investigation of default mode network role in automatic decision making

Posted comment on ´Default mode contributions to automated information processing` by D. Vatansever, D.K.  Menon and E.A. Stamatakis and published in PNAS October 23rd 2017 doi 10.1073/pnas.1710521114

SUMMARY

Vatansever, Menon and Stamatakis investigated the role of the Default Mode Network (DMN) in circumstances they defined as requiring automated decision-making ie. routine, predictable challenges that require fast and accurate responses. The authors of the article found that the DMN exhibited greater activity and connectivity between the hippocampus, parahippocampus and primary visual cortex areas in these circumstances and suggested that the DMN played an ´autopilot` role. This confirmed the findings of others.

In their study, Vatansever, Menon and Stamatakis used the cognitive flexibility task the Wisconsin Card Sorting Task (WCST). They took 28 subjects aged between 22 and 34 and presented them with 4 permanent reference cards and one alternating target card taken from a pool of 60 cards. The test participants had to sort the given card to one of the 4 reference cards according to a set of rules which included colour, shape, number and similarity. The participants were not told of the sorting criteria before the test and had to work it out for themselves as the test progressed. Each participant took part in 10 trials consisting of 4 blocks for each of the 4 rules. Feedback was given including indicating choice accuracy so the participants could work out the rule for each block. The results were attributed to two phases: the first known as the ´acquisition phase` where the rules were learnt by trial and error and the second phase the ´application phase` for the rest of the block when the correct responses had been learnt. Brain area activity was assessed using MRI.

Vatansever and colleagues assumed that their results would show that compared to the control condition, the percentage of correct answers in the task condition would be lower and they assumed that the participants` performances would be worse in the acquisition phase compared to the later application phase. Their assumptions were found to be correct with accuracy around 92% in the acquisition phase compared to about 99% in the application phase. They also predicted correctly that there would be a longer latency for the correct responses during the initial acquisition phase and this was also shown to occur with the control condition.

Brain area involvement during their task was also investigated by Vatansever, Menon and Stamatakis with emphasis placed on the activity of the frontoparietal, dorsal attention, cingulo-opercular, salience and visual networks. The authors assumed that DMN regions would be more active in the application phase rather than the acquisition phase because the task would demand at this time greater access to learnt, memory-based information in order to bring about the fast, correct responses. Connectivity of the areas would also reflect the different demands on the systems required at that time eg. perception. Vatansever and colleagues found that their experiments supported their hypotheses. Their MRI studies showed a highly symmetrical bilateral set of frontoparietal, insular, subcortical, and cerebellar brain regions more active in the acquisition phase compared to the application phase. This activity was associated with frontoparietal, dorsal attention, cingulo-opercular, salience and visual networks and supported previous work by others who found that these areas were involved in the successful performance of WCST tasks. Greater activity was found in the application phase in the regions associated with the DMN plus others (eg. ventromedial PFC, somatomotor networks and posterior cingulate cortex plus ventral anterior cingulate cortex, medial temporal lobe structures such as hippocampus, parahippocampal gyrus, right amygdala, superior and middle temporal gyri and the left middle occipital gyrus). A further seed-based connectivity experiment using a seed placed in the left posterior cingulate cortex/precuneus (PCC/PCUN) region showed that the two phases of task produced slight differences in levels of activity within the DMN areas ie. during the application phase there was greater activity in the PCC/PCUN, ventromedial prefrontal cortex, and left angular gyrus areas, but reduced connectivity with the bilateral insular gyri and right presupplementary motor area. The authors attributed the differences in brain activity observed to the different cognitive demands placed on the systems occurring during the course of the test. For example in the learning phase there would be low DMN activity and then the repetition phase when the rules are learnt, there is increased activity of the areas associated with the DMN.

Vatansever, Menon and Stamatakis also investigated the role of the dorsal attentional system (DAN) during their experimental task and its interrelationship with the DMN. The authors found that DAN activity was negatively correlated with that of DMN during resting state conditions. During both phases of the experiment the extensive DAN network encompassed activity of the frontal eye field (FEF – the seed of the seed-based connectivity experiment) and the inferior parietal lobe. However, differences were again observed between the acquisition phase and application phase with increased negative correlation with those regions commonly linked to the DMN in the latter phase to the former. Activity of the middle/superior temporal area and inferior/superior parietal gyri was observed to be lower in the acquisition phase compared with the later application phase. Accordingly, these observations were attributed to the alterations in functional connectivity in response to demands on the system for the two phases of the task and provided evidence that the task demanded changes in activity and connectivity not only for the DMN, but the attentional network too.

The authors continued their investigation into the activities of the DMN and DAN networks correlating them to behavioural performance measured through reaction times. It was assumed that DMN activity was associated with memory-based, automated decision-making and the DAN network with controlled, effort-requiring informational processing. An investigation into DAN connectivity and reaction time found that there was greater activity between the FEF and somatomotor regions (precentral and post central gyrus and paracentral lobe) in the acquisition phase which correlated with faster reaction times and better performance. This supported the view that connectivity between the FEF and precentral gyrus controls the saccades when processing visual information in visual searches and extraction of information during the rule-making acquisition phase. No correlation was found in the application phase. In the case of the DMN, no correlation was found between the PCC/PCUN with the parahippocampus, hippocampus, amygdala, primary visual cortices areas in the acquisition phase, but greater connectivity of the PCC/PCUN with these areas correlated with faster reaction times and better performance in the application phase. This supported again the view that the medial temporal lobe plays a role in context specific, memory based information processing and the visual nature of task during the acquisition phase whilst participants utilise learnt responses in the application phase.

Vatansever, Menon and Stamatakis concluded that their experiments supported the view that the DMN contributes to spontaneous internal thoughts during the brain`s state of rest, but greater connectivity and activity of the DMN areas is observed when individuals are required to access memory stores for a task. In the case of their experimental task then the DMN showed greater activity during the task`s application phase ie. during automated information processing than the acquisition phase. This was explained as automated information processing allowing individuals to use their own internal models of the world previously gained through experience to interpret their surroundings. This led the authors to describe the action of the DMN being in ´autopilot mode` in the application mode compared to ´manual mode` when the DMN failed to predict the current environment in the acquisition phase. This also provided an explanation as to why the DMN areas have ongoing activity when the brain is at rest and why it is active in certain situations where social interactions are important (eg. theory of mind, intuition, creativity and conscious sense of self).

The DMN`s cognitive functionality was attributed by the authors to be due to its extensive connectivity to the rest of brain which provided then a common workspace for the convergence of information from external sources and access to memory-based information. Therefore, their experimental results were interpreted as that the increased activity and connectivity of the DMN in the application phase of WCST task indicated the network`s ability to integrate memory-based information in order for fast automated decision-making, whereas the novel unpredictable situation of the acquisition phase demanded further attention and perception before the decisions were made. This would involve other networks eg. perceptual system and was observed with the roles played by the DMN areas and DAN areas during the two phases. Vatansever, Menon and Stamatakis went on to say that the differential involvement of the two networks may not then be described as just dependent on internally or externally directed cognition, but instead be described in addition by their dependency on the predictability of the environmental demands requiring either a memory-based (learnt) or perception-based (novel) response. Their  idea of duality in decision-making system under varying levels of predictability in the environment supports the views of others. For example:  Norman and Shallice argued for stored schemas that automatically take over processing in familiar contexts with the attentional system playing an intentional inhibitory role when the rules change; and Kahneman and Tversky`s work where they promoted a two system view with System 1 required for automatic decision-making to provide fast, best guesses and System 2 for calculated effort-requiring decisions.

Vatansever, Menon and Stamatakis concluded their article by saying that future studies were necessary to investigate the potential role of DMN in the formation of habitual behaviour in decision-making and to investigate its potential deficiency in cognitive disorders such as addiction, obsessive-compulsive disorder or clinical depression.

COMMENT

What makes this article so interesting is that it describes the relationship between two types of decision-making relative to a specific task and how the activities of the brain areas connected to the Default Mode network change relative to this task and the decision-making system in play. The results mean that we have to find an explanation as to why in the acquisition phase of a learning task and assumed to be where there is highly demanding cognitive processing this phase elicits lower activity in these DMN areas. Conversely, we also have to provide an explanation as to why in the application phase of the task these same brain areas elicit higher activity and connectivity at a point when cognitive demands are lower.

In order to do this we first have to look at what is going on in decision-making and what demands this places on brain functioning and hence, brain area activity. We can assume that the dual system of decision-making given by others is correct and that the task of card sorting used in Vantansever, Menon and Stamatakis`s experiments demonstrates to a large extent the difference between the two systems: the acquisition phase being System 2 which is slower, sequential and requiring central executive participation; and the application phase demonstrating to a certain extent System 1 type decision-making which is rapid, parallel and automatic. The actual stages of the decision-making process between the two phases remain relatively the same, but there are notable differences in the early stage and goal matching stage. Both the acquisition phase and application phase begin with the first stage of ´purpose and input` which equate to the individual knowing the ultimate goal of the task which has been given to him by the experimenters. The individual knows he has to perceive the card, identify its features and has to match it to one of the reference ones lying before him. The only difference is that in the acquisition phase the exact detail ie. the sorting criteria is not known. Experience tells him that there is no ´magic answer` available saying on which reference card he has to place his sample card since he has no previous examples to access. The only previous experience relevant to him at this stage is the awareness that the problem requires working out and this could take several steps. This is the difference in this stage of the decision-making process between the two phases since in the application phase the target sorting feature is known and the ´purpose` is defined as card matching according to a particular feature compared to the acquisition phase where the ´purpose` is essentially ´to find the sorting criteria`. Although most neurochemical systems are the same in this initial stage of the process, eg. visual input, perception, sensory memory store formation, both the attentional and consciousness systems demonstrate lower involvement in the later application phase due to the reduced demands placed on them since the purpose of the task has been identified and only matching is required.

This difference in cognitive demand between acquisition phase and application phase becomes even more apparent in the next stage of the decision-making process which is the ´solutions` stage where problem solving strategy has to be employed so that a decision can be made. In the acquisition phase this is highly demanding and it involves the perception of the real-time situation as where we are (eg. card in hand) and projection into the future of where we want to be (eg. placing the card on the correct reference card). From a neurochemical point of view it requires the formation of a short term memory store (termed input neuronal cell assembly, iNCA) representing the card in the hand and a purpose tNCA (transitional neuronal cell assembly) representing the unknown single sorting characteristic highlighted in the form of a card. (The rest of the card characteristics can be ignored and basically the visual search strategy centers solely on the required feature.)The purpose tNCA is formulated by applying a problem-solving strategy evoked initially from experience. Individuals have an array of strategies they use to make decisions eg. some people look at the good points or bad points of a decision (Plus versus Minus), or some look at the consequences of a decision (Cause and Effect) and it is the application of the optimal strategy which improves the chances of the correct decision being made. In the case of this experiment it is clear that the optimal strategy is the trial and error method for the initial acquisition stage so that the participants can work out what the target sorting feature is. However, other tasks would require more complicated strategies perhaps requiring a change during the process and may require the involvement of the emotional system and value comparisons. Naturally, the application phase does not require this cognitive processing stage since the purpose tNCA is already formed representing the target sorting feature and therefore, in this phase only the visual input in the form of a temporary sensory memory store has to be matched against the purpose tNCA.  The demands on awareness and attentional systems again are lower in this phase due to the sorting feature being known and hence, the stage becomes essentially automatic and to some extent subconscious.

From a neurochemical perspective the choice of strategy and making the decision are complex mechanisms and require the simultaneous functioning of multiple cognitive systems eg. working memory, error monitoring and emotional system. It is only the nature of the experiment described here that makes this stage relatively easy to follow. With reference to the DMN network, this stage requires the functioning of the higher order brain areas such as the prefrontal cortex and orbitofrontal cortex in the calculation of reward and value and a requirement for dopamine activity which shows some overlap between DMN connectivity and decision-making.

In the next stage of the decision-making process, the action is carried out which in the case of Vatansever, Menon and Stamatakis` experiment is the laying of the card on the correct reference card matching the target feature. The decision as to which reference card this is has been estimated in the first stages of the acquisition phase according to the trial and error strategy employed, but in the application phase the action is definitive and carried out according to the matching of features. The sensorimotor control is the same in both phases and involves the parietal cortex, basal ganglia and motor cortical areas, but the speed of action in the application phase may be increased. This is likely due to unconscious processing of the card`s features being faster than conscious processing so the action has begun before conscious awareness exists that it should be carried out. This is not new and we are all aware of situations where we have already begun some movement before we actually think we should move eg. trying to catch a falling glass. The demands on the attentional system are also reduced in this stage in the application phase and therefore, distraction or divided attention may force errors in movement.

In the case of trial and error learning, the final stage of decision-making is especially important and the application phase would take longer to reach (if it all) if it was not available. Learning which feature the cards should be sorted by is achieved using this strategy by guessing in the initial stages and receiving immediate feedback as to whether the choice was correct or incorrect. Biochemically, the iNCA formed from the given card is temporarily matched to the generic purpose tNCA of the reference card. Complex conscious decisions are made by matching cell assembly firing based on risk (the value of the decision plays a role), strength (a factor of how often the representation or features of the representation are observed – termed frequency) or similarity (how much the firing overlaps between the two assemblies due to matching characteristics and hence, how much stronger it is). When positive feedback is given ie. the choice of reference card was correct then comparison of the cell assemblies occurs according to how much overlap there is (ie. similarity of features) and the feature representing the strongest firing becomes the temporary ´ target feature`. The initial stages mean that correct and incorrect decisions are made and so the individual uses the feedback given to hone the number of sorting criteria contenders by monitoring the overlapping representations of the cards put before him by looking for similarity when he is told that the decision was correct (described as a form of reframing). This carries on until the iNCA formed matches the target/purpose tNCA which consists of one feature only. At this point the acquisition phase is completed and the application phase begins.  Feedback in the application phase is not as important since the individual knows the pattern to be followed and therefore, it is regarded more as a measurement of personal performance. As given before, errors in decision-making can occur due to distraction for example, but monitoring for errors occurs subconsciously. With reference to the DMN, error monitoring is attributed to anterior cingulate cortex (ACC) functioning which is located near to and has high connectivity with the posterior cingulate cortex (PCC)known to play a strong role in the DMN network.

    Therefore, we have seen that certain brain areas linked to DMN network functioning also play roles in the standard decision-making process and hence, changes in area activity would be expected as the demands of the decision-making process alter with the task at hand. We have already given in the description of the decision-making process above examples of the roles some of the brain areas which are linked to the DMN network play, but their functioning goes beyond the simple task used by these experimenters. For example, there is the post-cingulate cortex (PCC) with its differing functions according to location with the dorsal part responsible for involuntary awareness and arousal and the ventral part, SELF, cognition and thoughts and the frontal eye fields which are shown to have error-related activity and thought to respond locally leading on to the more general response elicited by the ACC. The parietal cortex is another area which is shown to be involved in the decision of movement or non-movement, but is also capable of temporarily storing, maintaining and manipulating information in the working memory (important in the formation of the temporary purpose and input neuronal cell assemblies) through either a cortico-cortico pathway or a subcortical pathway (supports flexible updating of working memory content). The medial prefrontal cortex (mPFC)  is also important where activity in the decision-making process is modulated to upcoming action values and its activity determines behavior, but not reward and the more important ventral medial portion (orbitofrontal cortex  – OFC. This is widely recognized as being involved in the System 1 decision-making system and important for the computation of the subjective value of events through organization by an anterior-posterior gradient corresponding to secondary versus primary rewards. Also individual differences in the degree of model-based control are attributed to the structural integrity of the white matter tracts leading from this area to the striatum. The temporal lobe also contributes with its hippocampal involvement (binding and relay station role for information) and theta oscillations over the participating connected areas important for the perception and processing of different parts of the information eg. features and values during non-spatial decisions.

However, decision-making also requires the activity of a number of areas which are not considered to be part of the DMN network. We have already mentioned the ACC which lies next to the PCC which is known to have DMN related activity. The ACC plays a role in detecting when strategic control is required. This includes autonomic regulation such as pain perception. It also has been shown to have an increased level activity before decisions are made in certain situations and this observation indicates its role in learning the value of actions and guiding voluntary choices based on past experiences. This evaluation process in decision-making involves weighing costs against benefits and this function has been linked to dorsal ACC activity and the striatum. Other areas are also involved in decision-making that lie in the vicinity of DMN networking areas. For example, other parts of the PFC such as the left side which is thought to be involved in planning, the right inferior area shown to be involved in System 2 decision-making and the lateral area thought to play a role in strategic control. However, an important area not part of the DMN network, but which has significant activity in decision-making is the dorsolateral PFC. This area is known for its involvement in plan generation (the right side) and plan execution (the left) with neurons in this area shown to encode a diverse array of signals relating to both task relevant and irrelevant features with only the former being encoded simultaneously with choice signals. Basal ganglia areas are also shown to be involved in the decision-making process, but not considered part of the DMN network. The striatum in particular is important with the strength of the cortico-striatal pathway with its dopamine dependent plasticity determining effectiveness of the decision-making process. Its role is related to the trade-off between computational simplicity and flexibility and the efficiency of using experience and involves competition between it and the prefrontal cortex. Connectivity between the striatum and hippocampus is associated with prediction and anticipation of reward. This link between basal ganglia functioning and reward in decision-making is further strengthened by the actions of the amygdala. The amygdala with the OFC area have both been shown to have altered activity in abnormal decision-making involving the risk of punishment. Lesions of the basal amygdala were linked with the increased choice of large risky rewards, but did not impair sensitivity to punishment whereas lesions of the OFC decreased risk taking. Stress which has been shown to impair the biasness towards larger rewards was shown to be blocked by temporary inactivation of the amygdala which reinforces the view that the amygdala plays an important role in the assessment of reward and risk in relation to decision-making.

Therefore, we have demonstrated that the process of decision-making requires a multitude of different brain areas some of which are said to be part of the DMN network and some not. Our next question is does the pattern of DMN functioning during the decision-making task given follow the pattern of other cognitive systems? Vatansever, Menon and Stamatakis showed that DMN activity and connectivity was decreased in the acquisition phase (termed ´manual mode`) and was increased during the application phase (termed ´autopilot` mode). This appears not to concur with what is thought to be happening in other brain systems. For example, working memory, attentional system and conscious awareness all appear to have placed on them a greater demand during the acquisition phase of this particular decision-making task and a lower demand in the application phase. This is in accordance with the progress of the decision-making task. For example, there is complex/high demand in the acquisition phase when there is no simple answer, a strategy is required where feedback is important and a method for comparing option and target on the power of strength of firing of complimentary features (´manual mode`). This is compared to the application phase where there is simple decision-making (does it match or not?) and achieved through previous experience and occurring primarily through unconscious processing or low level cognitive demand. This phase is more attributable to the ´autopilot mode` and lower demands on the working memory, attentional and consciousness systems. In the case of the visual system, memory mechanisms and motor control the demands on these systems appear to be the same independent of experimental phase and cognitive demand.

   So, since the DMN network shows opposite activity and connectivity levels to the other systems in play in the decision-making task, what can we conclude about what the DMN is and what it does?  (Before we go further though we should rule out the possibility that the DMN network is a figment of experimental procedures. Researchers have shown that there is a definitive group of brain areas connected by vast white matter tracts and these areas show the highest blood flow when the brain is considered at rest as expected. However, the DMN network areas are multifunctional.) Regarding the function of the DMN network, researchers follow two modes of thought. The consciousness theorists relate its action to the resting state of the brain ie. when brain functioning is not directed at any particular task and hence, when conscious awareness is likely to be low. In this way according to the consciousness theorists the DMN network then has ´no function`, just exists under particular conditions. When we become aware of it then this pushes the functionality of the brain to the higher level of cognitive thinking and hence, the brain is no longer considered as ´resting`.

The second school of thought comes from the researchers who hypothesise that the activity of the DMN network is linked to self-related cognition eg. thinking about the past, future and the SELF; bodily state monitoring eg. heart beat; and autonomic regulation eg. breathing.  If this is the case, then the DMN network may fill the role of ´monitor` of the SELF considered as both cognitive and physiological. We are not aware of this monitoring with subconscious processing leading to conscious processing if asked from an external source (eg.  ´How are you feeling?`) or forced (eg. by a change of circumstances). This is possible since the dorsal PCC which is a participant of the DMN network is equated with involuntary awareness and arousal. However, it is unlikely since the DMN network is linked to one system where monitoring would be important and that is the pain system. This view is supported because the brain areas involved in the pain response eg. ACC, dorsolateral PFC and somatosensory cortex are not part of the DMN network and acupuncture shows a decrease in connectivity and activity of the DMN areas even though increased self-awareness and decreased pain response occurs. The non-involvement of the DMN however,  could be explained by the pain system producing its own necessary fast responses.

Therefore, what can we conclude about the DMN network`s function? Well, it is probably a ´real` network which probably functions according to both views expressed above: resting state activity involvement and subconscious ´monitor` of the cognitive and physiological SELF. It is probably what keeps the brain in a state of readiness which would be vital to survival in a changing environment. Therefore, resting state would mean ´state of readiness` and not ´rest/no activity`. This view is supported by the evidence that shows that switching off neuronal firing activity induces adverse effects on neuronal cell physiology such as protein breakdown and even eventually apoptosis. By keeping areas that are highly connected eg. dorsal and ventral PCC, medial PFC, hippocampus and thalamus functioning even when there is no cognitive task requiring higher order area activity then the neuronal cells and neuronal pathways are always working to some extent and cell replenishment and renewal is constantly occurring.

As seen with Vatansever and colleagues experiments, the overall activity of the DMN network relates to the tasks underhand being active to keep the brain in a ´state of readiness` and deactivated when the higher order brain areas and systems such as working memory and attention are placed under high demand by for example a directed task. The network ´switches on` a fraction of a second after such a task is completed and the level of the network`s activity has been observed to be related to its previous experience and status (eg. activity was seen to have changed after a prior task that required the formation of memories). This confirms that the overall status of the network is constantly readjusting and fits in with the view that the DMN network represents a monitoring  ´readiness` state. DMN activity and deactivation is also reflected in certain conditions and states. For example, in the sleep stages where Stage 1 and the synchrony of thoughts means that an increase in DMN activity is expected and this is observed as the individual slips into sleep. However, in the later stages of sleep there is greater connectivity of the higher brain areas as synchronized firing occurs in order to form long-term memories then a decrease in DMN network activity is expected and this is also observed. Another clear example of the relationship between activity and low demand on higher order functioning is in the case of REM sleep and Alzheimer disease. In both of these cases, chaotic neuronal firing and connectivity leads to problems building coherent neuronal cell assemblies. This would be expected to lead to decreased DMN activity and this is observed. Problems with pathway connectivity between the PCC and mPFC areas also lead to decreased DMN activity and this is observed in autism where problems with social skills and poor empathy occur. However, the explanations for other examples of reported changes in DMN activity are not so simple. In the case of acupuncture, there is no goal directed task and one would expect if the theorists are correct that there is an improvement of status through meridian energy adjustment and also maybe pain responses. Therefore, an increase in DMN activity would be expected. However, studies show a decrease occurs and this could possibly be explained by the experimental conditions. For example, it could be that positive changes in concentration and awareness on treatment affect the network activity. Another example is depression where the individual is likely to suffer from misinterpretation of the status of the cognitive and physiological SELF and personal values but active cognitive thinking relating to the SELF still occurs. In this case, a decrease in activity of the DMN network would be expected, but instead an increase is observed. This possibly could be explained by the network reflecting the lower level of higher order cognitive thought taking place due to lack of interest by the individual, or by the individual blocking thoughts relating to the SELF.

Therefore, we can conclude that the DMN network is a probably a network of brain areas which function as a ´subconscious monitor` of the cognitive and physiological SELF and neurochemically maintains the brain in a state of readiness when there are no demands placed on the higher brain orders. This activity, which is not related to directed task or thought, prevents the breakdown of neuronal pathway firing and connectivity due to inactivity and keeps the brain areas functioning to some extent. High cognitive demands lead to the activity of this network being reduced in comparison so that available energy can be used for the higher functions. Further studies on the DMN network are important since it is clear that both sets of systems are required for a balanced, correctly functioning brain and it is possible that whilst research attention is centered on the more popular higher cognitive functions of the brain, the key to solving the problem of cognitive disorders such as Alzheimer`s disease or autism may actually lie in the brain`s ´quiet period`.

Since we`re talking about the topic …………..

……… work by Goh showed that aging leads to compromised activity in the frontal, striatal, and medial temporal areas having an effect on the reward system, impeding accurate value representation and feedback processing all critical for optimal decision making. It has also been shown that increased ventromedial PFC activity is positively associated with cognitive performance in older adults. If the experiment given here was repeated using older and elderly participants would we see changes in the DMN network activity not only in overall level of activity, but also in its timing in relation to when the test shifts from acquisition phase to application phase?

……can we assume that if the sorting criteria were two features and not just one, the acquisition phase would be longer and that one feature would take priority over the other? If a distraction was introduced, would this be even more detrimental to the decision-making process and we might see no increase in DMN activity in the application phase since the attentional system would be subject to higher demand?

…… would the DMN network ever demonstrate increased connectivity if the experiment given here was repeated but with a risk of punishment if the incorrect card was chosen?

……experiments have shown that when subjects are asked to report the times of their own decisions, there is a degree of inaccuracy on the times given. Imaging studies have shown brain activity correlates with the decision to move before a person reports that decision. If the experiment given here were repeated, but the participants asked to report their decision before carrying it out would we see a change in DMN network activity especially in the application phase when the initial stage of the movement may be subconscious anyway?

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feature binding in human visual working memory

Posted comment on ´Neural architecture for feature binding in visual working memory` by S.Schneegans and P.M. Bays and published in Journal of Neuroscience April 2017 vol 37 (14) page 3913 doi.org/10.1523/JNEUROSCI.3943-16.2017

SUMMARY

Schneegans and Bays propose in their article that populations of interacting neurons are required for feature binding in visual working memory and that non-spatial features are related to location by simultaneous conjunctive coding.

Their experiments involved cued recall tasks where subjects memorised object arrays composed of simple visual features eg. colour, orientation and location. Having learnt the items and after a short delay, the subjects were given one feature of the event and had to report on one or both of the other features. Swap errors (where the subject reported an item other than the one indicated by the cue) were determined as binding failures. In Experiment 1,  8 participants with normal or corrected- to-normal vision performed a cued recall task testing their memory for binding of colour and location. The stimuli were presented at a refresh rate of 13HZ suitable for learning. Each trial began with the presentation of a white fixation cross against a black background to maintain subject concentration and gaze fixation. This was then followed by a sample array of 6 coloured discs presented for 2secs each at random locations on the fixation cross. The display then disappeared for 1 sec and then one of the discs chosen randomly was presented on the display. In the report-location test then the subjects were asked to move the disc using a dial to the matching location. In the report-colour test, then the subjects were presented with a location and used the dial to select the matching colour. The responses were not timed. In Experiment 2, the same participants performed the cued recall task, but in this case with 2 responses so that the simultaneous presentation of more than one feature could be investigated. In this experiment, the subjects were presented with a sample array consisting of six bars with randomly chosen colours, orientations and locations. For the orientation-cue test the subjects were presented with a random orientation and had to use a dial to match colour and location. For the colour-cue test then the subjects were shown a colour disc and had to match the orientation and location using a single input dial.

Stimulus features were analysed and reported according to circular parameter space of possible values in radians. To allow easier comparison of the results then orientation values were scaled to cover the same range of colour and angular location. The recall error for each trial was calculated using angular deviation reported in comparison to the true value. Recall variability was measured using circular SD. To measure the distribution of deviations of reports expected by chance a randomisation method for each subject and condition was used where deviations of non-target features values from target feature values were randomly mixed and the deviations of corresponding responses were recorded. An estimate of chance distribution was calculated from over 1000 repetitions and this value was subtracted from the observed response frequencies.

In the case of Experiment 2, tests were additionally classified according to whether the spatial response was directed to the target (a spatial target test) or one of the non-target items (a spatial swap test). Therefore, neural population models were only fitted to spatial responses and for each test the probability that each item had been selected for spatial response generation given the response location was calculated. Only spatial target tests or spatial swap tests giving a probability of 75% were analysed further.

The population-coding model used by Schneegans and Bays in this investigation was an extension of that proposed by Bays in 2014 for memorising individual feature values. It was assumed that during the presentation of the array the memory features of each item were encoded in the activity of a particular population of neurons and therefore, the relationship between the feature and the mean neuron firing rate reflected the neuron`s preferred feature value and a tuning function which the authors assumed was normal. In recall, the memorised feature values were assessed according to the maximum likelihood of decoding which meant that the decoder observed the activity of the neuron population and reported the feature value that was most likely attributed to that particular pattern of activity. Recall errors were explained by random noise in neural activity that caused deviations from the encoded and decoded feature values. The authors assumed that the total neuronal activity was constant over the changes in the amount of information encoded and therefore, larger memory arrays produced fewer spikes for each item`s features and resulted as expected in poorer recall performance.

Using the population coding model, decoding from the neural population activity reproduced quantitative details of error distributions in the cued recall tasks. Distributions showed specific deviations from normality including increased proportion of large deviations from the memorised value accounting for response errors that could have represented guesses. It was found that the proportion of large errors increased as the number of spikes per item decreased which was explained as due to the higher set sizes. For feature binding then population codes were considered for feature conjunctions so that each neuron population had a preferred value and associated tuning curve associated with 2 features. Items that were memorised had separate population activity computed on the item`s feature combination and modulated by random noise. In the cued recall test then the decoded cue feature value closest to the given cue selected was considered the response.  The population coding model was also extended to accommodate binding of multiple visual features by combining several conjunctive population codes representing two features. The authors considered two extensions to the models to explain multiple binding features: they considered the direct-binding model where one conjunctive population existed for each pair of feature dimensions and therefore, the results would explicitly show binding between two features; and also they considered the spatial-binding model where one feature dimension (in this case location) takes a leading role in binding all other features together.

Mathematical analysis of the results obtained from both Experiment 1 and 2 was carried out to calculate the mean firing rates, normalisation of the neuron firing rates, tuning functions, and response probabilities. Analysis of the applied model was simplified by the authors who used rate coding with additive Gaussian noise, did not use the ´palimpsest` model instead computing an explicit distribution of response probabilities for distribution for each trial. The results were fitted to different models. With the colour-location task of Experiment 1 then two versions of the model were considered: the joint model (assumed that a single neuronal population is used to generate responses in both task conditions) or the independent model (assumed that there are two separate neuronal populations which give 6 parameters). Two models were also considered for Experiment 2: the joint model for colour location binding; and the direct binding model (values are assumed to be generated independently from the cue using one population code for one feature associated with location and the second cue to associate cue feature with report feature).  Spatial swap errors were assessed using a reduced model fitting only to the spatial responses from Experiment 2 linking cue feature (colour or orientation) to location.

The results of Experiment 1 investigating colour-location binding showed that there was binding between non-spatial and spatial features and that the memory representation was a single neuron population as shown by successful recall of the feature. The authors found that even though there were substantial differences in the shapes of the diagrammatic representations there was no significance in variability as measured by SD. This indicated that the responses were not noisy estimates of the target. A central tendency in the distribution plots indicated the presence of swap errors (the non-target was reported in place of a target) with the report-location condition showing a strong tendency with the report-colour distribution weaker, but still significant. This indicated to the authors that the swap errors were more common in recalling the locations than recalling the colours. The deviation of location estimates from a non-target`s location varied according to the similarity of the report colour to the colour of the target. Results were consistent with swap errors with the deviation significantly lower than chance, but only when target and non-target had similar colours. The deviation of colour estimates from the non-target colour varied with similarity of that non-target`s location to the location of the target. Therefore, the authors reported swap errors in the report-colour condition only for non-targets that were similar to the target in the cue feature ie. very close together in space. When the authors fitted their results to the neural population model they found that the joint model (ie. a single population for both conditions of the task by changing only the feature dimension either colour or space that takes the role of the cue feature and the feature dimension that takes the role of the report feature) had a better fit, but was not statistically significant for all subjects. The fit also indicated that swap errors were present – about 16% of trials for report-colour and 51% for report-location.

The results for Experiment 2 showed that in both conditions the error distribution for location had a significantly lower SD than for either non-spatial feature with colour producing the better result indicating that colour cues were more effective for reporting location than orientation. The results for colour compared favourably to those obtained in Experiment 1 which indicated that the additional demand of learning orientation did not significantly interfere with recall performance. The pronounced central peak in the figures representing response deviations indicated the occurrence of swap errors. These swap errors were found to be linked to similarity between the non-target and the target of the cue feature and the range of cue features values for which swap errors were reported was comparable to the reporting of the spatial and non-spatial cues. When the results were computed and applied to the models it was found that the spatial-binding model (ie. neural populations representing colour-location or orientation-location binding) provided the better fit for the observed results than the direct binding model. In this model, the spatial response is generated directly from the given cue and then this is used to estimate item location which is used as a cue to generate the non-spatial response. Two types of swap errors were observed to occur: when the spatial response was selected based on the cue information (same as observed in Experiment 1 for report-location task) which occurred 27% of the time; and secondly, the more common error when the estimated spatial location was used to select the memorised item for the non-spatial response (49% for colour-cue trials and 55% affected in orientation –cue trials). The higher percentage observed for orientation reflected, according to the authors, its general lower effectiveness to be used as a cue. Again fitting to the model, the authors found that the neural tuning curves for the spatial dimension were significantly higher than for both colour and orientation which also produced significant values between each other. This supported the observation of a higher proportion of swap errors for orientation than colour.

The authors also investigated the error distributions for both experiments and models since they found that nearly identical fits were achieved for both models. They stated that the differences between the two models were related to the pattern of swap errors across the two predicted responses. In the direct binding model, a swap error on spatial response had no effect on the response for a non-spatial feature and vice versa. However, in the spatial binding model, a swap error in the spatial response meant that the location of the selected non-target item would have been used to generate the response for the non-spatial response. Therefore, the non-spatial response should be centred on the feature value of the non-target at the selected location rather than the target. The mechanism predicts a strong correlation between swap errors and then absolute response errors in spatial and non-spatial responses. To examine this, the authors determined a Pearson`s product-moment correlation coefficient for the absolute response errors for all responses across the trials for all subjects and found that the correlation coefficients calculated showed that predictions of the spatial binding model were met. They did note however, that the results were reproduced only when tests were analysed with the non-spatial response produced first and the spatial response second. This indicated that the spatial response did not force the selection of the memorised item before the non-spatial response was initiated.

Schneegans and Bays concluded that their results showed that the model for feature binding that combined neural population representations with conjunctive coding was correct. Other studies showed that recall errors were as a result of noisy neural activity. The authors in their experiments saw swap errors both in cue and report features eg. a non-target item was judged as one most similar to the cue and attributed these swap errors to decoding noisy activity in their observed active neural populations. The errors seen in Experiment 1 either for the spatial responses with sharp distributions around the target location combined with a large proportion of swap errors, or wider distributions with fewer swap errors in case of the colour responses, were attributed to the different widths of neural tuning curves for the two features investigated.

The authors then used models (direct binding and spatial binding) to compare alternative mechanisms for binding non-spatial features and found behavioural results were fully consistent with the spatial binding model where non-spatial features eg. colour and orientation are bound exclusively via their shared location with no indication of direct binding between them.  This showed that location plays a special role in feature binding and that learning of the non-spatial feature occurs with the location. They also showed that it is only possible to recall one feature directly via the shared location of the second. Schneegans and Bays conclusions supported work by Nissen in 1985 on perception and also studies which showed that spatial attention is engaged when retrieving items from working memory even when cued by non-spatial features. Schneegans and Bays also went on in their discussion to compare favourably their results with the feature binding model for neural populations with conjunctive coding proposed by Matthey et al 2015. Differences between the two theories were explained by the fact that Matthey had not looked at spatial and non-spatial responses. The authors also ruled out other alternative models that were based on low error correlations with spatial cues and concluded their discussion with supporting evidence for their spatial binding model.

Therefore to summarise, Schneegans and Bays in their experiments showed that feature binding in visual working memory is satisfied by a model of neural population activity with conjunctive coding. Their investigations showed that binding of non-spatial features with location occurs and binding of one non-spatial feature to another occurs only via binding to a shared location.

COMMENT

What makes this article interesting is that it explores how visual information is linked together in human visual memory.  Schneegans and Bays found that the colour feature and/or orientation feature of a human visual event is stored and recalled with their respective locations in visual memory. Hence, it is tempting to compare the findings obtained here to the required binding of object information to location information for the more commonly carried out spatial memory experiments of mice. However, before we make any definitive conclusions we should think about what the experiments carried out by Schneegans and Bays actually mean. Schneegans and Bays subjects were presented with a definitive shape (the fixation cross) of a particular ´colour` (white) against a black background. This would in spatial memory experiments be classed as the object albeit the basic one. On the cross shape were placed either discs of colour or bars orientated in different directions and therefore, these would be treated by the subjects as mere additions to the basic shape common to all trials. Therefore, the information learnt would be a common shape with each one having a slightly different ´pattern`. This would correlate to the spatial memory experiments where the objects learnt are shapes (2D or 3D) comprising of visual features which may or may not include colour and different patterns. The objects of Schneegans and Bays experiments are naturally of much lower complexity.

The other piece of information stored with the colour and/or orientation according to Schneegans and Bays was the location. The word ´location` normally relates to a physical place or space and in terms of the mice spatial memory experiments it does actually mean the place where the object is because this is how the mice remember their way around the test maze. In this case, the location is for movement and this is the same if we were talking about moving to grasp something or stepping over something to avoid it. However, in Schneegans and Bays experiments the location is actually the place where the experimental colour disc or bar has been added. Therefore, the location is relative to the shape and outline of the cross and is more a location for object placement rather than that for movement described above. (Even if the fixation cross was not used then the location would be fixed relative to the boundaries of the screen and would still be classed as a location for object placement.) Therefore, again location in Schneegans and Bays experiments is of a lower complexity than that recorded and recalled in the mice spatial memory experiments.  We are not disputing that colour and/or orientation features are recorded with location just that the experiments carried out require the storage and recall of features much less complicated and of a different nature than those recorded in a known system already in place for successful spatial memory and associated with the word location.

Now, that we can see that human visual pathways and memory binds colour and/or orientation features to location we can look to see why and which mechanisms bring this about. Whereas location is bound to an object in the consideration of movement (eg. to achieve reaching and grabbing) colour and shape are bound to location or placement to give an object form and detail complexity. This is required cognitively directly for better perception and object recognition and indirectly for better higher order cognitive functions like creativity and decision-making. The characteristics of visual memory require features such as colour, size, shape, and location with binding (´chunking`) of this material into advantageous groups. The characteristics are bound together in time, but not necessarily in place meaning that many different brain areas and sensory systems may be involved and it is likely that stronger firing of particular features means that these take the role of the reference point or cue in later cognitive tasks such as helping in recall.

Neurochemical systems have to fulfil the demands of the brain`s required functions and for human visual input and memory this is no different. Just like with other forms of memory, the system relies on firing activity of appropriate neuronal cells. The general biochemical mechanisms involved rely on neurotransmitter release and neuronal pathways  of multiple neuronal cells linked by their axons and dendrites. The firing activity occurring at any point in time can form a neuronal cell assembly (Schneegans and Bay neural population) and this would represent the new input as well as learnt information recovered by reactivation of cells whose physiology has been altered by previous events. The synchronicity of firing of these neuronal cell assemblies provides the conditions for the binding of event characteristics in time. Most research suggests that cells represent specific features, but it has been suggested that some cells represent multiple features (eg. the place cells in the hippocampus that appear to represent both location and event features in spatial memory) or multiple cognitive functions (eg. Messenger`s cells involved in both working memory and attention). Although the idea would be logical (a cell that fires representing two features would be an example of energy conservation) there is no evidence of this type of capability for visual features eg. the cells forming the retinotopic map only represent single event features. However, brain areas have multiple layers with specific cells all having many dendrites and axons. It is more likely that in the case of location, that the feature is represented by the firing ´strand` and the location feature by where that strand ends in the relevant brain area, eg. a red rope tied to a post means red represents the colour and the post the location of its end-point. Binding of the information therefore, falls to the responsibility of brain area functioning and in the case of visual information this appears to mean firing from the hippocampus, an area known as a relay station with important roles in informational input, working memory and attention, to the lateral entorhinal cortex for the selectivity process of object information and the medial entorhinal cortex for spatial memory. The frequency of firing also relates to the functioning of these areas with regards to event characteristics with beta brain wave synchronicity in the former for encoding the information and between the lateral entorhinal cortex and medial prefrontal cortex for recall. Theta brain wave synchronicity is also observed between the medial entorhinal cortex and hippocampus in the case of spatial memory.

Although binding will bring event characteristics together in time, not all incoming features are stored for later use. We have already discussed relevance of features to tasks as seen above with reference points and in general since different forms of neuronal cell assemblies are produced during the memory process and so only the strongest firing representing the strongest or most relevant feature would survive. It should also be remembered however, that unattended information (ie. information of which the subject is not aware) could also be included. However, in the case of this experiment this is unlikely to occur since the target of focus is on a very small amount of visual information and the task is simple. The neuronal cell assemblies formed in Schneegans and Bays experiments also do not have to undergo the physiological changes associated with transforming short term memories into long-term memories since there is only a short delay between the input and recall (1 second) and with no interference and therefore, sustained activation to transform temporary firing populations into more permanent stores is not required. The experimental set-up also means that the neuronal cell assembly is not altered by working memory intervention since the features require no supplementary processing such as decision-making. This means essentially ´what is seen is what is learnt` (formation of sensory stores and short-term memory stores) and remembered (recall without processing). There is also limited emotional pathway involvement since there is definitive reward offered by successful recall only and an indirect positive emotional status reflecting self-satisfaction at the personal performance.

The above paragraphs describe the general neurochemical mechanisms associated with neuronal firing and input and recall of information on which the studies of Schneegans and Bays are based. However, the selectivity of the event information ie. colour, orientation and location rely on the firing of specific neuronal pathways associated with visual characteristics and leading from the basic eye (the conduit from the external environment to the internal) to the higher order brain areas associated with the more complex visual characteristics. The initial experimental set-up of Scheegans and Bays ie. the visualisation of the fixation cross evokes retinal firing relating to the object`s shape and outline (the role of the retinal rods) and colour (the cones). The pathway firing occurs in a forward sweep from the lower hierarchical areas  eg. the retina to the highest order areas that of the  visual cortices and this firing is neurotransmitter based, has particular brain wave frequency and exhibits saccades where firing is paused in cells due to cellular exhaustion and subsequent replenishment. Therefore, information received higher up in the hierarchy appears in ´bursts`.

Activation therefore, of the visual pathway hierarchy then provides the individual with the attributed features of form, shape and movement. Location is not recognised at the lower levels, but the characteristics of depth and size are. Therefore, these and movement perception give rise at higher levels to the event characteristic of location. In the case of movement and hence location, the hierarchy followed in the forward sweep of firing involves the retinal rods, bipolar cells (gives edges), M ganglion cells, LGN magnocellular pathway and parvointerblobs (gives orientation, movement and contrast) leading to visual cortex V1 magnocellular pathway (both orientation and direction sensitive) leading to the higher V2 area, then V3, V5 middle temporal lobe leading to eventually the MST which is known for linear motion. Whether all of the visual pathway for movement is involved in the input and perception of location in these experiments was not investigated, but we can assume that it follows the regime given above for other objects. The firing patterns observed relate to the models attributed to visual perception. The WHERE model (or perception-action model) relates to movement perception and location and correlates as expected to the involvement of brain dorsal areas (the V1 to the parietal lobe and medial temporal lobe). Milner and Goodale described this dorsal pathway as providing short-lasting and view point independent events which satisfies the results of Schneegans and Bays` experiments which showed that orientation as an descriptive event feature appeared to be less important than colour. This is understandable if we consider the example of a cup which we still recognise as a cup whether it is viewed from one side, the other or even upside down.

Therefore, we can say that the neurochemical quality that allows events to be perceived is the binding of its characteristics that are perceived by the systems available eg. in the case of human visual system colour, shape and movement. If we consider that only the colour characteristic of an event can be perceived, stored and recalled we can see that the capability of object recognition would be extremely hampered. Therefore, binding of characteristics simultaneously presented into a single population of firing neurons which may cover many systems is ideal and increases considerably the chances of recognition at a later date. Schneegans and Bays have therefore, provided by their experiments further evidence that in the case of the human visual system this at least occurs between colour and location and even, colour, orientation and location. However, nothing is ever that simple and so it also is the case with feature binding for human visual systems. There are at least three examples where the binding of location and event features are more complicated and these are:

  • The case of sequences. In this case, the event characteristic of location would vary with time and neurochemically, this can be portrayed by event content changing, but where location is replaced by the event having a particular order. Natural saccades give firing ´black outs` so that sequences of events are represented by a series of neuronal cell assemblies that portray a majority of features that are the same and a smaller number of features that are slightly changed. Binding of the features together to form the single population representing a point of time or order would be brought about by the firing between different parts of the hippocampus and the entorhinal cortex. For example, the input of depth and size information of the visual system would mean that movement of an object is accounted as well as support from changes in colour density and contrast. Therefore, the hippocampus would coordinate firing with the lateral entorhinal cortex for the object information and the medial entorhinal cortex for the location.
  • The case of expected location such as that seen with facial recognition. In this case, the location of features is already known since each face has features assumed to be in the same places for everyone. Variation of this placement may be slight eg. the width between the eyes or the face shape, but the characteristic that is most likely to change between individuals is colour. Therefore, the neuronal cell assembly has to assign colours to known locations and the face is remembered essentially as a ´whole`. This is why the details are less likely to be remembered individually unless changed or reminded by external prompts. Facial recognition requires a number of specific neuronal pathways including the fusiform gyrus which is known for face processing.
  • The case of feature exchange such as that seen with ´hybrid` images (eg. where the images of two famous people are swapped as the individual looks at the same location). In this case, it appears that the location is assigned different event features that appear linked to time. This is in fact an illusion and results from the visual system`s neurochemical capabilities. It is probably due to the visual features of one image causing firing of the appropriate neuronal cell assembly leading to recognition, but as that firing dies away because of the firing cells refractory period, firing of cells at millimetre distances away will start due to the visual system rule of priority given to firing of unattended cells. Therefore, even though the appearance of the second image occurs seemingly in the same external location and each location then appears to carry the feature of two images, there are differences in internal location of the firing cells.

Therefore, to conclude binding of visual features of an event is important and we can use this knowledge to promote learning and recall. It is for this reason that we have to be careful about how we experiment and what interpretations are made from the results we get particularly in the case of human beings. Schneegans and Bays carried out a series of experiments which on the surface appear to link one event characteristic to another, but only further examination of the details of the experiment show that this interpretation is not as clear-cut as first envisaged. Therefore, any experiments involving human subjects should be considered from all angles and appropriate controls put in place so that any results can be correctly interpreted.

Since we are talking about the topic …………………………..

…..can we assume that if in the experiment`s delay between feature presentation and recall the individuals were exposed to some form of visual interference the recall performance would be adversely affected and this adverse effect would be greater if it involved colour or location?

…..the cross modal effect is said to increase the amount of information a person can remember at any one time due to different sensory pathways being in play. Therefore, if auditory stimuli were included with the visual cue and the experiment repeated would we see no change with the performance of recalling location and colour/orientation, but if the sound was used as a cue would an observed difference reflect a preference between sound information and either colour or location?

…..would the introduction of more complex visual events including patterns and camouflaging lead to the expected change in recall performance due to an increased demand on the visual system?

…..can we assume that the use of reward would engage more brain areas in the recall performance?

Posted in memory recall, recall, Uncategorized, visual input, working memory | Tagged , ,

interbrain cortical synchronisation in motor areas encodes aspects of social interaction

Posted comment on ´Interbrain cortical synchronisation encodes multiple aspects of social interactions in monkey pairs` by P. Tseng, S. Rajangam, G. Lehew, M.A. Lebedev and M.A.L: Nicolelis and published in Scientific Reports vol 8 article number 4699 (2018)

SUMMARY

Tseng and colleagues in their article describe interbrain cortical synchronization (ICS) of neural firing representing spatial social interactions in pairs of monkeys observed whilst both subjects participated in a whole body navigation task. The authors began their article by describing the importance to individuals of observing behaviours of others in a group such as learning social rankings, recognising threats and allies and learning new motor skills. Previous research has showed that in primates observation of an action performed by another produced neuronal activity in certain brain areas of the observer eg. frontal and parietal cortices that mirrors that of the primate actually carrying out the action. Those firing neurons have been termed mirror neurons and were defined by Tseng and colleagues as those cortical neurons that respond in the same way when a subject performs or observes an action.

Tseng and colleagues wanted to investigate the neuronal correlates of spatial social interactions in primates and how the social interaction between a monkey pair was affected by whole-body movements of either the dominant or the subordinate animal. In order to overcome the common problem of obtaining concurrent neuronal recordings, Tseng and team used a whole body navigation task with a pair of monkeys, one of whom performed the task and the other observed. Three monkeys (C, K and J – the level of dominance in the group was determined prior to the experiment by the order of priority for food access) were used in the experiments and their brains were implanted with multiple cortical microelectrode arrays. For the experiments the monkeys were paired as C-K and C-J and they performed a whole body navigation task where one monkey (the passenger) was carried in a robotic wheelchair on a randomly computer generated route to a food dispenser giving out grapes while the second monkey (the observer) was seated stationary in a chair in a corner of an approx. 20 square metre room and observed.  Both received a reward at the same time eg. grapes for the passenger who performed the action and juice for the observer to maintain its attention. The monkeys swapped roles for each given task. As the monkeys performed the navigation task, concurrent neuronal connectivity recordings in the motor cortex (M1) and premotor dorsal area (PMd) were carried out. Episodes of synchrony of the neuronal firing (ICS) were said to represent specific aspects of social interactions in the monkeys.

The results of the experiments showed that there was episodes of ICS observed for each test and when all the results of the tests were amalgamated, the ICS observed depended on the wheelchair kinematics (whole body movements:  translational and rotational velocity and spatial location; room coordinates of the wheelchair), the passenger-observer distance and the passenger-food reward distance. Results showed differences with the various monkey pairing and assigned task roles. ICS episodes were observed for approx. 20% of the total session time for monkey pair C-K and 36% for C-J. For example, when monkey C (the more dominant monkey) was passenger and K was observer episodes of high ICS were more frequent when the distance between the monkeys decreased, but there were less frequent ICS when the monkeys swapped roles. Wheelchair velocity produced the same effect. The monkey pair C-J gave the same result pattern. Also higher levels of ICS were more frequent when monkey C, as passenger, was far away from monkey K and became less frequent when C approached J.

Tseng and colleagues also found that the probability of ICS was also influenced by the distance between passenger and grape dispenser (the position of the reward for the passenger). This appeared to be dependent on pairing since with the C-K pairing then the ICS episodes increased when either monkey was passenger and approached the grape dispenser, but with pairing C-J then ICS decreased when either was close to the grape dispenser. There also appeared to be variations in the results if the actual positions of the observer and grape dispenser were also altered. The probability of ICS also appeared to be dependent on wheelchair velocity. There was an increase in value when the wheelchair had a high rotational velocity for monkey C as passenger and monkey K as observer, but no change occurred if K was the passenger. The results of the C-J pairing were the same.

Tseng and colleagues also correlated their results of ICS to particular brain area activity. They found that with the monkey pairing C-K, when C was passenger then M1-to-M1 synchronisation (primary motor cortex-to-primary motor cortex) was the most frequent (approx. 61% of time) with M1-to-PMd (dorsal premotor cortex) synchronisation approx. 52% of the time and PMd-PMd only 43%. The same pattern was observed when K was passenger (53%, 47%, 44%). The authors also observed differences in brain area activity when the ICS was associated with the passenger`s position and velocity. In this case the dependence on neuronal firing rate was quantified using modulation depth. In these experiments Tseng and team found that the passenger had more velocity and acceleration modulated neuronal firing units in both M1 (velocity=  approx. 44% acceleration =  approx.16% ) and PMd (velocity=  approx. 41%  acceleration =   20% ). These values decreased when the monkey became the observer eg.  M1 (velocity=  approx. 1%  acceleration =  approx. 0% ) and PMd (velocity=  approx. 1%  acceleration =   0% ). They also found differences between the monkey pairs with monkey C modulation depth stronger in the PMd area than M1 when paired with K, but weaker when it was paired with monkey J.

The results also showed that there were brain area differences in neural firing when the position of the wheelchair was altered. Activity in both M1 and PMd units was found to be modulated in both passenger and observer brains. More units were modulated in the passenger brain to the observer (56% to 31%) and the presence of episodes of ICS increased spatial modulation strength (67% in presence, 44% in absence). It was also discovered that spatial modulation strength was higher for the more dominant monkeys.  Therefore, Tseng and colleagues showed that there were changes in firing rate associated with task and social rank. For example, it was shown that there was a change in firing rate of PMd units when the passenger was moved to different room locations. Spatial modulation patterns were also affected by the presence or absence of ICS episodes and the monkeys taking part. For example, the neuronal rate of the PMd unit of monkey C increased with decreasing distance between it and monkey K whether C was the observer or passenger. However, when monkey K was passenger then the firing rate of the PMd unit decreased when it approached monkey C, but increased when it approached the grape dispenser. When monkey K was observer then the firing rate of the PMd unit increased when it approached monkey C, but decreased when it approached the grape dispenser. For monkey K, the spatial modulation was stronger during the episodes of ICS observed.

In order to investigate social interaction further, Tseng and colleagues investigated the probability of ICS when the distance between the monkeys was less than 1 metre. This distance was used since the monkeys were said to experience different behaviour when others are within its extrapersonal space. Research from others shows that this is supported by neurochemical studies where mirror neuron modulation also appears to be different when actions occur within this extrapersonal space. In their experiments, Tseng and colleagues found that neuronal rates increased during the ICS episodes in both passenger and observer when this distance was used, but were lower for the more dominant monkey in the pair and higher for the observer compared to the passenger.

Therefore, Tseng and colleagues concluded from their experiments that cortical neurons modulated their firing rate activity in M1 and PMd areas according to whether the monkey was passenger or observer, and also from the experiment`s task wheelchair position and velocity.  Periods of transient synchronised firing were observed and these periods of high ICS were said by the authors to indicate social interaction between the monkeys. As the passenger moved then his M1 and PMd units responded to his change in physical position. The observer had equivalent cortical neuronal populations (mirror neurons) respond to represent the passenger`s movements and a period of synchronisation of firing occurred (ICS) between the passenger`s brain and the observer`s. The ICS periods reflected the social standing of the monkeys and the assigned roles in the task and varied both in probability and magnitude dependent on wheelchair location and speed (velocity and acceleration), distance between the monkeys and distance from the passenger to the reward.

With regards to social standing, Tseng and colleagues concluded that the ICS observed reflected position in the established monkey`s social hierarchy of their experimental monkeys and also assigned roles in the current task. With respect to behaviour, they found that the dominant monkeys roamed freely in their environment, while the more submissive monkeys suppressed their behaviours in that environment to avoid conflict. In their experiments, monkey C was dominant and strong ICS was measured when monkey C was paired with K especially when the two were close together. Social standing was also linked to the results pertaining to the distance between the monkeys. Activity was observed in the PMd area, but could change relating to the monkey`s position within the social hierarchy and therefore, the authors interpreted the activity as representing different approaches eg. ´someone entering my space` would produce a different neural pattern to ´me entering someone else`s space`. The experiment carried out where the distance between passenger and observer was less than 1metre showed more activity in the brain of the less dominant and when the monkey was the observer instead of the more active, passenger.

The authors also concluded that there is a significant amount of activity in the M1 and PMd areas which is modulated between observer and passenger and this observation is consistent with theories of others that mirror neurons are active in premotor and motor cortical areas and represent the observations of actions relating to wheelchair rotation and velocity. The authors observed that the M1 or PMd neurons had different tuning patterns when the monkey was either navigating or observing, but the modulation depth was comparative suggesting to them the existence of mirror neuron activity. The average modulation depth appeared to be higher when the monkey was a passenger rather than an observer and this they concluded could mean that self-motion elicits a stronger representation. Alternatively, the monkey when assigned the role as observer was not as attentive in the experiment and this reflected the experimental set-up which did not demand the observer to have full attention on the task. An attempt to rectify this was by only scoring when the observer monkey`s head was turned in to centre of the room.

With regards to passenger and reward distance, Tseng and colleagues found that neuronal  activity in the M1 and PMd areas was related to it and was modulated as to whether the monkey was passenger or observer. They proposed that social dominance determined food access and levels of aggression and therefore, the neuronal activity observed reflected a social interaction factor. Activity was attributed to dopaminergic activity which led to the suggestion that rewarded actions may explain the phenomenon of learning by observation and therefore, ICS could be a neuronal manifestation of social learning. It could therefore, facilitate the transfer of knowledge from one individual to another.

Therefore, Tseng and colleagues investigation was described as going beyond the ´mirror-neuron framework of observation mirroring action` since it showed that social interaction caused episodic ICS in multiple motor cortical areas. Since social interaction is described as a type of behaviour where actions are amalgamated with observations, the results of Tseng and colleagues were interpreted as indicating that social interaction between pairs of monkeys can be represented by widespread ICS which merges both action and observation as part of a neurophysiological interactive process taking place simultaneously in the motor cortical areas of multiple primate brains within a social group. Therefore, even though the authors identified that an amalgamation of results was necessary and also other behavioural aspects could have an influence on neural effects eg. eye contact, further research on ICS episodes in motor areas was suggested as a way of increasing knowledge about planned movements and execution in social environments. This research they indicated could also be applied to other species including humans. This was suggested as having implications on future clinical applications especially for disorders where social interaction deficits are known eg. autism. Although some of these disorders are known to be linked to deficits in the neuronal mirror system, according to Tseng and colleagues they could also now be linked to ICS episodes. Therefore, further research was suggested as being advantageous since according to the authors it could lead to the possible development of a diagnostic tool, or as a measurement of ICS for monitoring of treatment, or as part of bioneurofeedback therapy for improving social motor skills.

COMMENT

What makes this article interesting is the link between one of the popular topics in the neurochemical world in today`s times, that of neuronal area connectivity, with one of the popular topics of ten to fifteen years ago, that of mirror neurons.  In this article, neuronal activity of particular motor learning areas is associated with movement and reward of one individual and observation of that movement and reward in another. This is somewhat different from the most of the mirror neuron work carried out previously where emotional status of the observer is essentially associated with a specific action and elicited emotion of the other active individual. Therefore, brain areas investigated in this article were the motor learning areas of the primary motor cortex (M1) known as responsible for the initiation and execution of actions and the dorsal pre-motor cortex (PMd or dorsal pre-motor area, PMA) linked with motivational input, sensorimotor integration and movement planning. The former area is not as well-known for mirror neuron activity as the latter. Tseng and colleagues took their discussion of mirror neurons in these areas further by looking at the relationship between the neuronal connectivity of the M1 and PMd within and between animals and attempted to associate periods of neuronal synchrony with examples of social interaction and social hierarchy observed in their experimental animals.

Two trains of thought relating to mirror neurons and brain area connectivity arise from the experiments carried out: the first train relates to brain area activity associated with motor movement and observation of that motor movement and why there should be periods of mirrored neuronal activity; and the second relates to the emotional status arising from the action and resulting reward or observation of them and whether this in anyway is reflected by the neuronal synchrony observed in the motor learning brain areas.

An investigation into the first train of thought leads automatically to the question as to what is actually happening in the M1 and PMd areas during the experiment. From a physical point of view what we have is essentially similar action from both observer and passenger in obtaining the reward eg. extension of the arm and grabbing plus eating, but there is a difference in motor movement before the reward is given. In essence the actual motor movement in this stage in both observer and passenger is the same. This can be explained by looking at how the experiment is set up. The observer is stationary with the passenger approaching (ie. no motor movement) and the passenger is stationary in the robotic chair (also the same, no motor movement ie. no climbing, no running) and therefore in this case the ´movement` being registered is a change of location and distance between it and the reward dispenser or other monkey. Therefore, neural activity in both M1 and PMd reflects the movements taking place before the reward and during the reward with some neural activity observed reflecting bottom-up input and control eg. incoming visual information, hand positioning and some top-down eg. the perception and interpretation of the situation, attention and emotional status.

At some points during the experimental time period according to the authors there are episodes of neuronal synchrony within the primary motor area (M1) and the pre-motor area (PMA) during the task that was carried out simultaneously with both monkeys. This would be expected in relation to the reward part of the task since both monkeys received the reward at the same time and therefore, activity in the PMA region would represent the planning of the actions required to obtain the reward and the activity in the M1 region would represent the movements actually being carried out. This would be the same for either monkey. With respect to the time before the rewards were given, then the periods of neuronal activity reflect the different situations of the monkeys (ie. the passenger moves, but the observer is stationary) and implies that the ability to register and interpret the actions of another requires activity in areas related to the motor system for both planning (eg SMA and PMA, M1) and execution (eg M1).  This goes against what we know since neither monkey actually executes the movements required to bring it closer to the other or its reward and therefore, for this time frame M1 activity should be minimal representing only the planning part of the task. It is the PMA where the most activity should be observed since it relates to the planning and intention of movement, which is not initiated. The M1 activity observed possibly reflects the suppression of movements since some neurons of the M1 are facilitators and others suppressors (their activity facilitates during only action observation). In the case of the passenger active movement is suppressed because the robotic chair brings him automatically to the reward and in the observer, active movement is suppressed because he is secured to a chair and can only observe, cannot initiate active movement and has to wait for the reward he knows will come as soon as his ´partner` carries out the required task ie. the distance between it and the grape dispenser is crossed.

The periods of neural synchrony observed were attributed by Tseng and colleagues to mirror neuron activity. As we have said above in the case of the reward this is understandable since both monkeys strive to reach the reward and then consume it. In the period before the reward, then periods of synchrony are also explainable. In the case of the PMA area, mirror neuron activity ie. neuronal firing activity which mirrors that of another`s performing the action is well known and this is said to be due to the area being responsible for motor movement rehearsal. The case of mirror neuron activity in the M1 is less known, but is still observed. Therefore, the periods of neural synchrony seen in the M1 and PMd areas between monkey passenger and monkey observer would represent those periods where both monkeys share the same informational input and demands regarding motor movement. Hence, periods of ICS would be expected when both monkeys received the reward for example. Periods of synchrony before the reward may reflect the intention of movement as described above which would be the same for both monkeys, or an alternative explanation is that the movement of the passenger monkey is mirrored by the perception of movement by the stationary observer monkey. This would be in the same way that a passenger on a stationary train that is passed by another train that is moving perceives movement. In the case of the monkeys performing the whole-body navigation task, the level of mirror neuron activity which is low anyway, may represent only a few features of the overall event that are shared and would be responsible for perception of the event. This explanation is possible since if learning has occurred the level of mirror neuron activity decreases with repetition of the tasks in the same way repeated real-life action does. However, in Tseng and colleagues experiments this does not occur. An explanation for this lack of modulation may be that the results quoted are amalgamations of test results and said to be the probability of incidences of neural synchrony and therefore, reductions of discharge rates for the areas may be obscured by the way the results are formulated. However, with regards to movement in this task it is more likely that mental imitation in the motor areas M1 and PMd represent task perception and planning before the reward and same or similar neural activity representing the same movements carried out relating to receiving and eating the reward.

This leads on then to question what is happening with regards to mirror neuron activity in the M1 and PMd areas in relation to social interaction as suggested by Tseng and colleagues. Social interaction in the monkey world reflects social hierarchical status and Tseng and his team demonstrated that the monkeys used in their experiments exhibited social standings which manifested into the amount of freedom of movement each monkey had, access to food, grooming etc. From a neurochemical perspective this social order would be learnt and learnt behaviour would follow the order. Deviations from the social order are interpreted as inducing, just like they would in humans, changes in the emotional system particularly fear.  For example, dominant monkey C is unlikely to experience fear if as passenger he approaches monkey J, the lowest monkey in the social order, but monkey J would (and also if he is in the passenger role). The activation of the fear system in this case would produce known changes in brain area activity eg. activation of the amygdala area  as well as changes in the quality and quantity of information inputted at the time and working memory and attentional system performances. Therefore, it is likely that in the case of the whole body navigation task behaviour not in keeping with social standing would result in a reduction of the periods of synchrony of neuronal activity observed since the activation of the fear system of the less dominant monkey would have induced changes in firing patterns to accommodate the situation the monkey found itself in. The brain areas affected appear not just to be those top-down areas such as involved in working memory, attention and decision making such as the ventromedial prefrontal cortex as expected, but also the brain areas relating to movement planning and execution. The role of the PMA, is understandable – fear or distress would instigate a greater level of movement planning for example to get the monkey out of the dangerous situation and since the M1 is also involved in planning activity, activity in this area would also likely to be increased. However, activity of these areas may not be synchronised to that observed in the more dominant monkey who does not experience fear. Therefore, it would be less likely that neural synchrony would be observed in times of high emotion.

Tseng and colleagues took the relationship between mirror neuron activity of the motor learning brain areas and social interaction further by suggesting that this relationship could be manipulated and controlled to help individuals suffering from disorders where social interaction is negatively affected. The interpretation of results here indicates that social skills of one individual can be learnt by mimicking the actions and emotional responses of another. From a neurochemical point of view an explanation for the mechanism required is relatively easy to formulate. The ´tutor` would perform the action with appropriate emotional and social responses which the ´observer` ie. the person learning would watch. According to Tseng and colleagues study, mirror neuron activity of the observer would elicit synchrony of the PMA firing and to a lesser effect the M1 area. Repetition of the action by the ´tutor` would mean that the PMA and M1 firing has the same pattern and so neuronal cell assemblies would be formed in the higher brain areas to represent both the action (whether a single event or a sequence) plus a positive emotional effect. A repeat of the action once learnt would then lead the observer to perform the appropriate action through reactivation of the past experience which would lead to the individual being able to perceive the situation, understand the situation and know from learnt behavioural examples how to react in what would be learnt and regarded as the proper manner. This method is known and forms the basis of many therapies eg. bioneurofeedback. However, it is probably not the panacea for all suitable therapies for this type of disorder since a factor to consider is the problem of transference of results and conclusions from a monkey which lives in a relatively simple social environment particularly those bred for scientific experiments to humans who live in a complex, constantly changing highly social environment. For example, the experiments by Tseng and colleagues do show a constraint on such learning since neuronal synchrony between the learner and ´tutor` would be reduced if either party was distressed and the emotional fear system was active. This would affect the level of learning and therefore, optimal learning would have to be carried out whilst the individuals were relaxed and mood was positively stable – a situation difficult to achieve and effectively monitor. Hence, the results of investigations such as these provide more knowledge about neurochemical processes, but they should not be directly transferred to humans and be applied to and influence social conditions.

Therefore, to conclude this comment on the positive side we can see that activity of brain areas associated with motor movement such as the M1 and PMA occurs when either an individual performs an action or observes an action, but the function of these activated areas could be different. In some cases, the neuronal activity of the M1 and PMA may be synchronised and this is said to be the work of mirror neurons of the observer brain. This could reflect shared features of active or observed events or reflect suppression or facilitation of firing. In one case the activity causes initiation and execution of the action; in the other the perception and interpretation of the action. The level of firing in the motor areas appears to be affected by emotional status and this is understandable in that the prefrontal cortex, basal ganglia, anterior cingulate cortex areas are all important components of not only the emotional system, but are also important in the motor loops responsible for bringing about motor movements.

Since we`re talking about the topic…………………………………………………….

…..would the substitution of pain instead of reward in the case of the observer substantiate Tseng and colleagues results relating to mirror neuron activity in the PMA and M1 regions when the observer was the less dominant of the pair, but cause a change when the more dominant became the observer?

……would the use of opposite arms and hands by the observer and passenger produce differences in neuronal activity in the PMd and M1 areas and decrease the number of neural synchrony episodes because of changes in the microzonal firing observed?

….can it be assumed that if the observer`s eyes were covered for part of the distance covered by the passenger in its route to the grape dispenser that PMd activity would remain since it can function in the absence of visual stimuli, but the episodes of neural synchrony between the observer and passenger during this time would decrease because the firing would represent different input?

 

Posted in learning, mirror neurons, motor learning, neuronal connectivity, Uncategorized | Tagged , , ,

local translation and the role of the RNA binding protein

Posted comment on ´The function of RNA-binding proteins at the synapse: implications for neurodegeneration`  by C.F. Sephton and G.Yu and published in Cell. Mol. Life Sci. 2015 vol 72 page 3621 doi 10.1007/s00018-015-1943-x

SUMMARY

Sephton and Yu began their article by describing the important role of protein translation taking place locally in the synapse. This type of translation was explained as necessary to accommodate the changes in this area of the neuron brought about by the constantly changing micro-environment due to neuronal activation. In addition, localisation of mRNA in the synapse and protein synthesis has been shown to be critical for synaptic plasticity and memory formation. Control of nuclear translation can depend on different mechanisms such as upstream open reading frames, secondary structures or regulatory protein binding sites, but in their article, Sephton and Yu concentrate on the specificity of the translation of the mRNA by the RNA binding proteins present. This is the main group of proteins regulating mRNA transport and translation in this area and attention to this group has been brought about because of the assumed link between known genetic mutations (or gene deletions) of particular examples and certain neurodegenerative diseases. For example, sufferers of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) exhibit cognitive impairments and loss of motor neuron function and research has showed that there are strong associations between several RNA binding proteins eg. Fused in Sarcoma binding protein (FUS) and Transactive Response DNA binding protein (TDP-43) in both diseases resulting in dysregulation of synaptic function and initiation of abnormal neurodegeneration.

Local translation in neuronal cells is said to occur because of the distances between the soma (the cell body) and the dendrites which creates, what the authors  termed, a ´supply and demand challenge`  for proteins required in the dendrites for them to function, for example responding to neuronal firing and signal transmission. Therefore, local translation means that all requirements for the neurochemical process have to be located within the dendrites in addition to those in the vicinity of the cell nucleus. The components required include mRNA, ribosomes and translation factors. In the synapse, the mRNA is found to exist within granules. It is associated with the RNA binding proteins in the untranslated regions (5UTR or 3UTR) or coding regions to form messenger ribonucleoprotein complexes (mRNP) which can then assemble into more complex structures known as RNA (or RNP) granules. There are many different types and classification is based on composition, location, response to stimuli and function, eg. regulation of, distribution of, translation of and degradation of mRNA transcripts. The granules are constantly changing, forming different interactions and exchanging mRNPs, cytosolic proteins and polysomes, but research has shown that their presence is responsible for synaptic plasticity and communication in the functioning synapse.

Sephton and Yu continued their article with descriptions of particular examples of multi-mRNP complexes such as transport RNPs (tRNPs), stress granules or processing bodies. The transport tRNP granules contain mRNA and are said to be involved in the storage and transport of mRNA. They can also contain microRNA (miRNA). RNA binding proteins found associated with the tRNP type of granule are, for example Staufen 1 and 2, FUS and TDP-43. However, they are also said to contain at least 40 other proteins including motor transport proteins such as kinesin.  The transport of mRNA to the dendrite appears to occur with the mRNA in a translationally dormant state, but this can undergo alteration in certain circumstances. The authors describe the example of stress where mRNPs are exchanged within the tRNP with stress granules and processing bodies. The mRNAs are protected and once stress removed then the stress granules disassemble and mRNAs are repacked into translationally competent forms and their proteins are synthesised. Alternatively, they are selectively exported to another type of granule, the processing bodies which are responsible for degradation, translational repression and recycling.

The role of gene mutations of RNA binding proteins and the resulting neuropathological diseases were described by Sephton and Yu with reference to two mutations in particular, that of FUS mutations and TDP-43 mutations. The mutations of the specific RNA binding proteins in both cases resulted in negative changes in the cell`s mRNA population and protein translation causing adverse effects on synaptic structure and function and neurodegeneration. In both neuropathological diseases, no changes of the processing bodies were observed, but there were changes in tRNP and stress granule populations. FUS-mutations were found to cause an increase in number and size of the tRNP and stress granules. This was suggested as for example, resulting in more spontaneous assembly of tRNP granules that could have an impact on translation, or that it could make both tRNP and stress granules more insoluble. As a result pathological inclusions would be ´seeded` and this is reported as being the case in both ALS and FTD. Regarding TDP-43 disease mutations, larger, but fewer tRNP were observed and larger, but greater numbers of stress granules. Both were assumed to cause the same physiological effects as that seen with the FUS mutations.

In their article, Sephton and Yu then went on to describe how RNA binding proteins regulate local translation. RNA binding proteins are said to be a component of RNA granules and these transport mRNA to the dendrites in a translationally dormant state. This is supported by the presence of nucleic translation initiation factor 4AIII (eIF4AIII) with dendritic mRNA.  The mechanism by which translation is inhibited by the RNA granules was said not to be known since it has been shown that some granules contain translation components such as ribosomes and endoplasmic reticulum (ER). For example tRNP granules with the largest number of Staufen 1 and 2 pools contain ribosomes and ER and those containing the smaller number had kinesin, but no ribosomes and ER. It was assumed that this was the group of tRNP where translation was repressed. In this case, neural activity could therefore cause either release of mRNA from the tRNP granule to the polyribosomes where active translation would occur, or in some circumstances repression of translation would continue. The protein composition of the granule particularly the presence of RNA binding proteins appeared to dictate which path would be followed.  For example, ZBP1 associates with and transports beta-actin mRNA to synapses. Src phosphorylation causes dissociation between ZBP1 and mRNA, hence synthesis of beta-actin, which is required for both cell migration and neurite growth, is allowed. However, ZBP1 can also repress the joining of ribosomal subunits in the cytoplasm so translational initiation regulation can occur. Other examples described by the authors are the CPEB family of RNA-binding proteins where CPEB1 can act as both repressor and activator of translation and the binding protein, FMRP. FMRP mutations are associated with Fragile X syndrome and studies have suggested that FMRP plays a role in the linking between tRNP granules and polyribosomes. The phosphorylated form of FMRP is associated with stalled ribosomal translocation and the non-phosphorylated form is associated with actively translating ones. FMRP can also regulate transmission when in its phosphorylated form by an association with miRNAs eg. miR-125a and RNA-induced silencing complex (RISC) to repress synthesis of proteins such as PSD-95. Dephosphorylated FRMP can lead to the release of RISC from PSD-95 mRNA so translation can take place.

This link between RNA binding protein deficiency and neurodegenerative diseases was continued with Sephton and Yu looking at how the deficiencies are associated with changes in dendritic synaptic degradation and dendrite morphology, for example as seen in ALS and FTD sufferers.  For example Staufen-1 knockout mice are observed to have reduced numbers of dendrites and synapses in the hippocampus and have a defective dendritic delivery system of beta-actin tRNP granules. Sephton and Yu in their article looked at the cases of deficiencies of the FUS and TDP-43 binding proteins in particular.

FUS is a RNA binding protein responsible for the translation of proteins required for neuronal development and synaptic transmission. It is known to bind to thousands of cellular RNAs via two RNA recognition motifs (a zinc-finger domain and 3 arginine-glycine-glycine boxes). As it appears to exist in different ribonucleoprotein complexes it is thought to be involved in all types of translation processes eg. mRNA stability and miRNA biosynthesis. When the neuronal cell is in a steady state, the majority of FUS is localised in the nucleus of the cell where it exists bound with TDP-43 and SMN along the whole length of the nascent RNA. All three binding proteins in this case function in the maintenance of the spliceosome. It is also thought that FUS is involved in transcriptional elongation as it is closely associated with RNA polymerase II, binds to pre-RNA and mRNA at introns and coding sequences etc. Therefore, FUS mutated cells and FUS-null cells demonstrate dysregulation in mRNA and pre-mRNA splicing.

However, FUS can also be localised to cellular compartments other than the nucleus. It has been found in RNA granules and this localisation is facilitated by the non-classical proline-tyrosine nuclear localisation sequence (PY-NLS) and the nuclear export sequence (NES). The localisation results as a response to various stimuli and Sephton and Yu give in their article the example of hippocampal or cortical slices that had been stimulated by treatment with mGluR1/5 agonists. In the response to the agonist presence and hence, neuronal firing, FUS was found in the tRNP granules in the dendrites and also at the synapses where FUS was associated with NMDA receptors themselves. The role of FUS in the tRNP localised in the dendrites was described as being either repression or facilitation of the required translation and there appears to be examples of both.  FUS can also associate with cytoplasmic stress granules, but in this case, this only leads to translation repression.

As expected with a promotion or repression role in local translation, FUS is linked with neurodegeneration under some circumstances. The majority of familial ALS mutations are associated with the FUS encoding gene at its PY-NLS terminal which affects the localisation of the protein and its aggregation. For example, abnormal accumulation of FUS in the cytoplasm or nucleus of motor neurons in ALS patients is observed. The extent of the mutations appears to correlate to disease severity. Some mutations eg. those at the 3 prime end, can cause increased FUS expression. Mutations at the RGG1 and N-terminal region can also occur with FUS although this type of mutation is normally linked to FTD. In this case, the localisation of FUS is not as prominent as normally observed and abnormal aggregation can occur in some cases. Animal models expressing the various FUS mutations and their effects on neurodegeneration are available to support the research, eg. ALS-FUS mutations in D.melanogaster and C.elegans and examples of transgenic mice (eg. ALS-FUS R521G) which demonstrate motor deficits and motor neuron abnormalities. However, results from the animal models appear not to always follow expectations, eg. FUS mutations in transgenic mice appear to be regulated differently to wild type mice in response to neuronal stimulation, or could have altered dendrites. This is also supported by neuropathological studies which have shown as an example that pathological FUS aggregation does not occur in any animal model.

Animal models, however, are said by the authors to be of more value in studies on the reactions of stress granules to FUS mutations. ALS-FUS PY-NLS mutations show a correlation between disease severity and localisation in the cytoplasm and in the presence of these mutations larger granules are formed independent of the presence of stress. It has also been observed that FUS mutations may delay the assembly of stress granules and irreversibly sequester a variety of RNA binding proteins and mRNAs. No effect on the processing bodies or their associations has been observed. Therefore, suggestions have been made that the ability to bind mRNAs and sequester them into RNA granules may be one factor in disease neuropathology. Researchers have suggested that FUS mutations ´seed` protein aggregates which sequester more FUS and other proteins, hence depleting the cell of essential proteins and leading to cell death. This view however, requires according to the authors further research support.

The other example of RNA binding protein described in detail by Sephton and Yu in their article is TDP-43 which like FUS regulates gene expression. This binding protein can bind to over 4500 species of RNAs via 2 highly conserved motif regions: the RRM1 which is a major domain for binding of RNA and DNA and the region, RRM2. Like FUS, TDP-43 regulates transcription and multiple RNA processing mechanisms and hence, deletion leads to dysregulation of mRNA and pre-mRNA splicing. TDP-43 is expressed and located primarily in the nucleus, but it too can localise to different cellular compartments and RNA granules via classical NLS and NES binding. Various stimuli cause localisation to these other areas. For example, under basal conditions TDP-43 can be found in hippocampal dendrites and is co-located with RNA granules such as processing bodies. It can also co-localise with the post synaptic protein, PSD-95. The RNA granules containing TDP-43 contain RNAs including mRNA for beta-actin and CaMK11alpha, an important enzyme in neuronal cell plasticity. However, on depolarisation then RNA granules with TDP-43 co-localise FMRP and Staufen-1 in tRNP granules in the dendrites. In oxidative stress, TDP-43 localises in the cytoplasm and into stress granules. Again, no association with processing bodies has been reported even though reports of FMRP, Staufen-1 have been reported in these bodies in some circumstances.  There are also reports of TDP-43 being an integral component of different complexes eg. Dicer, Drosha and it is believed to be involved in miRNA biogenesis responsible for neuronal outgrowth.  Therefore, it is thought to act like FMRP in that its presence leads to translational repression via miRNA regulation.

Just like FUS mutations, TDP-43 mutations are also associated with neurodegeneration and neuropathology of some diseases.  There are more than 40 TDP-43 familial ALS mutations documented (mostly with mutations at the C terminal) and TDP-43 mutations have been observed in approx. 50% of FTD patients. Mutations do not normally lie in the NLS or NES regions, but instead mainly lie in the 3 prime end of the TDP-43 mRNA and these lead to increased levels of TDP-43. No impact on localisation occurs, but because the mutations lie in the glycine rich C terminal region then protein interactions are adversely affected. Animal models where the endogenous levels of wild type TDP-43 or expressed ALS-TDP-43 mutations exhibit drastic neuronal effects, aspects of ALS and FTD diseases are produced. For example, the depletion of TDP-43 in D. Melanogaster causes a decreased life span and locomotor defects due to alterations in dendritic branching and synapses, whereas over- expression can cause a loss of motor function and a reduction in the number of dendrites and synapses. Transgenic  TDP-43 mice expressing either wild-type or ALS associated mutations can also exhibit motor defects, which can be reversed if expression is inhibited. Mutations appear to be associated with abnormal neurites and decreased cell viability where depletion of TDP-43 leads to an increase in number of mature spines in hippocampal neurons, an increase in clustering of AMPA R at the dendritic membrane and an increase in neuronal firing. A link to Rac1 was established suggesting that TDP-43 could be an upstream suppressor of this spinogenesis regulator.

TDP-43 and ALS-TDP-43 mutations are shown to be actively recruited in response to stress to the other type of RNA granule that of the stress granule found in the cytoplasm. Both the C terminal glycine rich domain and the N terminal RRM1 region appear to be important for this association and therefore, both RNA binding and protein-protein interactions are required. Large stress granules are formed and like FUS, pathological aggregates of these stress granules occur suggesting a role in seeding for the TDP-43. TDP-43 mutations can also affect tRNP granule formation and migration and TDP-43 has been shown to be associated with RNA granules throughout the dendrites. Aggregation of TDP-43 is assumed to be important for the formation of neuronal tRNP granules and mutations found in ALS-TDP-43 models are shown to increase the size of the tRNP granules in rat hippocampal dendrites under basal conditions. Depolarisation with potassium chloride stimulates TDP43 granule migration into the dendrites with mutated forms demonstrating reduced density as well as slower speed of movement and shorter distances covered than the wild-type. Therefore, TDP-43 mutations can produce effects by eliciting the absence of important RNAs at sites of local translation or by producing dysregulated mRNA products.

Sephton and Yu concluded their article by looking at future perspectives regarding local translation, RNA-binding proteins and neurodegeneration. They state that the topic is gaining interest because of several reasons. There has been an increase in the identification of genetic mutations in genes encoding proteins involved in RNA regulation and associating these to the neuropathology of certain diseases. There also appears to be anomalies between in vivo disease models and in vitro ones and lastly that synaptic dysfunction precedes neurodegeneration and therefore, tRNP granule formation, localisation and protein translation dynamics, affected by RNA binding protein mutations could be the trigger to the neurodegeneration process.  In order to understand how this occurs, the authors investigated how RNA granules and RNA binding proteins are involved in the maintenance of the synapse and gave explanations to their function. They suggested that there is evidence that RNA binding proteins are important in transporting mRNA into granules which remain dormant until the mRNA transcripts are required to be locally translated on polyribosomes. The authors also hypothesised that the RNA binding proteins are also important for the presence of dendritic branches and spines since genetic deletions cause alterations in both. Therefore, Sephton and Yu concluded that the role of RNA binding proteins and granules should be researched more intently especially linked to the topic of local translation and downstream consequences at the synapse. This role was said to be especially important in light of evidence that linked RNA binding proteins and RNA granules with neurodegeneration and the pathology of particular neurological diseases such as ALS and FTD. However, the authors did admit that this area of localised mRNA translation would be difficult to study because RNA binding proteins like FUS and TDP-43 are associated with the regulation of thousands of cellular RNAs. Focussing on the area of neuronal stimulation and synaptic responses would they suggest make study of the topic slightly easier.

COMMENT

What makes this article interesting is that is looks at another site of protein synthesis in the neuron other than the common sites close to the cell nucleus. Researchers have proposed that local translation in neurons can take place and the authors of the article commented on in this Blog post describe the local site as being in the neuronal dendrites. The authors also link in their article the idea of genetic mutations of some of this process`s components to certain neurodegenerative and neuropathological diseases. We will concentrate in the comment here on the process of local translation in the dendrites and we begin with a short summary of the local translation process. This then leads on to questioning whether or not RNA granules indicative of this local translation mechanism are ´true components` and not an artefact of the experimental procedure used in cell component separation and if they are ´true components`  whether or not the neuronal cell actually needs local translation.

In order to ascertain whether or not RNA granules are ´true components` or not, we should begin with a brief simplified summary of protein synthesis and how local translation fits in with the general process in neuronal cells. The general process of protein synthesis in neuronal cells begins with the nuclear DNA which contains the information required for the protein`s structure and final location. DNA is transcripted into mRNA and we assume that this transcription process is the same whether the protein will be finally synthesised close to the nucleus or will be localised elsewhere.  The initial transcripts (the pre-mRNA) still within the nucleus contains introns (codes not translated into proteins) and exons (codes giving rise to the protein) and the introns, which can be regarded as ´guiding/regulating` signals can be spliced out. (Continued inclusion of these at this point could give rise to different end-of-process proteins.) The transcript of the message occurs in the mRNA form and this passes from the nuclear DNA site, through the nuclear membrane to sites where translation of that message into the designated proteins themselves can occur. These sites of translation are either close to the nucleus, or as suggested for neurons at sites well away from the nucleus such as the axons and dendrites. The site of protein synthesis if close to the nucleus is the rough endoplasmic reticulum (ER) which consists of stacks of membranes with ribosomes, the protein synthesis machinery, attached.  The mRNA transcripts carrying the protein-building information binds to the ER and is translated in a 5 prime end to 3 prime end direction. Translation means that the ribosomes bring the necessary amino acids of the final protein in the form of tRNA together to allow biochemical binding to occur to make the designated protein. Alternatively proteins, normally those dictated to finally reside in the cytosol, can be synthesised by free ribosomes which attach to the mRNA ´thread` itself to form complexes called polyribosomes. The proteins are synthesised in the same manner as those formed from ribosomes attached to the ER. Folding of the newly formed proteins occurs in the smooth ER followed by post-translational processing which can occur in the Golgi Apparatus (GA) which also is located close by. This is the site where proteins are sorted and processed accordingly to their ultimate destination.

Other researchers and Sephton and Yu in their article described one stage of the protein synthesis mechanism that of translation which was said could occur in the case of the neuron away from the nuclear site and nearer to where the proteins would actually be needed eg. the neuronal dendrites (or axons). This is termed local translation. In local translation, mRNA transcripts of the DNA material are formed just like with nuclear translation and are transported outside the nucleus into the cytoplasm. Although it is not known how long they exist as free threads or where, the next time they are observed is in the synaptic areas of the axons and dendrites where they appear associated with RNA binding proteins in the untranslated regions (5UTR or 3UTR) or coding regions to form messenger ribonucleoprotein complexes (mRNP). These can then assemble into more complex structures known as RNA (or RNP) granules. The complexity of the RNA granules and the range of locations, response to stimuli and function appear to be due to the RNA binding proteins and other components that make up these granules.

Sephton and Yu described in their article particular examples of RNA granules eg.  transport RNPs (tRNPs), stress granules and processing bodies. Each has a specific function, but the granules are constantly changing, forming different interactions and exchanging mRNPs, cytosolic proteins and polyribosomes. In each case the transcript exists primarily in the mRNA form and not in the pre-mRNA form and therefore, the presence of the polyadenyl translational signal would be absent having been spliced from the DNA transcript before leaving the nucleus. Instead, it is possible that RNA binding proteins take over this role and this will be commented on later. This is not uncommon and can been seen in other systems, eg. prokaryotes have promotor regions for every gene. It is also likely that the RNA transcript is in this stage of protein synthesis rather than for example the later elongation or termination stages since to have this level of control at these later stages would be a waste of resources and time. It is also unlikely that the granules are some manifestation of clumped microRNA (miRNA), which is formed from sections of mRNA folding back on itself and being spliced out. This is ruled out since the RNA content has been sequenced and found not to be complimentary not just to the 3 end of DNA as in the case of microRNA, but also that a small amount of microRNA is actually observed in the granules themselves. Other supporting evidence for the mRNA form in the granule and not the miRNA is that the latter can cause DNA modification, eg. histone modification, DNA methylation which RNA granules have not been reported to do. The mRNA in the granule dictates the proteins to be translated but as said before they are associated with other constituents into the granule form. What can definitively be said is that the functions of the granules are related to their molecular constituents eg. the RNA binding proteins and their presence is responsible for synaptic plasticity and communication in the functioning synapse. This explains why the contents of the granules are constantly changing and the content dictates the different outcomes eg. neurogenesis in hippocampus or memory consolidation in Aplysia.

So, now we have our mRNA transcripts in an area away from the nucleus and these are associated in granules whose function is dependent on their biochemical constituents. Probably from a neuronal firing point of view the most common form of granule is the most important, that of the transport tRNP granule type, which is said to be involved in the storage and transport of the mRNA. This type of granule can contain mRNA and also can contain microRNA (miRNA). RNA binding proteins found associated with the tRNP type of granule are for example, Staufen 1 and 2, FUS and TDP-43. However, the granules are also said to contain at least 40 other proteins and can be divided into two groups: those containing a low pool of Staufen 1 and 2, but contain the motor transport proteins such as kinesin; and those containing a larger pool of Staufen 1 and 2 and can contain ribosomes and ER. The transport of mRNA to the dendrite appears to occur with the mRNA in a translationally dormant state. In response to neuronal stimulation for example, the mRNA is released from the tRNP and forms polyribosomes so that translation can occur. This leads to local production of the necessary proteins in the synaptic area. It is thought that the mRNA that is translated is associated with the tRNP granules with the larger pools of Staufen 1 and 2 and the translationally dormant form is linked to the kinesin containing ones. It is also believed that the binding protein FMRP may provide a link between the tRNP and polyribosomes and its phosphorylative state may dictate whether translocation occurs or not, eg. the non-phosphorylated form of FMRP is linked to active translocation, the phosphorylated form with stalled. Following translation, the mRNPs can assemble back into the tRNP state, can be degraded or assembled into another form of RNA granule, the processing bodies. These are responsible for mRNA degradation, translational repression, and recycling or decay and hence, contain many proteins involved in these processes. Alternatively, neural stimulation can lead to repressed translation. Again the nature of the granule components eg. the RNA binding proteins determine the action ie. if protein translation occurs or whether is is repressed.Under stress conditions, another form of RNA granule is formed, that of stress granules. In this case, mRNPs are exchanged within the tRNP with stress granules. The mRNA transcripts are therefore protected and once the stress is removed then the stress granules disassemble and mRNAs are repacked into translationally competent forms and their proteins are synthesised. Or, alternatively, they are selectively exported to the associated processing bodies and the mRNA is degraded and hence, translation is repressed.

Although researchers are certain that RNA granules exist, the changeable physical nature of the system must lead to the question as to whether RNA granules actually exist in the natural neuronal environment or whether they are in fact just an artefact or a product of the experimental preparation techniques employed to investigate them or other neuronal components. It could be that otherwise free mRNA observed closer to the nucleus is bound with RNA binding proteins released due to cell separation techniques and the mediums employed. For example, the biochemical components may take the form of a granule due to the pH or ionic composition of the medium which could cause unnatural folding and bonds being formed. Aggregation of the granules is observed naturally and is believed to be related to greater degradation, but is not seen in some neuropathological cases or animal models. Alternatively, the experimental preparation process could remove blocking proteins and fatty acids and allow unnatural ribosomal binding to occur whether it is at the stage of the initial 30S subunit or later where the subunits actively combine. The result of this could be that unnatural translation would occur. The evidence however, does lead to the conclusion that although dynamic and dependent on the role of the cell at that time, the granules are genuine physical structures and the system is a justified mechanism for RNA translation.

Now that we have established that the RNA granule and local translation are real biochemical components and part of a genuine biochemical process we have to look why they are necessary when established mechanisms are already in play. One advantage of this type of translation as discussed by Sephton and Yu is that local translation would provide a quick protein production and transport mechanism to the sites where the proteins are actually needed, eg. the synaptic membrane so that the cell can respond quickly to the functional demands placed on it due to signal transmission. However, it must be said that in some cases of neuronal functioning this is not applicable and there is no difference in length between neuronal cells and other cells. Also, quick responses are believed to be required only in the transmission of the firing signal and longer term responses like memory occur over an extended period of time. Therefore, it is not necessary to have  a separate translating system from the more standard 2 nuclear systems. However, another advantage of such a system is that there would also be a reduced set of needs on such as system such as post-translational processing by the GA which dictates the proteins location since the mRNA is already there in the vicinity where it functions and hence, speed of synthesis and complexity of the protein support mechanism would not be required. It should be noted however, that this implies that the protein structure itself of a protein translated locally may have constraints. It may either have to be functional alone with minimal post-translational processing, or it will be part of a ´super complex` with other molecules required to form the viable active protein.

Another possible advantage of the local translation process to that of the nuclear process is that that RNA granules provide the equivalent of a ´protection system`  for free mRNA in a location away from either the smooth ER and GA. The cytosol is rich in ions which can be detrimental to protein structure and therefore, it is biochemically not advantageous to have mRNA floating around in it for any length of time. Also neuronal cytoplasm due to the mechanism of firing suffers  extreme changes in ionic composition and electrical charge during the course of depolarisation and re-establishment of electrical normality. Therefore, mRNA would have to be protected if it has to travel any distance in this environment from its nuclear source. Binding it with other components eg. RNA binding proteins to form granules would protect the mRNA thread from the possibly destructive biochemical environment of the synapse.

Therefore, although localised translation in areas well away from the nuclear site can be advantageous in terms of speed of reaction and protection, is it necessary to have another separate system in order to carry out this function? The cell already has two types of translation in place: that on the ER and then with GA modification; and that using free mRNA with the ribosomes attaching themselves in the cytoplasm onto the mRNA thread. Local translation is a separate system and involves the mRNA being physically associated with many other components into ´granules`  (eg. RNA granules) in for example, the dendrites. It is likely that this ´granule` system is based on a system already in place to transfer proteins and molecules in other cells. This transport system uses vesicles and the molecular motor systems associated with microtubules and microfilaments. Vesicles can hold enzymes for synthesis and degradation of molecules and is a quick and efficient intercellular transport system. Therefore, it can be considered that the RNA granules described in the dendrites are the mRNA equivalent to the normal vesicle system observed for other biochemical compounds. However, there are some differences and a problem. Whereas most vesicles are considered ´active` eg. lysosomes, the mRNA carried in RNA granules may not be. In tRNP granules, mRNA appears to be transported in a translationally dormant state and the granule acts as a carrier and provider of conditions for a change to occur when the mRNA is required to become translationally active.  This presents a problem because mRNA becomes active when a signal is received, but because of the constant breakdown of the proteins and other components in the granule which is normal for any biological molecule or system, the mRNA cannot be held in a ´ready` state too long. Therefore, there is a constant need for renewal if the aim of having this type of system is to maintain the cell in a continued state of readiness and this requires the biochemical machinery and energy to do this. Also it appears that the RNA granule transport is one directional and that is away from the nucleus unlike the vesicular system which is bi-directional. This implies that the mRNA is not brought back to the nucleus in order to influence the DNA as is observed in the case of some viruses.

Now that we have established that local translation is a viable mechanism and is dependent on RNA granules of different types we will conclude this comment by taking a quick look at one of the components of the granules which is said to be important for neuronal function and cellular plasticity. Sephton and Yu showed in their article the RNA binding proteins content of the RNA granules present at any one time can dictate whether protein translation is repressed or initiated and the type of granule observed eg. tRNP has binding proteins,  Staufen 1 and 2,  FUS and TDP-43 plus mRNA in transitionally dormant state.  RNA binding proteins can exist at the untranslated end regions or in coding regions of the mRNA and as expected such binding will affect maintenance and translation. The presence of either RNA polymerase I or II makes a difference as to whether the region is translated or not and therefore, RNA binding proteins could dictate which polymerase is allowed to bind. (This of course assumes that RNA granules require the same conditions for mRNA translation as that closer to the nucleus.) The RNA binding proteins could also revert the mRNA form back into a local pre-mRNA form so that the initiation of translation is prevented. Pre-mRNA has specific  5 and 3 ends (5 end – cap structure added, 3 end  – poly A tail) which are required for aiding binding of the mRNA to the ribosome, protecting from premature destruction by ribonucleotidases and as a signal for transport to the cytoplasm. Therefore, specific RNA proteins could take on these roles. In nuclear translation splicing occurs to remove these additions and to remove introns so that exons become joined together. This is also likely to be the case in RNA granules when the signal is given to translate the transcripts. This implies that the signal is in some form that can remove RNA binding proteins from the relevant sites. Other RNA binding proteins determine how mRNA interacts with its environment eg. the binding protein FMRP and mRNA and ribosomes. This implies that the binding of RNA binding proteins promotes conformational changes to the mRNA transcript that opens the attachment sites of perhaps the ribosomes. Phosphorylation of FMRP means no translation whereas removal of the phosphate group means that translation occurs. This mechanism is also seen with miRNA and is a common mechanism in other molecules since addition or removal of molecular groups eg. phosphate groups, or disulphide bridges will create changes in tertiary  and quartenary molecular shapes that promote or decrease function.

The number of RNA binding proteins and the number of different roles that they play make them ideal targets for manipulation whether natural eg. gene mutations leading to decreased numbers of the binding proteins or experimental, eg. FUS knock-out transgenic mice. Sephton and Yu described in their article some of the RNA binding proteins and their links to neurodegenerative diseases eg. FUS mutations observed in ALS, TDP-43 mutations in frontotemporal dementia. It is clear that anything that affects mRNA formation, transport and translation will have an effect on the functioning of the neuronal synapse. These effects may be short-term or long-term. The question that has to be answered is whether long-term changes in neuronal function and plasticity are caused only by permanent changes in local translation of the nature described above (ie. caused only by gene mutations), or not. It is possible that temporary ´blips` in protein synthesis in the area of synapse will have immediate effects that alter current neuronal firing, but may not be sufficient to cause permanent effects which are only elicited through the system located closer to the nucleus. Only further research will answer this question, but it is an interesting topic and again indicates how complicated neuronal cell mechanisms are.

Since we`re talking about the topic……………………….

…….if we could carry out RNA sequencing at the 5 prime end region of mRNA from free mRNA, ER-bound mRNA or RNA granule-bound mRNA would we see differences in genetic code dependent on the source or will they all have the same promotor sequences and would these differ in content as expected to the promoter regions of prokaryote mRNA?

…….if we use cortical slices and subject the cells to different conditions eg oxidative stress, drugs, then separate out the mRNA from the RNA granules of those cells and subject them to Northern Blots will we be able to see alterations in the mRNA transcripts necessary for the cell adaptations required to cope with each condition?

……can we assume that the mRNA transcript length as measured by agarose gel electrophoresis is the same whether the mRNA transcript undergoes ER/GA translation close to the nucleus or at a site away from it?

……since mutations of some RNA binding proteins are associated with increased neurodegradation can we investigate whether the apopteric enzymes necessary for degradation eg. annexin V are actually translated from mRNA associated close to the nucleus or are they locally translated? If RNA granules are removed would the absence of annexin V labelled with fluoresceinisothiocyanate (FITC) be proof that local translation is responsible for the production of apopteric enzymes?

Posted in neuronal cells, protein synthesis, RNA binding proteins, translation, Uncategorized | Tagged , , ,

neuronal firing relating to pain and the influence of anxiety

Posted comment on ´Determining the neural substrate for encoding a memory of human pain and the influence of anxiety` written by M. Tseng, Y. Kong, F. Eippert and I. Tracey and published in Journal of Neuroscience December 2017 vol. 37 (49) p. 11806

SUMMARY

Tseng and colleagues began their article by saying that the perception of pain is dependent on the individual, but the neurochemical mechanisms involved are common. Little is known or clear about these neural mechanisms that encode the information about painful stimuli so that it can guide future behaviour and allow the individual if possible to avoid dangerous and life-threatening situations. Therefore, Tseng and colleagues looked at the process involved in working memory encoding and non-encoding of somatosensory information in painful or non-painful situations.

In their investigation, Tseng and colleagues used fMRI with a delayed discrimination task. Results were obtained from 20 healthy subjects (male and female, mean age around 27) who had no previous experience of thermal or vibrotactile stimulation experiments. Before the fMRI experiment was carried out, each subject was assessed for their anxiety level and awareness to pain. Painful stimuli were elicited through the application of rapid increases of temperature (30 degrees centigrade in 0.8sec) and non-painful stimuli from vibrotactile frequency discrimination. Each subject was attached to a piezo tactile stimulator by having the left palm on 4 stimulators with the tips of the index and ring finger taped securely on the probes and the bases of both fingers taped on thermal stimulators. Three behavioural sessions were carried out. The first session was to find out the stimulation temperatures of each subject within a range of 1 degree centigrade to 42 degrees and then the highest temperature the subject found tolerable. In the second session, each participant was subjected to vibration frequencies between 5 and 50HZ where flutter sensations are reported. The third session had each participant being subjected to alternating pain (42 degrees centigrade to the highest tolerable temperature) and vibrotactile stimuli. The participants were asked to grade the stimulus intensity directly after each application on a visual scale.  Using this scale four pain stimulus magnitudes and 4 vibration stimulus frequencies were calculated for each subject. In the trial period each participant was subjected to a cue period of 3 secs where they were presented with either a red (encoding trial) square or green (non-encoding trial) square. An 8 sec delay followed and then they were presented with the first stimulus. In the encoding trial participants had to keep the first stimulus in mind when they were presented with the second cue period consisting of the same red square and second stimulus. The participants had to decide if the presented second stimulus was higher or lower in pain intensity or vibration intensity than the first. In the non-encoding trial, the subjects had to judge the second stimulus without reference to the first. Intensity in this case was determined by deciding whether it was higher or lower than the 25th or 75th percentile of the visual scale determined for each earlier. Participants were trained to use both right and left fingers to signify response and once this was satisfactorily learnt then in total 32 fMRI were carried out.  The fMRI were to investigate whether the same brain areas were activated in both pain and vibration trials. Small volume corrections were applied for example to the somatosensory cortices, thalamus, insula and anterior cingulate cortices (ACC), amygdala and hippocampal areas. Once completed psychophysiological interaction analyses (PPI) were performed to measure interregional functional connectivity between the bilateral thalamus, the right ACC and left somatosensory cortex (left SI) areas. In order to examine the sensitisation of responses the participants were asked to rate task difficulty for non-encoding trials also using a visual scale. Various statistical analyses were performed on all results to examine whether memory task performance was significantly different from the 50% chance level.

The results obtained by Tseng and colleagues using their experimental techniques showed that the painful stimuli gave moderate pain and this did not change across the trials so no sensitisation had occurred. Error rates were significantly lower than chance so it could be said that subjects performed the tasks correctly and attention remained consistent between the application of the first and second stimulus. The reaction time was found to be slower in the case of the painful condition compared to the vibration condition and this was explained as the skin taking a longer time to return to normal after excessive heat stimulus. Task difficulty was found to be assessed by the participants as significantly higher between the pain encoding vs non-encoding situations.

Regarding brain area activation, Tseng and colleagues studies showed that activation was dissociable between neural encoding of painful versus non-painful stimuli. The activity of the bilateral midline and mediodorsal thalamus and rostral portion of right ACC were enhanced during the encoding of painful thermal stimuli, but not with the non-painful vibrotactile stimuli. Encoding of vibration however, led to an increased response in the left SI which did not show increased activity with pain. Both the left and right amygdala areas were activated in the pain trials, but activity was not significantly different between the encoding and non-encoding tasks. The bilateral hippocampus area was not significantly activated for either pain or vibration trials. The results of the PPI analyses showed that the medial PFC was the only region with enhanced functional coupling with the thalamus and ACC during pain encoding trials compared to non-encoding trials and this was not observed during the vibration trials. There was no significant correlation between medial PFC activity and participant`s perceived view of task difficulty error rates and response latency.

Tseng and colleagues also looked at how anxiety would affect the encoding of pain. During the pain encoding trials participants with a high level of anxiety and those who showed trait anxiety levels showed significantly decreased error rates than those with a lower level. This was not observed with the pain non-encoding trials, or either type of vibration trial and error rates did not differ between the different levels of attention towards pain either. Subjects with high state anxiety scores showed a significantly faster reaction to detect the offset of the first stimulus and significantly reduced the reaction time during high pain stimuli, but not during low pain stimuli. The response latency was not affected by different levels of trait anxiety in any trial and neither was task difficulty. The results suggested that inter-individual differences in state anxiety and trait anxiety related to pain encoding behaviour with subjects having higher levels of anxiety performing better on pain encoding trials and reacting faster in detecting painful stimulus. FMRI analyses showed that the activity of the left amygdala was negatively correlated to the level of state anxiety during the pain encoding trial, but not in the pain non-encoding trial or either vibration trial.  The results were only significant in the high level pain trials and the activity of the left amygdala with anxiety in the pain trial correlated to the extent of coupling between the thalamus and mPFC. In the case of trait anxiety, the results showed a trend towards positive correlation between individual trait anxiety and pain encoding thalamic activity with significance achieved only in the case of high pain levels.

Tseng and colleagues summarised their findings by hypothesising that there were distinct brain areas such as the thalamus, SI and ACC active in the encoding of information in both painful and non-painful situations. However, in the pain situation because of the strong emotional contribution then activity should be present or stronger in pain-encoding areas and emotional related areas such as the amygdala. They found that working memory activity was dissociable and dependent on task ie. whether the stimulus that was to be learnt was painful or not. Differences in area activity were observed. In the case of the encoding of the vibrotactile stimulus SI activity was observed and this was also observed in the encoding of pain with activity in the medial thalamus and rostral anterior CC and with significantly increased connectivity between the medial thalamus and medial prefrontal cortex. The hypothesis given by others that the increased SI activity observed with encoding and working memory involvement was associated with attention was found not to be case in this study since the task reaction times were not significantly different for the different trials. The authors also found differences in activity in the SI areas eg. the bilateral SI responded to vibrotactile stimulation as expected through suggested uncrossed ascending tracts or transcallosal connections, but only the ipsilateral area participated in the encoding process. The role of this area was said to be unclear, but it was considered unlikely that it was involved in sensory perception. The activity of the area was shown to vary across different cognitive states.

The authors went on to explain the roles of the various areas shown to be involved in pain encoding. The rostral ACC, suggested as an area participating in the processing of threat-related stimuli, was also now attributed with a role in acute and chronic pain processing. The medial thalamus, associated with pain encoding, contains nociceptive specific neurons and hence, is involved in mediating the emotional aspects of pain. This view was supported by others suggesting that the area had a range of cognitive functions including attentional modulation of nocicieptive processing and working memory.  The authors also concluded that the connectivity between brain areas was also affected during pain encoding with significantly enhanced connectivity between the mPFC, thalamus and ACC. The mPFC is known to be involved in working memory and in regulating emotion and cognition and the medial thalamus was suggested as acting as an interface between the mPFC and hippocampus during the encoding process. The encoding process was said not to be driven by self-monitoring or attention instead the authors suggested that there was a distinct stream in the brain to sub-serve working memory of pain encoding and the emotional part of pain experience receives preferred processing when pain needs to be transformed into neural construct.

Tseng and colleagues also found in their experiments that the level of anxiety experienced by the subjects enhanced the task performance on pain encoding with the more anxious participants demonstrating significant performance advantages. The modulated brain responses were associated with the increased connectivity between the medial thalamus, mPFC and amygdala especially during trials with high pain. The activity of the amygdala area inversely predicted and negatively correlated to the degree of thalamic-mPFC coupling and this was said to correlate to emotional learning.  This supports the view that amygdala neurons project to the hypobasal forebrain area of the periaqueductal gray (PG) to modulate responses to aversive stimuli and is therefore suggested as a coping strategy that attenuates perceived distress. Therefore, inter-individual differences with exhibiting anxiety show that the emotional state engages distinct neural mechanisms. Anxiety also appeared to elicit a more efficient performance in encoding pain with lower error rates and faster reaction rates.

Therefore, Tseng and colleagues concluded their investigation by saying that working memory neural constructs are different for pain than non-pain encoding situations and that anxiety can affect the process.

COMMENT

What makes this article interesting is that we know that processing of sensory information and sensory memories can be altered by the individual`s emotional system, but this article confirms that the pain system which we think of as a basic biological system, its perception and its ´recording` can also be affected by the individual`s emotional state. (In the article discussed here the emotional state is that of anxiety.) This confirms views that if the emotional state can be controlled then the perception of pain can also be affected and hence, this is another avenue by which modulation can occur.

The discussion here begins with a look at the similarities and differences between information being received in the brain for pain compared to that of the sensory system, its processing and recording and effect on future events plus the effect of anxiety on those systems. Discussion will only be at the fundamental level since the systems are extremely complicated, but it may give areas where perhaps research should focus in the future. We begin by investigating the input and perception of pain and comparing this system to that for other sensory information. It is known that both arise from the input of relevant information: pain from real- time pathway activation from the source at cell level, the nociceptors; and sensory information from receptor and sensory cell activation followed by real-time pathway stimulation or, alternatively sourced from reactivation of appropriate cell memory stores. Hence, the two systems are similar in that they are sourced at specific cell levels, but differ in that the pain system becomes active in using real-time information only.

The evolutionary pathways for sensory information appear to be stable with only the capability having changed with development. Specific pathways lead from the sensory cells to higher brain areas and functionality and activity of those brain areas are linked to informational input plus factors such as attention and emotional state. Activity occurs through the firing of the neuronal cells and involves the release and action of different neurotransmitters such as glutamate, acetylcholine, 5HT, dopamine and GABA. Some areas and certain neurotransmitters lead to inhibitory firing eg. GABA and interneuron function in the hippocampus and some excitatory eg. dopamine and the prefrontal cortex. The group of cells firing together and bound together in time and activity is known as a neuronal cell assembly and this is equivalent to the neural representation of the information from the environment that is being acted on at that time. These initial firing groups are equivalent to the memory sensory stores and exist through sustained firing of the relevant cells. This leads on to the formation of short-term memory stores which are capable of further processing eg. the addition of more information. This is just a simplified version of what is occurring, but we can see that information progresses from the external environment into a neural representation that can be for example manipulated and stored or just decays and disappears. Two important physiological systems affect the neuronal firing, group dynamics and what is acted on or eventually what is stored and these are the attentional system and more relevant to the article discussed here, the emotional system. In a way these two systems are linked because attentional state dictates what brain areas and which cells have increased stimulation. There are likely to be three attentional states: normal – occurs when our minds are flitting from one external event or topic to another; focused both diffuse and concentrated where diffuse focus means attention is on a number of objects within an event rather like gist and concentrated where focus centres on one event; and finally the fear attentional state where attention is appropriate to the fight or flight response. Attentional state is dictated by the activity of certain brain areas such as the ACC, lateralintraparietal cortex (LIP), temporoparietal areas, medial temporal area, PFC (dorsolateral and orbitofrontal areas amongst others). This activity affects the quality and quantity of information being considered in real-time so that it is relevant and maximised for the task at hand. In the case of the fear attentional state, there is an increase in quantity through an increase in volume, but not necessarily and increase in quality, since the level of non-relevant material is higher as well as relevant and gist becomes more featured rather than concentrated focus.

This fear attentional state is linked to the fear emotion experienced at the time and therefore, it can be said that emotional state affects sensory information quality, quantity and processing. This is just one example of emotional state affecting brain functioning and as known, emotional state can also relate to positive emotions such as pleasure.  The two emotional systems can be said to be a balance of two neurotransmitter systems eg. dopamine (pleasure) and noradrenaline (fear) and the action of particular brain areas eg. medial prefrontal cortex for pleasure and amygdala for fear. The emotional state experienced at the time is supposed to be recorded simultaneously with the informational characteristics so that memories have an emotional ´value` to them. This can be called the ´emotional tag` and this value is likely to be recorded in the PFC. It has been suggested that the structure of the PFC relating to emotional value can be considered like a ´sliding switch` with pleasure having graded values appropriate to the different levels of positive emotional response an individual can hold, but fear is allotted to just one grade since it is either present or not.  The ´switching on` of the different grades results in a set of values for all experiences and this type of mechanism is important in cognitive functions such as decision-making as well as behaviour. Just like with informational characteristics, emotional values can be modulated by various factors eg. past experiences. There is support for this view with medial PFC activity required for recording in conditioning and the OFC linked to hedonistic experiences of reward. With relation to the pain system and its modulation, Zhou and colleagues extended this work by looking at reward signals and dorsal raphe nucleus activity and found that the intensity of the response of the OFC was affected by the frequency and duration of dorsal raphe nucleus stimulation.

Tseng and colleagues looked at one emotional factor in relation to the pain system that of anxiety and the sensory system like the pain system is affected by this emotional state. Anxiety is defined as a maladaptive response to threat, stress or fear and it can lead to an unpleasant state of anticipation, apprehension, fear and dread. The response may be real or imaginary and may be disproportionate to the actual stress or threat at that time. Although anxiety disorders appear to stem from past behaviour, there is also some evidence that sufferers may have a hereditary disposition eg. 60 gene regions of chromosome 15 are duplicated in about 90% of family sufferers which may lead to an over-sensitivity of neuronal communication. There are many physiological symptoms, but what are of interest here are the psychological ones, eg. fear, dread, obsession, distress, unease, and difficulty in concentrating. The physiological mechanisms relating to anxiety and these psychological symptoms being experienced include reduced activity in brain areas such as the frontal cortex and prefrontal cortex, but also increased activity in areas such as the amygdala and hippocampus.  Several neurotransmitters appear to be involved, for example: acetylcholine with cholinergic systems being increased in the hippocampus in aversive memories; and GABA binding to the GABA A receptor and acted on by barbiturates and benzodiazepines in association with anxiety directly. Barbiturates potentiate GABA induced increases in chloride conductance due to an increased affinity for GABA at the GABA A R. The reticular formation appears to be an important site of action with the middle area forming the pontine region which activates cortical areas whereas the medulla suppresses. The actions of noradrenaline and 5HT are however more interesting with changes in emotional state relating to increased release or decrease of the relevant neurotransmitter.

Noradrenaline acts at the locus coeruleus and results in anxiety when stimulated and arousal and vigilance when threatened. This area has alpha noradrenergic 2 receptors (alpha-AR2) which when blocked increase the release of noradrenaline and when stimulated decrease its release. In an elevated maze experiment binding of antagonists to the alpha-AR2 appear anxiogenic whereas agonists (eg. clonidine) are anxiolytic. The blockade of post synaptic beta-noradrenergeric receptors (beta-AR) appears to have opposite effects since inhibitors appear to be anxiolytic whereas agonists are anxiogenics. This effect is possibly mediated by peripheral receptors which mediate the peripheral autonomic effects of anxiety such as increased heart rate tremor and perspiration, but do not contribute to the conscious awareness of anxiety. They may contribute indirectly by autonomic activation leading to feedback which could be interpreted negatively eg. the beta-AR antagonist, propranolol, which is used to treat some of anxiety symptoms such as tachycardia, but not psychological symptoms or affects the conscious experience of anxiety.

In the case of 5HT, depression, which can be interpreted as being at the opposite scale of emotional disorders to anxiety, results from a decreased 5HT level in neurons of certain brain areas and decreased numbers of the inhibitory serotonin receptors. Studies showed that reduced numbers of 5HT1A receptors are also involved in the mechanism of anxiety whereas antagonists at the 5HT2 and 5HT3 receptors produce anxiolytic effects. The effect on receptor number mirrors the situation in depression and the effect of SSRIs.

The importance of this balance of neurotransmitters and activity of brain areas can be seen if we consider one cause of anxiety that observed in the case of conditioned aversive stimuli. In this case the behavioural inhibition system (BIS) is responsible for anxiety due to increased sensitivity to the non-reward or punishment stimuli and this may be relevant in the experiments described in this article which rely on the administration of pain. The BIS is a septo-hippocampal system which involves the neurotransmitters GABA, 5HT and NA and a competition between fear and anxiety. The fear response is controlled by the amygdala response to immediate threat whereas anxiety is mediated through a septo-hippocampal system that deals with future threats. Anxiety is therefore created by competition between conflicting goals requiring resolution and leading to uncertainty. The hippocampus, an important relay station for informational input and binding, tries to reduce this by helping to inhibit responses that may put the individual in danger eg.  approaching for food even when there is a chance of threat, and to assess the risk whilst employing reactivation of past experiences to facilitate the resolution of the conflicting goals. However, it has been shown that anxiety causes an exacerbation of pain associated with increased activity in the hippocampus and therefore clinical strategies have been suggested to reduce pain by disengaging the hippocampus during potentially painful clinical procedures. One method used by the BIS to resolve conflict is to increase the negative value of stimuli and to associate this with the emotional state of worry and anxiety. Therefore, the individual is more sensitive to negative stimuli which create activity in BIS which in turn increases the sensitivity to negative stimuli. It is likely that this involves inappropriate emotional tag storage at the time of the initial event or inappropriate processing of the PFC sliding switch scale of the previously stored emotional tag at the time of recall.

From our simple descriptions of the sensory information mechanisms and brain areas and those affected by anxiety we are able to see points where there is overlap with the pain system and where manipulation of the system can occur. The mechanism of pain or nociception also like the sensory system begins with areas on nerves that are sensitive to outside factors and these here in the case of pain are the nociceptors. These respond to damage and transmit neural signals identifying location, intensity and duration of the stimuli. Melzack in 1990 developed a theory of pain which has similarities to that developed for consciousness. He proposed the ´neuromatrix` consisting of neural firing loops between the thalamus and the cortex and leading to a ´neurosignature` of activity. This neurosignature corresponds to all information from the various areas and it includes input from attentional systems, emotional systems and senses. Melzack concludes that neurosignature projects to the ´conscious experience` area and this he termed the ´sentient neural hub`.

The pathways responsible for the input and perception of pain can be divided into two groups: ascending and descending and this too reflects to some extent those that are involved in sensory informational processing. In this case, the ascending neuronal pathways refer to that carrying the sensory input and the descending pathways, the influence on that firing from higher cortical areas such as the PFC as seen in decision-making and other systems such as working memory, attention and emotions. The ascending pathways for pain reflect different uses eg. pain control is the responsibility of the ACC and mPFC, but information about the pain sensation eg. about temperature differences that cause pain is the responsibility of the spinothalamic pathway. This pathway channels signals to the thalamus, then ACC, somatosensory cortex and dorsal cortical areas. Other ascending pathways include: the spinoventricular pathway to the reticular system and then cortex responsible for coordinating the responses to pain eg. turning the head; the spinotectal pathway leading to the superior colliculus; and the trigeminal pathway responsible for signalling between the face and the thalamus before ascending higher. Therefore, brain areas responding to pain perception include the ACC (intensity of pain, self-administered vs administered different areas), cortex, thalamus, somatosensory cortex (exact location) and insular cortex (pain integration). The functional disruption of one system leads to augmentation in the pain-induced activation of one or more other pain relevant brain regions including the PFC.

The neurotransmitters that appear to be involved in pain transmission are glutamate which is a common signalling mechanism in sensory information and dopamine already discussed as important in emotional pleasure expression and firing in the PFC and basal ganglia. Studies of pain processing have shown that there is also dopaminergic activity in the basal ganglia and this is linked to variations in the emotional aspects of the pain stimuli. Nigrostriatal dopamine DA2 receptor activation can be attributed to the sensory aspect of pain, while mesolimbic dopamine DA2/DA3 receptor activity can be related to the negative affect of pain and fear. We have also discussed how anxiety affects GABA firing in the reticular formation and since one ascending pain pathway involves the reticular formation then this is one area of overlap where anxiety could have an effect on the level of pain being experienced by an individual. GABA could also be involved in the pain pathways observed via the action of interneurons which can be either excitatory (eg. activated by glutamate for example in sensory systems) or inhibitory (eg. activated by GABA for example in sensory systems). The activity of the latter plays a role in the Gate Theory of Pain. This explains that certain neurons are excited by large sensory neurons and inhibited by pain axons. For example, there are multifunctional neurons in the substantia gelatinosa dorsal horn (admittedly, not the brain) that are excited by pain and also excited by other input leading to interneuron firing (excited by neurons and inhibited by axons). Therefore, if the neuron is excited by sensory stimulus it is excited and then fires its interneuron and the pain signal suppressed.

Attention just like with sensory information plays a role in the quality and quantity of information transmitted. The level of attention applied is determined in the case of the pain system by the threat value of the event. This can produce conflict when there is a need to disengage from the pain signal in favour of more important signals such as those for the ‘fight or flight’ response and survival. On the other hand, attentional bias towards the pain signal can be modulated by: the nature of the stimulus itself and previous experience (eg. heat is worse than cold), novelty and through anticipation and uncertainty; the individual and his/her own personal characteristics; and the environment in which the pain occurs. For example, attentional bias has been shown by studies that show increased engagement to pain signals and difficulty disengaging from for example by cognitive interference associated with pain-related words and visual-processing bias to the pain location. This prioritisation of pain over other stimuli is an innate response to threat. The threat value of pain may be modulated cognitively by providing information about the pain in advance and this may be the case in the experiments used in the Tseng`s study described above since the individual is aware of intending pain administration. Therefore, the individual has an expectation of pain which can alter activity and patterns of connectivity of relevant brain areas. Negative expectations can also affect activity in the PFC (particularly medial PFC and OFC), ACC, hippocampus, insular cortex, nucleus accumbens, amygdala, thalamus, somatosensory cortex, head of caudate, cerebellum, and the PAG – all areas associated with pain pathways. Hence, pain is anticipated and these expected levels of pain can alter the perceived levels of pain. On the other hand, positive expectations only affect activity in the dorsolateral PFC, ACC, striatum and frontal operculum.

The pain signal can also like the sensory signal be modulated by emotional state and this is relevant to the experiments described in Tseng and colleagues` article where they investigated the change of perception of pain with the emotional state of anxiety. Pain is also an emotional reaction since there is a mental feeling of pain and this feeling is individual and cannot be imagined. It can, however, just like sensory information come under cognitive control and like the case of values, the perception of pain can be manipulated. Pain perception arises from nociception although feelings can be absent even if the pain signal is transmitted. It is an immediate reaction and just like with sensory events and consciousness only one emotional value can be perceived at any one time. The pain system responsible for this emotional recognition is also like the emotional system for sensory information dependent on a descending pathway. Strong emotional stress for example can suppress the feelings of pain through the activation of several brain areas, but one of the most important appears to be the periaqueductal gray region (PAG) which is a zone of neurons in the midbrain which receives input from several brain areas that have a role in transmitting emotional state eg. medial PFC, hypothalamus, amygdala and locus coeruleus. PAG neurons also send descending axons to various midline regions of the medulla and these neurons project axons down to the dorsal horns of the spinal cord which depress noradrenaline activity and also particularly firing to the raphe nuclei. Therefore, the PAG area can be modulated by descending pathways that arise from brain areas responsible for emotional state such as the mPFC and amygdala and this ultimately results in pain signalling effects lower down the neuronal hierarchy. Whereas cognitive modulation may alter both intensity and emotional feeling of the pain being experienced, the emotional modulation of pain is more likely only to change the unpleasantness of it.

   The question is therefore, where pain fits in with the medial PFC sliding switch of pleasure/fear values attributed to information events. We know that pain values exist, are individual and have a threshold only above which can pain be consciously experienced. This threshold can be lowered by certain factors (eg. ill-health, cold, hunger, pain from another source, fear, worry, anxiety, boredom, insomnia, depression and frustration) or raised (eg. by  painkillers, acupuncture, heat application, anaesthetics, alcohol, excitement, concentration, interest, self-confidence and faith). We assume that like emotional values, pain is attributed to medial PFC activity and is part of the grading of ´emotions` experienced portrayed by the ´sliding switch`. It is likely that in addition to the ´sliding switch` having graded values relating to positive pleasure emotions and one grading for fear, it also has one grading for pain. This can be explained by the observations that one experiences fear or pain, but there is no grading ie. there is no little pain or a little fear. The role of the PFC in this function is supported by another pain pathway, the cortico-cortical modulatory pathway which is known to involve the higher areas of the brain and demonstrates connectivity in prefrontal regions such as the dorsolateral PFC and ventrolateral PFC. This pathway is responsible for the cognitive and emotional modulation of pain and does so at these higher brain areas rather than changes in the lower pain relevant regions.

Support for the modulatory role of the cortico-cortical pathway comes from looking at the functions of particular brain areas already known to be associated with the emotional system. The PFC is said to play a role in ´keeping pain out of the mind` and it is thought this is achieved by the modulation of the cortico-subcortical and cortico-cortical pathways, employing both somatosensory (non-emotional) areas and areas that process emotionally salient stimuli. The perceived control over pain activates the dorsolateral PFC during the anticipation of pain and the ventrolateral PFC during painful stimulation. The activation of the latter is negatively correlated to pain intensity and it acts as a controller of attentional engagement and so is linked with amygdala action. The other region of the PFC involved in the emotional system and pain perception is the OFC (synonymous with the ventromedial PFC) which is linked to the attribution of emotional values and reward. In relation to pain, this particular area may play two distinct roles. In the case of distracting tasks carried out at the same time as administration of pain, then the perception of pain is higher and this arises from decreased PAG activation which would normally decrease pain perception, but a more dominant increased activation of the OFC which is linked to increased sensitivity. Zhou extended this view by looking at reward signals and dorsal raphe nucleus activity and found that the intensity of the OFC response was affected by the frequency and duration of dorsal raphe nucleus stimulation and we have already shown that this particular region is linked with arousal and pain. Hence, the OFC modulates in two different ways the perception of pain.

Another area linked to the emotional system and the pain system is the amygdala region. The amygdala is part of the descending pain modulatory pathway and is known demonstrate increased activation in the case of threat to the individual ie. essentially the ´fight or flight` response. This is achieved by a reduction of PFC influence. Anxiety appears to reduce the activation of the PFC and hence, increase amygdala activation under a lower perceptual load and hence, sensitivity to threat is heightened. The amygdala has also plays a role in conditioning involving aversive and emotionally charged events and this is relevant to the experiments described by the authors in the article. In this case, the subjects are conditioned to respond to the pain experienced by pressing the relevant buttons. It is thought that in fear conditioning the amygdala is the place where the unconditioned stimulus (UCS) and conditioned stimulus (CS) is formed and responses have been shown to be linked to synaptic changes in the basolateral amygdala. Recently, distinct neuronal circuits within this area have been identified to differentially mediate fear expression versus inhibition and this has led to suggestions that there might be specific pharmacological target areas for inhibiting fear and enhancing fear extinction.

Therefore, we can see that there are a number of areas where there is overlap, similarities and differences between the systems in play for the input and perception of sensory information and that for pain. The same could be said for the memory mechanisms. In the case of sensory information, it is stored in the form of neuronal cell assemblies where the individual cells represent features of the event and have been physiologically changed to reflect there binding with other cells. The processes that lead to this are complex, but begin with the sustained firing of the cell in response to the appropriate feature and resulting in long-term potentiation (LTP) or long-term depression (LTD). The sensory information is stored with the emotional tag, a record of the emotional worth of the event information to the individual. Recall of the stored information means firing of the same cells that made up the initial input so that a neural representation is formed. This neural representation can be modulated, manipulated, added to and then re-stored if required.

Not much is known about how ´pain memory` is stored. It has been suggested that neuropathic ´pain memory` lies within the peripheral nervous system and evidence suggests that this may be correct to some extent. Plasticity in the pain system can occur at the primary afferent nociceptor just like with primary sensory cells and the involvement of the neurotransmitter receptors such as NMDA R and AMPA R. The mechanism here involves long-term potentiation changes which cause physiological adaptations resulting in long-term changes in cellular functionality responsive to certain stimulus. Changes at the lower levels translates to signal alterations higher up in the neuronal hierarchy and in the case of pain, LTP has been observed in synapses activated by C-fibre afferent activity such as in the dorsal horn. Although the LTP observed shares the same physiological mechanisms as that for sensory information, there is one important difference. Low frequency afferent stimulation causes LTD at most synapses in the brain, but low frequency stimulation of C-fibres, their normal firing frequency in most cases, causes a two-stage LTP at a subset of dorsal horn neurons. It appears that it is the early stage LTP that causes the typical physiological changes eg. gene transcription and translation alterations and it therefore requires the activation of CaMKIIalpha, PKA and PKC leading to the phosphorylation of AMPA receptors. The changes in gene expression cue the transition to late LTP which is less well-researched. However, it is thought that this stage involves an atypical PKC isoform which is involved in the trafficking of the AMPA receptors to the synaptic membrane for the sustained glutamate signalling required for the long-term cellular changes to occur.

Although the dorsal horn may demonstrate physiological changes to pain consistent with sensory information memory, it is unlikely that what we would call ´pain memory` actually resides there. If we think of what we mean by pain memory, we actually think of information that is to us painful or will elicit pain. Therefore, pain memory is likely to be as suggested above a recording of the ´emotional tag` relating to the pain grade associated with the neural cell assembly corresponding to the event we associate that pain with whether in its entirety or just in part. It is likely that the quality and quantity of information recorded at the time corresponds to the same level as that observed in a fear situation. This supports in part the view of pain researchers who suggest that the other possible location for pain memory is at the level of the neurons receiving nociceptive input throughout the brain.

Another approach to looking at the memory mechanism involved in pain is to look at fear conditioning. Tseng and colleagues` experiment carried out here also required memory storage of the pain experienced albeit of a short-term nature and therefore, we can look at the memory mechanisms taking place if we consider it as a case of fear conditioning of an operant nature. Fear conditioning means that the reinforcement is pain/fear rather than reward. The presentation of the pain and the fear conditioning process produces neuronal responses area in particular brain areas. For example: the hippocampus is required for contextual associations, but activity in the raphe nuclei results in no ripple activity in the hippocampus, an area important in the consolidation of memory; the amygdala is important in response to the emotionally charged reinforcement and it was found that the central area directly projects to the PAG inhibitory neurons; medial PFC neurons also project to this area so it is susceptible as shown above to medial PFC activity; and the other area is the ACC which demonstrates theta brain wave activity in fear expression.

We can assume that fear conditioning memory has the same mechanisms as those used to form other informational memories. For example: a reliance on protein phosphorylation and dephosphorylation; increased CREB phosphorylation; changes in spine morphology (observed in amygdala); persistently active protein kinases; microtubules requirement; and calcium/calmodulin-dependent protein kinase II requirement. However, there are one or two differences. Whereas memory formation is associated with NMDA or AMPA R LTP and/or LTD, in the case of fear memories NMDAR LTD is required for their consolidation, but not their acquisition. Fear learning however, does force AMPA R into the amygdala as expected. The presence of NMDA antagonists not AMPA in the amygdala leads to the prevention of the fear memory becoming labile or recalled (Mamou). There is also heightened activity of hippocampal extrasynaptic GABA A receptors in contextual fear conditioning which would normally impair fear and memory, but in this case enabled state dependent encoding and retrieval.  Also sleep which has an important role in normal memory formation has a different structure to that seen in fear conditioning. Here, REM sleep is disrupted and hence, there is higher fragmentation resulting in a disruption of any extinction learning that might follow. This has been observed with PTSD sufferers also. Extinction of fear conditioning is believed not to be an erasure of memory as thought, but a formation of a new memory. This requires the involvement of the hippocampus (ie. the remapping of place cells) and beta-adrenergic receptor and glucocorticoid receptor activity.  Successful fear extinction also requires ventral medial PFC gamma brain wave activity. The extinction of conditioned fear by the glucocorticoid agonist DEX is blocked by NMDA R antagonists which suggest that the conditioned fear mechanism has a different requirement for acquisition than for consolidation in the case of the amygdala and this supports work given above. This also explains the action of the GABA A R agonist, muscimol, whose administration leads to the disruption of extinction whereas the antagonists have no effect. The addition of a partial NMDA R agonist with muscimol reverses the effect.

The experiments carried out here by Tseng and colleagues also involved decision-making and therefore, we should look at how decision-making and working memory capabilities can be affected by pain. The results of the experiment showed that the level of performance errors was reduced with pain administration implying that the level of performance was higher through better accuracy and judgement. Many brain areas are involved in decision-making and working memory eg. left PFC for planning, slower right PFC for execution of those plans, medial PFC for value and upcoming action (dopamine dominant), OFC for the encoding of values (GABA and glutamate dominant), the ACC/CC for mediating response to the visual field and learning the values of outcomes and the amygdala where lesions lead to an increased choice of risky reward and the caudate involved in trade-offs.  Also connectivity between particular areas appears to be important eg. connectivity between the OFC-hippocampus-amygdala whose activity can lead to increased dissatisfaction with results, or the thalamus-medial PFC where a decision based on STN activity can be modulated. Therefore, any factor that affects the activity of any of these areas will have an effect on decision-making and working memory capabilities and this is observed in both the cases of pain and anxiety eg. anxiety causes the reduction in the activation of the PFC, or threat increases the activation of the amygdala.

The above simplified explanations of the mechanisms involved in the sensory information and pain pathways show how both are perceived and ´stored` in order to benefit the individual. The pain mechanism is what it is – a mechanism for the protection of the individual from physical harm, but due to the nature of the pathway it can be modulated by top-down systems that also control how sensory information is inputted and processed. We have seen how the neuromatrix loops for the pain pathway relate to the views of consciousness and how perception of pain can only occur in one area just like conscious awareness. But, we have also seen that unlike consciousness pain can be ´stored` although not as an event itself, but as an emotional value, which is as individual as the emotional values attached to pleasure and this is stored with the sensory information and characteristics of the event to which it applies. There are neuronal differences in the pain mechanism eg. interneurons stimulate firing, GABA neurons lead to excitation, but there are also many similarities. Therefore, bearing pain therapy in mind, advances in neuronal research that can lead to modulation and manipulation of sensory systems can only help advancements in knowledge of this equally important pain system.

Since we`re talking about the topic…….

….bacteria loaded with antibodies and magnetic particles can be guided through the blood to appropriate parts of the body to provide local treatment. Can we assume therefore, it would be possible to do the same sort of thing using neurotransmitter agonists or antagonists to provide localised analgesia so that pain signals are not given out? Capsaicin can cause analgesia by desensitising pain fibres so could this also be used in this way?

….each individual has his/her own pain threshold that can be manipulated by certain factors. Would it be possible through hypnosis to manipulate the threshold to such an extent that the individual becomes desensitised to pain due to injury or disease and not suffer from a general feeling of numbness?

…..it is said that only one site of pain can be perceived at any one time and there can be pain signals without the perception of pain. Could an individual or the pain mechanism itself be manipulated into perceiving pain from a lesser source in preference to a more serious source by increasing the importance and perception of the former and by also giving methods of management so that the more serious source is ignored?

Posted in anxiety, emotions, neuronal firing, pain, Uncategorized | Tagged , , ,

inhibition of visual input by top-down modulation in the case of conscious awareness of information in working memory

Posted comment on ´Attention, working memory and phenomenal experience of WM content: memory levels determined by different types of top-down modulation` by J. Jacob, C. Jacobs and J.Silvanto and published in Frontiers in Psychology volume 6 Article 16033 October 2015

SUMMARY

Jacob, Jacobs and Silvanto explored in their article the role of top-down attentional modulation of the content of working memory and concluded that the representation of the original memory in the centre of the focused attention achieved conscious awareness and this process also requires the suppression of all incoming visual information via inhibition of the early visual cortex.  They began their article by defining their accepted model of working memory (model of Cowan, 1988) where working memory is seen as an activated long-term memory able to retain a number of activated representations in parallel some of which may be re-enacted long-term memory representations. They also quoted the extended model of working memory by Oberauer (2002) which suggests a store of reactivated long-term memory representations plus a capacity limited short term store (zone of direct access) and a store containing a single item linked to focused attention (FOA) which provides the content for goal-directed processing and is the only item to reach conscious awareness. The content of this working memory was said to be experienced as an image with qualia and could be scrutinised and modulated. The authors argue that reaching conscious awareness involves more than just modulation of the actual memory trace involving attention and requires in addition inhibition of visual input.

Jacob, Jacobs and Silvanto continued their article by proposing own hypothetical model relating working memory to attention. They explained that information exists in different states dependent on the level of attention and that this translates to memory. Three memory levels were proposed with relation to attentional control: one level with non-attended, non-conscious memory with no attentional modulation; second level with attended, phenomenally non-conscious memory with an enhanced actual memory trace due to attention; and the third level with attended, phenomenally conscious content with an enhanced memory trace and top-down suppression of visual input. The authors claimed that their model was distinct from previous ones because of distinct, non-conscious memories and conscious, attended memory states.

What followed was evidence indicating a dissociation between attention and phenomenal experience of memory content. Jacob, Jacobs and Silvanto began with the proposal that non-conscious items can be attended to, encoded and maintained in working memory. Research supporting that view was quoted as coming from the work by Soto et al. (2011) where subjects were able to maintain encoded information and use it later even when at the time they were unaware of the cue and distractors were present in the maintenance period. This indicated to the authors that attention was on the information held in the memory store (ie. FOA) in the delay period. Further research from Feredoes et al. (2011) showed that the working memory trace maintained in presence of distractors was brought about by the top-down facilitation of visual cortical regions maintaining the working memory content. Other brain areas were also reported as being involved in maintaining non-conscious items in working memory as changes in the right mid-lateral prefrontal cortex, orbitofrontal cortex and cerebellum were observed with activity in the dorsolateral prefrontal cortex, anterior prefrontal cortex and posterior parietal regions observed in visual working memory. Also, it was found that subliminal shapes in visual working memory guide attention and facilitate working memory performance. The authors therefore concluded that working memory can have non-conscious representations and so information in the FOA may not be necessarily conscious. This supported the view that attention may be allocated to working memory content without the content being conscious and hence, additional processes are required for a phenomenal experience to occur.

The dissociation between attended and conscious representations was reported in the article as being because once memory content has been brought to conscious awareness then it interacts differently with external input than with non-conscious memory content even if both are attended. The encoding of concurrently presented visual information could be changed by for example raising the level of the threshold at which information is detected and this could be independent of the similarity between the mental image and visual input. This was said to prevent the mental image from being weakened, but the reverse could also occur with external input impairing the conscious experience of memory content. The encoding of external stimuli matching working memory content was reported as being enhanced and reaching visual awareness more effectively whereas dissimilar information was said to be suppressed. In this way, working memory was said to act as a ´gate keeper`.

Jacob, Jacobs and Silvanto continued their discussion about their model demonstrating that consciousness and attention are dissociated and that more than just attention is required to bring content to conscious awareness by saying that their 3 state model supports well-known work on consciousness by Dehaene et. al. (2006). Dehaene et al. 2006 described information processing as being subliminal, preconscious (both considered unconscious forms) or conscious. Subliminal was defined as limited bottom-up processing due to attenuated stimulus strength potentially interacting with top-down attention. Preconscious was described as being dependent on stronger signal strength, but was limited, or had no top-down attentional modulation, but did have a potential for conscious access. Conscious processing was described as involving top-down and bottom-up activation beyond a sensory threshold and consistent with working memory models with information assigned to the FOA and activated long-term memory representations as being non-conscious. Jacob, Jacobs and Silvanto stated that Baars classic Global Workspace Theory was not a model of working memory since it described how visual input reaches awareness, but they did quote it in their article as providing evidence of the dissociation between consciousness and attention. In the Global Workspace Theory subliminal, attended information processing is short-lived and there is no attended but non-conscious content whereas in their model, non-conscious, attended representations in the working memory exist and are maintained for longer periods and can survive distractor appearances.

The article then went on to discuss the different requirements for the achievement of conscious awareness with reference to the 3 memory level model put forward by the authors, Jacob, Jacobs and Silvanto. As given above, it was stated that in the working memory model, attentional modulation of the content with conscious awareness requires an additional factor and this was proposed as the suppression of visual input from external sources via early visual cortex inhibition. This view was supported by fMRI studies where subjects carried out visual motion imagery and had were observed to have reduced early visual cortex activity, but enhanced activity in the motion-selective extrastriate region V5/MT. This was explained by the necessity of knowing the source of the content. The authors said that working memory would have to know that the conscious material was internal (the so-called Perky effect where external input is confused as part of mental imagery). They claimed that this was important so that external information is not concurrently consciously experienced as it would have a stronger neural signal than the competing conscious trace from the same item sourced from visual imagery.  Therefore, when the content of the working memory needs to be brought to conscious awareness there should be a bias towards internal, reactivated information and this is achieved by inhibiting the visual input from real-time external events.

The authors concluded their article by re-stating their hypothesis that there is a relationship between phenomenal experience of content in working memory and attentional control and that conscious awareness involves the creation of a second, distinct representation (previous work) generated and by the top-down facilitation of the original memory trace and favoured to incoming external information by suppression of incoming visual input at the cortical level. They concluded their article by suggesting a possible future neuroimaging study to prove their hypothesis.

COMMENT

What makes this article interesting is the continued exploration of the cognitive capability, working memory. This article by Jacob, Jacobs and Silvanto links working memory with attentional modulation and conscious awareness and they propose two things: one, that the working memory is multi-facetted with facets having different attendance and awareness characteristics; and two, that the facet relating to attended, conscious information obtained from the reactivation of long-term memory relies on the suppression of incoming information from the external environment in order to reduce neural competition and this suppression is elicited by top-down attentional system modulation.

Before we can discuss their hypothesis, we have to look at and provide a neural mechanism as to what working memory is and what affect attention can have on it. My view is that working memory is not like Jacob, Jacobs and Silvanto suggest that of a ´melting pot`, a single area or brain ´splodge of activity`, but a state where multiple areas are active at the same time working on multiple ´tasks` most of which are unconscious, but at least one can reach conscious awareness. (I say at least one, because divided attention can mean that conscious awareness flits between at least two cognitive events and may appear virtually simultaneous, but from a neurochemical basis are not). Working memory is not a process, but is a ´condition` where processes can occur and these processes involve common, shared tools such as sensory input, attention and decision-making and involves information which can either come from reactivated long-term memories or from newly inputted information from the external or internal environment. The idea that multiple processes are involved is not new and well-known descriptions of the working memory (eg. that of Baddeley and Hitch, 1974) has it as a group of capabilities such as the central executive (synonymous with attention), episodic buffer, phonological loop and visuospatial pad. Jacob, Jacobs and Silvanto concentrate on only the attentional system`s contribution to function, but other tools participate as well in either the bringing of the information into the working memory state or in the information`s scrutiny or manipulation.

The informational content of the working memory can be from differing sources such as reactivated long-term memories including associated emotions and event value (Jacob, Jacobs and Silvanto`s equivalent of information with quale) as well as newly sourced material from incoming input from the external environment or internally sourced information or created material via manipulation. Whatever the source, we can assume that the time the information spends in the working memory state is dependent on the firing mechanisms in play and to an extent, its relative importance to the individual whether from task performance or personal value. For example, information said to be in the short-term memory store according to Jacob, Jacobs and Silvanto is from the neuronal firing of relevant cells and therefore, the period of activation would be dictated by the time the neuronal cells could fire before chemically being exhausted and they shift into their refractory periods to replenish. The information would then fade. Repetition or manipulation of material would lead to sustained neuronal cell firing and the holding of that information in the working memory state for longer periods just like in the case of formation of long-term memories.

In neurochemical terms, the firing of neuronal cells depends on the source of the material eg. visual working memory activates different brain areas to language and this allows the scope of the working memory state to broaden if multiple skills and senses are involved in the content. The cells themselves are considered to be multi-tasking (Messenger) which allows cells to be representatives of information, but also susceptible and instigators to tools such as attentional modulation and the whole state representing an event depends on connectivity of multiple areas so that items are inputted, maintained and manipulated. Visual working memory is said to involve many areas such as the prefrontal cortex, cingulate cortex, hippocampus and entorhinal cortex (for relaying the signals, synchronicity and binding), fornix and thalamus (for basic sensory information relays), V4 and medial temporal lobe and inferotemporal lobe (for visual pathways and visual attention) as well as the cerebellum (for procedural memory and movements). Manipulation and holding of material is the responsibility of the lateral- and post-parietal cortex.

Whether the informational content of the working memory state has conscious awareness or not depends on several different factors and is independent of the source (eg. internal or external) or type of information (eg. visual, auditory). Jacob, Jacobs and Silvanto hypothesise 3 working memory levels dependent on whether material is attended or not, or conscious or not. What selects information for conscious awareness is essentially the strength of firing and strength of firing is dependent on quantity of information (ie. the more cells active, the greater the chance of reaching conscious awareness) and quality (ie. the more characteristics available including emotional status and value, the greater the chance of reaching conscious awareness) and the task at hand (ie. the more difficult the task or more complex for example the greater the chance of conscious awareness). In the case of, for example, the simple recall of a procedural memory like riding a bike then this is unlikely to evoke conscious awareness especially if other more challenging input is available at the same time, but it will and will enter the working memory state if it is coupled with learning to ride a new bike with a different gearing system. Therefore, conscious awareness of one facet of the working memory will reflect the strength of firing of the information independent of its source. However, it should be remembered that conscious awareness represents only one ´draft` of an experience if the multiple drafts theory for consciousness is to be believed. This can be compared to working memory which can also be considered as one ´draft` of information with manipulation, the addition of supplementary information (associated with ´filling in` of consciousness) providing the other ´drafts`. The link is also supported by looking at the brain areas involved. Consciousness involves the firing of particular brain areas such as prefrontal cortex, cingulate cortex and parietal cortex and these are as shown above the same areas as those said to be involved in working memory. Therefore, the two capabilities can be said to be linked although in most cases dissociated.

So, we have looked at what working memory is and how conscious awareness of some of its content can be brought about, but Jacob, Jacobs and Silvanto expanded this by hypothesising that working memory and consciousness relies in 2 cases of their 3 memory model on the involvement of the attentional system. Only the non-attended, non-conscious form had no attentional modulation according to them and this can be is explained by considering this form of working memory as being associated if at all with recall of memories without the need for any further processing. The only disadvantage of this definition is that it is unlikely that in this case the working memory state at all since the working memory state is usually associated with information manipulation and pure recall does not require any further processing to be totally effective. One of the other forms of memory described by Jacob, Jacobs and Silvanto states that information is attended, but phenomenally unconscious and so attentional modulation can lead to enhancement ie. more processing, but still remains under the threshold for conscious awareness. It is only the final form where conscious awareness is attributed to working memory content and this attended information is in the centre of attentional focus and can be scrutinised and modulated. The link between attention and consciousness is not new with Dehaene and Changeaux`s 2011 Neuronal Global Workspace Theory proposing that attention acts as a selecting mechanism for conscious contents and working memory as a specific store.

Although we think of attention as a discernible force, biochemically it is not. It is a mechanism that instigates the strengthening of certain information and the weakening of other rather like a dial so that some information is attended and other not. This is an important quality if we want a working memory state where some of the information active at that time can be scrutinised and manipulated and the rest ignored or simply carries on. Competitive selection of information can be based on feature strength or even biased because of stimulus colour, movement or emotional value. The level of attention can also vary with a low level described by Marchetti as being with or without consciousness or a high level as associated with selected events, manipulation and decision-making for example. It can also be bottom-up based on the stimulus features and lower level sensory pathways and automatic recall of values, memories for example, or top-down meaning that the allocation of the resources is under the control of the higher cortical areas and dependent on memory, values, associations and decision-making. Independent of which attentional system is exerting its control, the first 270 milliseconds of any event is neurochemically the same and it is only after this time that the allocation of further deciding attentional resources occurs. This early period could be described as the preconscious period for some events with them either reaching conscious awareness later if focused attention is applied (after 300 milliseconds) or if no attentional resources are directed at them being the ´never-conscious`.  Attention may not also be considered as a single focused capability centred on a limited area since Marchetti also described attention as being ´diffuse` ie. like ´gist`, spread over a number of aspects of one event. In Jacob, Jacobs and Silvanto`s model the focus of attention is the conscious event and the diffuse attentional state produces no conscious awareness which differs from Marchetti and others who say that even in this condition, conscious awareness can occur. This is credible if we consider diffuse attention as ´gist` – we may not be exactly aware of all facts, but we have an overall understanding.   Therefore, linking information in working memory and how some of this reaches conscious awareness and some not relative to the amount of attentional resources aimed at it is understandable.

However, Jacob, Jacobs and Silvanto went further with their working memory model by saying that the attentional system inhibited certain informational input into the working memory state if the informational content was of a particular kind ie. was information obtained from reactivated long-term memories for the same event as being observed in real-time. This inhibition was brought about to reduce the competition for working memory capability from the incoming information from the environment which they said would produce naturally stronger neuronal firing and therefore, evoke conscious awareness in preference to the reactivated familiar material. Two copies of the same material would exist with the copy of the newly inputted material being stronger. Therefore, Jacob, Jacobs and Silvanto´s inhibition hypothesis is understandable since: competition would increase the perceptual load and therefore, certain characteristics may be ignored; it provides a reason why material is not processed again if it has already been processed and stored (eg forms capability of object recognition); and the reactivated information may have more recalled associated material with it such as emotional state and personal value than the new input. The hypothesis is supported by the observation that we are often unaware of a change in a person`s appearance for example (attentional blindess). This implies that our recognition of that person relies on recall of stored information in response to cues from the real-time encounter, but close examination of the person`s appearance in real-time does not occur. Although this may be negative in that changes are not observed, using recalled information has the advantage that it is more than just visual characteristics for example and that other information and associations are also part of the reactivated information and hence, the capability of the working memory to process material is strengthened and so is also the chance to reach conscious awareness. However, this inhibition may not be possible as already indicated if the reactivated image is too different from the incoming information and may be detrimental since no updating of the stored information from sensory information will be possible.

The mechanism hypothesised by Jacob, Jacobs and Silvanto as bringing about the inhibition was top-down attentional control at the level of the early visual cortex V1, but herein lies a problem. Jacob, Jacobs and Silvanto compared the informational content in the working memory of the reactivated memory as visual imagery and visual imagery is associated with activation of the V1. Therefore, suppression of activity in this brain area by the attentional system would automatically inhibit the working memory state and suppress the visual information being recalled. Possible explanations to explain this discrepancy if this hypothesis is correct are: that the neurons firing in response to input in the V1 are not the same ones of the neuronal cell assembly group representing the stored image; or that the inhibition occurs at a lower level than the V1 cell hierarchy so that visual features are not discernible. Further research into visual imagery particularly using real-time neuroimaging is required to explore the capability.

Therefore to conclude, Jacob, Jacobs and Silvanto`s model for working memory and the levels of information relating to attendance and conscious awareness has merits in that it provides support for the view that working memory is not a single item capability, but is multi-facetted with each facet having different characteristics regarding informational content, conscious awareness and attentional system involvement. In the case of conscious awareness of one type of working memory content, that of reactivated visual images, then Jacob, Jacobs and Silvanto`s model proposes that top-down attentional modulation occurs that inhibits the input of real-time visual information if the event being observed is the same as that of the reactivated store. This has the advantages that competition for cognitive resources from the stronger real-time image is removed and that associated information such as emotional worth is also recalled and is available for processing and manipulation in addition to the event features. This strengthens the firing and is likely to increases the chance of bringing the content to conscious awareness. However, inhibition of visual input also has the disadvantage that the updating of stored information from real-time external events is prevented. Inhibition of this type could be detrimental for those people that suffer from memory recall problems since the information reactivated may be sufficient enough to trigger inhibition, but not substantial enough to aid working memory performance. Therefore, the topic deserves further investigation.

Since we`re talking about the topic………………..

…..can we assume that the same type of inhibition can be observed with auditory memories under the same conditions and also that the same type of inhibition occurs when the events are multi-sensory?

…..if the experiments are repeated using the same reactivated event as the external event being experienced, at what level of dissimilarity of content is the inhibition of the external input removed? Does this correlate time-wise to when the subject becomes conscious that a change has occurred? Does this occur even if that change refers only in a change in emotional worth?

……the administration of ketamine leads to an increase in irrelevant information in working memory. Therefore, if ketamine is given and the experiments repeated, would conscious awareness be on multiple events akin to divided attention and how would this affect the suppression of real-time visual input? Is it possible that attention is diffuse rather than focused, or is it focused on multiple events (like divided attention) and hence, not reach the threshold value for visual input inhibition?

… what effect could working memory training have on the inhibition of V1 activity?

 

Posted in attention, consciousness, Uncategorized, visual input, working memory | Tagged , , ,

discussion about hypothesised link between the menopause and Alzheimer disease

Posted comment on ´Changing your mind`  by J. Hamzelou and published in New Scientist 3141 2nd September 2017 p.36.

SUMMARY

Hamzelou began her article by describing some of the symptoms experienced by some women going through menopause. She stated that the cognitive changes observed in menopause, eg. migraines, mood swings, anxiety, short-temper, forgetfulness and insomnia, resemble the presenting symptoms observed with sufferers of Alzheimer disease and may in fact signal the start of that disease. In order to support her view Hamzelou quoted work by Brinton, a Californian scientist who studies the hypothesised link between the menopause and Alzheimer disease. Brinton hopes to develop therapies that artificially boost hormone levels that would lead to protecting the brain from the detrimental changes that could lead to dementia later in life.

Hamzelou continued her article by describing the biological basis of menopause and listed the common non-cognitive symptoms observed, eg. fatigue and weight gain. She said that in comparison to those obvious symptoms, cognitive symptoms are often overlooked since they occur at a time when other reasons can be given to their appearance eg. ageing and also because society demands a level of expectation and endurance when considering mental health problems. However, research given by Hamzelou as being carried out in the last decade, has shown that a decrease in oestrogen level has effects on memory, mood and even what has been termed the  ´brain health` of men and women. Research by Brinton and others has shown that reduced levels of oestrogen are correlated to alterations in the type of energy the brain cell uses and to a reduction in the production of energy. Under normal conditions, oestradiol increases the activity of the mitochondria in brain cells involved in normal cellular energy production and therefore, it helps cells recover from damage associated with normal ageing. Grimm of the University of Queensland, Australia supports Brinton`s view and was quoted in the article as saying that the drop in oestrogen makes the brain more sensitive to damage that could lead to death of neurons. Brinton believes that the fall in oestrogen that occurs in the menopause causes the brain to produce less energy and to change the type of energy it uses. Glucose is the normal energy source of brain and this is reduced by 25% in tissues of menopausal sufferers. To overcome the shortage of glucose the cells, according to Hamzelou and Brinton, begin a ´starvation` response and use fats as their energy source instead. They are also believed to use myelin as well which can be found in the protective shield around the neurons themselves. Although their studies were carried out on mice, Brinton suggested that the results could also apply to humans and some research supports this. A decrease in glucose metabolism, a change in white matter volume and grey matter volume and an increase in beta amyloid production relative to men have been observed. The switch in energy source was also suggested by Brinton to provide an explanation for some of the other symptoms of menopause. For example, the metabolism of fat because it is a less efficient energy source than glucose creates more heat and this excess heat in the brain was suggested in some animal studies as possibly triggering the menopausal non-cognitive symptom of  hot flushes.

Hamzelou continued her article by describing why some researchers link the supposed protective effect of oestrogen on cognitive function and hence why the menopause and its cognitive symptoms can be linked with symptoms observed in Alzheimer sufferers. Brinton investigates why women are more susceptible to Alzheimer`s illness and thinks that the hormonal transition occurring  in the perimenopause stage and full menopause may be the cause and start of Alzheimer illness in some women. Studies have shown that two thirds of people with Alzheimer`s illness are women and even though the disease is diagnosed when they are in their seventies, the disease actually starts around 15-20 years earlier when the natural menopause occurs.  The link to energy production during the hormonal transitions occurring in menopause was supported by work from others. For example brain scans measuring how much glucose is being metabolised across different brain regions were carried out in 2005 by Mosconi and colleagues of the New York University and they observed reduced glucose metabolism with Alzheimer sufferers and women who were in perimenopausal or postmenopausal stages. These observations compared favourably to Brinton`s observations in mice and suggested a link between a decline in glucose metabolism in the menopause, ageing and Alzheimer illness.

Hamzelou then went on to describe the natural progression of such results – if oestrogen has a brain effect when it falls, then what happens when it is replaced? Some studies suggested that hormone replacement therapy (HRT) could prevent dementia, but a trial of 7500 women in 2005 by the Women`s Health Initiative Memory Study found that HRT actually quickened cognitive decline and increased the risk of not only dementia, but also breast cancer and cardiovascular disease.  Hamzelou quotes researchers who believe that the study was flawed and describes the study by Pinkerton at the University of Virginia who looked at women given conjugated equine oestrogens. They stated that the negative link between HRT and cognition was incorrect since the administered oestrogen was obtained from pregnant horses and therefore, not an appropriate hormone source for premenopausal women and that the women taking part in the study were already over 65 and were therefore, too old to be described as suitable menopause subjects. They said that their brains had already adapted to low oestrogen levels and that the number of relevant receptors had already decreased. Pinkerton went on to say that there appeared to be an optimum time for HRT treatment (termed ´window of opportunity`) and that this time period was limited to between the appearance of the menopausal symptoms and the time when the brain was still responsive to treatment. They said that oestrogen can work better on healthy cells and therefore, HRT works better when women take it around the time of the menopause. In response to the increase of detrimental side effects observed with HRT administration, Pinkerton said that in the case of breast cancer, administration of HRT was linked to only an increase in breast cancer of under one case in a thousand. Pinkerton concluded by saying that HRT should be used only if women experience unpleasant symptoms, but the view of ´lowest dose for shortest amount of time` should be replaced by the caveat of ´making sure that the treatment is appropriate`. The determination of what is appropriate has not yet been made. Hamzelou continued by suggesting that the better solution may be to use oestrogens that only work on specific organs eg. one that works on brain, but by-passes breast tissue. She quoted in her article work by Raber of Oregon Health and Science University in Portland who reports that drugs of this nature are already in development. Hamzelou also quotes Brinton who suggests a nutritional approach to protect the brain from the effects of hormone loss. This view is linked to food obtained from the diet and brain function. For example, ketogenic diets appear to benefit epilepsy sufferers. In the case of the menopause, a high fat diet is not advised for people at risk of weight gain and against the view of a healthy diet rich in fruit, vegetables and grains being good for brain health. She also recommended exercise and keeping active, which has been shown to boost mood and cognition and can increase bone mass.

The article concluded with Brinton describing the future with individually tailored hormone therapies given at the right time to treat menopause symptoms and prevent Alzheimer`s illness.

COMMENT

The menopause can be regarded as a ´sensitive` topic at the best of times particularly with women, but when it is linked in scientific research to the appearance of Alzheimer disease then the feelings it evokes are intensified. Therefore, any research into the association between these two topics should be rigorously examined because unlike other factors causing changes in memory and cognitive capability (eg. the administration of certain drugs or a stroke) the natural decline of a hormone due to increasing age is something that transcends effects under the control of the person herself. Experimentation into the menopause in humans is beset with problems. For example, because the onset is variable and the occurrence of relevant symptoms is individual. We know that natural occurring menopause is clearly defined as existing one year after the last menstrual period, but definition of the ´last menstrual period` is difficult to define itself since women experience differing forms of menstrual periods in the perimenopausal phase. The definition of the beginning of menopause is therefore easier to establish when it occurs through surgical intervention eg. hysterectomy or also through disease such as polycystic ovarian syndrome. Even if the beginning of menopause can be determined accurately time-wise the variation in symptoms whether physiological, cognitive or emotional makes interpretation of results difficult in humans. Physiological symptoms such as hot flushes or loss of sleep are probably easier to see and measure, but the cognitive symptoms (eg. irritability, loss of spatial memory) on which this Blog is focussed are more difficult since they are in part ascertained through self-reporting which can be unreliable and are subjective with daily variations and differences depending on personal situations. However, we can say that the menopause is a physiological condition or state brought about by decreased levels of circulating oestrogen/oestradiol and therefore, we can assume that whatever symptoms are observed then they occur as a result of this decrease in circulating hormone.

Oestrogen is produced from progesterone by the ovaries and instigates a wide variety of effects in the whole body. However, it is also produced in the brain, blood vessels and bone synthesised from cholesterol to various intermediate compounds eventually to pregnenolone which then is converted to 17alpha-hydroxyprogesterone then to androstendione (to estrone),  to testosterone and eventually to oestradiol. Since this Blog focusses on the brain and neurochemical processes we shall concentrate here in this post on effects of oestrogen in the brain and on neurons. It can be said that the presence of oestrogen in this organ and on these types of cells has a general effect on neuronal firing and is said to elicit intracellular effects associated with changes in the DNA, membranes and from the article reviewed here on cellular energy production. This general positive synaptic effect translates into an influence on firing and is said to provide a protective effect on neurons and their functions. Exposure to oestrogen or oestradiol can mean that cells are more likely to survive hypoxia, oxidative stress and exposure to neurotoxins for example and hence, also elicit a protective effect against the development of certain mental illnesses such as multiple sclerosis, Parkinson´s disease and dementia.

When considering the effect of oestrogen on brain cell firing we should assume that the effect is not major since for example there are other systems in play which have far more wide-ranging effects (eg. NMDA concentration, glial cell functioning) and that there is a natural variation in oestrogen level anyway with the menstrual cycle with no major signal transmission shut down when oestrogen is at a low level. Therefore, we should probably consider oestrogen more as an instrument of ´fine tuning` of the neurobiological system in the same vein as the emotional system (eg. a positive influence from dopamine on the emotional system and neuromodulation of prefrontal cortex firing) or like the effect of tiredness and sleep deprivation. In order that such an influence can occur the cells in question must have oestrogen ´acceptor` capability and this will be described in more detail later on. The possession or absence of such a capability could explain why some brain areas are affected by oestrogen and why some are not and hence, why some cognitive functions are affected and others independent from oestrogen influence.

For now in the context of a positive effect on synaptic firing, oestrogen has been shown to increase neuronal firing due to the growth of neurites (increases cell viability) and an increased number of dendritic spines. For example in the case of the hippocampus, the number of spines varies with the level of oestradiol in vivo with both peaking together. The presence of oestradiol also shows that the area grows more excitatory synapses and the new spines have more NMDA receptors on them. Hence, the long-term plasticity of the hippocampus is increased in the presence of oestrogen. Also oestrogen can initiate its effect directly in the hippocampus by depressing the synaptic inhibition mechanism. Oestrogen receptors have been found on the inhibitory interneurons in the area which do not grow more spines on exposure.  The oestradiol causes the inhibitory cells to produce less GABA so there is less inhibition of firing and hence greater general neural activity which somehow triggers an increase in spine growth in the area and increases the number of excitatory synapses on the pyramidal cells. In the presence of low oestrogen then decreased spine density and a decreased number of NMDA receptors is observed as expected, but also increased acetylcholinesterase activity is seen. This implies that an effect on the cholinergic firing mechanism in the area is also influenced. These effects on the hippocampus give an explanation in part as to why certain memory systems are said to be affected in menopause since the hippocampus is believed to be responsible for the relay of information in the brain and with the neighbouring entorhinal cortex area responsible for the binding of information together. Hence, effects on object recognition and verbal memory in menopause where there is a reduced level of circulating oestrogen are seen.

Another brain area said to be affected by oestrogen is the prefrontal cortex. It has been found that dopamine activity in this area is enhanced by oestradiol and in its presence then bigger synapses are observed. The effect is associated with the presence of oestrogen receptors of the alpha type. Therefore, in this case oestrogen could influence the neuromodulatory control associated with this area and dopamine, thus explaining in part the observed cognitive symptoms in menopause linked to the emotional pathway eg. irritability, and lower decision-making capability eg. assessment of values of events.

The synaptic and firing effects observed in the presence of oestrogen are brought about by intracellular processes involving the hormone. These are believed to be associated with DNA binding and/or cellular membrane effects and also as suggested by the authors in the article reviewed in this blog, by changes in the energy producing mechanisms taking place in the cell`s mitochondria. The DNA effect is well documented and begins with the transfer of the hormone through the cell`s membrane – a process that is simple due to its non-polar molecular structure. Once inside the cell it binds to a highly specific soluble receptor protein in the cell`s cytosol. These oestrogen receptors are of the alpha or beta type and are known as nuclear oestrogen receptors (ERalpha, ERbeta). It is thought that it is the alpha type in the hippocampal CA1 area that is linked to the increased synaptic plasticity described above. The hormone/receptor complex then interacts directly with specific binding sites on the DNA called oestrogen response elements (EREs) and ultimately, this binding modulates gene transcription. The DNA binding domain is highly conserved with 9 cysteine residues, 8 of which bind zinc ions which stabilise the structure of the domain (called zinc finger domains). The ligand binding site exists at the carboxyl end and is comprised of alpha helices. Ligand binding in a hydrophobic pocket in the centre leads to conformational changes that allow the recruitment of a coactivator protein such as SRC-1, GRIP -1 or NcoA-1. These have a common modular structure and bind to the ligand binding domain of the receptor dimer. Binding to the DNA ultimately changes gene transcription so that certain proteins are either down- or up-regulated so that the oestrogen influence on the cell is realised.

The other known cellular effect of oestrogen is its binding to membrane-bound receptors (mERs) eg. GPER (GPR30), ER-X and Gg-mER. These receptors can be rapidly activated on exposure to oestrogen and their effects are believed to be associated through the attachment of caveolin-1. Complexes are formed mostly with G protein coupled receptors, striatin, receptor tyrosine kinases (eg. EGFR, IGF-1) or non-receptor kinases (eg. Src) and each causes different effects. Although binding through G protein coupled receptors eg. GPR30 has an unknown role, binding to other structures cause cellular effects eg. through striatin – some of the membrane bound oestrogen receptor complex may lead to increased levels of calcium ions and nitric oxide; through receptor kinases – signals sent to the nucleus via the mitogen activated protein kinase MAPK/ERK pathway and the phosphoinositide 3 kinase (PI3K/AKT) pathway; and finally through glycogen synthase kinase 3 (GSK- 3beta) which inhibits transcription by the nuclear oestrogen receptor by inhibiting phosphorylation of serine 118 of the nuclear oestrogen alpha receptor. Phosphorylation of the GSK-3beta removes its inhibitory effect and this is achieved by PI3K/AKT pathway and MAPK/ERK pathway via rsk.

Another possible mechanism involving the membrane is the oestrogen receptor complex`s effect on the lipid domain as a whole and the subsequent increased or decreased action of other neurotransmitter complexes existing in that same lipid domain. It is known that oestrogen elicits an effect on NMDA receptors in the hippocampus, but it also affects acetylcholine binding to the M2 acetylcholine receptor. In general, however it is likely that the overall effect of oestrogen by binding to membrane-bound receptors is increased firing activity as described above.

The third action of oestrogen at the intracellular level is that suggested by Hamzelou and researchers such as Brinton who hypothesise that the presence of oestrogen supports the use of glucose as fuel source in the cell in its energy production mechanisms, but its absence causes a change in fuel source to fats and even myelin and a decrease in mitochondrial function. What does this actually mean? In the brain, the sole source of fuel for cells is glucose under normal circumstances and we have to assume that there are normal circumstances even in the low levels of oestrogen in parts of the menstrual cycle because of diet and that the glucose transport into the cells is below maximum capacity and hence, an increase in brain cell activity will still keep glucose transport into the cell within its limits. As already described in another Blog post, cell energy production mechanisms can change according to certain conditions eg. conditions of low oxygen/high altitude. In this case, the lack of oxygen means cellular adaptation of the biochemical processes supplying energy to the cell occurs.  In low oxygen conditions, the normal mechanism of energy production means that glucose is still being metabolised by a chain of enzymatic reactions (called glycolysis) to produce pyruvate just as that occurring in aerobic respiration (ie. in the presence of oxygen),  but the second stage of the process is altered. This stage is where the pyruvate is converted by another chain of reactions into the energy molecules, ATP.  If oxygen is not present at the level required for this aerobic mechanism, then a process called lactic acid fermentation is initiated (anaerobic respiration). Lactic acid fermentation means that pyruvate is then converted to lactate by the enzyme lactate dehydrogenase (LDH). However, this anaerobic process does not produce the same number of ATP molecules as normal aerobic mechanisms, but it does provide some. The other potential problem of this scenario is the build-up of lactate which is observed in muscle cells. However, it is likely that in the brain which is dependent on a constant supply of glucose and energy that a safeguarding mechanism is in place called the Cori cycle which transports the lactate out of the cell, back to the liver where it is converted into glucose by a process known as gluconeogenesis. Again the LDH enzyme is involved and this conversion could explain the lack of appetite experienced by some when undergoing rising altitude.

In the case of the menopause, Hamzelou and researchers such as Brinton suggest that the source of fuel in the brain cell changes from glucose to fats when oestrogen levels are low. Normally, fatty acids are bound to albumin in the blood and cannot cross the blood brain barrier, but under conditions such as starvation for example,  ketone bodies are generated by the liver and transported in the blood across the blood brain barrier to partly replace the glucose as fuel in the brain cells. Therefore, Hamzelou and Brinton suggest that ketone bodies are used as fuel source. The acetyl coA formed in fatty acid oxidation enters the citric acid cycle only if levels of fat and carbohydrate degradation are balanced. This is because of the availability of the substrate oxalocitrate which forms citrate, the next substrate in the cycle. Oxalocitrate concentration is lower if carbohydrates are not available.  In fasting or diabetes, oxalocitrate is used to form glucose by the gluconeogenic pathway and therefore the substrate is not available for acetyl coA production. Therefore, acetyl coA is converted to acetoacetate (by a 3 step mechanism) and D-3-hydroxybutyrate (formed by reduction of acetoacetate in mitochondrial matrix) which with acetone (formed from slow spontaneous decarboxylation of acetoacetate) forms compounds known as ketone bodies. The major site of production of ketone bodies is in liver mitochondria and these are transported via the blood to other tissues. They are used as fuel sources in the muscle, renal cortex and brain in cases of starvation (75% of fuel in prolonged starvation) and insulin-dependent diabetes mellitus. In the latter, the absence of insulin means that the liver cannot absorb glucose and as a result cannot provide oxaloacetate for the fatty acid derived acetyl coA process and cannot prevent fatty acid mobilisation by the adipose tissue. Therefore, the liver produces large amounts of ketone bodies which are strong acids and the presence of such high levels causes severe acidosis. This results in a decrease in intracellular pH which impairs tissue function – a condition already described in a previous Blog post when considering cell function in high altitude conditions. The brain begins to use acetoacetate after 3 days of starvation (a third of energy needs met), but after several weeks it is a major source. The advantage is that ketone bodies are built from released fat and this preferable to breaking down muscle instead.

Although the hypothesis by Hamzelou, Brinton and supporters about the switch from glucose to fat and even myelin may be true and that glucose metabolism is reduced in the brain in low oestrogen, then if this hypothesis is correct, then we must assume that in menopause, the brain cells are not getting their normal fuel source because of the lack of oestrogen. Therefore, under normal conditions oestrogen would then aid the transport of glucose into the cell by affecting the insulin signal on the glucose transporters, or by directly effecting the glucose transporters themselves. Is there any proof of this? There are no reports of significant effects on insulin sensitivity or levels or glucose levels in the menopause. However, there is a report of the change in insulin metabolism. Therefore, the cause of effect could be indirect through reported changes in diet in menopausal women where diet is altered to counteract the increased weight gain and fat deposits observed around the middle. A strict diet could translate into starvation conditions and hence, changes in fuel sources as indicated above could be observed. It is likely that if a normal diet is maintained then such an effect on fuel source would not be seen.

Therefore, the overall conclusion about the action of oestrogen in the brain is that it is a molecular compound that affects cell functioning of susceptible cells by either binding to the cell membrane or by internally binding to receptors which bind directly to the DNA and affect gene transcription. This can result in either a negative or positive effect on cell functioning. If the cell has oestrogen acceptor capability then oestrogen can affect that cell, that area and ultimately have an effect on cognitive function of some sort linked to that brain area eg. oestrogen influences the activity of the hippocampus by inhibiting the interneurons and hence, increasing synaptic firing and increased plasticity of area in question. An absence of the hormone will lead to observed changes in verbal memory, object recognition, spatial memory (only rats), short term memory, learning new associations, long term memory, working memory plus depression. However, the action of oestrogen should be considered more in terms of ´fine tuning` systems and mechanisms already in place rather like the effects of tiredness. This would in part explain why there appears to be a neuroprotective effect with oestrogen ie. cells are more likely to survive hypoxia, oxidative stress, exposure to neurotoxins for example or protection against diseases such as multiple sclerosis, Parkinson`s disease and dementia if exposed to oestrogen or oestradiol. The positive oestrogen effect on gene transcription and synaptic firing would counter-balance the negative effects caused by the cellular stresses.

This leads on to the hypothesis proposed by Brinton and others and explained by Hamzelou in her article about a link between the menopause and Alzheimer`s disease. It is said that there are several similarities between the two conditions eg. the start of menopause is considered to be linked to the same time as the start of Alzheimer`s disease; women are far more susceptible than men; and the presenting symptoms relating to cognition appear to be the same or similar. Therefore, we must question whether this is just circumstantial or whether there is a real link. With regards to timing, the menopause or reduction in oestrogen as described above could initiate some minor temporary changes in physiology which could lead on to changes in sleep patterns, depression and anxiety and small changes in performance of some cognitive functions. Although the physiological changes seen with Alzheimer`s disease are known for times later on in the disease progression, the physiological changes associated with the early stages are to date not clearly defined. It could be that these are actually the same changes as those observed in menopause ie. changes in sleep patterns, susceptibility to depression and anxiety, reduced levels of interest and hence, lower levels of mental stimulation etc. and therefore, the timing of the menopause and onset of Alzheimer disease would appear to be the same. Of course, it should be remembered that not all women who experience the menopause go on to develop Alzheimer disease and menopause and Alzheimer disease  are associated with more elderly people and hence, timing could be a reflection of the normal ageing process and the changes in life style, aspirations, emotional stability that could accompany this particular life period.

The second association between the menopause and Alzheimer disease according to Hamzelou, Brinton and others is the observation that Alzheimer disease is more prevalent in women and understandably, the menopause is a female condition. Since brain neurochemical mechanisms are independent of gender then we must assume that the difference is due to either physiological differences between the female and male brain, or possibly could the reflect the way in which men and women mentally approach and carry out events. The latter is probably a product of the former and therefore, the observation that oestrogen level has an effect on the performance of the hippocampus (described above) could explain why there is a gender difference in the appearance of Alzheimer disease. The hippocampus is an important brain area with multiple roles in cognitive functions such as information intake and binding, memory mechanisms, working memory and decision-making and is known to be progressively and extensively negatively affected as Alzheimer disease progresses. Women appear to have naturally larger hippocampal areas and therefore, this could provide a possible reason why women appear to suffer from Alzheimer disease more than their male counterparts.

The third similarity proposed by Hamzelou, Brinton and others linking menopause to Alzheimer disease is that the cognitive symptoms of the menopause are similar to those seen with sufferers of Alzheimer disease. Both appear to be a collection of cognitive symptoms linked to relaying information taken in, binding of information together, value assessment for example and hence, the similarity of symptoms of for example lack of memory, decision-making problems and emotional status changes are understandable. As stated above, circulating oestrogen appears to affect synaptic functioning and as with the timing association physiological changes would instigate observable performance changes. Since both produce to some extent permanent changes in physiology eg. menopause causes minor changes due to its ´fine tuning` role and Alzheimer disease massive changes because of amyloid deposits and abnormally high apoptosis of neurons then symptoms would be appear to be the same.

There is however a difference between menopause and Alzheimer disease in relation to whether cognitive performance can be restored by treatment of oestrogen replacement therapies. With Alzheimer disease in the later stages of the disease, administration of oestrogen replacement therapies appears to have no beneficial effect. In the former however, treatment can reverse some of the symptoms eg. verbal memory, short term memory are improved and there are positive effects on sleep and emotional state disturbances, eg. depression is reduced. This is understandable since falling levels of circulating oestrogen are being boosted by the administered oestrogen compounds and hence, the positive effects on DNA transcription and synaptic firing are being restored. Increased expression of the oestrogen alpha receptor in hippocampal CA1 area and increased NMDAR synaptic transmission have been observed with the administration of oestrogen compounds to mice menopausal models. However, most research appears to suggest that the positive effect of this oestrogen administration appears to be limited to only a short period when falling levels are minimal (called the ´window of opportunity`) which implies that in the long-term other changes are occurring in the synapse and brain areas that are not associated with the fine tuning mechanism brought about by the presence of the oestrogen hormone. These physiological changes could be those linked with natural ageing for example or instigated through life-style changes brought about by a variety of reasons. This may be important because other things appear to be beneficial for reduction of menopausal cognitive symptoms eg. exercise, proper diet, social contact, mental stimulation, appropriate sleep patterns. These can possibly restore the balance or counteract the loss of oestrogen experienced in the menopause. One factor that should be considered relating to this is the importance of zinc in brain cell functioning. Zinc deficiency is known to cause anorexia, lethargy, diarrhoea, impaired immune system, growth restriction, intellectual disability, depression, loss of appetite and disorders of fear conditioning. There is a range of effects because zinc ions have important functions in general in nerve conduction in the brain, roles in correct enzyme functioning such as carbonic anhydrase, aspartate transcarboamylase, aminoacyl –tRNA synthase, metalloproteases and in neurons in particular an important role in the phospholipid cell membrane signal and in relation to menopause in  steroid binding to the receptor as seen in the case of oestrogen. It is possible that menopausal women could suffer from zinc deficiency due to dieting and/or poor diet. Food stuffs containing zinc are bread, eggs, oysters, liver, meat, dairy products and pulses and weight gain associated with falling oestrogen levels may mean that the diet is restricted of these zinc containing foods. This deficiency could lead to the wide range of effects attributed to the multiple cellular roles of zinc.

Therefore, can we definitively say that oestrogen reduction in menopause is linked with Alzheimer disease? It is likely that oestrogen is not a major player in neuron function rather it provides a ´fine tuning` mechanism for synaptic physiology and function in the same way as tiredness or emotional state changes can. It appears that its effect in the brain is limited to particular areas such as hippocampus and prefrontal cortex which play important roles in cognitive functions such as memory and decision-making. Therefore, the presence of oestrogen may provide a neuroprotective effect on certain neurons which allows these cells to more likely survive extreme negative conditions such as those seen with  hypoxia, oxidative stress and exposure to neurotoxins. This of course naturally translates then into positive changes on cognitive functions so that oestrogen is said to have a protective effect against certain mental illnesses such as multiple sclerosis, Parkinson`s disease and dementia. It is then understandable that conditions where there is an absence of oestrogen or where levels are low such as the menopause lead to minor effects on cognitive performance. However, the effect could be also attributed to normal ageing processes being experienced at that time. The association between menopause and Alzheimer illness, although symptoms appear similar, is likely to be indirect with general ageing, certain conditions such as stroke and lifestyle changes being the main causes of the appearance of the disease. Therefore, it is understandable that boosting the level of oestrogen when it is naturally falling can provide some positive effect on certain cognitive functions, but only temporarily. Probably of more benefit to women experiencing the menopause is the continuation and maintenance of good life style practices.

Since we`re talking about the topic………..

…..if synaptic firing is enhanced by the presence of oestrogen because of the inhibition of hippocampal interneurons can we assume that the administration of a GABA antagonist preferably targeted to the hippocampal area simultaneously with the administration of oestrogen to ovariectomised mice will block this positive effect? Would the expected behavioural changes relating to restored spatial memory also be absent?

…..using real-time functional MRI would it be possible to chart connectivity between certain brain areas eg. hippocampus, amygdala and prefrontal cortex during the course of a problem-solving type task using menopausal subjects and to monitor the effects that the administration of either oestrogen or progesterone pre-testing would make on those connectivity patterns?

…..performance of place recognition tasks was found to be reduced in female rats who were in the proestrus (high oestrogen) phase of their oestrous cycle. An excessive consumption of sugar sweetened drinks daily beginning 14 days before testing was found to protect the rats from this negative change. This was attributed to the sugar consumption causing functional changes in the hippocampus. Object recognition appeared not to be effected. Can we assume that the same pattern of results would be unlikely to be observed with human females because of the effect of insulin, but may produce a problem in those that suffer from diabetes?

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