hippocampus firing defines event boundaries in continuous experiences

Posted comment on ´The hippocampal film editor: sensitivity and specificity to event boundaries in continuous experience`  by A.B. Yakov and R.N. Henson and published in Journal of Neuroscience 2018 vol 38 (47) p. 10057 doi.org/10.1523/JNEUROSCI.0524-18.2018

SUMMARY

Yakov and Henson in their article described the results of their investigation into hippocampal functioning during continuous sensory experiences. The authors hypothesised that the hippocampus would be sensitive to event boundaries which they defined as the moments in time between sensory events and their experiments confirmed this.

The experiments carried out involved two groups of subjects: one large group of 131 females who viewed an 8.5 minute long film (an abridged version of a Hitchcock film) that was compelling and unfamiliar to them (termed the Cam-CAN study) and a smaller group of 16 females who watched a familiar 120 minute long film (´Forrest Gump`) which was divided into 8 segments of  15 minutes (termed Studyforrest). (Eight participants watched this film in German, their native language and 8 watched it in English and dubbed in German. However, a lack of difference in results of the two language groups meant that the results were combined.) Film segmentation was achieved by the participants pressing a key when they felt that one event (termed the meaningful unit) ended and another began. A reaction time of 0.9 seconds was subtracted from the logged key press times. If more than 5 different observers marked boundaries within approx. 2 secs of each other (termed 1 TR) the period was treated as a single boundary. In the Cam-CAN study 19 boundaries were identified separated by 6.5 to 93.7 seconds. The observed boundaries were divided into 3 levels according to the number of observers that identified them (the salience). There were 7 low salience events (5-6 observers), 5 medium (7-12) and 7 high (13-16 observers). In the Studyforrest 161 boundaries were defined and these were separated by 4.9 to 167.7 seconds with 60 low, 43 medium and 54 high salient values.

The neuronal functioning of the hippocampus was assessed using functional MRI. The imaging conditions (eg. type of scanner, TR length, axial thickness) were slightly different for the two studies, but considered comparable. Regions of Interest (ROIs) included the anterior and posterior hippocampus where the end results were averaged, the visual cortex V1, auditory cortex and angular gyrus (AG). The AG was included in Yakov and Henson`s experiments because other researchers had reported a change in activity patterns in this area which appeared to correspond to event boundaries. Data from the imaging was pre-processed for the purpose of correlation and then various statistical analyses were performed to determine significance. Various other analyses were performed relating to event boundary characteristics such as isLoc/isTemp (shift in location/time), visDist (visual distance between frames immediately preceding and frames immediately following a boundary), visCorr (visual correlation between frames immediately preceding and frames immediately following a boundary), visHistDist (distance between the colour histograms of the frames before and after a boundary), lumdist (difference in overall luminance before and after a boundary), DCNN (the correlation between the layers of a deep neural net run on the frames before and after a boundary), psdCorr (correlation of the power spectral density before and after a boundary as a measure of auditory similarity), psdDist (the distance of the PSD across a boundary), absVolDist (absolute difference in volume across a boundary), V1 and A1 betas (average V1/A1 response across participants to each event) and isAG (binary predictor indicating whether a boundary coincided with a pattern shift in the AG). Therefore, the role of hippocampal activity during continuous experience was assessed for both sensitivity (the hypothesis-driven approach via examining hippocampal response at event boundaries defined by the observers) and specificity (the data-driven approach involving the identification of hippocampal events based on the amplitude of the hippocampal firing response and by testing the overlap between these events and subjective boundaries).

Yakov and Henson found from their experiments using film watching to simulate continuous experience that there was a strong hippocampal response at event boundaries for both film watching studies. This was determined by an overall higher response when a larger number of observers marked the event boundary (ie. high salience). For the Cam-CAN study, the size of the response was small, but was still significant. The authors continued their investigation by looking at the effect of various predictors such as shifts in time, location and sound for example between one event and the boundary and the following event. They found that if all the predictors were added together then the results were not significant, but when each predictor was added to the model separately then both the number of observers in total (termed nObservers) and the number of observers of each group reflecting salience of high/medium/low values remained significant. Most event boundaries were found to be characterised by a change in location such as that determined by the predictor isLocTemp and the authors found that the predictor and nObservers results were highly dependent and therefore, their relative contributions could not be dissociated. Therefore, it was concluded that no definitive statement could be made about whether modulation by boundary salience could be accounted for by other perceptual factors such as spatial and temporal change.

The results obtained for the Studyforrest differed slightly from the Cam-CAN study in that an increase in boundary salience was observed, but unlike the shorter film study the value remained significant even if the predictors were added together or considered separately. This included the predictors for spatial and temporal changes. The authors therefore, concluded that in both studies that the hippocampus was sensitive to boundary salience and to the number of observers who reported the event shifts. The difference between studies was attributed to the Studyforrest being longer and having an increased number of event boundaries which then made the assessment of the relative contributions of various additional drivers easier. Therefore, it was found that although changes in visual/auditory contributions or location and time occur and modulate hippocampal activity they did not account for its sensitivity to boundary salience. The modulation of hippocampal response by boundary salience was concluded to be sensitive to other factors.

Yakov and Henson continued their investigation by looking at the effect of AG activity on the hippocampus since it had been suggested by other researchers that a possible hypothesis for the increase in hippocampal activity at event boundaries was that event boundaries elsewhere in the brain cause cortical pattern shifts which have an effect on hippocampal activity. In their experiments, Yakov and Henson found that there was an increased hippocampal response to overall AG pattern shifts, but this increase was only found for AG pattern shifts that matched event boundaries. Therefore, they concluded that both AG pattern shifts and increased hippocampal activity are driven by event boundaries rather than patterns of AG activity changes and hippocampal activity being directed related. Other brain areas were also investigated for this type of effect on hippocampal activity. 55 ROIs were tested in total by the authors, but only 5 produced possible correlational event boundary firing and these were the hippocampus, the posterior cingulate cortex, precuneus, posterior parahippocampal cortex and lingual gyrus. Further analysis however showed  that only the hippocampus and posterior cingulate cortex results remained significant when adding in the perceptual and objective shift predictors or by testing with the nObserver analysis condition.  Yakov and Henson also tested for the specificity of event boundaries and found significant results with 58% of hippocampal events matching predefined boundaries in the Cam-CAN study and 38% in the longer Studyforrest.

Therefore, the authors concluded that event boundaries play a key role in shaping activity during the neuronal encoding of continuous experiences. The hippocampus was an area of interest since it was found to be sensitive to event boundaries. Yakov and Henson found in their experiments that there was an increase in hippocampal activity relating to event boundaries and this correlated to boundary salience. It did not however, appear to reflect the differences in the degree of perceptual change since changes in factors such as visual information, auditory change, time, location did not produce significant changes to the observed event boundaries. The increase in hippocampal activity was also found to coincide with shifts in cortical patterns observed with the AG where also event boundaries had been identified with continuous experience. Therefore, it was concluded that event boundaries and not shifts in cortical firing patterns per se drive hippocampal activity. This observation was also recorded for the brain area, posterior cingulate cortex.

COMMENT

What makes Yakov and Henson`s article so interesting is that gives information about a neural event that is normally not considered ´an event` and by doing so elucidates further the role of one particular brain area, the hippocampus in sequence memory (or continual experience).  What we mean by ´a neural event not normally considered as an event` is that in the case of sensory input relating to an external event we normally concentrate on neuronal activity linked to characteristics of that event such as visual and auditory information and even personal emotional worth. This information is all bound together in a period of synchronised firing in the different relevant brain areas and is stabilised to form a neural representation of the external perceived event. Yakov and Henson have shown with their experiments that this indeed goes on and we are well aware that this synchronised activity and neural representation is extremely important for cognitive capability such as in the stages of perception, memory, decision-making and behaviour for example.  But, what Yakov and Henson claim to have done in their experiments is to look at what happens to neuronal firing when one external event ends and another begins which is a natural occurrence in continuous experiences (or sequences). These ´periods` are termed event boundaries and they are said to correspond to the shift, the break or the interval between one event and the next. In Yakov and Henson`s experiments these event boundaries were identified by their participants being aware of a change in external events and reporting it to a third party (hypothesis-driven approach) and the neuronal firing responses in the hippocampus measured using fMRI (data-driven approach). Yakov and Henson reported that these two events occurred simultaneously and that hippocampus firing modulated strongly with the identified event boundaries.

It is unlikely that this is a true reflection of what is occurring at this time and to explain why we have to look at how information is inputted from external event sequences or continual experiences. If we consider the initial event of a sequence (Event 1), information about that event is inputted via the sensory pathways and ends up activating cells in the higher brain areas where the information is processed (working memory) and identified (perception, object recognition) or simply stored short-term. In this case, the hippocampus acts as a relay of information sending it on its way to the higher brain areas  or providing conditions via its connectivity to the adjoining entorhinal cortex, parahippocampal cortex and rhinal cortex areas and its own complex physiological structure to allow information binding and sustained firing to occur. There is likely to be conscious awareness to begin with plus functioning attentional and working memory systems. At the higher brain areas a neural representation of this Event 1 would be formed from the synchronised firing at that time all bound together in one active unit. The next event in the sequence would also present with incoming information and again the information would be relayed via the hippocampus to the higher brain areas or directly to them via the thalamus. However, since many of the elements would be the same as those of the initial event it is likely that this information is not treated as a separate entity but as a continuation of Event 1. This means that it is likely that the matching active cells as part of the Event 1 neural representation would continue to fire with their firing strengthened by this continuation and the novel information which would be the subsequent changes, are added to it in order in the form of complementary representations.  (The continual repetition of the unchanged elements of the event means that the conditions required for memory formation such as constant firing are fulfilled.) This process would continue for all incoming information where the majority of characteristics of the initial representation remain. With reference to Yakov and Henson`s experiment this would refer to the time from onset to the end of the first event.

What happens when the external event massively changes as in change of film topic if we relate it to Yakov and Henson`s experiments? If we consider it only from the point of neurochemical firing at this point, we can see that there is a huge change in characteristics of the neural representation for the elements of this Event 2. The hippocampus again relays the incoming information to the higher areas or provides the conditions via its connectivity to the surrounding entorhinal cortex, parahippocampal cortex areas and its own complex physiological structure to allow binding and sustained firing to occur. Again a neural representation of this Event 2 would be formed from the synchronised firing at that time in the higher brain areas. However, the expected continued firing of the already present representation (Event 1 plus changes) would not occur and this would then begin to die out with the stronger representation of Event 2 taking its place. It is unlikely that an ´error signal` is instigated at this time by the higher brain areas as seen with conditioning experiments and unexpected reward because no stored representations exist to which the input can be compared. However, the formation of multiple competing representations would bring about changes in attentional systems, working memory (leading to updating) and more importantly with relation to Yakov and Henson`s experiments, conscious awareness. This change in awareness would be the point when the experiment`s participants would be aware of a change in topic and would report the change to the third party (ie. the subjective event boundary, hypothesis-driven approach).

So, here as we can see we have a slight problem in the so-called simultaneous event boundary as indicated by the participants and the hippocampal response as indicated by the fMRI (data-driven approach). If the above description of event elements and neural representation is correct for sequences then the subjective recognition of the event boundary should come after the change in neuronal firing representing the shift from Event 1 to Event 2. That is, a person can only know that something is changed after it has happened in real-time. Therefore, the subjective event boundaries would be later than the neuronal firing responses.

This leads to the question as to what the observed modulation of hippocampal firing actually represents. It is likely that the hippocampal firing observed at so-called event boundaries is likely to represent the multiple representations present at the time shortly after event shift. That is, it would represent the dying out activation from the input relating to the previous topic and the growing stronger activation of the novel topic. These would counteract one another so that strong modulation of firing would on average be seen. Any attempt to separate the images would need extremely accurate and small scale neural imaging techniques that may not be available at this time. This is supported by Yakov and Henson who demonstrated that changes in predictors such as location produced no significant effects on firing. The shared event boundaries observed between the hippocampus, AG and post-cingulate cortex (PCC) observed by the authors can be explained by considering the conscious experience. They probably represent the roles of the AG and PCC in conscious awareness with AG known to be linked to linking the SELF with episodic memories and PCC with the DMN and are independent of event content, but are dependent on shared timing also with the hippocampus and other cortical areas.

Therefore, in conclusion Yakov and Henson`s experiments have added to the limited knowledge we have about sequence memory by reaffirming that the hippocampus acts as a neuronal firing relay station in this type of memory in the same way it does with single events. It is likely that it can hold multiple items simultaneously albeit at different strengths and this is logical if we consider that we can simultaneously input and process information consciously and unconsciously. The area`s connectivity with other brain areas allows information to be processed, perceived, remembered or used according to the demands placed upon it and modulation of it whether structurally or through other areas functional connectivity can have wide-ranging cognitive effects. Therefore, it remains an area of great importance and great interest.

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

……….Yakov and Henson`s experiments featured real events pictured on a screen. Can we assume that an imagined sequence of events instigated through auditory commands would produce the same effects on other brain areas, but the input relay system of the hippocampus would not show event boundary activation?

…….certain factors can upset the synchronisation between brain areas such as jet lag, endocannabinoid agonists. Is it likely that such factors would also disrupt the subjective and objective event boundaries observed in the hippocampus especially since this area is known to be affected?

…..sensitivity to time is reported with things like caffeine (increased arousal leading to a longer perceived duration) and mindful meditation (increased sensitivity to time and a lengthening of time). If the experiments of Yakov and Henson were repeated would a change in event boundary similarity between participants be observed and can we assume that individual event boundaries would still relate to strong hippocampal firing modulation?

Advertisements
Posted in binding, hippocampus, input memory, neuronal firing, sequences, Uncategorized | Tagged , , , ,

role of angular gyrus in autobiographical memory and the Self

Posted comment on ´Specifying a causal role for angular gyrus in autobiographical memory` by H.M. Bonnici, L.G. Cheke, D.A.E. Green, T.H.M.B. FitzGerald and J.S. Simons and published in Journal of Neuroscience 2018 vol 38 (49) p. 10438 doi.org/10.1523/JNEUROSCI.1239-18.2018

SUMMARY

In their investigation, Bonnici and colleagues investigated the role of the parietal cortical area, angular gyrus (AG), in brain memory. Studies carried out by others show that dysfunction of this particular area does not lead to amnesia, but impairs aspects of episodic memory and hypotheses have been developed in relation to how this occurs. These include the reduced ability of memories to capture attention spontaneously, or the reduced ability of subject to ´re-live` personal events.  Inconsistencies in the different study results exist with some finding that parietal lobe lesions lead to deficits in free recall of autobiographical events, but not with cued recall and others showing that the inhibitory stimulation of AG has no effect on free or cued recall. Bonnici and colleagues therefore sought to resolve the conflict in opinion.

Twenty-two healthy individuals aged 19-35 were used in the authors` experiments. The subjects were subjected to continuous theta burst stimulation (cTBS) of the left AG and a control vertex location and memory tests of free and cued recall of autobiographical memories and word pairs were carried out. Testing of the two conditions was one week apart for all individuals and each session was structured to begin with the autobiographical memory gathering phase (AM) leading on to a  study phase for the word pairs task, the cTBS procedure, the autobiographical memory recall phase and the word pairs test phase. For testing of autobiographical memory function, the authors used the modified version of the Autobiographical Interview test where each participant had to name 5 personal significant events from four life periods.  Recall was free and cued after the stimulation and scored according to the number and type of details each recollection contained. For the testing of word pair memory performance, subjects were presented both audibly and visually to 2 sets of 64 noun pairs.The participants had to create a sentence within 10 seconds that contained both words and say it aloud. Free recall meant participants had to remember as many words as they could within 2 minutes and cued recall meant that participants were presented randomly with 1 word out of the pair and had 3 seconds to remember the second. The cTBS procedure consisted of 3 pulses at 50Hz every 200msec for 40secs at 70% of the participants motor threshold and was aimed at the AG or at a vertex centre both of which were determined by the neuronavigation system, Brainsight.

The results obtained showed a selective reduction in free recall of autobiographical memories after cTBS of the AG and this manifested as fewer details recorded. Further examination showed that the effect centred around fewer internal details with no differences in the production of external details. Cued recall brought about no reduction in remembering the personal events after cTBS of the AG and therefore, it was concluded that the cTBS of the AG affected the production of relevant details.

Bonnici and team performed experimentation to further explore the nature of the details that were lacking. They found that subjects reported fewer autobiographical episodes from a first-person perspective after cTBS of the AG and therefore, concluded that the AG enables the subjective experience of remembering by integrating memory features within an egocentric framework. This result supported work carried out by others on patients with parietal lesions who were found to be impaired where egocentric spatial navigation tasks were concerned, but not with map-based spatial tasks that were sensitive to hippocampal damage (ie. reliant on the memory of facts).

The results of the word pair memory experiments were clear-cut. It was assumed that a greater impairment of free recall of word pairs than cued would indicate an effect on attentional processes whereas a greater impairment of source recollection confidence than accuracy would be consistent with the subjective experience view. The authors found that subjects produced significantly fewer word pair responses in free recall than cued independent of the stimulation condition. This was suggested to be due to the highly demanding nature of the task on the attentional processes rather than the effect of the stimulation of the AG. Therefore, it was said that the results indicated no effect of AG stimulation on word pair memory which supported the work of others.

Bonnici and colleagues concluded that their experiments supported the view that the AG is part of the network of brain areas responsible for episodic recollection. Their experiments were designed to try to resolve the dispute in observations between parietal damage and cTBS of AG on memory function. They found that because of the differences observed in the results obtained between the autobiographical memory and word pair memory tests after AG stimulation that the AG played a role in autobiographical memory, but not word pair memory. In addition, Bonnici and colleagues concluded that the AG plays a role in the integration of memory features within an egocentric framework forming a representation from the first-person perspective so that a person can remember events subjectively from their own past. Their results support the subjective-experience account of AG function. The findings are less indicative of the so-called attention-to-memory hypothesis since the memory deficits appeared to be related to internal words than external words, deficits were in first-person perspective events and there was a lack of effect on word pair recall. Therefore, Bonnici and colleagues concluded that the AG plays a role in the subjective experience of remembering.

COMMENT

What makes this article so interesting is that it explores further the neurochemical basis of the ´Self` or what makes up the concept of ´Me, Myself, I`. Normally we look at the Self from the point of view of consciousness, but if look at it relating to information processing and memory for example we can see that the concept of the Self from this first-person perspective can actually involve functioning of different brain areas. What we mean by this and what is shown by the work of Bonnici and colleagues is that ´I` referring to my real-time conscious experience which includes awareness of my surroundings, my feelings, physiological status (eg. breathing rate) is brought about by activities in multiple brain areas (eg. medial prefrontal cortex, insular cortex and anterior cingulate cortex), but these may vary from those that are active when we consider for example the awareness and role of  ´Me` in the recalled events from my past. In this case the memories reactivated indicate events and characteristics that apply to ´Me` or are relevant to ´Me` and Bonnici and colleagues have shown that this first-person perspective involves the functioning of the parietal brain area, the angular gyrus (AG). This idea is not new in that consciousness theorists have long proposed the concepts of proto-Self (neural patterns mapping the state of the being moment to moment), core-Self (the transient entity re-created for each object to which the brain interacts) and autobiographical-Self (involving learning and recall of memories). We assume from the work of Bonnici and colleagues that the AG is involved in the autobiographical-Self part.

So, what can we say about the role of AG in autobiographical memory and how this area provides the awareness of personal involvement in recalled events? The AG sits in the parietal cortex and is in a strategic neural location for processing speech, touch, auditory and visual information. Therefore, the information that we deem as necessary for the definition of ´Me` such as what do I see, what do I hear, how can I tell others what I see and hear require activity of this AG area. Extending this idea the AG has been found to provide the salience of spatial features in attention and even the ability to infer intention in decision-making tasks. These roles appear to be subtleties of systems taking place elsewhere. For example, emotional content and assessment of value is undertaken by the medial prefrontal cortex areas and striatum for example and attention the parietal and prefrontal areas amongst others. Therefore, the neuronal networking required to fulfil these capabilities do not include the AG, but activity of the AG means that a personal ´I, Me` perspective is acquired. This is achieved through two characteristics of AG functioning: the first is, as said above, that AG functioning collaborates in the recall of autobiographical memories to bind ´Me` to that recall; and the second is that the activity of the area is associated with speech and the processing of language.

With regards to the first, the recall of autobiographical memories occurs under the same conditions as recall of third-person memories and both sets of memories are stored in the appropriate cortical areas. Reactivation of the memories also initiates the recall of emotional feelings (´emotional tags`) and values that may be associated with them whether they apply to the person or to another. The application of the first-person perspective appears to give distinctive qualities to the memories envisaged in that there is a ´detachment` between ´I`, the now, conscious first-person and this applied ´Me` perspective. This is supported by the observation that when recalling autobiographical memories we appear to experience them from the third-person perspective, detaching ourselves from the fact that we actually did the action, or experienced the event. Also on reflection we rate the past as ´inferior` to now even though the event may be the same, or the past event provided more pleasure than the same contemporary one, and pain cannot be re-experienced on recall of a painful event only the description of it. Therefore, the quality of the Self applied to autobiographical memories is different to the conscious awareness of the Self in real-time experiences and it appears that the AG is required for the first, but not the second. This capability of having two separate areas (or groups of areas) allows the two ´I`s to function at the same time. For example, there can be real-time awareness of heart-rate, incoming visual information for example as well as recall of autobiographical memories in the pursuit of a solution to a problem for example. However, only one will have conscious awareness at any one time although switching is possible just like in the case of divided attention. The ability to ´detach` the first-person perspective from memories from real-time first-person experience is also an advantage in certain cognitive functions such as decision-making since it allows decisions to be made objectively, separate from the real-time emotional state at the time.  It could also explain the development of self-recognition and empathy which allows the transference of experience from the first-person perspective to explain the actions of others.  (In the case of empathy, this may make use of the neighbouring locations of the AG with the supramarginal gyrus which is a major contributor to the empathy function.) It may also explain why some people report ´out of body` experiences and self-transcendence.

The second aspect of first-person perspective is speech and language processing of which the AG plays a role. Conscious awareness can be reported verbally using speech and language or proven by unreported actions or physiological measurements. However, descriptions whether of facts or feelings and problem-solving for example require language (common to others and learnt) and speech. The AG is required for speech processing and therefore, the Self requires the functioning of this area if the first-person perspective is to be communicated to others. The AG is not alone in this function and is part of a network of active areas responsible for the movements of muscles involved in speech, even language structure and phonetics.

Therefore, to summarise, the AG is an area that appears to be involved in the expression of the Self`s ´Me, Myself, I` and it appears to have two distinctive roles.  The first is that activation of the area in the recall of autobiographical memories leads to the application of ´Me` to those memories. This is carried out in such a way that there appears to be a ´detachment` between ´I` being recalled and ´I` existing now and part of the conscious experience. Such a ´detachment` is brought about by AG activity in the former only and allows the two perspectives to occur simultaneously. Hence, we can recall the past and our involvement in that past and be aware of what is happening in the present at the same time even if only one is conscious at any time. The second role of the AG is simpler neurochemically, but is even more fundamental to the concept of the Self in that activity in this area is required for speech processing. Without it our ability to communicate verbally and describe our memories, feelings and so to others or internally to think and solve problems for example is not possible. Therefore, the AG should be ear-marked as a key player in the neural correlates of consciousness and the Self. The continuing development of real-time neuroimaging techniques and computer analyses will no doubt in the future make it possible to investigate this area further and maybe lead to identification of spatial divisions within it that are specific for the two functional roles it appears to plays.

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

….Bonnici and colleagues asked their subjects to recall pleasant autobiographical memories and by doing this they saw the involvement of the AG. Would the recall of unpleasant autobiographical memories instead lead not only to the involvement of the AG, but also to activity in the amygdala even though this area is not believed to be neurally connected to the AG?

……can we assume that if we examined AG activity in a repeat experiment with subjects suffering from autism there would be a correlation between weaker AG firing and lack of first person and third person awareness?

……would we be able to distinguish areas within the AG for conscious awareness and unconscious awareness if the experiment was repeated using subjects suffering from blindsight instead and exposing them to pleasant personal photographs on their ´sighted` side and unpleasant personal photographs on their blind side?

 

Posted in angular gyrus, consciousness, memory recall, Uncategorized | Tagged , ,

reduced ventrolateral prefrontal cortex gray matter volume correlates to decision-making irrationality in aging

Posted comment on ´The reduction of ventrolateral prefrontal cortex gray matter volume correlates with loss of economic rationality in aging` by H-K. Chung, A. Tymula and P. Glimcher and published in Journal of Neuroscience 2017 37(49 12068

SUMMARY

In their article Chung, Tymula and Glimcher describe the results of their investigation into whether a reduction in efficiency of economic decision-making observed for adults aged 65 years and above correlated with a decrease in gray matter volume found in the brain area ventrolateral prefrontal cortex (vlpfc).

Fourteen healthy males and 25 healthy female adults aged between 65 and 92, who recorded Mini-Mental states of between 26 and 30 and had no recorded medication for any psychiatric condition, developmental disorder or dementia, took part in the study. The subjects performed a behavioural experiment to determine the degree of their rationality in a decision-making task. This meant that the subjects had to perform 11 different trials, each one of which was to select a bundle of 2 goods from 3-7 different alternatives. The goods in the bundles were carefully selected by the experimenters to be equally desirable for the subject being tested. This was achieved by first presenting the subjects with a range of objects thought to be favoured by elderly people eg. hand cream, pocket tissues, teabags, chocolates, puzzle books and asking them to rate the goods by their desirability. This test was performed twice. Bundles were then constructed of two types of goods (eg. sudoko books and crossword books) with variations in the number of each type (eg. one bundle would have 3 sudoko books and 4 crossword books and another bundle would have 6 sudoko book and 1 crossword book) for each participant and for each trial. The experimenters asked the participants to choose the bundle they liked the most. This experimental set-up assumed that the subject would choose the bundle that had the best selection for them personally and would reject the bundle with more of the not so good. Trade-offs between the 2 classes of goods and the amounts of the 2 goods would have to be made with the assumption that people would trade-off the quantity of one good choice for the other good one. Once the decision had been made, the subjects were then asked to press the number of the corresponding bundle onto a numeric keyboard and the result was displayed on a screen. The subjects were then told to recheck their choice and press enter to continue. No time limit was placed on this choosing period so that choice was not based on either the inclusion of undesirable objects, lack of interest or time pressure. At the end of the trial each subject was ´paid` according to his/her choice made in one randomly selected trial.

In Chung, Tymula and Glimcher`s experiments choice was correlated to the increase in number of Generalised Axiom of Revealed Preference (GARP) violations. Participants were considered as violating GARP when the chosen bundles consisted of one favourite and one non-favourite object rather than two favourite objects. Mathematical analysis was performed and 2 quantifiable measures were used to determine the level of irrationality of the choices made. These were: frequency of irrationality where the number of selected bundles violating GARP were calculated; and severity of irrationality dictated by the Houtman-Maks (HM) index which rated the assessment of the loss from small to severe. Whilst the subjects performed the behavioural tasks described above,  MRI neuroimaging was also carried out. Voxel-based morphometry (VBM) analysis was performed using a VBM8 toolbox for identifying gray and white matter and CSF followed by SPM8 and Matlab for adjustments and analysis. The brain activities of particular areas and their co-activations were identified using Neurosynth.

The results of the technical rationality experiment carried out showed that there was a decline in rationality in the older subjects compared with the younger control subjects. The elderly subjects presented varying levels of irrationality in choices with 42% making two or more GARP violations from the 11 trials. In comparison to other studies, elderly people appeared to violate rationality slightly more than college undergraduates (35%), the same as younger people with blood alcohol levels at or above 0.8% of the legal limit, less than second-grade children (74%) and less than patients with ventromedial frontal lobe lesions (89%). The severity of the irrationality correlated to its frequency. Further investigation showed that although there was a decline in rationality compared with younger subjects, chronological age itself did not correlate to irrationality levels observed either in frequency or severity. Participants made on average 1.37 irrational choices with one participant making 6.

Having established that technical irrationality was increased in older subjects, but did not correlate to chronological age, Chung, Tymula and Glimcher investigated the relationship between economic rationality and gray matter density of the vlpfc using whole-brain VBM analysis. The authors found that there was a reduction of gray matter volume of a cluster in the left vlpfc  (identified as lateral Brodmann 10, rostral 46 and lateral 47 areas) correlating to the irrational economic behaviour observed. This area corresponded to more GARP violations in both frequency and severity than other brain areas. Testing of the right vlpfc produced a less robust MRI signal and therefore, functioning of the right vlpfc was examined by other means. Using these alternative methods, the authors found the same results for the right vlpfc  as for the left and therefore, concluded that there was significant correlations between frequency and severity of irrationality for both left and right vlpfc.

The final part of their study saw Chung, Tymula and Glimcher investigating brain area connectivity associated with economic rationality by creating a reward associated map using Neurosynth. The authors found that the left vlpfc was associated with reward regions and co-activated with other brain areas traditionally linked to decision-making processing including the dorsal lateral prefrontal cortex (dlpfc), striatum and posterior parietal cortex. These areas were also shown to demonstrate strong co-activation patterns to the right vlpfc as well.

Chung, Tymula and Glimcher concluded their article by summarising the impact of their study on current cognitive thinking and the impact that this type of cognitive deficiency has on a society that is getting older. They showed that in older adulthood, the decline in decision-making rationality was not reliant on memory or complicated processing rules, but occurred because of unknown reasons. They looked at whether a change in decision-making capability for known alternatives was caused by physical changes in brain structure and found that for their experimental set-up it did. Using a task that involved multiple cognitive capabilities mainly relating to value, Chung, Tymula and Glimcher showed that the region left and right vlpfc is involved in decision-making including the elderly and co-activates with other decision-making and reward associated brain areas. The authors found that in the case of economic rationality, irrationality in their decision-making task correlated to a decrease in  volume of gray matter in the vlpfc of their elderly subjects and therefore, concluded that the vlpfc area is linked to the mechanism required for utility maximisation.

COMMENT

What makes this article interesting is that it describes the role of the lesser known ventrolateral prefrontal cortex region (vlpfc) in the type of decision-making that requires a person to choose between two things that they deem to be of equal value. According to Chung, Tymula and Glimcher this particular brain area has been found to be physically affected by age which could explain the observations of decreased  performance in  ´success` of this type of decision making when  ´success` is defined as being consistent choice. Therefore, the vlpfc area is indicated as having functional importance in maintaining decision choices. The experimental set-up of Chung, Tymula and Glimcher was aimed at looking at economic rationality based on personal value of an object, but the authors extended this beyond the realms of ventromedial prefrontal cortex (vmpfc) functioning by making their subjects choose from between two objects which from initial testing were given the same value rating. The experimental set-up with emphasis on object recognition, emotional worth and physical movement also removed the need for language and language processing. This is important to note since the vlpfc is known as the Broca region and is associated with language production. In the experiments carried out by Chung, Tymula and Glimcher articulate speech was not required since the subjects were not asked to provide explanations of why their choices were made, there was no complicated, step-wise problem solving and the decision was ´reported` to the observers by a physical action. The behavioural part of the experiment was accompanied by neuroimaging which gave an indication of firing activity during the course of the task. Therefore, from their experiments, Chung, Tymula and Glimcher were able to associate physical changes in their studied brain area to behavioural changes. It should be noted here, that the experimental results support to some extent the observations of others, but there are difficulties in consistency of results and interpretations simply because different tasks have different cognitive requirements, or require other levels of engagement and therefore, it is necessary to know exactly what the nature of the task is before any conclusions can be made. Also, experimentation using human subjects leads to another set of problems because humans can alter their behaviour for a number of reasons such as tiredness, inattention, boredom, or even over-confidence. From the neurochemical side, real-time imaging of small areas particularly of human beings can be difficult and the interrelated activity of multiple brain areas can suffer from inaccuracy through the averaging analysis for example. However, Chung, Tymula and Glimcher investigated a particular example of decision-making and strived in their experiment to remove inconsistencies.

In order to interpret what the authors found out we have to first look at what is going on in the decision-making mechanism their subjects faced and see where the differences lie between elderly and younger people. We assume firstly that the method used for both groups is ´head` based. By this we mean that the participants do not just look at the object bundles and choose on gut feeling (ie. ´heart` based decision-making) which bundle to take. Instead there is some processing deduction carried out to see which bundle is best for them personally. This occurs because the objects in the bundles are deemed to be of equal value to the subject according to what was ascertained in the preliminary testing phase.  Therefore, each subject has set before them a bundle of two equally favourable objects, but the amount of each varies. The first stage of the decision-making process is the input of sensory information relating to the objects and this we assume employs the same mechanisms whether in the elderly or younger people. Only the efficiency may change. The second stage of the decision-making process is also independent of the age of the subject. This stage involves what is termed the ´construction of options`. This means that the strategies that can be used in order to solve this decision-making problem are identified. We all have a series of strategies that we use to problem solve and the chosen one varies because it has to match the situation or sometimes because we favour one method more than another. In both the elderly and the younger subjects the ideal strategy in this decision-making task is the same and is either comparison of personal emotional value of the objects, or less likely the assessment of plus and minus points of the objects. The former involves the subject choosing the object bundle which provides him with the highest reward status. Other possible strategies such as consequence and sequel, agreement and disagreement, other people`s views are in this experiment not applicable as there are no consequences of action apart from personal gain. This is why it is important to determine what the needs are for the task involved for any experiment since one cannot associate brain area functioning with behaviour if the cognitive demand is different.

In Chung, Tymula and Glimcher`s decision-making experiment the third stage of the decision-making processing is the mental ´running through` for all the bundles on offer using the core features of the objects such as title of book or object characteristics with the acknowledgement of the end result of each of the options ie. the intended reward status for each option. This is the first area that may be affected by age and hence, a difference may be observed in performance between the elderly and younger participants. Age is known to affect working memory (ie. processing power) and sensory input and hence, if information is being inputted and processed, the greater the amount and level of detail, the better the electrical representation formed in the brain areas. Biasness towards one feature or another can sway the electrical trace formed and may lead to inconsistencies in decision-making rationality.

Stage 3 then leads on to Stage 4 which is neurochemically an important stage and is where the physical firing traces of the objects are ´constructed`. Behaviour comes from neurochemical reactions and Stage 4 is the acknowledgement that decisions are based on, according to the hypothesis proposed by the author of this blog, by an assessment of firing activation of the individual traces. The assessment is carried out by comparison of the strength of firing through extent, similarity and emotional response. Strength of firing is achieved through the amount of activated cells obtained from frequent firing ie. something that is liked will possibly be visited more than once and according to neurochemical firing rules the trace will exhibit stronger firing. Similarity of firing is attributed to the number of shared characteristics of the traces being reactivated at that one time with the greater  number of characteristics being shared leading to the stronger firing. In the case of decision-making, similarity can also be assessed by the strength of firing of the option trace compared to the goal or purpose trace. The third neurochemical firing comparator is the firing associated with emotional response accompanying the information (ie. the ´emotional tag`). This represents the personal emotional value attributed to the characteristics of the representation encoded in the firing trace. Therefore, there are three ways in which an option can be judged from a neurochemical perspective. The first two depend on firing traces reactivated at the information storage and processing sites and the third through reactivation of the object`s  emotional tag stored in the pfc (likely the orbitofrontal cortex area, ofc) alongside the information. Again, this is another stage of the decision-making mechanism that may differ with regards to elderly and younger participants. Firing details, for example biasness towards one feature or another and emotional values eg. personal value of object may be subject to inconsistent input, poor recall or renewed skewed processing and this may provide several opportunities for the traces of new bundles to contradict ratings of previous ones. It is likely that the younger people show greater consistency in their assessments of objects during the course of the experiment and this therefore, would be one stage where the decision-making mechanism can differ between the elderly and younger participants.

In the experiments carried out by Chung, Tymula and Glimcher Stage 5 of the decision-making mechanism is the stage where the participant decides yes or no to a particular bundle. This decision is based on looking for the bundle which gives the his/her the highest reward value and therefore, I requires that the emotional tags associated with each bundle are compared. Once this decision is made, then the subjects are asked to perform a physical action (Stage 6), but are then asked re-check their decision. This involves repeating Stage 5 and possibly Stage 4. Stage 5 like Stage 4 is dependent on the relevant information and emotional responses reactivated at the time. Therefore, it too can be affected by age. The mechanism remains the same, but in the case of Chung, Tymula and Glimcher`s experiments inconsistencies of choice occur in the elderly participants in comparison to the younger. This indicates that the values assigned to the bundles is skewed in the subsequent presentations and irrationality of deciding which bundle provides the best reward is induced. Unlike Stage 5, Stage 6 which is the action stage (ie. performance of a physical motor action to select the option chosen), does not differ between the subjects since the action is the same independent of age. Under some circumstances, but not here, the decision-making mechanism concludes with one further stage that of feedback (Stage 7). This is where there is acknowledgement of the outcome of the decision-making process based on physical and emotional feedback. Stage 7 is not carried out here because virtual bundles are used and there is no opportunity to provide feedback.

Now that we have seen how decisions are made and what stages could be affected by changes in neurochemical functioning and structure we have to ask if aging affects the behavioural decision-making process in Chung, Tymula and Glimcher`s experiment, where does the small brain area vlpfc fit in? Essentially we are looking to see what happens when this area is not functioning correctly. Previous research supports Chung, Tymula and Glimcher`s findings since it has been shown that vlpfc lesions leads to a loss in strategy based performance. This is a more complicated type of decision-making than the one demonstrated here, but it still links performance on the basis of rationality of decisions. Aging is also associated with: the decreased ability to multitask which could be attributed to matching the goal to the input (Stage 5); and decreased capability to input novel information in real time which could relate to the input of option material (Stage 3).

There are various areas where changes in vlpfc functioning may give rise to behavioural changes. Chung, Tymula and Glimcher gave one such effect in their article and that is the loss of gray matter material in the vlpfc with increasing age. Functional changes can be attributed to loss of gray matter in both right and left vlpfc areas and this is important because for example, the pfc in general is known to play a role in many cognitive functions eg. working memory, emotions, central executive, attention and decision-making. It achieves this through neuronal and interneuronal firing and glial cell functioning at the micro level scale and connectivity between appropriate areas at the macro scale. Firing has different characteristics dependent on general brain area and even at the local level such as via neurotransmitter trigger involved, balance between excitation and inhibition and the presence of multitasking neurons. Underactivity or overactivity of areas can lead to different psychological disorders such as depression (linked to 5HT and overactivity of the pfc) and stress (pfc underactivity). Therefore, the decrease in gray matter volume of the vlpfc and reported substantial decrease in number of cells can have effects on the excitatory/inhibitory balance of the area. The observed decrease in gray matter in the lateral pfc lies at a 3% loss in volume per decade and the pattern of gray matter loss shows it beginning with the frontal areas leading to later loss in the temporal areas with a sparing of the sensory areas. The number and volume of white matter cells are also observed as decreased with increasing age, but to a lesser extent than gray matter and the pattern of loss in the frontal areas is reported later than for the gray matter.There is however, a greater loss with age in the vmpfc, which is the area known to be linked to personal values and comparison of subjective values.

On the macro scale connectivity between brain areas is important for cognitive function. For example it is known that pfc and hippocampus connectivity is required for information context guided memory. This capability is required here in Chung and team`s experiment since the assessment of the bundles relies on object recognition memory in addition to emotional memory. Connectivity of brain areas leads to stimulation of neurons that are not initially activated by the stimulus and also leads to the synchronisation of firing between cells and brain areas to produce brain waves. These are said to reflect the cognitive function at the time. For example theta waves are required by the pfc areas for maintenance of information, alpha waves for maintenance and recall, and gamma waves indicating possible manipulation of material. A number of areas are reported as being connected to the vlpfc, eg. vmpfc, dlpfc, striatum, post parietal cortex and others. Therefore, disturbances in connectivity brought about by reduced number of brain cells can have a major effect on cognitive function associated primarily with other areas. For example, the anterior cingulate cortex (acc) (which also demonstrates reduced volume and activity in elderly) is known to be linked to monitoring and fear response, but it also has connectivity to the vlpfc.  Effort invested in attaining rewards is demonstrated to be linked to activity in the vmpfc and acc and acc lesions produces normal responses to reduced reward with improved behavioural strategy not being maintained. This indicates that the acc is essential for learning the value of actions and guided voluntary choices based on the history of outcomes and actions. Lesions of the area made monkeys unable to sustain rewarded responses in a reinforcement guided choice task and to integrate risk and payoff in a dynamic foraging task. It was also found that vlpfc and dlpfc demonstrate acc connectivity that is decreased in repeated retrieval of information. Therefore, any change in vlpfc volume as indicated with age could cause changes to dlpfc and acc connectivity and ultimately affect informational input. This could impact decision-making rationality as demonstrated here.

    Therefore, physical changes such as cell number, firing and connectivity  can have an effect on cognitive capability. Several areas relating to Chung, Tymula and Glimcher`s experiment could be affected. We have already discussed briefly the first in our description of the biochemical mechanism associated with decision-making and that is that the change in value assessment demonstrated from trial to trial by the elderly participants in the experiment has an effect on rationality performance. We assume that the value of an object remains the same from the preliminary test to the end of experiment and this is likely to be the case with the younger test subjects. They are more likely to stick with their choices assessed in the preliminary testing phase providing there is no change to the information given or the experimental conditions. Therefore, economic rationality is observed with the younger participants.  In the case of the more elderly test participants, the expectation that the choices they made early on in the preliminary testing phase remained constant was not realised. It appeared that the value of the objects changed in the course of experiment leading to inconsistent choices and a demonstration of economic irrationality (GARP violations). The biasness towards certain choices in the later trials that was observed with the younger participants did not occur with the elderly test subjects and instead there was a move toward the less frequent / less automatic responses. This observation has also been reported with visuomotor conditioning and correlates to changes in pfc functioning. In the case of Chung, Tymula and Glimcher`s experiments the irrationality observed in the elderly subjects could mean that there is a change in emotional tag associated with the presented objects, or there is a change in the mechanism rating the emotional tags. In the biochemical decision-making process described above, Stages 3, 4, or 5 could be affected in the elderly. With regards to Stage 3, this could mean that the assessment of the bundle as a source of reward could be affected, but this is unlikely since the participant will still choose one and still acknowledge that the reward would come from that bundle. With Stage 4, the alteration in function could relate to the assessment of each bundle as level of reward achievable and could lead to different ratings for each bundle during the course of the experiment. Finally, Stage 5 would also be affected if the personal value ratings were different during the course of the experiment. Since the decision is taken to provide the participant with the highest level of reward, it is still assumed that the participant will choose the bundle with the highest reward rating, but the values are not consistent throughout the experiment and therefore, at the decision-making stage inconsistencies in choice would be seen. The value system relies on vmpfc functioning which is known to be involved in comparing values of different units of goods on the same scale as would be the case here. Vlpfc and vmpfc exhibit two-way directional interconnectivity and therefore, reduced vlpfc functioning would influence vmpfc region functioning or at least have an effect on vmpfc firing and connectivity.

A second area where there could be an influence on decision-making in the elderly in this type of task is the effect of age on attentional processes. This does not mean concentration, but refers the level of relevant and irrelevant information taken in. It is known that aging leads to an increase in the input of irrelevant information. This would have an effect on the decision-making process because in Stage 1 where core features are required to define the electrical representation, the traces would be swamped with additional information that is irrelevant to the task involved, eg. colour of sudoko book cover. This means that at the later stage where the decision is actually made, there may be a shift from neurochemical emotional value strategy to similarity strategy because irrelevant features would strength the matching of the firing traces from an information perspective in comparison to a weaker emotional value perspective. The lack of suppression of irrelevant information observed in elderly people could lead to inconsistency of choice compared to the younger participants who do not suffer from this increase in irrelevant informational input. Since attentional filtering relies on functioning and interconnectivity of parietal, temporal and prefrontal cortical areas, it is likely that any physical change in an area such as vlpfc will have wider ranging effects in attentional functioning whether in informational strategy shifting or content.

We have already discussed in the effect of attention another area which may lead to inconsistencies in choice demonstrated by more elderly participants and that is the choice of decision-making strategy. It is said above that the rise in irrelevant material in an object`s biochemical electrical representation may lead to the decision-making strategy going away from emotional value to one of similarity. The experimental set-up is such that the emotional value of the reward (ie. the bundle) dictates the choice made for each individual and we have said that this is a decision-making method reliant on using the ´head` meaning that informational processing is involved. We assume that this method is used continually during the experimental task, but it could be that with elderly participants there is a shift to a ´heart` type decision-making method during the trials. This may still rely on emotional value assessment, but it can indicate non-processing such as in the case of where ´gut` instinct takes over. Gut instinct can be based on ´false` presumptions of value for the reason given above that irrelevant information is taken into account that would bias the decision-making or lead to relevant information being ignored. Younger people, because of the nature of the experiment are more likely to remain in ´head` decision-making mode for the entire test period whereas the more elderly participants due to the reduced level of vlpfc would succumb to the higher influence of the vmpfc functioning linked to subjective values.

The final area discussed here as possibly influencing decision-making in the elderly is the lack of feedback with Chung, Tymula and Glimcher`s experimental set-up. The absence of Stage 7 could indicate that feedback is more important for elderly people in sustained decision-making tasks. Feedback requires monitoring mechanisms which involve the acc and we have already seen that this area too is affected in volume and cell number in elderly people. Therefore, in the absence of feedback, the acc is not stimulated and irrationality of decision-making in the elderly is neither noticed, nor compensated for. If the conflict in decisions was made apparent to the elderly participants then may be rationality would be restored. Younger people appear to be more capable of sustaining consistency in the absence of feedback for the period of trial undertaken here. This could reflect normal acc functioning in this age group rather than the vlpfc .

The comment given here supports the observation of Chung, Tymula and Glimcher that in the case of decision-making between objects of equal value vlpfc functioning is involved and is required for consistency of choice over an unmoderated experimental test period. With relation to Chung, Tymula and Glimcher`s findings that there is a loss of vlpfc gray matter in the elderly, we have also identified where a loss in grey matter of the vlpfc whether volume or firing cells has effects on firing of that region and interconnectivity of that region to other brain areas resulting in the described cognitive effects. Unfortunately, it appears that loss of function of the vlpfc (and even the acc) because of structural changes although not directly dependent on chronological age is related to aging and therefore, under normal circumstances loss of cognitive capability will occur to some degree. Structural changes may not be directly halted, but it could be that the loss of vlpfc functioning can be compensated for cognitively by changing our approach to information and information processing. For example, the stimulation in interest generally may lead to improved performances of object recognition, spatial memory for example. We know that the more something is processed the better it is remembered. Another compensatory method may relate to value assessment and the use of our own definitive judgement relating to value, reward and risk on a more regular basis in particular in making clear and repeated decisions. Another possible way to compensate for decision-making inconsistency may be to provide or demand feedback and constantly monitor the results of decisions made and assess the success or failure of those decisions. This maintains awareness and attention and leads to increased engagement, increased memory capability and information processing. We may not be able to hinder the functional changes linked purely to age of tissue, but we may be able to compensate for the effects that such changes in structure have on our cognitive capabilities.

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

….can we assume that if the choice made was based on fear and risk and not on reward and personal gain that the level of irrationality would not be decreased to such a level observed by Chung, Tymula  and Glimcher because the vlpfc functioning would not have the same influence on the decision-making mechanism since the amygdala would be involved instead?

…..the pfc requires glutamate, dopamine and GABA neurotransmitters for its functioning. Would it be possible to ascertain whether the decrease in vlpfc functioning in Chung, Tymula and Glimchers`s experiments was dependent on lower dopamine functioning by pre-administrating COMT? This would assume that the pre-administered COMT would lead to compensatory increased activity and restore economic rationality in the elderly participants? Would the pre-administration of ketamine which would block glutamate receptor functioning show involvement of NMDA receptors in vlpfc functioning during the experiment by a weakened signal or firing in the information representation observed?

…..would the use of optogenetics of layer 5 of the prefrontal cortex neurons during performance of the task allow the selective effects on dendrite activity of the elderly participants to be observed and show a difference between them and the younger participants?

 

Posted in ageing, decision-making, prefrontal cortex, Uncategorized | Tagged , ,

firing differences of tonically active interneurons in the striatum to reward events

Published comment on ´Differences between dorsal and ventral striatum in the sensitivity of tonically active neurons to rewarding events` by K. Marche, A-C. Martel and P. Apicella and published in Frontiers in Systems Neuroscience 24th July 2017 doi.org/10.3389/fnsys.2017.00052

SUMMARY

Marche, Martel and Apicella investigated a small group of cholinergic interneurons found in the striatum called TANs. These interneurons are tonically active and even although they are only 3% of the total number of neurons present they act to modulate the local striatal output. The TANs are known to be involved in stimulus-reward events and can be identified by their distinctive firing characteristics ie. the stop in firing in response to stimulus and reward (termed pauses) and the following rebound to baseline activity (termed rebounds). In their article, the authors described their study into whether the TAN responses are uniformly distributed throughout the dorsal striatal region (motor and associative striatum) and ventral region (limbic striatum) and how TAN firing responses can differ.

In order to study the striatal TANs in a stimulus-reward type scenario, Marche, Martel and Apicella used a Pavlovian conditioning task and 2 adult male monkeys (F and T) as experimental subjects. The task consisted of a given visual stimulus (0.3secs) and the restrained monkey having to perform reaching arm movements to receive a liquid as reward response. Test conditions were: fixed reward timing (FRT) – the liquid reward was given at a fixed time interval (1sec) after the visual stimulus independent of any behaviour shown;  variable reward timing (VRT) – the interval between the stimulus and reward was varied; and unpredicted (free) reward timing (URT) – where the reward was given independent of stimulus. Blocks of 30 to 40 tests with each trial lasting 6 secs were carried out with randomly alternating conditions. The locations of the total 62 TANs observed were plotted for each monkey (Monkey F, had 37; Monkey T, 25) and firing was recorded. The neuronal activity observed was analysed by detecting changes in the TAN firing. A test period of 100ms was monitored in 10ms time periods starting at the presentation of the stimulus or delivery of reward and the average spike counts within that interval was calculated across all trials. The onset of a modulation was taken to be the beginning of the first of at least 5 time periods that showed a significant difference in spike activity compared to the control. The offset of modulation was taken to be the first of 5 time periods when activity returned to control values and the magnitude of change in TAN activity was measured by counting the firing spikes between the onset and offset and expressed as a percentage above or below baseline activity. The activities of the populations of TANs were pooled across the samples for the different striatal regions corresponding to the functional territories conventionally defined for a primate. The striatal regions investigated were: the motor region (corresponded to part of the posterior putamen to anterior commissure) and contained a total of 26 neurons; an associative region (included dorsal pre-commissural parts of both the caudate nucleus and putamen) – 21 neurons; a limbic striatum region – the ventral part of the caudate nucleus and putamen rostral to the anterior commissure – 15 neurons.

Marche, Martel and Apicella found that the mean firing frequency of the TANs was similar in all three regions (approx. 5.3 spikes/sec). Their study also showed that 68% of the neurons had changes in activity after the presentation of the visual stimulus and 69% after the delivery of the reward in the FRT condition which indicated that the fractions of responsive TANs within the regions did not differ between the stimulus and reward. Twenty one out of the total 62 neurons responded to only one event, ie. either to the stimulus or reward. The proportion of TANs displaying a response to the visual stimulus were said not to vary significantly among the striatal regions (limbic striatum 80%; motor 58%; associative 71%), but the percentage of TANs responsive to the reward showed some variation with the limbic striatum having a higher proportion of firing TANs than the other two regions (100% limbic; 65% motor; 52% associative). The proportion of TANs responding to both stimulus and reward was higher in the limbic striatum, but the responses were not specifically related to the stimulus. This was shown by the excitatory component of firing not being influenced by the subsequent delivery of the reward.

Marche, Martel and Apicella also looked at the durations and magnitudes of the pauses and rebounds of the TAN responses they observed. They found that the duration of the pause responses to stimuli were significantly longer in the limbic region than in the motor and associative regions. The magnitudes of the pauses were also greater in the limbic and associative regions than the motor region. Investigation of the rebounds from the pauses initiated by the stimuli showed similar patterns with the durations of the rebounds being longer in the limbic and associative regions than the motor area, but the magnitudes did not significantly differ although it was said that there was a trend towards the limbic region. In the case of the pauses and rebounds of the TAN firing in response to the rewards, then few differences between the areas were shown. Only the durations of the pauses following reward gave a significant difference with the highest values being obtained in the limbic and associative areas.

An investigation into the average activity of the entire population of TANs regardless of responsiveness to stimulus or reward found that there was clear modulation of the whole sample recorded in each striatal region after each task event. Population activity was found to be the same for all regions for the control period at 110-120ms after the onset of the stimulus, but the duration of the pause differed with 70 msecs for the motor region, 110msecs for the associative area and 160msecs for the limbic striatum. There were no differences in the duration of the rebound period. After the delivery of reward, again population activity appeared at 110-120msecs and the durations of the pauses were again area specific with the motor region giving a pause length of 70msecs and the associative and limbic regions both 120msecs.

Marche, Martel and Apicella also investigated whether there were differences in TAN responses between the regions when the timing of the reward was changed (ie. the VRT condition). In this case under VRT conditions the same number of neurons and the fraction responded to the stimulus and reward as in the FRT condition. Durations of the pauses in response to the stimuli were the same for all three striatal regions, but the magnitudes of the pause responses were significantly different in the VRT condition with a higher result obtained for the limbic region than the associative striatum. No differences in the durations or magnitudes of the TAN responses to reward were observed under the VRT condition for all 3 regions. When the population activity was investigated as a whole (ie. averaged values of all 27 TANs activities) then the durations of the pauses after stimuli were different with 30msecs for the motor striatum, 80 msecs for the associative striatum and 120msecs for the limbic striatum. Following the reward the population responses ranged from 50msecs for the motor striatum to 110msecs for both the associative and limbic regions.

The third condition investigated by Marche, Martel and Apicella was with the reward being delivered randomly in their conditioning task (URT). This condition ensured that any activity observed was linked to the reward and was independent of the stimulus. The authors found that in the URT condition approximately the same proportion of neurons responded to the reward as in the FRT condition. In the URT condition, the duration of the pause response to the random reward was significantly different for the studied regions ie.  longer durations in limbic and associative striatum areas compared to the motor area. This was different to the results obtained for the FRT condition and was supported to some extent when the activities of the populations as a whole were investigated. The averaged population activity of the duration of the pauses in response to the rewards was found to be 170msecs for the limbic region, 80msecs for the associative area and 70msecs for the motor area.   The limbic area neurons also produced the stronger responses.

The authors concluded their article by saying that in the striatum`s role of processing motivational information, the ventral striatal TANs exhibit stronger responses to rewarding stimuli than those found in the dorsal striatum. Not only was a higher proportion of neurons in the ventral striatum responsive the TANs present also demonstrated particular response features eg. the duration and magnitudes of the pause and rebound periods for stimulus onset and reward delivery were greater in the ventral area than in the dorsal striatum. This supported the view that the ventral TAN system is different to that found dorsally with possible greater local cholinergic input that could parallel the functional specialisation within the striatum. Marche, Martel and Apicella continued their discussion by noting the similarity of the TANs observed in rodents to those found in their primate subjects and went on to consider whether the afferent signals into the areas may be responsible for the pause response of the TANs. They also considered how TAN activity could be linked to reward predictability and action selection.

COMMENT

What makes Marche, Martel and Apicella`s article interesting is that it again brings to attention the fact that a brain area serving a particular function does not necessarily mean that all neuronal cells in that area act in the same way. Marche, Martel and Apicella describe in their article a small subset of neuronal firing cells called TANs that are located in the striatum and that are tonically active (ie. active without specific synaptic input), but have distinctive excitatory firing characteristics compared to the other excitatory cells present. This comment explores where TAN activity fits in with the cognitive functioning of the striatum and why the identifiable pauses and rebounds in firing are necessary for the neurochemical functioning of the area.

The experimental basis used by the authors to investigate TAN functioning was a conditioning task with a visual stimulus, motor movement (arm reaching) and reward (liquid). This type of experiment is ideal for looking at brain area and neurochemical functioning linked to learnt behaviour based on emotional value and expectations in relation to a reward given after a specific stimulus – core functions associated with the striatal region. The dorsal region (traditionally said to consist of the caudate and putamen) is linked with motor and associative functions (ie. cognition involving motor function),  certain executive functions (eg. inhibitory control and impulsivity) and stimulus response learning. The ventral region (traditionally said to consist of the nucleus accumbens, hereafter termed NA, and the olfactory bulb) is said to be linked to mediating reward cognition, reinforcement, and motivational salience. There is a small degree of overlap as the dorsal striatum is also a component of the reward system that, along with the NA core, mediates the encoding of new motor programs associated with future reward acquisition (eg. the conditioned motor response to the reward cue). Before we continue it should be noted that the divisions of the striatum used by Marche, Martel and Apicella in their article do not agree with more common striatal partition. For example, the motor region is described by the authors as that area between part of the posterior putamen to the anterior commissure; the associative area as the dorsal pre-commissural parts of the caudate and putamen; and the limbic area as the ventral part of the caudate and putamen rostral to the anterior commissural. This means that the ´dorsal` term used here reflects the dorsal parts of the caudate and putamen and the ´ventral` term the ventral parts of the same areas. Cognitive roles attributed to the dorsal and ventral areas therefore relate to the caudate and putamen regions and not the more well-known NA.

When we are looking at the striatum and it`s roles in motivationally salient events we can see that it is linked to reward value, the expectation of that reward and the updating of the value of that reward. The input and interpretation of the information relating to the reward itself is not the responsibility of the striatum alone with multiple brain areas involved including sensory systems for informational input and associative areas for assessment, eg. the ventral tegmentum (VTA) with its dopaminergic system (work by Mortig et al, Wittman et al). In the VTA case, the lateral hypothalamus is said to send projections to the VTA which are believed to be important for behaviour and corticotrophin releasing factor (CRF) acts on the VTA to decrease the motivation to work for rewards also by regulating the DA activity (Tyree et al. and Wanat et al.). Also the prefrontal cortex (PFC) plays a major role with immediate and delayed rewards separately represented in the dorsal medial PFC (mPFC) and compared in the ventral PFC in order to guide decisions (Wang et al.). The orbitofrontal cortex (OFC) is known to be linked to the waiting for rewards and their subjective value (McGuire et al). The activity of this area is believed to be controlled by various neurotransmitters such as 5HT, GABA and glutamate eg. citalopram (a 5HT inhibitor) pretreatment is said to lead to increased OFC responses to reward (Delben et al).

The importance of the reward lies not just in its characteristics, but the value placed upon it by the individual. The determination and storage of values also require the inter-connectivity and inter-functionality of multiple brain areas. For example, the encoding and retrieval of values occurs through the firing and  connectivity of particular brain areas such as the basal amgydala and the insular cortex. Connectivity here mediates encoding and retrieval of outcome values with the amygdala encoding and the insular cortex retrieving such values in guide choices (Parkes et al.). The PFC again has a number of different functions eg. the mapping of the value of events on a common scale (Gross et al.) and particularly the OFC which links events to reward values (Winecoff et al., Favonik et al.). This area allows the representation of contexts that guide memory retrieval and the comparison of the subjective value of outcome (Hornick t al.). The anterior cingulate cortex also plays a role and is important for encoding competitive effort ie. learning the value of actions.

It is in the role of values and the subtleties of the value system that the striatum shows like the PFC, different functionality for different local areas. The traditionally termed dorsal striatum is said to comprise of the caudate and putamen. In the case of the caudate, activity relating to values was said to rely on dopaminergic functioning eg. Tai et al. found that stimulation of the dorsal striatal dopamine D1 and D2 receptor–expressing neurons during decision-making in mice introduced opposing biases in the distribution of choices. The effect of that stimulation on choice was dependent on recent reward history and mimicked an additive change in the action value. Other researchers found such subtleties in the dorsal striatal functioning relating to values. For example Wunderlich et al. found that the computational processes underlying forward planning are expressed in the anterior caudate nucleus as values of the individual options in a decision tree, ie. competing values of available options. In contrast, the team found that values represented in the putamen pertained solely to the values learnt during extensive training. During the actual choice stage, both of these areas demonstrated functional coupling to the ventromedial PFC which was consistent with it acting as a value comparator integrating the outputs of the two striatal regions. This link with the PFC functioning was further corroborated by Campbell-Meiklejohn et al. who showed that values computed from choices were weighted by their associated confidence and specifically represented in the ventromedial area of the PFC. The tendency to self-monitor predicted a selectively enhanced response to accordance with other`s results in the right temporal-parietal junction  and therefore, indicated that using cues of the reliability of other peoples’ knowledge to enhance expectation of personal success generated value correlates that were anatomically distinct from those concurrently computed from direct, personal experience. It also indicated that representation of decision values in the ventromedial PFC was sub-organized according to computed values. Meder et al. found another subtlety of the value system. They found that when gathering valued goods, risk and reward are often coupled and escalated over time. This requires increasing activity and connectivity of a cortico-subcortical ´braking` network that increased with gains and included the caudate as well as the pre-supplementary motor area, inferior frontal gyrus and subthalamic nucleus. The putamen was also linked with value computation and definition although less so than the caudate and more in the area of information processing and storage. Jang et al. reported a link between the putamen and hippocampus with the inter-regional connectivity associated with binding of more abstract information such as attentional state, emotional state that could be recorded with the specific data information to the stimulus and reward in order to increase the chance of perception and appropriate behavioural response.

The description given above for the role of the caudate and putamen relates to the traditionally called dorsal striatum although in the case of Marche, Martel and Apicella`s study this covers both the limbic and associative striatum areas. Although they do not include the NA in their studies this particular area (traditionally termed the ventral striatum and linked with the olfactory bulb) is known to play an important role in values and again relies on the activity of the dopaminergic neurochemical system. Wieland et al. found that phasic dopamine is involved in the assignment of values to stimuli and synaptic plasticity was specific depending on input. West et al. also found that NA neurons encode features of stimulus and behaviour learning and selection associated with rewards. Cue-selective encoding during training by the NA core neurons reliably predicted subsequent behaviour whereas NA shell neurons significantly decreased cue-selective encoding in the devalued condition compared with the non-devalued condition. This indicated that even the NA demonstrated regional specificity to value.

In addition to its role in encoding and retrieving value, the striatum has also been shown to play a role in expectation of reward and the subsequent updating of information on the basis of feedback. These functions can be adequately investigated using conditioning experiments, one of which was carried out by Marche, Martel and Apicella. For example, the striatum is known to play a key role in reinforcement learning specifically in the encoding of the teaching signals such as reward prediction errors (RPEs). The attribution of incorrect values is associated with impaired coding of RPE and an increased turnover of dopamine in the striatum (particularly in the ventral striatal region) and prefrontal cortex connectivity (Boehme et al.). Both negative and positive expectations are linked with striatal activity. For example, negative expectations influence behaviour with activity of the head of the caudate plus NA. In addition activity in the ACC, PFC (including mPFC and OFC), left hippocampus, insular cortex, insular cortex and amygdala plus others is observed whereas positive expectations are linked with the striatum and particularly the NA as well as the dorsolateral PFC, ACC and frontal operculum. The list shows that multiple areas are involved and hence, it is difficult to distinguish unique roles for the dorsal and ventral striatal regions alone. Therefore, higher levels of research are carried out on the other strong cognitive areas such as the ventromedial PFC, VTA and ACC.

However, it is known that the dopaminergic system is involved in the expectation and receipt of expected reward as well as unexpected reward timing and character and striatal areas are known to have dopaminergic functioning. The ability to predict favourable outcomes is said to require dopamine release once the conditioned sequence of events is learnt. At first, dopamine release occurs with the presentation of the reward leading to signaling a predictive cue onset after learning (Day et al.).  Eschel et al. found marked homogeneity among individual dopamine neurons with their responses to both unexpected and expected rewards following the same function, but just scaled up or down. As a result, they were able to describe both individual and population responses using just two parameters. Research narrows this type of functioning to the traditionally termed ventral striatum and particularly the NA, which was not part of the experimental model used by Marche, Martel and Apicella. However, NA neurons demonstrated a subtlety of the system with subsets of NA neurons being responsible for particular responses. Owesson-Wright et al. found that DA neurons that projected from the VTA to the NA fired in response to predicted and unpredicted rewards or cues. Both cue presentation and action caused dopamine release that predicted reward delivery, but distinct populations of NA neurons were found to encode the behavioral events at the same specific locations selectively. This was thought possible through different dopamine receptor population activity. A subset of DA-2 receptors mediated responses to the cue whereas the dopamine responses acting after the performance of the required behavioural action was found to be the DA-1 receptor.

This capability of expectation can also be linked to the opposite, that of unexpected reward whether relating to timing or character. Again, such a function is correlated to dopaminergic system functioning in the striatum such as in unpredicted reward in conditioning experiments (Redgrave et al. and Steinberg et al.). The signal requires a reporting of errors in reward prediction (Niv et al.) which Steinberg et al. found required strong activation of midbrain dopamine neurons. The phasic signal which signals the discrepancies between actual and expected outcomes (the so-called reward prediction error) represented an opportunity for new learning (ie. updating) which is well known in conditioning experiments, as that occurring in extinction for example. Again it appears that the VTA-NA connectivity is important. Chowdhury et al. found that abnormal expected values resulted in healthy elderly subjects exhibiting incomplete RPEs in the NA and this signal was tightly coupled to inter-individual differences in the connectivity between the VTA and NA as indicated above. In their experiments the administration of the dopamine precursor L-DOPA increased task performance as it restored the necessary dopaminergic system. Such errors in reward prediction or unexpected rewards whether due to timing or character require the updating of stored information and this too, appears to require striatal involvement particularly the caudate (Chiu et al.) and its dopamine functioning (Diederen et al.).

Therefore, we can see that the striatum is important in the encoding and retrieval of values and particular regions have specific functions. We can assume that these areas function like other brain regions and have a number of excitatory and inhibitory cells whose activity is linked to the demands of the area`s function at that time. There is a lot of research on excitatory neuronal cells particularly the glutamate and dopamine regulated cells and also certain inhibitory cells particularly the GABA regulated ones. Marche, Martel and Apicella instead investigated a particular subset of excitatory neurons found in the striatum, which we have to assume are important for the correct functioning of those areas. Therefore, we have to ask what are these TANs there for particularly when there are other excitatory mechanisms in place? However, before we continue we have to discuss the work on TANs of this region by others, particularly that of Garr (2016) because not all Marche, Martel and Apicella`s conclusions support the views of others. Garr also described a subset of tonically active interneurons (TANs) in the striatum that are also cholinergic and were said to modulate medium spiny neurons (MSN) excitability and sensitivity to cortical input. Just like Marche, Martel and Apicella, these interneurons were found at a population level, to respond to motivationally relevant stimuli with a pause in firing followed by a subsequent increase in firing above baseline (the rebound). In the case of Marche, Martel and Apicella this population was found to be just 3% of the total striatal neuronal cell population and only 62 cells responded in total with 21 responding to only one event. Both Garr and the authors here appear to agree that the patterns observed in the population may not accurately represent the responses of the individual cells and Marche, Martel and Apicella compared both in their study.

On closer investigation of the conditioning experiments used by both sets of investigators, certain discrepancies appear. In the case of Garr, the action of the TANs was investigated using a conditioning task where the two monkeys used were trained to press a lever for a water reward in the presence of the following two cues: one that signaled the amount of force required for a successful response (high or low force) and one that signaled the magnitude of the reward to be earned (high or low reward). The experimental set-up gave four cue combinations: low force/high reward; high force/high reward; low force/low reward; or high force/low reward and after training the results collected from the TANs showed that the majority of them were located in the caudate nucleus. This correlates to the results of Marche, Martel and Apicella who found that limbic and associative regions both containing caudate regions had higher levels of TANs (71, 80% for stimuli response and 52%, 100% for reward response) than the motor region that consisted only of the putamen region.

The TANs in both studies demonstrated the typical TAN firing responses of pause followed by increase in firing (rebound) in response to both the cue and reward. However, in Garr`s study the TANs activity was investigated further and they were found to be separate groups modulated during the two task demands ie. the TANs that paused in response to the cue presentation were generally not the same cells that paused in response to reward delivery, and the same was true for increases in firing. Garr found that few TANs showed a pause during cue presentation, but many showed an elevation response. This could indicate that other excitatory TAN cells are fired rather than just those exhibiting the distinctive firing pattern. These firing cells could relate to the information processing capability of the striatum in binding information of the event with the activity of the hippocampus as given above. Following the delivery of the reward, a similar number of TANs responded with pauses and rebounds which indicate that these are the interneurons associated with reward value and expectation as indicated above to elicit striatal motivational salience function. This supports Garr`s interpretation of results which showed that the majority of pauses preceded the lever press (behavioural action required in this conditioning experiment) and the majority of rebounds coincided with the lever press. If the above view is correct then the ´rebounds` would coincide with the assessment of the value of performing the lever press to obtain the reward (expected and feedback) and may not reflect the same cell firing as that experienced by the reward response.

Garr`s experiments also looked at the effect of effort in performing the action (ie. force modulation) against the size of the reward (eg. high or low water). He found that the magnitude of the pauses and rebounds during the cue and reward stages were dependent on the trial type with separate groups of TANs modulated by the amount of required force and the amount of reward. Only few TANS responded to the cue with pauses, but more were found to rebound. The vast majority was modulated only during the high-force trials and these were seen to be separate from those showing reward magnitude-modulated rebounds during cue presentation where the vast majority of these were modulated during low-reward trials. Therefore, Garr interpreted his results as the presence of two separate groups: one firing for high-force trials, and one for low-reward trials. This correlates to the interpretation given above that the TANS ´rebounds` would correlate to value assessment of the lever press reward.

Therefore, Garr concluded that there were different populations of TANs in the striatum responding to different stages of the condition task and even to different conditions (eg. effort and reward size). Marche, Martel and Apicella did not go into the same depth of conditions that Garr did preferring to look at reward timing, but they did show to some extent a difference in TAN populations that went beyond the limbic and associative striatal location. They found that in relation to their fixed interval reward trials that the proportion of TANs responding to the stimulus was on average 70% and to reward 72% and indicated that the same cells are likely to respond to both even though they showed that  a third of their TANs only responded to one or the other. They showed that the proportion of TANs responding to the stimuli did not vary significantly with region (58-80%) whereas they did for reward (65% motor, 52% associative and 100% limbic). In each case the subtle differences may be masked by averaging. For example, a closer look at the results shows that more cells in the associative region respond to the stimuli than to the reward (71% to 52%) and conversely, more cells respond to the reward in the limbic system than to the stimuli (80% to 100%). The duration and magnitudes of the responses were the same so it can be said that the studied responses are TAN cell responses. The results obtained indicate a difference in caudate and putamen functioning dependent on location and task demand. Dorsally located TANs in the caudate and putamen are more active during stimulus presentation and stimulus response and ventrally located TANs in the caudate and putamen are more active during reward presentation and reward response. This may be loosely translated to the functions attributed to dorsal and ventral striatal regions given above although we must assume that term ´ventral` relates to caudate and putamen areas and not just nucleus accumbens as given in the more traditional definitions. Dorsal areas are linked to the computing of options and personal reward value as well as binding of abstract information that may aid in the assessment. Therefore, this definition of function supports the observation that TANs are more active in the dorsal caudate and putamen (associative areas) during the stimulus presentation ie. when value of cue and learnt reward are assessed. The observation that ventrally located TANs in the caudate and putamen are more active during reward presentation and response may support the role of ventral areas in expectation of reward, but may be indicative of specific location of function as given by Garr in his study above where cells responded to different levels of effort and reward.

So we have discussed why the TANs are there functionally, but why are they present from a neurochemical perspective? Why not just a set of normal-acting firing excitatory neurons or interneurons rather than cells that fire, stop firing and then fire again? We know that the mechanism for firing is acetylcholine binding and this results in dopamine release and we have to assume that the pauses are brought about by mechanisms responsible for hyperpolarization of firing cells (eg. GABA binding or chloride ion/potassium ion channel opening). These characteristics are shared by other neuronal cell types that are also present in the striatum. In fact, the principle type of cell in this area constituting 95% of the population are medium spiny neurons (MSN) which are GABAergic inhibitory neurons. They too can be influenced by dopamine and this leads to 2 subpopulations of neurons depending on the presence of DA1 type and DA2 type receptors with 40% of cells having both. The interneuron population consists of excitatory and inhibitory cells. The inhibitory interneurons are GABAergic and are of many different types. The best known are the parvalbumin expressing interneurons (also known as fast-spiking interneurons) which are responsible for fast feedforward inhibition of the principle neurons, but there are also types responding to tyrosine hydroxylase, somatostatin, nitric oxide synthase and neuropeptide-y. The excitatory interneurons of which TANs are a subset also influence striatal cell firing and are influenced by both dopamine (via DA5 receptor) and acetylcholine. For example, large aspiny interneurons release acetylcholine and respond to the salient environment with stereotypical responses which are temporally aligned with responses of DA neurons of the substantia nigra.

Therefore, the first possible reason for the presence of TANs in the striatum is the fine control of firing within the striatum itself and between it and other brain areas. The striatum must perform neurochemically to fulfil its cognitive demands which in the case of conditioning means reacting to sensory input, informational processing in the working memory state of input and value of reward, long term storage of information and its subsequent recall on repetition of trial conditions plus monitoring of expected reward.  The striatum must also be linked to other areas that perform these functions. Interneuron functioning allows control of overall striatal functioning by switching on and switching off adjoining neurons and interneurons and this type of modulatory control can be seen in other areas. For example the firing of the GABA interneurons in the hippocampus is linked to short term responses to repeated stimuli, also a requirement in the learning the task in the conditioning experiments carried out in this article. In the hippocampus, pyramidal neurons represent the major postsynaptic target of most interneurons, whereas a small fraction of synaptic contacts (5–15%) from interneurons is made onto other GABAergic cells. The GABA interneurons that innervate each other (the so-called interneuron-specific interneurons) are controlled via specific inhibitory mechanisms.  In this example, information reaches the pyramidal cells of the CA3 region in the hippocampus via mossy fibre synapses made by dentate gyrus (DG) cell axons whereas small terminals and filopodial extensions target GABA containing interneurons. These synapses are unusual since they exhibit low basal release probability, pronounced frequency facilitation and exhibit a lack of involvement of the glutamate receptor, NMDA receptor in long-term potentiation (LTP). Synaptically released glutamate mediates both negative and positive feedback acting on presynaptic metabotropic glutamate receptors and kainite receptors, but no post-synaptic activation of NMDA receptors.  LTP at the mossy fibre pyramidal synapses occurs through the increase in neurotransmitter release, presynaptic binding, presynaptic calcium ion release and activation of presynaptic adenylate cyclase. The mossy fibre interneuron interaction range of short-term responses to repetitive stimulation goes from pronounced depression to modest facilitation of the firing. This is also seen in the excitatory cholinergic interneuron firing of the TAN subset of cells in the striatum. In the case of the hippocampus cells, glutamate released here activates post synaptic AMPA receptors and this calcium-permeable AMPA receptor activation leads to NMDA receptor-independent LTP and presynaptic decrease in neurotransmitter release whereas activation of calcium-impermeable AMPA receptors shows robust NMDA receptor-dependent LTP and down regulation of post-synaptic population of AMPA receptors (Nicoll et al.).

The TAN firing can also control activation that stems from outside the region. For example in the case of hippocampal long-range input interneurons, the activity of these cells can be controlled by extrinsic GABAergic projections: one arising from the medial septum (MS) and the other from the medial entorhinal cortex (MEC). Activation of the septal GABAergic afferents produces a silencing of interneurons and is associated with rescinding inhibition in the adjoining pyramidal cells. Two distinct populations of MS interneurons have been identified for this function: fast-firing and burst-firing cells. A subset of these cells expresses hyperpolarization-activated and cyclic-nucleotide-gated non-selective cation channels and exhibits firing and rebound spiking in response to rhythmic inhibition. These MS interneurons show a different phase preference during hippocampal theta activity and are thought to target different types of hippocampal interneurons cells that are active at the positive peak of the theta oscillation control dendritic inhibition of the CA1 pyramidal cells.

Therefore, TANs may be a unique subset of interneurons that control firing in the striatum via their own short-range interactions or long-range connectivity. The second reason for having TANs is that the TANs may keep the firing of the area ´ticking over` when there is no outside stimulus since the interneuronal subset also demonstrates tonic activity. The experiments of Marche, Martel and Apicella were performed with a conditioning task and therefore, there were definite responses to stimuli and reward. However, TANs can fire spontaneously and this has been observed with other neurons and interneurons within the striatum. Yorgason and colleagues showed that certain neurons in the NA (part of the traditional ventral striatum) demonstrate spontaneous DA release which is regulated by a number of factors including voltage-gated ion channels, DA2-autoreceptors, and nicotinic acetylcholine receptors (also seen in TAN cells) on cholinergic interneurons. The spontaneous release was described as infrequent (0.3 per minute), but the rate and amplitude of the firing were increased after blocking the potassium Kv channels. The firing of these cholinergic interneuron cells could be increased by a number of different factors eg. blocking glutamate reuptake, but it was found that only the effect on potassium kv channels influenced dopamine release. This could indicate that spontaneous dopamine release via cholinergic interneurons in the NA area are independent of stimulus activated firing and could be responsible as suggested above for ´ticking-over` like functionality, a role played by TANs in the caudate and putamen striatal regions observed by Marche, Martel and Apicella. However, this hypothesis is unlikely since the area is inclined to be permanently active to some degree because of its functioning (eg. value assessment) and connectivity to the major sensory input and processing brain areas (eg. VTA, PFC).

Another possible reason for the existence of TANs is that their activity may also compensate for the refractory periods of activated neuronal cells during task performance. This would allow continuity of firing that could satisfy the conditions required for learning (eg. sustained firing) or information processing (eg.  theta wave synchrony of multiple brain areas). This hypothesis could be supported by the fact that the pauses come after the stimulus or reward at times when there is peak activity from other firing cells within the striatum and established connectivity to other areas and rebounds when those cells reach their refractory periods and stop firing in order to replenish their neurochemical stores and restore firing capability.  In the case of LTP, a condition for long-term memory required in the correct performance of conditioning tasks, the continued activation of the area may require not only synaptic plasticity of the neuronal cells, but also of the interneurons too. LTP and long-term depression (LTD) have been found at GABAergic interneuron synapsing onto other cells eg. the pyramidal cells of the PFC, but the mechanisms of plasticity at these GABAergic synapses may differ significantly from those formed onto neuronal cells. For example, it has found that LTP occurs in both RAD interneurons and CA1 pyramidal cells and can be induced by theta-burst synaptic stimulation, but they are regulated differently. In the pyramidal cells, LTP is mediated by the activation of both GABA B receptors and a group of metabotropic glutamate receptors, whereas in interneurons, neither is required. This compensation for refractory periods can also be linked to the maintenance of firing so that synchronous firing of multiple brain areas can occur. We have already seen the extent of connectivity of the striatal regions in the establishment of values and their assessment and this has been linked to theta brain wave activity. For example, Murty and colleagues found that there was increased connectivity of category-selective visual cortex with both the VTA and the anterior hippocampus that predicted associative memory for high- but not low-reward memories and therefore, maintenance of synchronous firing is important for the value assessment process.

A fourth reason for the presence of TANs and their distinctive firing pattern relates again to their possible role in switching on or off neurons by aiding the establishment of temporal coordination of firing. In conditioning tasks and others where there is a distinctive order eg. stimulus-to-behaviour, the switching on-off ´pulsing` of interneuron firing could be akin to that seen in Morse code signaling. The switch-off periods would stop firing for a period after the stimulus or reward as given in the case of Marche and colleagues experiments, hence providing a distinctive period for informational content for the two parts of the task. This would of course be contrary to the third reason for their presence given above that of sustaining firing during the refractory periods since it would require the shut-down signal to be shared simultaneously by other cells in the area and this has to date not been shown. However, temporal coordination of neuronal activity via several types of cortical GABAergic interneurons has been shown in the hippocampus and extra-hippocortical areas. For example, Unal et al. suggested that oscillatory septal neuronal firing at delta, theta, and gamma synchrony frequencies during stimulus may phase interneuron activity and Jacobs et al. has also linked hippocampus cells to informational timing. Allen et al. showed that hippocampal activity differed depending on the temporal context of items. Salz et al. found that CA3 cells exhibited robust temporal modulation similar to the pattern of timed cell activity in the CA1 and the same populations of cells also exhibited typical place coding patterns in the same task. Middleton et al. investigated further and found that silencing CA3 cells disrupted temporal coding in the CA1 with gamma synchrony important for information binding temporal context and background timing was given by theta synchrony. The hippocampus may, because of its natural spiral physical structure and forced order of firing (eg. DG firing to CA3 to CA1), have natural conditions for timing and order that cannot be achieved through the striatum`s physiology and therefore, the TANs may establish the firing conditions necessary for temporal order of information.

Therefore, we can conclude that TANs although only a small subset of excitatory interneurons in the striatal regions must have a function important to the role that these brain areas have in event salience. They again, confirm the complexity of neurochemical systems in the brain and the need for detailed investigation and understanding of not only neuronal firing, but internal and external brain area connectivity. It is unlikely that manipulation by external means of such a small subset of neurons is possible in order to gain cognitive advantage not only because of their population size but also because they appear to work and be regulated by the ´giants` of the neurochemical world such as acetylcholine and dopamine. However, investigation does lead to a greater understanding of how brain area cognitive functioning relates to neurochemical mechanisms and should be continued.

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

………can we assume that conditioning experiments of the type performed by the authors, but demonstrating successive approximation learning or extinction would lead to no change in the distinctive TAN firing pattern for either if the function of the TANs is to exert fine control over general striatal firing or compensate for refractory periods of other firing cells so sustained firing is achieved?

………if fear conditioning experiments were performed instead of positive reward ones would striatal firing be reduced and hence, a reduction in TAN firing population be observed because of an increase in firing to other brain areas such as the amygdala rather than the striatum?

……..would the administration of the GABA blocker etomidate establish a role of GABA in TAN firing in the conditioning experiment?

…….sleep deprivation is said to increase motivation for reward. Therefore, if the conditioning experiments were performed with sleep deprived subjects would an increase in TAN population number be observed and if so, would this reflect that the extent of TAN firing itself reflecting personal values rather than being a product of firing of other excitatory cells present in the striatal areas?

 

Posted in conditioning, interneurons, neuronal firing, striatum, Uncategorized | Tagged , , ,

tau pathology effects on neuronal firing

Posted comment on ´Pathological tau strains from human brains recapitulate the diversity of tauopathies in non-transgenic mouse brain` by S. Narasimhan, J.L. Guo, L. Changolkar, A. Stieber, J.D. McBride, L.V. Silva, Z. He, B. Zhang, R.J. Gathagan, J.Q. Trojanowski and V.M.Y. Lee and published in Journal of Neuroscience 2017 37 (47) 11406

SUMMARY

Narasimhan and colleagues reported in their article the results of their investigation into three different structural conformations of tau aggregation (called tau strains) and their cell-to-cell transmission in non-transgenic mice (non-Tg mice). The tau strains they investigated were all linked to neurodegenerative diseases with known tau pathology eg. Alzheimer disease (AD-tau), supranuclear palsy (PSP-tau) and corticobasal degeneration (CBD-tau). The authors began their article by describing the similarities and differences between these illnesses from a neurochemical point of view.

In their study, Narasimhan and colleagues injected into female non-Tg mice purified tau obtained from the post-mortem grey cortical matter of patients suffering from either Alzheimer disease (AD-tau) or corticobasal degeneration (CBD-tau). In the case of PSP-tau, the injected material was purified matter from the lentiform nucleus (globus pallidus and putamen) of patients who had suffered supranuclear palsy. The authors also set up primary neuron cultures from the hippocampus of CD1 embryonic mice. Investigations carried out were to determine the differences in tau strains relating to potency, the cell type specificity of transmission, brain region development and the timing of tau pathology.

The results obtained showed Narasimhan and colleagues that there were differences in the potency of the three tau strains considered. Using Western blots with anti-tau antibodies, the authors found as expected in AD-tau the 6 isoforms of tau with 3 prominent bands of 3R and 4R tau. Both CBD-tau and PSP-tau demonstrated 2 bands corresponding to the 4R isoform. All isoforms were found to be hyperphosphorylated as expected. The CBD-tau appeared to contain some 3R isoforms and this was attributed to overlapping Alzheimer disease (AD) pathology in the frontal cortex found with this disease. Using an assay with increasing guanidinium chloride (GuHCl) concentration and protease digestion, the authors were able to perform a conformational stability assay on the three tau strains. They found that the Western Blot for PHF-1 used to determine protease resistant bands showed the three strains had different banding patterns without GuHCl. AD-tau had smaller tau fragments (15-20kDa) and CBD-tau and PSP-tau larger (approx. 25kDa). Incubation with GuHCl led to differing PK resistance with CBD-tau being the least stable, AD-tau more stable and PSP the most stable although the two PSP-tau cases used gave different results. Narasimhan and colleagues therefore concluded that there are different strains of tau pathology in the three diseases investigated.

In their second set of experiments, Narasimhan and colleagues seeded non-Tg primary hippocampal neurons with the different tau strains and looked at the subcellular localisation of the tau aggregates. They found that insoluble tau was required in each case and that these corresponded to strains found in humans eg. AD-tau had 3R and 4R isoforms. The AD-tau produced thread-like immunoreactivity in the axons of hippocampal neurons with rare perikaryal inclusions whereas CBD-tau produced frequent perikaryal and axonal inclusions and PSP-tau the most (approx. 300 times the potency of the other two). Again, the PSP-tau showed discrepancy between the two samples by not inducing tau pathology in all cases.

The investigation of in vivo localisation of endogenous tau aggregation required the tau strains to be injected into the hippocampus and overlying neocortex of the non-Tg mice. As a result, Narasimhan and colleagues found over the 3 month experimental period differences in potency between the tau strains. Case 1 of the PSP-tau strain again presented different characteristics in that it was the most potent at propagating tau aggregation. CBD-tau induced less extensive tau pathology and AD-tau the least. Both PSO-tau and CBD seeded tau aggregates in more neuronal subtypes in the hippocampus (eg. dentate granules, hilar neurons and CA3 neurons) whereas AD-tau primarily seeded tau aggregates only in the hilar neurons. The same cell-type specificity was exhibited in non-Tg mice as in humans with AD-tau aggregates observed in neurons and PSP-tau and CBD-tau in neurons, oligodendrocytes (oligodendrocytic inclusions in the white matter tracts such as fimbria and corpus callosum and resembling the oligodendrocytic coiled bodies found in humans) and astrocytes (astrocytic plaques similar to human CBD or in the case of human PSP, tufted astrocytes as well as astrocytic plaques).

In both the PSP-tau and CBD-tau strains neuronal tau aggregates were observed 1 month after seeding. The investigation of the tau pathology in these tau strains showed that injection of human tau led to endogenous mouse tau aggregation in the neurons and glia. An investigation of the tau aggregates formed was carried out using antibodies specific to the tau conformations seen at the different stages of pathology. (The pathology of tau aggregation is believed to follow a pattern with tau becoming first hyperphosphorylated. This is demonstrated by antibodies AT8 or AT180 dependent on the position of the phosphorylation eg. pSer 202/Thr 205 or pThr 231. Then, misfolding and aggregation occurs which is demonstrated by the antibody MC1 for misfolded conformation around the interaction of the N- and C- terminals of tau and TG3 antibody for misfolded conformation around the pTHr231 site. Finally, the tau aggregates form neurofibrillary tangles comprising of beta sheet structures and this is demonstrated by the amyloid-binding dye, ThS).  In their experiments, Narasimhan and colleagues found that in the case of AD-tau a small set of the seeded neuronal aggregates were weakly positive for AT180, TG3 and MC1 at 3 months. CBD-tau aggregates instead at this same time period demonstrated strong positive results for AT180, MC1 and TG3 antibodies, but only rare occurrences of binding of the amyloid-binding dye, ThS. However, PSP-tau showed all. Therefore, the authors concluded that the pathology was different for each tau strain. An investigation of the glial effect also supported the differences between the tau pathology of the three tau strains investigated. CBD-tau astrocytic tau aggregates were found to be positive for AT180 and mildly positive for MC1 whereas oligodendrocytic tau inclusions were positive for AT180, MC1 and TG3. None of the tau strains were positive for the amyloid binding dye ThS demonstrating that the final stage of neurofibrillary tangles had not been reached within the one-month time period.

The authors continued with an investigation into the spatiotemporal transmission of the tau strains. The pathology for all 3 strains was found to increase and spread from 1-3 months to other connected CNS regions, but the number of seeded tau aggregates did not increase any more from 6-9 months. The number of PSP-tau aggregates was found to be stable from 3-9 months and the number of AD-tau and CBD-tau aggregates actually decreased. No significant neuron loss was found in AD, CBD and PSP tau over the post-injection time intervals with the PSP-tau strain retaining more tau inclusions than the other two in the ventral hilus at 9 months post-injection. The investigation of transmission showed that all 3 tau strains transmitted tau aggregates to sites connected with the injection sites with the AD-tau strain producing a narrower spatial pattern than the other two (less cortical regions) at 3 months, but more at later stages. All three tau strains demonstrated transmission to the olfactory bulb which was not connected to the injection sites.

An investigation into the spatiotemporal transmission of tau pathology relating to the glial population involved Narasimhan and colleagues seeding glial tau inclusions in non-Tg mice by injecting CBD-tau or PSP-tau. The transmission of tau for both presented similar properties. CBD-tau mice developed more astrocytic tau pathology compared to PSP-tau which had more oligodendrocytic tau inclusions. Both remained stable even at 6-9months. The astrocytic tau pathology in CBD-tau injected mice spread with time from the ipsilateral ventral hippocampus observed at 3 months to the contralateral hippocampus and cortical regions at 6-9months. This was contrary to results observed with neuronal transmission. Mice injected with PSP-tau oligodendrocytic tau aggregates presented with significant transmission from the ipsilateral to contralateral side of the white matter tracts including the fimbria and corpus callosum.

The authors concluded their study by looking at whether the site of initiation and neuronal connectivity of that site determines the distribution and spread of tau aggregates in taupathies rather than being dependent on the tau strain. Therefore, the authors injected AD-tau, CBD-tau and PSP-tau aggregates into the dorsal thalamus of non-Tg mice. Six months later the authors found the same distribution of tau aggregates as described above when the injection sites were the hippocampus/cortex areas. PSP-tau was still the most potent strain and CBD-tau induced glial-tau pathology whereas AD-tau did not. The spatial distributions of the neuronal tau aggregates were similar for all three strains, but the spatial distribution of the tau pathology was different. This supported the authors` hypothesis that the site of initiation determines the distribution and spread of the tau aggregates and is independent of the actual tau strain. In the case of the injection site being the thalamus, the astrocytic tau pathology spread in the same brain regions as the neuronal pathology suggesting that neuronal-to-astrocytic propagation of tau pathology is involved in the spread of astrocytic tau pathology.

The authors ended their article with several conclusions. They began by describing the value of their research (eg. the use of authentic tau strains; the importance of using non-TG mice; the significance of their work being the only study that describes tau transmission) and stated that they had found that tau strains have different folding patterns and hence, have different neurochemical characteristics. The taupathies observed probably reflect these differences and are not likely to be linked to whether 3R or 4R tau isoforms are present. Narasimhan and colleagues` studies also showed that the PSP-tau form was the most potent (300 times more potent than other strains), but it is likely that heterogeneity of PSP-tau strains exist. The tau strains were also found to produce different pathologies in non-Tg mice and these matched results of human studies. PSP-tau was the most potent in vivo propagating more neuronal tau aggregates to anatomically connected areas than the other strains independent of the location of the injection site. This appeared to support the results of clinical syndrome studies.  The authors also observed differences in tau pathology relating to the development of tau aggregates between the three tau strains in the non-Tg mice and this gives information about the diversity of human tauopathies. For example, the formation of tau aggregates of PSP-tau and the slightly lower potent CBD-tau relate to the shorter clinical course of both resulting diseases compared to AD where there are likely to be fewer aggregates developing in the earlier stages, but with an accumulation over long periods of time. According to Narasimhan and colleagues all three tau strains appeared to have similar spatial distributions of neuronal pathology independent of the site of injection. This did not support research by others which use artificially derived tau strains and who found that regional differences were observed. The authors also concluded that all tau strains were capable of inducing neuronal tau aggregates in the same brain regions and this was dependent on the site of injection. This observation too was not supported by others who showed that the trans-entorhinal cortex is the earliest site for AD tau pathology, striatum and prefrontal cortex for CBD and the brain stem in PSP. The authors suggested that the different sites of initiation lead to development of unique tau strains and spread to anatomically connected areas. As far as glial tau pathology of CBD-tau and PSP-tau were concerned the authors found converse effects between glial and neuronal tau pathology in selected brain regions. This suggested to them that either the transmission of pathological tau seeds goes from neurons to neighbouring glial cells namely astrocytes or that astrocytic tau pathology spreads from one astrocyte to another through the gap junctions between them. Transmission of oligodendrocytic tau aggregates was suggested to be due to an unknown mechanism spreading from glial cell to glial cell through the white matter tracts. The authors concluded their article by stating that their results aid tau-targetted therapies for those neurodegenerative diseases known to be linked to the aggregation of tau.

COMMENT

What makes this article interesting is that it continues to show the complexity of the brain and how the mechanisms of neurochemical systems and reactions cannot be considered foregone conclusions for all cells and all brain areas.  The article commented on in this blog reports the results of three tau strains and their pathology both neuronal and glial. It shows that even though the general tau neurochemical mechanisms may be the same, something about the particular structure of the tau molecules, the cells and even the brain areas cause diversity in the consequences of their actions. Particularly in the consideration of taupathology because of its link to Alzheimer disease (AD) we have bear in mind that what we see in the test-tube, the neuronal cell line, the rodent model may not at the end of the day be directly transferrable to what happens in humans. But we have to start somewhere and in the case of taupathology we have to ask several questions: What does tau do under normal conditions? What causes it to go ´rogue`? What happens under pathological conditions? And whether there is any hope of stopping this and even if we were able to, would neurodegeneration still occur under those conditions, just brought about by other means? We will consider these questions only from the perspective of the brain and cognitive functioning since AD is given as an example of tau strain in the Narasimhan article and the main focus of this blog is this particular organ of the body.

So to begin, we look at tau as the protein it is functioning normally in the brain`s neuronal and glial cells. Tau exists in isoforms the most common of which are the 3R and 4R forms. It is a Microtubule Associated Protein (MAP) meaning that it is membrane bound and associated with the cellular cytoskeleton in both neuronal cells and glial cells. Tau acts as a ´bridge` for microtubules one of the components of the cytoskeleton so that they lie straight and aligned in the intracellular environment. Microtubules with attached molecular motors are part of the cytoskeleton responsible for vesicular transport within the cell and cellular endocytosis and exocytosis. These functions are important in neurons particularly for the nerve signal transmission, the transport of metal ions, neurotransmitters and receptors, but are not necessarily linked to the transport of ions like sodium or potassium involved in neuronal action potentials since these have their own transport systems in the form of pumps and channels. There is an exception, however to this, since calcium ions can be found in intracellular stores and vesicles and are released by exocytotic mechanisms. It should also be noted that tau itself can be found in the extracellular environment since in some cortical cell lines tau has been found free-floating and un-aggregated outside the cell. These tau proteins appear not to be full length as those existing intracellularly, but are present as C-terminal fragments. Research shows that these fragments are released from not only active neuronal cells, but dead and dying ones too. Even though their function has been described as being unknown, we will see later that they can be linked to the spread of tau pathogenicity and so should not be disregarded.

Therefore, ´normal` tau is essential for the required exocytotic and endocytotic mechanisms important for neuronal and glial functioning. As research shows at some point, tau goes ´rogue` meaning that the pathological tau ie. a tau form that can cause neurodegeneration occurs. This pathological tau form appears to be of the 4R isoform type in most cases and this is supported by the work of Narasimhan and colleagues. Although all their examples have this 4R form, not all the studied taupathies demonstrate the same potency indicating that the tau strains have different structural conformations. Since structural conformation is based on different amino acid constituents we have to assume that these pathological tau forms have to a certain degree different amino acids which lead to different binding and different tertiary and quartenary structures.

Therefore, what can cause the normal tau protein to turn ´rogue` and hence, demonstrate different binding and functioning? The initial stage of the pathological process has been found to be the hyperphosphorylation of the tau protein. Hence, one possible cause of naturally producing pathological tau is a mutation of the tau gene leading to ´rogue` isoforms being formed that are prone to being phosphorylated. Two other suggestions have been made. The first is the one that people most commonly favour because of its link with AD and that is the presence of beta amyloid. This is described in more detail later, but one factor is that the presence of beta amyloid induces the hyperphosphorylation of tau proteins by glycogen synthase kinase 3 (GSK3) whose production is promoted by beta amyloid. Beta amyloid can also increase the release of extracellular tau aggregates and tau fragments. These can be taken up subsequently by the synaptically connected neurons and induce further intracellular tau hyperphosphorylation so that the taupathy spreads through the connected cell network. This is a natural process, but Narasimhan and others use the mechanism experimentally to investigate taupathy by seeding neuronal cells whether in vivo or in vitro by injection or exposure to pathological tau and hence, can induce pathological changes in the cellular networks that they can control.

The ´rogue` tau produced has a negative influence on the neuronal and glial cells ending with cell death. Taupathology reflects the working of the brain area which is based on the ´speciality of the cells` present plus the connectivity of the cell and the area to other cells and areas within the brain. Tau pathology can be seen before any symptoms of cognitive deficiency and therefore, provides a mechanism for early diagnosis of neurodegeneration if it can be measured reliably. It begins at the cellular level and as said above is dependent on hyperphosphorylation of the tau proteins which requires the action of a protein kinase (for example the beta amyloid linked glycogen kinase 3). The hyperphosphorylation causes different amino acid binding and different tertiary and quartenary conformational structures that lead to the misfolding and aggregation of the tau proteins. Such changes begin with the axons where neurophil threads appear. Using silver staining preparations researchers have shown that inside a normal cell, one or more single fibres in the axons leading to the soma are prominent through their thickness and silver impregnability. As the pathology advances, then many fibrils are arranged parallel to one another and demonstrate the same changes. They then accumulate forming thick bundles and neurofibrillary tangles are observed in the soma. Taupathology can also occur in glial cells with astrocytes forming plaques or a ´tufted` astrocytic appearance or for oligodendritic cells the characteristic oligodendrocytic coiled bodies. As Narasimhan and colleagues described in their article, the highest level of damage was caused by tau that had the greatest conformational stability and this view is shared by others who describe the severity of AD correlating to the number and distribution of the neurofibrillary tangles. As the tau pathology progresses eventually the nucleus and cytoplasm disappear and only the bundle of tangles of aggregated fibres remain. This type of cell destruction appears to occur by a different mechanism to that of the more common apoptosis and necrosis.

The effect of taupathology on brain functioning and the cognitive symptoms observed depends on the brain area involved and its connectivity to other regions. Unlike other destructive measures like injury or stroke, tau pathology appears under natural conditions to spread and in the case of AD, this spread seems to be of a particular pattern. AD is said to begin with the region of the perirhinal cortex (which receives input from the parietal cortex and visual cortex) and spreads to the entorhinal cortex and then to the hippocampal areas of the dentate gyrus (DG) then CA1 and CA3. The fornix, which is the area receiving the major output from the hippocampus, also appears to be susceptible. Narasimhan and colleagues also reported such a spread in the case of their induced taupathies and also noted that in each taustrain the olfactory lobe was affected.

Therefore, with such devastating effects at the cellular level and with its capability of spreading to adjacent brain areas, we have to ask whether there is any hope to stopping taupathology once it has begun? Does the cell itself try to overcome tau phosphorylation or misfolding for example by gene expression changes as a reaction to pH changes or the increased action of protein kinases responsible for the initial phosphorylation? Or would increasing the production of new, unadulterated tau or even inducing higher production of new cells in the case of the hippocampus which is known to exhibit neurogenesis in response to neuronal activation.  It appears not and also the natural response to neuroinflammation seems not to be functioning normally. In AD there is an observed increase in stress markers. Savage and coworkers found that there was a robust inflammatory response caused by the accumulation and subsequent deposits of beta amyloid in the brain. This inflammation leads to cognitive deficits as also observed with injury and stroke for example andmarkers for activated microglia show increased neuroinflammation consistent with the spread of AD. Under normal conditions, the microglia perform immune-like actions and migrate to and put out processes within the beta-amyloid plaques as they would with any other cell ´invader`.  However, they are unable to efficiently perform phagocytosis and cannot clear the presenting plaques. Therefore, the local neuroinflammation response is abnormal in taupathies and this supports the success of anti-inflammatories to decrease the AD effect. For example the anti-inflammatory etanercept  leads to an improvement after 3 months and is believed to work through action on tumour necrosis factor alpha (TNF – alpha) which binds with beta amyloid.

Although it appears unlikely that there is any natural mechanism to prevent taupathology from causing cell destruction and spreading to other cells, in the case of AD, plasticity of the brain cells and redundancy in the neuronal cell system is likely to ´protect` the individual from the highly negative effects on cognition until about 80% of degeneration has occurred and this probably occurs in other taupathies as well. From the perspective of administration of medicines, the use of anti-inflammatories appears to have some success in limiting taupathology as described above. Methods involving the reduction of pathological tau by enforcing its removal also appear to have some success since research has shown that immunotherapy using specific antibodies against tau oligomers will lead to their removal and reverse memory deficits in Tg2576 mice. However, we have to ask even if we stop taupathology, would it stop neurodegeneration being caused by other means? Is pathological tau then the limiting factor in this type of neurodegeneration or it is just one factor of a number that have the same results? This question has to be answered because of the relationship which we have already indicated between tau and amyloid. For example as described above beta amyloid causes oxidative stress of the cell and increased quantities of all forms of extracellular tau and in the case of AD, changes in amyloid appear to be the initial stage of the disease.

The amyloid precursor protein (APP) like tau is a membrane-based protein. It is a highly conserved protein expressed in many tissues and concentrated in neuronal synapses. Amyloid is an intergral part of the cell membrane and has an important role, like tau, in the endocytotic mechanism with its interaction with the molecular motor, kinesin and therefore, is part of the cell signalling, LTP and cell adhesion mechanisms.  It is also important according to some researchers for iron transport in the neuronal cell. The APP either possesses ferroxodise activity facilitating iron export from the cell through its interaction with ferroportin, an activity blocked by zinc which is accumulated by beta-amyloid presence as in AD, or by APP acting to stabilise ferroportin in the plasma membrane. APP is also reported to be linked with intracellular copper where APP expression decreases brain copper levels, but increasing copper levels decreases beta amyloid and APP (Maynard et al.)

The pathological form of amyloid is said to be the beta form (beta amyloid) which is formed by splitting the amyloid molecule by 2 membrane based enzymes, beta-secretase and gamma-secretase. The splitting process occurs twice so that the resulting beta amyloid is released and forms a layer on the outer membrane. Beta amyloid therefore is a 37 – 49 amino acid based protein which has a beta-pleat conformational structure consisting of 2 or more beta strands connected by hydrogen bonds. Although it is known to be the pathological form in taupathies, beta amyloid contributes also to normal brain functioning and it is possible that it is an imbalance of this that causes the pathology. Under normal conditions, beta amyloid aids recovery of brain cells by binding to toxic agents such as metal ions and excessive amounts of brain neurotransmitters both of which can cause abnormal neuronal firing.  Like APP, there is again a link to metal homeostasis with iron and copper. Wan et al. observed that beta amyloid increased the levels of intracellular iron in a certain cell line that over-expressed the APP protein. This was linked to an increase in the expression of the iron transporter, but not transferrin. In the case of copper, Maynard et al. found that like APP, beta amyloid expression leads to decreased brain copper whereas increased brain copper leads to decreased levels of beta amyloid and amyloid plaque formation. In the case of removal of toxic agents, the beta-amyloid pleats clump the negative agents to form plaques so they are easier to remove from the cell. It is said that this ´mopping up` turns amyloid into a powerful enzyme that forms hydrogen peroxide which itself can kill the neuronal cell in a reactive oxidative stress reaction. It is believed that instead of this clumped beta amyloid form it is instead soluble beta amyloid that is the problem in the initial stages of AD (Selkoc and colleagues) since there is greater link between this and dementia.  However, the formation of plaques plays a role in AD where too much beta amyloid is produced. This has been reported to occur via several mechanisms eg. through high activity of gamma-secretase, incorrect timing of amyloid splitting, or a mutation of gamma-secretase so that the amyloid molecule is split in the wrong place forming the ´toxic` form of beta amyloid.

The logical inference is that because beta amyloid accumulates excessively in AD, its precursor protein APP would be elevated as well. However, it was found that neuronal cell bodies contain less APP as a function of their proximity to amyloid plaques. This finding indicated that this deficit in APP results from a decline in production rather than an increase in catalysis and it is this loss of a neuron’s APP that has been said to affect the physiological deficits that contribute to dementia. We must balance this however with the observations that treatment with beta amyloid leads to a 5 times higher number of hyperactive brain cells and worsens AD symptoms as well as having the detrimental effect on the formation of pathological tau.

Therefore, in answer to the question that if we stop taupathology would we prevent neurodegeneration by other means it looks unlikely since beta amyloid would be likely to form that would also lead to detrimental affects on endocytosis and result eventually in cell death. Several methods have been suggested to reduce beta amyloid and these have been linked to slower AD progression, but like tau these too are not natural. For example, small-molecule inhibitors of the beta-secretase enzyme (eg. BACE1, JKL inhibitor) have been found to lower beta amyloid levels and reverse deficits in conditioning memory deficits. These are also associated with increased microglial functioning, hence increasing the neuroinflammation response. Inhibitors of insulin –like growth factor receptors have also been found to improve spatial memory and reduce anxiety in a knocked-out-neuronal IGF-1R  APP/PS1 mice model. Fewer amyloid plaques and lower levels of beta-amyloid were observed. Less traditional methods have also been found to have some success such as the drug, aducanumab which has been found to decrease AD progression and the deposition of beta amyloid. A compound from grape skins, resveratrol, has also been found to lead to decreased levels of beta amyloid in the blood as well as ultrasound which has been used in mice and shown to cause the breakdown of plaque formation.

However, even if we are able to stop toxic beta amyloid production and plaque formation, we still have not eradicated the other factor observed in AD and taupathology and that is the hyperexcitability of the neuronal firing observed in the affected areas initially afflicted. Hyperexcitability is linked to a number of different factors. There is reported acetylcholine dysfunction in the areas of the EC, forebrain (could be parietal and visual areas) and PFC as a result of for example excessive acetylcholine, the depletion of K+ channels in the dendritic hippocampus (a decrease in potassium pumped out of cell occurs resulting in continual firing) and increased SK channel inductance. A continuation of LTP excitation instead of the switch to inhibitory LTD has also been reported in the case of the hippocampus which is the area badly affected in AD and linked to the cognitive deficits seen. In AD itself, the observed presence of beta amyloid leads to a number of neuronal cell changes that cause hyperexcitability such as increased calcium ion entry, increased glutamate release and decreased uptake and overactive mGlu5 receptor activity. Even if it were possible to remove excess acetylcholine or glutamate, tau or amyloid pathology may not be preventable since noradrenaline dysfunctioning has also been observed in the region locus coereleus in AD so the situation may be even more complicated than previously thought with multiple neurotransmitters being affected.

And so this is where we are. It would be nice to put AD in a box labelled ´caused by pathological tau or caused by pathological beta amyloid` but as this brief comment shows the subject is immensely complex with multiple players with both positive and consequential negative effects. Success in solving this problem will come to those that can look at it from multiple angles not only at the cellular level, but also at the level of brain area connectivity and for this to happen the interrelationships of thousands and thousands of factors have to be discovered, their boundaries investigated and then considered not singly but as part of a functioning whole.

Since we`re talking about the topic……

……can we assume that if AD-tau is seeded using the non-Tg mouse we will be able to see the same reduction in theta brain wave synchronisation between the hippocampus and prefrontal cortex during a spatial memory task as that using a knock-out APP mouse? Would exposing the mouse on a regular basis to a light flickering at 40HZ as given by Boyden and team`s experiments protect the mouse from the AD-tau pathology and restore the theta brain area synchronisation and memory performance?

……..the administration of clioquinol is said to lead to reduced abnormal beta-amyloid synaptic targeting and a reduced level of ZnT3 which also reduces abnormal beta-amyloid levels and plaque formation. This implies a link between intracellular zinc ion levels and Alzheimer disease but there have been disputes about whether a rise in zinc ions actually occurs. Would seeding with AD-tau and measuring intracellular and extracellular zinc levels in the hippocampus and the prefrontal cortex clarify the situation particularly if the levels of ions were measured over the course of the development of the pathology?

….a mutation at site A6737 in the APP gene is said to protect against the development of Alzheimer disease pathology. Can we assume that if we investigate how this mutation translates into alteration of amino acid content and hence, tertiary and quartenary APP molecular conformation we may be able to induce on a local scale the same amino acid alteration by using enzymes or more long-term by specific DNA manipulation?

Posted in Alzheimer disease, hippocampus, neuronal firing, Uncategorized | Tagged , ,

the structure and function of the cerebellum

Published comment on ´The Secret of You` by C. Williams and published in New Scientist issue no. 3185 7th July 2018 p. 36

SUMMARY

In her article Williams describes the change in view of the worth from a neuroscientific perspective of the brain area known as the cerebellum. She begins her article by explaining the initial assignment of function relating to sexual desire and area size linked to degree of sexual deviance that was attributed to it in the nineteenth century by phrenologists. This initial hypothesis led to the more common and more widely accepted association of the area with movement that was proposed by the neurologist Gordon Holmes during World War I. His work led to the view that the cerebellum functioning was related to fine motor control, but had no role in thinking and it provided the basis for the perception of the cerebellum as a ´trusty sidekick` or ´support act` for the more ´important` cortical areas that were linked to cognition. However, this hypothesis changed in the mid-1980s when neuroimaging experiments showed activity in the cerebellum even when the subject was not moving but was thinking. Although largely ignored or explained as being neural activity associated with eye movements, a link to cognitive function could not be overruled with the results of experiments in the 1990s when people with damaged cerebellums had no trouble with movement but did exhibit cognitive and emotional problems such as depression or inattention. Investigations into neural connectivity showed that only a small proportion of the connectivity was associated with the motor cortex and hence, attributed to motor control whereas the largest proportion was to areas of the cortex linked to cognitive skills, language and emotions. Of particular interest was the connectivity between the cerebellum and the prefrontal cortex (PFC) which is an area widely linked to personality and other human qualities such as impulse control and emotional intelligence. The neural connectivity was also observed to be loops between areas with information being inputted, processed and then re-sent.

Williams then went on to describe the work by Barton, an evolutionary neuroscience who looked at the unusual characteristics of the cerebellums of the ape species compared to other species. In other species the size of the cerebellum increases at the same rate as the rest of the brain but with apes the increases are much larger. For example, the cerebellum of humans is 31% larger than expected and contains 16 billion neurons more than that of a monkey. This increase in growth was suggested to be as the result of changes in movement demands. For example, apes had to swing through trees due to its larger size and hence, required to forward think and have fine sensory motor control whereas monkeys could run along branches. Therefore, the cerebellum was required to be physiologically structured to be able to calculate the most likely outcome of a manoeuvre based on previous experience (the so-called forward models), which can be updated and amended as required.

This was only one reason given for why a relative increase in cerebellum size occurred.  Another reason given was the cerebellum`s role in social interaction and emotions. This association came from not only observations that subjects with damaged cerebellums exhibited emotional problems, but also from the physical structure of the cerebellum itself. It was seen that the structure associated with motor control consists of organised rows of highly branched neurons (Purkinje cells) linked by parallel fibres (responsible for sensory input) and vertical climbing fibres (linked to error messaging and updating the forward models). This type of structure was found to be the same over the whole cerebellum and therefore, scientists put forward the suggestion that social interaction or personality would have the same mechanisms as those seen for motor movements. The only difference was the connectivity to other parts of brain. Therefore, they assumed that subjects perform complex mental  computations that apply to social and emotional interactions in the same way as they would for movements. This mechanism would allowed complex behaviours to develop and hence, was likely to be an important evolutionary step. The first behaviours were probably according to Barton centred around planning sequential movements to reach a goal leading to understanding sequences and developing into decoding the gestures of others and even language. This view, according to Williams, led to the claim that the cerebellum could be behind the greatest human achievements like science and culture.

More recent thinking expands on this initial role and links cerebellum functioning to understanding how the brain builds up a picture of the world around us. The brain understands the sensory information inputted by using past experience to make predictions of what is happening and in this way a picture of the surrounding world is constructed. Therefore, according to the philosopher Clark the cerebellum is ideally structurally and functionally placed for the coordination of movement, language and thought for this to occur. It is accepted that most of this is unconscious processing, but the cerebellum is suggested as being instrumental in joining unconscious processing occurring using the rules and models obtained from previous experiences and the conscious experience. Therefore, the cerebellum would free the brain to use the limited conscious resources on other tasks that have a high attentional demand. These roles in movement and complex thought by the cerebellum led Stoodley to reappraise how thought is itself defined. She proposed that thought should be considered as a kind of movement that is ´ trapped inside the brain` and hypothesised that the cut-off point at which point the planning process is part of movement or part of cognition is arbitrary.

William`s brought her article to a close with a description of some of the conditions now considered as being associated with cerebellar functioning and dysfunctioning, eg. differences in how the cerebellum and prefrontal cortex are connected are known to affect the focusing capability of ADHD sufferers or how in schizophrenia cerebellum changes could result in defective capability in balancing internally generated models of reality from incoming sensory information. She also quotes how cerebellum functioning in attention can be positively altered by the application of transcranial magnetic stimulation.

COMMENT

What makes this article interesting is the attempt by Williams and others to bring the workings of the cerebellum, which is pattern or model based, to the same ´status` as the cortex with its link not only to memories, but also to real-time thinking, emotional values, reasoning and creativity. No one disputes the worth of the cerebellum. Without its neuronal capability involved in model based learning and recall of sequences, the smooth and quick flows of muscle contractions and relaxations and the resulting movements would not be possible. However, the complexity of processing and memory storage in real-time and non-real time achieved by the cortex far outweighs the cognitive capability of learning by doing carried out by the cerebellum. Therefore, the claim of Williams and others that the development and functioning of the cerebellum were probably the reasons for science and culture may be an over-exaggeration. However, we can say that the cerebellum certainly plays a role in providing knowledge to these two areas of human achievement with its involvement in patterns of movement and behaviour and  equally important in its role in language production which is a fundamental tool in the art of human thinking. In addition, it could possibly be said that the cerebellum is one of the brain areas which links human beings to the more basic biological species such as the amoeba which cannot think, but are capable of motor learning and recall and certain types of deliberate behaviour.

We argue that, putting aside the claims of its alleged pivotal role in science and culture, the cerebellum is an interesting area of the brain from a neurochemical point of view.  It may be suggested that the cerebellum, in a manner similar to the hippocampus, functions as a relay centre for neuronal firing. It is not a storage site for memories, but acts as a hub to unite information relating to a single event (in this case relating to a specific movement) and relating it to the next movement forming sequences. This is essentially the same approach as that of the hippocampus which acts as a hub to unite sensory information relating to a single event. This unifying function allows information (in the form of comparing goals of intended movements and calculating sequences of movements to achieve those goals) relating to a single event to be processed, learnt and then finally stored in memory sites separate to it. Recall of movements or sequences of movements requires the information to be reactivated from its external storage site whether by external stimuli or by internal means. Then the cerebellum hub works as a relay centre to restore binding and timing of information to pass on the information to the areas responsible for motor movement as well as providing a means of monitoring for errors. It also allows adaptation of these learning patterns since the memories are stored elsewhere and the cerebellum would act as a conduit of relaying change that can be then duly stored. It also can lead to prediction. If the cerebellum regulates sequences of movements then by knowing one movement then the one after it would also be known. Therefore, one could predict what will happen next or what one will do next. This is valuable not only to predict movements, but also to predict behaviour by the individual himself and more importantly, by projecting these patterns onto what is being observed, on others too. Williams describes this in her article. Learnt behaviour follows patterns and is often sequential in nature. Therefore, a brain area that can relay models allows behaviour to be repeated, organised and understandable.

In order to carry out its memory based function, the cerebellum requires a physiological structure that allows it to receive input from multiple sources, associate it with timing and re-send signals to multiple neuronal destinations where it can be further processed, stored, or acted upon. This occurs by having multiple layers of different types of neuronal cells, a firing reduction mechanism as in the form of LTD and widespread neuronal connectivity with other brain areas so that information can be stored or movements initiated. If we look at connectivity, the cerebellum is a part of many networks such as the Default Mode Network (DMN -Purkinje cells), motor loops, conditioning (expectancy pathway), working memory (cerebellar node for re-processing of information) and emotional pathway (pons-cerebellum loop responsible for interpretation and responses to of movements). However, as depicting its status as a pattern-forming and pattern-following area it is not part of many other networks that depend on real-time information provision and manipulation such as attention,  visuomotor network, pain, consciousness and the important emotional-value allocation stages of the decision-making process.

   The capability of bringing motor information together is dependent on its complex, but extremely regulated physiology. The brain area sits on ´stalks` (peduncles) arising from the pons and it has close connectivity to this area. Although it is only 10% of the total volume of the brain, it has 50% of the total number of the brain neurons, which gives an indication of how much ´computing` power it has. The cerebellar cortex which is the visible part consists of 2 layers of cell bodies just under the surface of the cerebellum called the Purkinje cell layer and the granule cell layer. These are separated from the pial surface by a molecular layer. The Purkinje cells have multiple dendrites which only extend into the molecular layer where they branch out like a tree flattened in one plane. Input to the Purkinje cells comes from two sources. The first is via the pons which sends mossy fibres into the granular cell layer to connect to granule cells. These go into the molecular layer forming parallel fibres that connect to the dendrites of the Purkinje cells. Only one parallel fibre forms one synapse to each Purkinje cell. The other input into the Purkinje cell layer arises from the medulla (inferior olive) which integrates information from muscles and is therefore, important for motor control. Each climbing fibre is connected to one Purkinje cell, but the climbing fibres themselves have many excitatory axons and therefore, an extra large excitatory potential can be formed that always strongly activates the Purkinje cell. Spiking activity from climbing fibres has complex characteristics and work by Tsutsumi and colleagues shows that it requires aldolase C expression. Therefore, firing creates a high frequency signal amplification and computer modelling work by Ostojic and colleagues shows that the modulation amplitude of the Purkinje cell layer can increase up to high frequencies displaying resonance at 200HZ. The output of the Purkinje cells consists of axons synapsing on neurons in the deep cerebellar nuclei which then send output to the brain stem and so the area has the capability of being a major modifier of behaviour.

   What makes the cerebellum interesting from a neurochemical perspective is the interplay between the two opposing neuronal plasticity mechanisms that of long-term potentiation (LTP) and long-term depression (LTD). Few brain areas rely on LTD where long-term plasticity of neurons demonstrates inhibition of firing. The mechanism employed normally involves the neurotransmitter GABA compared to glutamate, a mainstay of excitatory neurons. In the case of cerebellum, the activation of climbing fibres synapsing on the Purkinje cells cause a large postsynaptic excitatory potential that always stimulates the Purkinje cell to fire an action potential. This is brought about by activating sodium ion channels via glutamate binding to NMDA receptors and AMPA receptors so that sodium ions enter the dendrite.  Depolarisation also activates voltage gated calcium channels that also exist in the membranes of the Purkinje cell dendrites and results in an influx of calcium ions which according to Okubo and colleagues requires the endoplasmic reticulum playing an important role. However, the Purkinje cells are also activated by the axons of parallel fibres and stimulation of these causes a release of the excitatory neurotransmitter, glutamate. This also binds to the NMDA receptors and AMPA receptors present as well as to metabotropic glutamate receptors that are also present.

Activation of the different receptors by the binding of glutamate has different effects within the Purkinje dendrite. As already said, binding to the NMDA receptors and AMPA receptors causes sodium and calcium ions to enter the dendrite. The increased sodium ion concentration results in an action potential and depolarization and the continued transmission of the firing signal. The increase in calcium ion concentration results in the activation of the calcium calmodulin protein kinase II enzyme and activation of calcium modulin II which ultimately leads to the insertion of more AMPA receptors into the postsynaptic cell membrane. This is linked normally to LTP where there is higher sensitivity of the cell fire on stimulus by released glutamate. The increase in calcium ions has the opposite effect with the activation of protein phosphatase which causes protein dephosphorylation and ultimately the internalization of AMPA receptors from the membrane surface. This leads to the reduction in number of possible active membrane-bound AMPA receptors and hence, possible decrease in signal transmission. In the same vein, the cerebellum Purkinje cells also have metabotrophic glutamate receptors on their dendritic membrane. Metabotrophic glutamate receptors are coupled via G proteins to phospholipase C and therefore, stimulation of these receptors by released glutamate from the parallel fibre axons leads to activation of this enzyme and the production of the second messenger, diacylglycerol (DAG), which then activates protein kinase C (PKC). The DAG kinase enzyme physically interacts with PKC and with the postsynaptic density protein 95 and functionally suppresses PKC by metabolizing DAG. Lee and colleagues suggested therefore, that DAG kinase is localized in the synapses keeping PKC in a non-active state until it is released during LTD. The active protein kinase C phosphorylates proteins and is likely to lead to the down-regulation of AMPA receptors and reduced calcium channel functioning.

Changes with receptors can be supported by firing frequency alterations. An investigation by Sgritta et al found that LTD and LTP were linked to spike pairing of the cerebellum firing pathway. They found that that spiked timing dependent plasticity requires repeated low frequency oscillations (6HZ) of an excitatory nature between mossy fibre and granular cells. The NMDA receptor dependent for LTD also required mGluRs and intracellular calcium stores. Both LTD and LTP of this form occurred within a 25 second firing period with excitatory potentials formed leading action potentials in LTP, but where action potentials led to excitatory potentials in LTD. The spike dependent plasticity occurred at 6-10HZ and was not visible at frequencies greater than 50HZ or less than 1HZ, nor when excitatory potential/action potential pairing was randomized or with high calcium ion buffering. Research by Kamikubo and colleagues also showed that mGluR1 is a G protein receptor and that LTD mediated by the mGluR can be blocked by adenosine 1 R agonists which inhibit the binding of glutamate, but have no effect on the calcium dynamics of cell.

Therefore, the Purkinje cells of the cerebellum appear to have 2 systems promoting increased dendritic signaling (ie. climbing fibre and parallel fibre activation via glutamate NMDA and AMPA receptors functioning via sodium ion influx) and 2 systems working against (climbing fibre activation of glutamate receptors linked to calcium ion influx and parallel fibre activation of metabotrophic glutamate receptors.) The situation of the Purkinje cell activation is seen rarely in brain areas which predominantly require LTP to occur. The lesser common LTD occurs because of plasticity of the AMPA receptor population due to the physical structure of the Purkinje cell and paired firing of both mossy fibres/parallel fibres and climbing fibres. This means that during pairing input from the excitatory climbing fibres, activated because of medulla input from the brain stem responding to motor movements,  is linked to input from the pons and responses from the cortex via the mossy fibres and parallel fibres. After pairing, it was found that stimulation of parallel fibres alone caused Purkinje cell activation, but the post-synaptic response was smaller. This was caused by a down-regulation of the AMPA receptor population and so LTD was said to have occurred. (AMPA receptor functioning was also shown by Rigby and colleagues to be affected by transmembrane AMPA regulatory proteins such as stargazin which regulate presynaptic AMPA receptors in the molecular layer interneurons by causing increased spontaneous GABA release. McGee and team found that stargazin acts as an auxiliary subunit and enhances receptor function by increasing single-channel conductance, slowing channel gating, increasing calcium permeability, and relieving the voltage-dependent block by endogenous intracellular polyamines. Another protein however, GSG1L which is a transmembrane auxiliary protein and part of the AMPA receptor proteome, was found to have an opposite effect by reducing the single channel conductance and calcium permeability and increasing polyamine dependent rectification.  Therefore, channel functioning of the molecular layer cells attached to the AMPA receptor complex can be increased or decreased according to associated proteins and hence, demand even if AMPA receptor number is not affected.)

What do these adaptations mean in terms of input and output? They mean that during learning both input from the climbing fibres (motor movements and direction) and mossy fibres/parallel fibres (movement and cortical input relating to sensory information eg. sensorimotor cortex) are required. However, once a movement or sequence of movements is learnt, recall requires only input from the latter which always activate the Purkinje cells. Also of importance is that Purkinje cells have multiple dendrites even though one dendrite interacts with only one parallel fibre. Therefore, this fits in with observations by Spampinato and colleagues that there is a specific somatotopic area connectivity meaning that learning a movement with one muscle determines that the learning of other muscles that are also required will occur automatically. The firing of the Purkinje cells then lead to output via the deep cerebellar nuclei. The impact of this is that learning allows movements and sequences of movements to be repeated with or without conscious awareness. This is realised by neurochemical chain firing which gives rise to order and ´timing` and internal motor loops which allow repetitive firing to satisfy the conditions for learning criteria and connectivity to other brain areas in order to initiate, learn and recall specific movements. The system receives sensory input and information from the pyramidal and extrapyramidal systems from the cortex and projects to deep cerebellar nuclei and vestibular nuclei which connect with motor neurons in the brainstem and sub-thalamic nuclei which innervates motor neurons in the motor cortex and the ascending pathways. The ascending pathway consists of the spinocerebellar pathway which projects to the reticular formation and the vestibular system and to the thalamus circuits interacting with the motor cortex. This provides information about muscular activity relating to complex voluntary movements. Motor loops exist through the lateral cerebellum with layer 5 from areas 4,6 somatosensory cortex and the  post parietal cortex and these project to the pons (pontine nuclei – 20 million axons) that feed the cerebellum. The lateral cerebellum feeds back to the motor cortex via the ventricular lateral nuclei of the thalamus and this system is required for the proper execution of planned, voluntary, multi-joint movements.

   The physiological structure and mechanisms of the cerebellum allow its functions to successfully occur. The nature of the mechanisms and the formation of internal models formed enable prediction and expectancy in behaviour as indicated by Williams in her article. If motor movement processing is based on learning of sequences (motor models) then this type of learning can be used for predictions of outcome ie. memories formed and based on past experience show a sequence which indicates what will happen in the future. Therefore, behaviour whether a sequence of motor movements or a sequence of responses, shows what is intended to happen one step at a time and this of course can then be compared to what has happened. The cerebellum with its regulated physiology and wide connectivity is therefore, ideally structured to be the instigator of motor learning and patterns of activity that can then provide the knowledge base for more abstract thought. This and problem-solving processing can occur that will allow behaviour to outstrip the boundaries of previous experience. Therefore, although the cerebellum may not be the reason for science and culture as alleged by Williams and others it certainly plays a part providing a base on which creativity can build. Studying the complexities of the mechanisms involved may aid the world of robotics to come closer to the wondrous extent of human capabilities.

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

……intention tremor is said to be the result of cerebellar dysfunction. Treatments to reduce tremors are said to include the administration of isoniazid (a GABA aminotransferase inhibitor – leading to an increase in GABA) or buspirone hydrochloride (a serotonin agonist). Is it possible that the long-term administration of either results ultimately in the down-regulation of AMPA receptors because of the LTD mechanism above so that the input into the Purkinje cells is at a lower level?

……the cerebellum is known to be linked to eyeblink conditioning and methods that temporarily inactivate neurons such as the use of a cooling probe or locally infusing muscimol or lidocaine can lead to investigation of the learning and execution of the conditioned response. Can we assume it would also be possible to use such methods in the investigation of learning and execution of particular behaviours and also when the subject merely observes them?

Posted in cerebellum, LTD, neuronal connectivity, neuronal firing, Uncategorized | Tagged , , ,

Self-awareness and the mirror self-recognition test

Posted comment on ´The why of me` by S. Deleniv and published in New Scientist issue no. 3194 8th September 2018 p. 29

SUMMARY

Deleniv begins her article by describing how only a few biological species are capable of self-recognition which she said is believed by some to have evolved in only higher order species with the largest brains and represents the pinnacle of mental complexity ie. consciousness. She goes on to say that others question this idea and this scepticism has found support from the recent work of Chang and team at the Shanghai Institutes for Biological Sciences. Chang`s team discovered that a small group of Rhesus macaque monkeys that were previously not able to identify themselves in a mirror (the well-known face-mark test or mirror self-recognition test, MSR) could actually easily learn to do so. Chang`s team fitted bulky, protruding neural recording devices to the monkeys` heads and trained the monkeys to link food reward with a projected laser dot. The experimenters started by projecting the laser dot in visible places and progressed to places which could only be seen reflected in a mirror. At this point, when the MSR test was repeated then the monkeys passed since they were observed to contort and display themselves as well as tugging facial hair all whilst watching their reflections in a mirror. Therefore, Deleniv proposes that the link between the MSR test and the mind should be reviewed.

MSR is a widely used test for self-awareness with the sense of Self leading to the individual recognising that there is a dye mark on his face and then attempts to rub it off if they can. Animals which pass this test are thought to be intelligent eg. chimps, orangutans, Asian elephants, European magpies, killer whales and bottlenose dolphins. But, there are evolutionary gaps where some species unexpectedly fail eg. gorillas and some species that unexpectedly pass eg. pigeons, manta rays. Although explanations for these anomalies can be given in some cases, the monkey test of Chang and team showed Deneliv that the mirror test for self-awareness is flawed and that self- awareness may be more prevalent than originally thought.

Deneliv continues her article with a discussion about consciousness level and mental complexity.  She says that psychologists and anthropologists believe that the hierarchy of consciousness corresponds to increasing brain complexity. The lowest level could be attributed to sensory experiences and perceptions relating to hunger, colour, warmth and fear with little awareness of meaning. Introspection is suggested at a higher level, but even this may be linked to a limited view of self. This leads to the highest level of mental complexity where minds are capable of constructing narratives of experiences around the concept of the Self.  Evidence of the hierarchy exists through physiological structure such as size and complexity and Deleniv said that disparity between species could depend on the demands faced by those animals in order to survive eg. from the physical environment. Evolution of the brains of the higher species could have been driven by the demand on the individuals to deal with the minds of others. At this point Deneliv quoted Dunbar and social brain hypothesis which proposes that life in tight-knit communities is especially challenging since it relies on the individual being able to understand what is going on in another community member`s mind. For this to occur then the brain must be evolved to take in more than sensory information and become an observer. According to the neuroscientist, Graziono, in order to achieve this the individual must build a model of the brain which not only represents the individual`s own mind, but also that of others. The model used requires an accurate representation of factors in play having made assumptions according to their contribution and relative importance, run a simulation and assessment of validity of the result making adjustments if necessary. The capability of model building allows the individual to make real-time representations in order to make future predictions and according to Graziono they can also be built and used for social interactions. Therefore, Graziono and Deneliv propose that if correct then self-awareness is the conscious state of simulation of the individual`s own mind and is therefore, not higher order or intrinsically more complicated than consciousness, but just another example of consciousness. Essentially, the mind is just something the brain can model and become aware of. It is therefore, difficult to establish whether self-awareness is then due to complicated brain physiological structure or not especially since consciousness itself has not been accurately physiologically defined.

There is agreement from other researchers about the brain working by generating simulations, but there is also disagreement in that consciousness is only a part of this modelling machinery. Instead it is thought that consciousness is an unintended by-product of extensive neuronal firing and closed loops of neural connectivity and exists without serving a particular purpose. Therefore, self-awareness is then not a simulation, but described as just a ´hall of mirrors`. Deneliv goes on in her article to describe other emergent phenomena observed in nature where structures emerge purely as indicators of other forces in play, eg. the collective behaviour of groups of flying birds and bacterial colonies growing in petri dishes. Therefore, self-awareness may be an apparently complex phenomenon emerging as multitudes of neurons interact with each other and the same neurons representing the sense of Self are active when the mind`s of others are also being considered.

Deneliv continues her article by saying that not all animals are the same and the complexity of their brains does not mean self-awareness, the sense of Self or the ability to understand the minds of others. She gives as an example the case of cephalopods which includes the species octopus and cuttlefish. Godfrey-Smith describes that the particularly large brain of the common octopus is shaped because of the demands placed on the animal by living in an environment dominated by vertebrates. Such an environment may have induced evolutionary development of physical self-awareness, but this self-awareness of the octopus must be considered different to that of the human. The octopus`s self-awareness could not be measured by activity before a mirror and therefore, the MSR test is only a test for self-awareness akin to that found in humans. Even that statement can be considered as not 100% correct since humans show varying levels of self-awareness with age. Developmental psychologists show that children can acknowledge themselves in a mirror at the age of 3 yet do not recognise themselves in a video taken a few months earlier and also do not appreciate that they have lived in the past until a few years later.

Therefore, Deneliv shows in her article that only a few species can pass the mirror self-recognition test and argues that if we proceed with the idea that self-awareness is only of this form and measured by this particular test then we will omit or ignore other higher order biological examples with mental complexity and flexibility of mind developed in response to environmental demands.

COMMENT

What makes this article interesting is the question it raises about the effectiveness of a particular psychological laboratory test (that of the mirror self-recognition test, MSR) at providing an answer as to whether self-recognition and self-awareness are linked to higher mental capability or not. Deneliv proposed two ideas in her article which we would like to discuss here from a neurochemical perspective. The first is that self-awareness measured with the MSR test is linked to animal intelligence and higher order brain complexity and structure. Bigger brains were said to lead to consciousness. However, in her article Deneliv did discuss this hypothesis as not ideal since there are exceptions for a variety of different reasons eg. lack of motor capability and motivation to actually remove the applied facial dye mark and even evolutionary gaps between some animal species that pass the test and higher order species that fail. Therefore, she came to the conclusion that the MSR itself is not a reliable indicator of self-awareness. The second idea linked the topic of self-recognition to self-awareness, the SELF and consciousness.  The SELF (whether according to Deneliv represented by self-recognition or self-awareness) and consciousness were both described as emergent ´behaviours` dependent on brain neuronal firing and connectivity. This led to the introduction of the idea for higher species of the formation of mental ´narratives` and ´models` from the observed real-time firing which Deneliv said could be involved in social interaction success.

If we look at the first idea then we should begin with a neurochemical basis of the MSR test. The test requires the visual system (responsible for visual input and processing, perception), the attentional system (focus on the reflected image), memory mechanisms (reactivation of stored face memory) and working memory (monitoring for visual feature discrepancy) from the collection of cognitive systems required plus the motor system (action of rubbing the dye mark away).  The mechanisms involved in self-recognition can be thought of as subservient to the more general face recognition  with face recognition essentially a third person activity whereas self-recognition is the ability to see and identify oneself. Whether one is a supporter of the face recognition models of either Bruce and Young (1986) or Ridditch and Humphrey (2001), the ability to recognise a face whether one`s own or of another individual relies on the reactivation of stored memory information relating to physical attributes such as facial features, voice, name and other characteristics. Both models begin the face recognition process with the comparison of incoming information from the visual system of the external event (either the person before them or the reflected image in the mirror) and firing neurons representing these reactivated stored memories learnt from previous encounters. From a neurochemical perspective previous encounters have led to the storage of neuronal firing patterns representing the visual input of colour, form and depth as well as binding of those features. It is thought that right fusiform gyrus activity is essential in the identification of the information as a face. This information is stored in the form of a neuronal cell assembly (sNCA) representing the person`s features (the face recognition units of the Bruce and Young model or the structural descriptions of the Ridditch and Humphrey model). Additional information is also associated with the physical characteristics such as Person Identity Nodes (Bruce and Young 1986) including information such as occupation and interests or the semantic system of Ridditch and Humphrey (general knowledge about the person). Therefore, the sum of the information stored is ´this is XXX` and ´he/she does XXX`. Presentation of the person again (whether another person, the individual reflected or as a photo) promotes firing of particular patterns of neurons which reactivate the stored memory and through the matching of the  incoming information with the strongly firing reactivating neurons representing the features of a previous encounter, recognition of the ´individual` will occur.

 

Therefore, self-recognition can be interpreted as an adaptation of the basic face recognition process. The initial encounter allows a memory to be informed as to what the individual looks like at that time. This can be carried out by looking at reflections such as in a mirror, glass, water or photos although the latter has been reported as producing slightly different brain area activity. (This could be due to moving pictures compared to static and reflected features being on the opposite side to real so that some spatial adjustment is necessary.) In the case of real-time events mimicking movements or matching expressions induces learning by copying and Deneliv reported this in her article with the caveat that although it may be possible that this type of learning may induce imitation of movement, it does not necessarily mean that the self-recognition capability is present. In the case  of human individuals, we are told even if we do not appreciate the fact ourselves  that this ´person` before you is you and therefore just in the same way as the face recognition process of others, packets of appropriate information relevant to personal appearance are stored with the appreciation that this is you. Two things can be said here. The first is that this view proposes that face recognition is not a special case of information storage. It just applies the same processes as we have seen above that are important for identification of others except that it from a personal perspective becomes part of the wider information available about the SELF. The second is that the face recognition system is flexible and capable of reading and identifying multiple images that can differ only slightly from one another. This allows us as humans to essentially still recognise a person or ourselves when appearances or even expressions are changed. (Expressions are actually considered as an important feature of both face recognition models with expression analysis after structural encoding in the Schweinburger and Burton model (2003) and view normalisation in the Ridditch and Humphrey model (2001).) From a neurochemical point of view, variations involve characteristics or parts of characteristics that are minor to the identification task eg. mouth position but not eye colour and indicate that there is possibly a ´generic` image stored with certain feature areas not stored, stored with a low priority or stored with an information level of ´gist` instead of accurate detail.

So, what happens to face recognition if visual information is not available such as that for blind people or in animal species that do not come into contact with reflecting surfaces?  It is clear that in the case of blind people that touch is important so the face recognition mechanism exchanges visual information for tactile information and long-term memory stores are built on this other sensory information.  In other species, as shown by the Rhesus monkeys described by Deneliv in her article, there can be appreciation of self-recognition even if they do not appreciate the more complex idea of the SELF as agent. This is what the MSR test showed: that species capable of visual processing were either able to remove a superfluous mark or not dependent on whether they were capable of identifying the presented event as part of themselves. Therefore, self-recognition can rely on more than visual information processing, but the MSR test does not show it.

Deneliv extended the discussion of self-recognition to that of self-awareness. Self-awareness is described as a part of consciousness and the SELF and is linked to self-concept. Self-concept consists of self-esteem (concerned with the feelings that an individual has about himself) and self-image (concerned with the knowledge the individual has about himself, which is essentially self-recognition and self-awareness). Self-awareness (and consciousness) develops dependent on availability of physiological complexity and structure and the demands placed on the individual from internal and external sources. Therefore, just like consciousness, self-awareness can be considered a ´state` (Deneliv`s ´hall of mirrors`) rather than a behavioural simulation or model. The relationship between self-recognition (essentially based on physical attributes and other personal characteristics) is subservient to the more comprehensive self-awareness that can encompass additional personal information and physiological features such as emotional reactions or heart rate responses. Bearing in mind the definition of self-recognition given above then self-awareness can exist without visual-based self-recognition since the individual must not be capable of knowing what they are physically from a third person perspective nor must they appreciate that they are an agent of their own activity. Therefore, self-awareness can be a capability held by a variety of species and at a number of different levels. For example an amoeba could be described as self-aware due to its avoidance response to a dangerous acidic external environment , but this self-awareness level is nowhere near the capability that a higher species like the chimp may have.

According to Deneliv, the capability of self-awareness can be discussed in terms of social interaction. From a neurochemical perspective, social interaction leads to long-term memories being formed of social learning models achieved through either trial and error (simulated models) or real-time instruction (from others or from the individual). In this case, self-awareness would then include the perceived position of the individual in a social grouping and the long-term memories would give the models of behaviour that should be followed for social success or to ensure personal safety. Face recognition can be a part of this not only through recognition of other members of the social group, but also through expression analysis of them. Therefore, the MSR which only measures visual recognition and ´self as agent` capability may not as Deneliv rightly indicated be an all-encompassing  measurement of how individuals cope with the social demands of their environment. Self-awareness and models formed from previous encounters also play a role.

The final point to discuss is the link between self-recognition, self-awareness and consciousness. Deneliv indicated that self-awareness is an emergent property of human brain neuronal firing and connectivity in the same way that consciousness is. She described different levels of consciousness from the low sensory perception-no SELF, intermediate level of introspection-limited SELF to the highest form of introspection with ´narrative building/behavioural models` and the level of consciousness we associate with the human species. She described consciousness however, as having ´no purpose`. First of all let us try to see where the link is between self-recognition, self-awareness and consciousness. From a neurochemical perspective, for conscious awareness to occur, certain brain areas demonstrate simultaneous firing activity culminating in a global conscious experience (the global workspace theory of Baars and others). Multiple areas are in play such as the medial prefrontal cortex, parietal area, temporal areas, amygdala, cingulate cortex and hippocampus. This can be compared to brain areas active in the face recognition process such as medial prefrontal cortex and temporal areas (part of visual processing mechanism), but with a strong dependency on the right hemisphere only and particular activity in the right fusiform gyrus. Therefore, from a neurochemical point of view regarding brain area activity only, face recognition may be only a small part of the conscious experience. Self-awareness on the other hand can be seen as conscious awareness turned in on its self. Therefore, brain area activity can be more widespread (eg. represents neuronal firing responsible for the inclusion of more information such as emotional value, heart rate response)and can be directed (eg. focus on the pain in one physical area). It, like consciousness, can be considered as an emergent property and a ´state`. It can be directed (by the higher brain areas such as the prefrontal cortex or the emotional system) or occur naturally as real-time active firing groups reach a sufficient strength to go over the awareness (conscious) threshold.      Whether associated with self-awareness or not, a lot of information processing is unconscious and therefore, other species may also exhibit a level of non-conscious self-awareness that they cannot report. It can be assumed that like with humans, if self-awareness is at this level then it too has a purpose, because we disagree with Deneliv`s opinion that consciousness has no purpose. Conscious awareness does have a neurochemical purpose because for example individual´s cannot solve particular problems without conscious awareness or cannot take a course of action without conscious awareness especially if this demands going against previous or logical experience. Consciousness may not provide the means to combat cognitive situations, but it represents the condition by which it occurs or is a signal that the condition is appropriate. In this way this explains  why consciousness is often linked with attention and we should consider consciousness as the ´state` and attention as a biological system. In much the same way we can consider self-awareness as the ´state` and self-recognition as one of the biological systems.

Therefore, by investigating the relationships between self-recognition, self-awareness and consciousness we have come to the same conclusion as Deneliv that the MSR may indicate self-recognition capability in a particular group of animals, but it is not a measure of the entire capability that the species may have. Both self-awareness and consciousness demonstrate physiological and cognitive capabilities that may not be explored completely by a single psychological test. The interaction of systems and their functioning in a number of different situations have to be explored before any conclusions can be made and the definition of ´higher order species` may need to be re-defined.

Since we`re talking about the topic………

…..would neuroimaging of the various brain areas associated with face recognition show differing levels of activity in individual`s demonstrating irregular self-recognition results such as those suffering from brain injury, Capgras syndrome or eating disorders for example?

….would antidepressants that alter medial prefrontal cortex executive activity have any effect on the ability of an individual to recognize himself?

…the visual reality capability of computer games allows players to assume different gaming roles and visualize themselves acting out these roles. Therefore, self-recognition would be assumed to adapt to the character played. Would a time delay be introduced if the MSR test is repeated, but with the character image, as the individual adjusts to his new ´self-image`? Would a manipulation of expression of the mirror reflection character induce relevant emotional changes in the individual watching?

……if the MSR test was repeated with chimps and the chimps were shown reflections of themselves, but with simultaneous exposure to the smell of a predator or higher ranking animal, would learning occur resulting in the subjects becoming frightened of their own images or would they take avoiding action?

 

Posted in consciousness, neuronal connectivity, neuronal firing, Uncategorized | Tagged , ,