lithium in drinking water and dementia incidence

Posted comment on ´Association of Lithium in Drinking Water with the Incidence of Dementia` by L.V. Kessing, T.A. Gerds, N.N. Knudsen, L.F. Jorgensen, S.M. Kristiansen, D. Voutchkova, V. Ernsten, J. Schulllehner, B. Hansen, P.K. Andersen and A.K. Ersboll and published in JAMA Psychiatry 2017 74(10) 1005-1010 doi 10.1001/jamapsychiatry.2017.2362

SUMMARY

Kessing and colleagues` article addresses the question of whether the incidence of dementia in the general population of Denmark co-varies with long-term exposure to micro-levels of lithium in the drinking water. Their study suggested that higher levels of lithium could be associated with a decreased incidence of dementia.

A nationwide, population-based nested case control study with 733,653 control individuals and 73,731 dementia sufferers was performed in Denmark. Dementia sufferers were aged between 50 and 90 years of age and had received a diagnosis of dementia from 1st January 1970 to 31st December 2013 and had hospital contact either as an in- or  out-patient. Each dementia sufferer was matched by both age (median age 80.3 yrs old) and sex (approx. 60% female and 40% male) to 10 controls. All control subjects had to be alive and have no diagnosis of dementia when their matched subject had been diagnosed. The residence of each study subject was recorded from 1986 and each location was cross-referenced to the level of lithium in the drinking water at that time measured according to municipality between 2000 and 2010. The lithium level was assumed to be stable within the study period.  Data was analysed from 1st January 1995 to 31st December and Kessing and colleagues looked at 4 levels of lithium exposure (2.0uG/L to 5.0; 5.01 to 10; 10.1 to 15; and greater than 15) and recorded the incidence of dementia in their test groups.

Kessing and colleagues found in their study that the level of lithium exposure was lower for patients with a diagnosis of dementia than for the controls (dementia – median 11.5uG/L whereas controls -median 12.2 uG/L). Also, this association between exposure and dementia was found to be non-linear. A comparison of incidence rate ratio (IRR) for dementia for lithium exposure of 2.0 to 5.0uG/L to exposure greater than 15.0uG/L showed a decreased value for the higher exposure (IRR 0.83, 95% CI, 0.81-0.85 P>0.001). However, the ratio value was the same for a comparison of exposures between 10.1 to 15.0 uG/L (IRR 0.98, 95% CI, 0.96 – 1.01; P= 0.17), but the value increased when it was compared to 5.1 to 10.0uG/L (IRR 1.22, CI 95%, 1.19-1.25 P>.001. Similar patterns were obtained for both Alzheimer and vascular dementia sufferers.

Therefore, Kessing and colleagues summarised that their results suggested that increased lithium exposure in drinking water may be associated with a lower incidence of dementia in a non-linear manner. However, they described the association as not being definitive since the nature of their study set-up meant that patterns and links could be identified between factors, ie. lithium exposure and incidence of dementia, but no definite conclusion could be made because other factors may have influenced  their results.

COMMENT

What makes this article interesting is that it looks at the topic of dementia not from cause or treatment, but from protection. This article hints that naturally occurring high levels of lithium in drinking water can have a protective effect from dementia, ie. there is a slightly lower risk of dementia occurring with exposure to high levels of naturally occurring lithium in this case sourced in the drinking water. Others have jumped to suggesting that adding lithium to drinking water may provide some form of protection from developing this disease, but it should be said that Kessing and colleagues indicate that their results are not definitive and the incidence of dementia could be influenced by other unknown factors. Unfortunately, because of the nature of the disease and the fear that it induces, the field of dementia research and non-academic thinking is awash with different hypotheses about causes, treatment and protection and also unfortunately, although we may be gradually explaining its biochemical basis, we are still not far enough forward to finding a ´one-tablet` cure. The problem with dementia is it`s elusiveness – many causes and triggers, many possible culminations of causes and its wide-ranging physiological effects that may be individual in both degree and nature. The value of this article is that it provides another element to studying this disease in the hope that every fragment of information is another piece of the puzzle.

The article commented on here in this blog is about the effect of lithium exposure on dementia. The beneficial health effects of lithium comes about from studies on bipolar disorder which is where sufferers experience periods of mania and periods of depression.  Sufferers of this particular disorder exhibit associated negative cognitive effects with both of the two phases and it often develops later into dementia. Treatment with chronic administration (but not acute) lithium helps the sufferers by stabilising moods so that the periods of mania and depression are reduced and has also been found to delay the onset of dementia if occurring.

The first point about lithium exposure and dementia is that cognitive disorders, especially those not genetically predetermined and where the emotional status and system are involved, seem to appear over a period of time and require treatments that are also needed over a period of time before reduction of symptoms is observed. This delay between administration and effect means that the long-term ´fix` is not reliant on instantaneous changes of biological processes. There may be observable influences on processes after single administrations, but these may not be the same as those occurring after long-term administration, or even be related to symptoms.  The exploration of the effects of lithium exposure on dementia hence requires two approaches – the instantaneous effects observed perhaps in the ´test-tube` although more advisable on the whole body after single exposure and secondly, what is happening at the biological and physiological levels after a longer period with or without further administration of the ligand or sustained administration. This type of exploration mirrors the approaches to research on depression where we know that the beneficial effects of antidepressant administration require 3 weeks to occur whereas certain biochemical changes are instantaneous eg. the immediate effect on neuronal adenyl cyclase activity compared to long-term effects on neuronal connectivity and neurotransmitter receptor number . The therapeutic effect of any drug (or therapy) has to either reverse these changes in order that treatment is deemed successful, or provide alternative means by which correct neuronal functioning may occur. In the case of some treatments this reversal is also temporary and administration must continue. Without sustained administration the biological processes and systems can fall back into ´dysfunctioning status` resulting in the reappearance of symptoms and an example of this is lithium itself and its role in the treatment of bipolar disorder.

If we are to understand what is happening in dementia we have to look at the conditions that cause it and the mechanisms that are subsequently put into play. Kessing and colleagues` article prompts an investigation of the metal, lithium and its supposed effect on the incidence of dementia. We have to assume that the biological mechanisms involved in dementia and associated with cell death and destruction of neuronal pathways are independent of the cause of the disorder, ie. the biological mechanisms with abnormal functioning are the same whether the dementia observed is a result of long-term bipolar disorder or injury for example. In the same vein, as given above treatments can either normalise the dysfunctioning processes or provide alternatives that compensate for those dysfunctions caused by the disease or injury. If we apply this hypothesis to lithium and dementia, the biochemical effects of lithium which lead to the proposed neuroprotective effect can then be grouped according to whether they affect neuronal firing (eg. reducing levels of over-excitation in neuronal firing in certain brain areas as observed in dementia) or by reducing cell apoptosis (ie. reducing the mechanisms employed in cell degradation and cell death, also perceived as important in dementia). Therefore, lithium could be said to promote a reduction in susceptibility to dementia by affecting one or both of these groups of functions.

If we look at the first group of functions relating to lithium exposure that of effects on neuronal cell firing we can see that the action of short term administered lithium acts at many different neuronal sites and cellular functions. Biochemically, neuronal over-excitation results in excessive firing in individual cells or in groups of cells within brain areas at a level which is outside normal expectations for that cell or area. For lithium to have an effect on this mechanism then it must interact with normal firing mechanisms. (Here in this comment we concentrate just on the brain and neuronal systems since we are looking in general at possible mechanisms for cognitive failure, emotional system upset and dementia.) Lithium ions due to their size and electronic charge can act where two other common ions in the brain act. These are sodium ions (a cation like lithium with a single charge) and magnesium ions (also a cation, but with an electronic charge of two, however having the same ionic and hydrated radius as the lithium ion). Since a lithium compound is medically administered then the lithium is already in ionic form and so is already able to accept an electron. Therefore, electronically lithium ions can replace sodium ions in firing mechanisms.  One way in which it can do this is to transfer through the sodium channels of the neuronal cell membrane. The possible result of this action is cell depolarisation and this is observed in brain areas where hyperpolarisation occurs. Researchers have also found that lithium can regulate the expression of different isoforms of sodium channel and therefore, effect on firing through increased presence of sodium channels is possible. Cellular firing levels can also be affected by lithium causing the release of the neurotransmitters, serotonin (5HT) and noradrenaline (NA) which can result in cells that are activated by these molecules actually depolarising. This is important since in some brain areas, 5HT and NA have an inhibitory effect on cell activity, or have an inhibitory effect on cells further down the pathway.  In the absence of these neurotransmitters for whatever reason, the administration of lithium ions could substitute for their loss and firing whether excitatory or inhibitory results. It should be noted however, that under normal firing circumstances, lithium ions do not cause excessive firing and cellular depolarisation.

Whereas lithium ions can directly replace sodium ions in neuronal firing mechanisms, their action relating to magnesium ions is a little more complicated since they disrupt processes important in cellular firing that rely on the involvement of magnesium ions for correct molecular conformational structure.  For example, lithium ions can have an effect on cell firing via its action on sodium potassium ATPase ( Na+K+ATPase) which is magnesium ion sensitive and responsible for ionic gradients across the neuronal cell membrane during firing and another transport system, the mitochondrial sodium-calcium exchanger. The sodium-calcium exchanger is important in the removal of calcium ions from the mitochondrial matrix. The activity of this depends on previous action of the Na+K+ATPase which pumps sodium ions out of the cell and potassium ions in during firing. This action allows the entry of calcium ions into the cell in the presence of high concentrations of sodium ions (ie. at depolarisation). Hence, failure of Na+K+ATPase action (as observed as impaired in bipolar disorder) leads to potassium ion depletion inside the cell and sodium ion accumulation. Therefore, the sodium calcium exchanger begins to pump calcium ions in leading to an increase in the cells hyper-excitability (also observed in bipolar disorder). Lithium ions in this case activate Na+K+ATPase, hence normalising cellular ion concentrations and depolarising the cells where hyper-excitability was previously observed.  It was found that lithium ions at high doses actually replace the magnesium ions in the complex. The presence of the metal ion is important because of its role in the nucleotide phosphorylation stage of the process eg. in the breakdown of ATP (the transport of ions requires energy) in this case. Nucleoside triphosphates require magnesium ions or a manganese complex to be active since the magnesium ions neutralise some of the negative charges present on the physical polyphosphate chains of the molecules, hence reducing non-specific ionic interactions between the enzymes and the polyphosphate groups of the nucleotide.  This link with nucleotides, magnesium function and lithium action as a substitute ion can also be observed with cellular cyclic nucleotides, eg. in the case of adenyl cyclase and the production of cAMP. Here the enzyme interacts indirectly with the magnesium ion through hydrogen bonds to coordinated water molecules. The inhibitory effects or activation effects of lithium ions on adenyl cyclase (cAMP levels are reduced in depression and increased in mania) relates to G protein binding (likely to be inhibited) and hence, this is another example where lithium has an effect dependent on what the normal functioning of that area or system is.

Therefore, we have seen how lithium ions can work at the level of cell firing, but how does it reduce levels of neuronal firing over-excitation and ultimately, levels of excitotoxicity where cells begin to die? Both over-excitation and increased excitotoxicity have already been reported as involved in dementia and occur in areas such as the entorhinal cortex and hippocampus. The end effect can be local cell death and in dementia there appears to be a natural progression of cellular degradation as inactivity and death in one area leads to underactivity and cellular death in another and so on. It should be remembered that only the hippocampus appears to be capable of high levels of neurogenesis (new cell formation functionally linked to memory) and hence, any loss of cells in other areas will have serious effects on firing of the system as a whole.

So, how can lithium ions reduce over-excitation where needed? The different mechanisms influenced by lithium ions will lead to different effects on neuronal cell functioning restoring normality. The administration of lithium ions can lead to neuronal firing in areas which would normally have an inhibitory effect or could force depolarisation in areas which are normally hyperpolarised. Neuronal firing forced by lithium ion presence could be by substitution with sodium ions in membrane-bound sodium channels leading to cellular depolarisation directly or through its action on neurotransmitters 5HT and NA release. Normalisation of the other ion transfer systems required such as Na+K+ATPase and sodium-calcium exchangers can also be induced. The overall effect is that lithium ions substitute for dysfunctional firing systems and cause neuronal activation which leads to inhibition of cells further down the pathway. This knock-on effect on others could remove the higher levels of excitation seen in some conditions that lead to excitotoxicity and cell death. The overall inhibition of hyper-excitable cells may also explain the activation effects of lithium on adenyl cyclase (cAMP levels are reduced in depression and increased in mania) where G protein binding is inhibited and also reports where lithium administration causes an increase in calcium ions through likely activation of the PI3 / Akt pathway as observed with action against alpha-bungarotoxin and muscarinic receptor binding. Therefore, a reduction in firing occurs and since areas work together and firing is interconnected then functioning patterns of firing will also change and long term alterations ensue. Since in the case of lithium ions and bipolar disorder, mood is stabilised on long-term administration the physiological changes that occur after the sustained period of administration of the metal ion result in a likely ´normalising` action of individual cell firing and ultimately, connectivity patterns seen in the disorder.

The second area of functioning where lithium ions may have their effect is on reducing cell apoptosis or death.  Cell death methods can be intrinsic (eg. requires transcription factor effects and subsequent DNA transcription changes) or extrinsic (eg. requires extracellular receptor binding and caspase cascades), but independent of cause (eg. injury, cell membrane signalling detrimental changes, neurotrophic signal changes) they appear essentially to have the same mechanisms. A change in apoptosis because of lithium treatment leads to a change in mitochondrial/ER dysfunction, reduction of negative epigenetic effects, reduction of glial dysfunction, reduction of oxidative stress and inhibition of the enzyme, glycogen synthase kinase- 3 (GSK-3). For example it has been reported that there is a change in IP3 in bipolar disorder which results in a change in calcium ion signalling in offending cells. The rise in intracellular calcium (also reported in bipolar disorder) results in dysfunction of the mitochondria (observed by increased Bcl-2 levels and decreased Bax levels) and increased oxidative stress of the cell (an effect that can be decreased by glutathione administration also observed in bipolar disorder). The increased level of oxidative stress results in cellular apoptosis. Lithium is reported to inhibit GSK-3 which is one of the factors controlling cellular apoptosis and it can either increase activity or decrease it depending on circumstances. This again could be interpreted as lithium ions ´normalising` function in dysfunctioning cells, or having no effect if the cells are functioning normally.

Increasing apoptosis occurs by the disruption of correct mitochondrial functioning and affects the regulation of the expression of the mitochondrial transcription factor, Bcl-2. Decreased apoptosis occurs in the case of lithium administration when GSK-3 inhibits the early phase of the caspase cascade (caspase 8) or by having an active PI3-AKT pathway which leads to a rise in calcium ion release or an increased beta-catenin/wnt pathway. The inhibition of the GSK3 enzyme has wide ranging effects since the enzyme has multiple functions such as phosphorylation of glycogen synthase and ultimately regulation of glucose metabolism, effects on transcription factors such as cJun and cell cycle mediators such as cyclin D. There are also observations that GSK-3 affects proteins bound to microtubules and this could relate to an observed build-up of beta-amyloid protein in the cells – a process which is linked to the observation of dementia.

Therefore, the administration of lithium ions can result in decreased apoptosis or a protective effect  against apoptosis. The latter may be the case if the cell senses that the normal firing function is defective and another unnatural ion has taken the system over as suspected in the case of lithium ions actions on cellular functioning described above. The result is that there is a normalisation of function in the relevant areas which in bipolar disorder may exhibit extensive induced apoptosis. The increase in cell number and functioning cells is supported also by the observation that lithium administration leads to an increase in volume of the hippocampus, an area whose function is observed to be reduced in dementia. Renewing cells is essential particularly for the hippocampus as stated above since neurogenesis here is linked to the cognitive input of new experiences, binding of information and memory. An increase in growth of the hippocampus has been observed with lithium ion administration and this counteracts the area`s shrinkage which has been reported in depression. An increase in volume implies the production of new cells.

Therefore, lithium ions exhibit a wide range of cellular effects, but their action appears to be linked to only those brain areas whose neuronal functioning is abnormal, ie. areas which exhibit over-excitation or are subjected to over-inhibition. Exposure to the lithium ions can ´normalise` firing and also ´switch off` the apoptosis mechanism so that cells can return to normal functioning levels even if not by natural mechanisms. Cells that are functioning normally appear not to be affected and this is supported for example by the action of lithium ions on guanylyl cyclase which is part of the photo-reduction mechanism in humans and important in visual processing.  Guanylyl cyclase activity leads to an increase in cGMP keeping sodium ion channels open whereas light reduces the level of cGMP causing the sodium channels to close and the cell to hyperpolarise. Sufferers of bipolar disorder report reduced visual motion perception, but lithium has no effect on photo-reduction or on ocular functioning even though it is a potent inhibitor of guanylyl cyclase activity. Therefore, just like with neuronal firing, this brings about a discrepancy since the biochemical effects described for lithium ions are independent of cell status and therefore should occur whether the cell is in either an over-excited/over-inhibited state or not and the action should be on all cells and not just those exhibiting excitotoxicity. The action of lithium ions therefore, implies that those cells exhibiting excitotoxicity or lack of inhibition have already different functioning or different physiological characteristics that favour lithium ion action and binding in preference to its action on normal cells. It could mean that the propensity of binding of lithium ions is lower on these abnormally functioning cells than the cells own natural substrates and hence, it only works when these natural substrates are not available. Since the first port of call of the lithium ion is the neuronal cell membrane then it is possible that the over-excited or over-inhibited cell already has a different cell membrane structure or functioning and it is here where the natural substrates are normally favoured, but when absent, then the presence of lithium ions after administration will have an effect. This hypothesis is supported by the biochemical property of magnesium ions which is that they coordinate groups of ions or molecules so that the correct arrangement and correct conformation of the molecule is attained. This could be of importance when looking at lipid rafts and protein conformations as part of the cell membrane physiological structure and functioning. Lithium ions could replace magnesium ions if absent to re-stabilise the neuronal cell membrane lipid raft structure so that normal firing mechanisms can be induced.

Therefore, a look at the biochemical basis of lithium ion action in bipolar disorder and the mechanisms behind its mood stabilising effect gives an indication to the complexity of dementia and to the problem of defining biochemically what causes it and what can be done about it. Lithium ion administration can be described as a ´blanket` treatment affecting many different systems even if the overall affect is a reduction of emotional upsets and a stabilisation of mood. Its possible mode of action is likely to be through normalising cellular firing in areas experiencing over-excitation or lack of firing and this could be caused by imbalances of sodium ions and magnesium ions. It could also act by affecting the cellular apoptosis processes that may be called into play if cell death is ordered due to cell dysfunction or to prevent cell death ordered because of the abnormal lithium ion effects on the neuronal firing mechanisms. Lithium ion exposure also does provide some insight into a possible initial ´limiting` cause for dysfunctional cell firing and this appears to be the cell`s exterior membrane.

Since we`re talking about the topic….

…would it be possible to use radioactive lithium to map areas of neuronal cell action with time using mouse or rat models of disorders such as dementia? Would such studies give an idea of where the ´limiting` brain area for dementia is?

……lithium is said to be an important inhibitor of glycogen synthase kinase 3 which is known to phosphorylate the voltage gated potassium channel type, KCNQ2. Phosphorylation of the channel decreases its activity and ultimately affects the transport of potassium ions important in depolarisation. Therefore, can we assume that lithium ion action ultimately has an effect on KCNQ2  channels` number or function and this can be observed by measuring potassium ion transport and concentration in the neuronal cells using inhibitors or blockers of KCNQ2?

……prolonged lithium ion treatment is said to lead to probably indirect inhibition of PARP-1 (poly-(ADP-ribose) polymerase) by inhibiting 3´5´-phosphoadenosine phosphatase (pAp-phosphotase) function. This causes a progressive accumulation of pAp in the cell that binds to the PARP-1 inhibiting it. However, the hypothesis is under dispute. Can we assume that deletion of pAp phosphatase gene could clarify the observation since in its absence poly -ADP-ribosylation activity inside the cell would be normal and PARP-1 inhibition would not be observed?

 

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network reorganisation of procedural memory during sleep

Posted comment on ´Network-wide reorganisation of procedural memory during NREM sleep revealed by fMRI` by S. Vahdat, S. Fogel, H. Benali and J. Dovon and published in eLife 2017;6:e24987 doi 10.7554/eLife.2498

SUMMARY

Vahdat and colleagues described in their article their investigation of how learnt motor sequences formed memory traces during sleep and how there was a gradual shift of memory representations from a temporarily activated cortical pattern to a downscaled, more interconnected subcortical pattern.

For their experiments, Vahdat`s team used 13 healthy volunteers and recorded the development of memory traces of their subjects in real-time using the techniques of  functional magnetic resonance imaging (fMRI) and electroencephalography (ECG). The subjects performed two different finger movement tasks one week apart. In the motor sequence learning task (MSL), they practiced a self-paced, known 5-item finger sequence task and this was compared to another task in which the subjects were asked to produce simultaneous movements of all four fingers at the same average frequency. This was termed the motor control task (CTL). The initial training session was held at 22.30 (termed ´learning session`, S1) and this was followed by a retest session at 09.00 the following morning (termed the ´retest session`, S2). Resting state conditions were met by the subjects staying awake with their eyes open and these were carried out before and after each practice session in the evening (termed RS1 and RS2) and the following morning (RS3 and RS4). A simultaneous EEG-fMRI recording scan was made lasting a maximum of 2.5 hr while the subjects slept in the scanner. Offline BOLD-fMRI imaging was carried out during the task learning, sleep and repetition of the task. Functional connectivity of brain areas was estimated using the overall co-activation of brain areas within a network during the different fMRI sessions.

The results of Vahdat and colleagues` experiments showed that specific neuronal firing activation patterns representing the learning of the motor sequences occurred in brain areas during the learning practice session (S1). This sequence related pattern was termed the ´learning pattern`. It was also observed that there was greater activation with the MSL rather than the CTL and although the speed of performance appeared faster for the MSL than the CTL, when the speeds of the MSL and CTL were intentionally matched, then no difference in average speed was observed. The summary of the activation peaks observed during the learning pattern was found to be related to the pattern observed representing consolidation. When the subjects were retested after sleep (S2), the authors found that the performance of the MSL had improved. They interpreted this as evidence of memory consolidation taking place during sleep. No such improvement was observed with the CTL. They also measured the accuracy of the performance of the MSL by looking at the percentage of incorrect key presses and it was found that in general there were a very low number of errors even after the overnight sleep. This was explained by the motor sequence being explicitly known by the subjects. An investigation into performance variability also found a significant decrease only in the MSL.

Vahdat and colleagues then looked at the pattern of activation of brain areas associated with learning and retesting of the motor sequence and found that the pattern of activation was similar for the learning session (learning pattern) and the retesting (consolidated pattern).  Each had similar patterns of activity in the sensorimotor core areas, but there were significant differences between the cortical and subcortical areas. It was found that the consolidated pattern had increased activity (approx. 4 times as much) in sub-cortical areas (mostly basal ganglia and cerebellar areas) and decreased activity (approx. half as much) in cortical areas (mostly fronto-parietal sensorimotor regions). A volume-based analysis of the activated areas showed that two cortical clusters (including superior parietal lobule and anterior intraparietal sulcus) had greater activation during the learning session than during retesting and conversely, subcortical regions (including putamen and cerebellar regions) had greater activation during retesting. An estimate of functional connectivity (where strength was given as the connectivity index, CI) showed that there was a significant effect for both learning and retesting patterns with the latter being greater. A significant effect was also observed for the CTL. The authors showed that the effect was specific for consolidation by observing four recognised brain networks: the default mode, visual network, and left and right fronto-parietal. The CIs calculated for the resting states and during NREM sleep for both conditions showed no change in CI values. Therefore it was concluded that the changes observed were not due to overall changes caused by time, learning or sleep, but were specific for the tasks being undertaken.

Investigation of the CI analyses carried out using dual regression analysis of the fMRI allowed the authors to identify specific brain areas within the connectivity patterns that were recruited for each condition during the learning session and the Stage 2 NREM sleep period. Vahdat and colleagues found that transient reactivation in a cortically dominant pattern formed during learning was followed by downscaling of the functional connectivity in these areas followed by a gradual reorganisation of the representation towards a subcortically dominant consolidated trace during NREM sleep. The functional connectivity of the ventrolateral putamen area was significantly increased within the consolidated network during NREM sleep in MSL compared to CTL. A gradual increase in the strength of connectivity was observed and this was significantly related to the amount of gain seen with the motor performance. However, the pattern of the brain area connectivity during learning did not show significant change during the NREM periods with either task. Therefore, although the consolidated patterns were not increased immediately after training it was already significantly elevated before the retesting session indicating that the memory consolidation process took place during the sleep phase and was not caused by the practice during the retesting session after sleep.

Vahdat and colleagues also found that the V-VI areas of the cerebellar cortex also showed increased functional connectivity for both tasks during NREM sleep and the retesting session and this was also associated with the overnight behavioural gains observed. The functional connectivity within the posterior parietal cortex (more activated during the learning session) also demonstrated increased values in the resting state condition following the MSL.

Therefore, the authors concluded from their investigations that there is a gradual shift of the patterns of neuronal activation representing the motor sequences during the memory process from a transiently activated cortical pattern in learning to a downscaled, more interconnected and more subcortically dominant  one during NREM sleep and after during recall. This indicated that NREM sleep is necessary for two complementary processes to consolidate human motor memory. They are: the suppression of the initial memory trace formed during learning; the restoration and reorganisation of the newly-learnt information in a more stable state following. The ventrolateral putamen appears to play a central role in the emergence of the consolidated pattern during NREM sleep. The authors concluded by indicating why sleep deprivation leads to impairment of motor skills and motor memory plus also indicated the possibility of enhancing newly learned skills by manipulating brain circuits during NREM.

COMMENT

What makes this article interesting is that it supports the view that sleep plays a beneficial, some might say even essential, role in the formation of long-term memories not only for explicit and implicit memories, but also as shown here for motor memories. Vahdat and colleagues demonstrated in their experiments that the learning of a five-finger sequence of movements produced as expected a specific pattern of neuronal activation of appropriate brain areas relating to motor memory. This activity pattern at learning represents the different complementary functions required for such tasks eg. control of muscle contraction and expansion and movement plus cognitive functions associated with visual pathways, attention, reasoning etc. that all occur simultaneously during the learning period. During the sleep phase (in particular the NREM phase) consolidation of the memory formed leads to greater performance of the motor sequence during the retesting phase. This was accompanied by reduced levels of variability in the performance and lower levels of recall error. With regards to the neuronal firing patterns, not only did the authors notice an increase in connectivity between the relevant areas in the retesting phase, but they also observed a shift of level of activity from the higher cortical areas (50% of the activity observed earlier for frontoparietal sensorimotor areas) to increased levels of activity in the subcortical areas (4 times increase in the basal ganglia areas and cerebellar V-V1) and particularly in the area of the ventrolateral putamen. Therefore, in order to understand  their observations we need to look further at two areas in particular: the brain regions involved in motor movements and memory of those motor movements; and the role of sleep in the memory consolidation process.

Brain region activity related to motor learning and memory

Vahdat and colleagues in their article showed that there was a change in area activity between the learning and consolidation (retesting) phases of their experiment with a shift from cortical motor areas to subcortical structures eg. striatum and a gradual increase in connectivity between all participating areas. Area activity relates to the different functions occurring during the learning and retesting stages and these can be divided into two sets of processes: those relating to motor movements which one can assume are the brain areas associated with muscle movements and the second set which are those areas associated with cognitive functions such as visual processing, learning, reasoning, monitoring etc. plus those associated with emotional status and conscious awareness. This division is supported by McDougle, Bond and Taylor who separated the cognitive demands of motor memory into explicit and implicit learning all sub-serving the task performance. They described fast and slow processes with fast being associated with the explicit memories involved.

With regards to motor movements and motor memory of motor movements, it is known that the neuronal activation pattern follows area activity associated with what is known as the ´motor loop` – a group of interacting, sometimes interrelated brain areas where some areas influence excitatory effects on others in the loop and some inhibitory effects. Therefore, the greater the activity of some brain areas means other areas exhibit higher levels of inhibition and so, a change in activity of one area alone can have an overall effect on the performance of the motor loop as a whole. Another feature of this system is that specific activity leads to specific movement eg. force and direction since somatotopic maps are in present and also that  because of the population coding scheme, the larger the active population, the finer the control of the movement. In the experiments discussed here the neuronal activation patterns representing the motor movements alone can be assumed to be the same whether the individual is in the learning phase or retesting phase.

Vahdat and colleagues found in their experiments that the brain areas, ventrolateral putamen and lobule VI of the cerebellar cortex, were mainly involved in the reorganization process following the learning phase of the motor sequence. This change in bias of function was linked to a change in functional connectivity of the putamen within the consolidated pattern during the NREM sleep phase, as well as during the post-sleep resting-state periods, and was thought to be related to the level of improved behavioural performance in the retesting stage following sleep. These results support the important role of the putamen in the motor loop and the observations that abnormal activity of this area is involved in certain disorders/diseases linked with abnormal or restricted movements eg. Parkinson`s disease, Tourette syndrome and stroke. Activation of the putamen is affected by the other member areas of the motor loop.

The motor loop consists of a group of brain areas whose activation patterns create the conditions required to perform motor movements. There is a group controlling strategy (eg. the higher levels of the brain such as the neocortex and basal ganglia), another controlling the tactics such as the movement of the muscles, the contractions, the organisation of sequences all based on previous experience (eg. the motor cortex and cerebellum) and the last group, the actual execution which involves the activation of the muscles (eg. the spinal cord and brain stem). Therefore, specific activation of the frontal (sensory cortex) or prefrontal, parietal or motor cortex areas leads to excitatory firing of the appropriate areas of the basal ganglia caudate region (through the somatotopic map condition). These activated caudate neurons then cause specific firing of areas of the globus pallidus or more importantly relating to the investigation described here, areas of the putamen, which then causes firing of the global pallidus. The globus pallidus is connected to the ventral lateral nucleus of thalamus (VLo), but global pallidus firing is inhibitory and therefore, any activation of the putamen increases the inhibitory effect on the VLo. The VLo is connected to the dorsal lateral thalamus which at rest is inhibited and hence, activity of this area undergoes excitation as the putamen activity relaxes the enforced inhibition. Át this point, the signal carries on through the motor loop to the higher cortical areas of supplementary motor cortex (area 6) and the premotor area. Areas 4 and 6 then send excitatory axons to the corticospinal tract which then activates the spinal cord. The other area involved in the motor loop is the M1 motor area whose layer V pyramidal cells receive input from other cortical areas and the thalamic ventral lateral nucleus. The output from such pyramidal cells activates the lower motor neurons such as those of the spinal cord and the subcortical sites for example associated with motor processing (brain stem). Hence, firing of the motor loop goes from the strategic areas to the execution areas.

Both of the two areas of the motor loop investigated by Vahdat and colleagues, that of the putamen and cerebellum, are capable of their wide-ranging involvement in motor movements and motor memory due to their complex physiology. The putamen has extensive connectivity to other brain areas, for example to the substantia nigra (an area linked with Parkinson`s disease), the globus pallidus, the thalamus (inhibitory effect from the putamen and excitatory effect to the putamen), and to the cortex (sends information to the putamen in multi-fibre pathways plus the area has numerous parallel circuits for cortico-subcortico-cortico communication loops). A look at the area at the cellular level shows a wide range of axons and dendrites which are highly arborized and exhibit a topographical organisation eg. anterior to posterior, and lateral to medial for functional and somatotropic gradients, diffuse terminal outputs, segregated terminals from adjacent regions and finely interconnected terminals from distal cortical regions in an overlapping function. It also has a varying range of active neurotransmitters, eg. GABA, which controls the inhibitory effect on the thalamus and dopamine, which acts presynaptically and influences the substantia nigra.

The functioning of the motor loop areas relates to each of their roles in motor movement and motor learning and memory and therefore, specific movements produce specific patterns of neuronal activation whether in learning or retesting. The other group of brain areas where activation could be observed relate to the cognitive demands placed on the individual because of the task. These include visual processing, and areas associated with cognitive functions such as learning, reasoning, monitoring etc. plus those associated with emotional status and conscious awareness. Even after sleep in the retesting session activity of this group of higher brain areas constitutes 50% of that reached during the learning cycle. Therefore, some cognitive processes are still active and repetition of the activation pattern is appropriate to memory recall of the learnt sequence. With regards to the putamen and its increased activity in retesting said to be linked to consolidation of the motor memory during sleep, we have already seen that learning is linked to putamen activity. This area integrates with other areas to complete tasks and an injection of muscimol (a GABA agonist) leads to a decrease in learning. In the case of reinforcement learning, then cholinergic interneurons in the area fire during stimulus with an impulse rate of 0.5 -3 and with regards to rule based tasks, then lesions of the area disrupt the recall due to the damage of the required hypothesis testing system.

The cognitive demands of the task not only relate to memory input, formation and recall they are also linked to emotional state which undergoes many changes during the learning and recall stages with each stage associated with different brain area activation. This means that the overall neural representation of the sequenced task will vary according to the emotional state observed at the time. However, only the overriding emotional status will be recorded in the form of an emotional tag alongside the information and this is the emotional status recalled along with that information. The concurrent emotional status observed due to real-time system activation will be expected as relaxed during re-testing due to successful recall. The changes in emotional status will also be mirrored by a shift in level of conscious awareness between the learning and retesting stages which will also be reflected by the activation pattern of the appropriate areas. As the learning of the motor sequence progresses then it is likely that there is a shift in conscious awareness to subconscious. Both conscious awareness and emotional status will be subconscious and neutral with any changes initiated through feedback (eg. monitoring for errors in the recalled sequences), a lack of the continuation of the sequence initiating the required prompts/reminders and increased speed although essentially the task sequence is order dependent and not time-dependent. The retesting session although proceeding with recall of learnt material with no variability demanded still requires visual input and other sensory input eg. body positioning, balance and error monitoring and therefore, increased activation in these areas should be observed.

Therefore, the activation of brain areas during the task given by Vahdat and colleagues reflects the requirements for motor movements plus for the cognitive demands placed on the individual due to the nature of the task. Whereas in other types of memory, the more connectivity observed the better the recall, ie. more details, more associations, in the case of motor memory, the more the connectivity then the finer the motor control.

Role of sleep in motor memory implied from the investigation

Sleep is believed to aid the consolidation of the learning neuronal firing patterns and this can have a beneficial effect on recall. For example, researchers have shown that a 10 minute nap can lead to a better recall of a story. In the experiments performed by Vahdat and colleagues regarding motor memory, sleep aids the consolidation process of the motor memory and a shift of strength of area activity is observed with lower levels observed for higher brain areas eg. the reasoning, strategy areas to the lower areas that of the putamen and cerebellum. There are many studies on sleep and memory and there are various hypotheses that cover the area of memory consolidation, including the trace reactivation, synaptic homeostasis, and systems consolidation hypotheses. The Trace Reactivation Hypothesis assumes that the repeated reactivation of a recently formed memory representation during sleep replayed in the slow-wave-sleep phase leads to a gradual strengthening of the learning-related connections (Guidotti and colleagues) especially in the hippocampus (an area known to exhibit activity even if the movement is not being carried out) and hence, to long-term storage of the memory trace.

Procedural memory has been found to be positively linked to sleep both in the sleep spindles and slow wave phases of sleep. This hypothesis is in contrast to the Systems Consolidation Hypothesis that proposes that sleep engages an active reorganization process that stabilizes the unstable neural representations formed of a new skill into a consolidated memory trace. In this hypothesis, protein synthesis is required. In the third hypothesis, the Synaptic Homeostasis Hypothesis it is proposed that local neuronal networks are potentiated and eventually become saturated during learning. In order for new information to be encoded, sleep is involved in the restoration of these local networks by downscaling the strength of synaptic connections. The authors of this article support this view by showing that there is a shift from the higher cortical areas to lower brain areas.

All three hypotheses however can be linked to the process involved in the consolidation of the memory traces even if the processes may be more complicated than suggested. The repetition of the sequence leading to the learnt sequence and then its recall in the retesting stage means that the temporary changes occurring in the initial learning stages cause physiological changes that mirror the translation of the memory from short-term to long-term memory. Consolidation comes through recall and retesting and it is disputed whether protein synthesis is required for this process or not, but it is known that histone acetylation is required.

In the experiments described here retesting occurs after sleep and the changes in connectivity imply that the consolidation process during sleep is the same as that which occurs when repetition is carried out whilst the individual is awake. The advantage of the consolidation process occurring during sleep is that the cognitive resources of the brain are minimised during sleep by the reduced sensory input, eg. visual pathway shut-down. Therefore, sleep is linked to reducing cognitive load allowing resources to be focused on the consolidation of memories formed from events recently experienced during the waking period. This implies that there is a point at which the cognitive demand outweighs the available resources and priorities have to be set. This point should be explored. We know at what point activity is not high enough for conscious awareness (through default network observations and minimally conscious individuals) or cognition (through dementia or coma sufferers), but the point at which the cognitive load is so great that the brain shuts down or sets its own cognitive priorities gives another angle to how the functioning of the brain could be manipulated.

Conclusion

Therefore, to conclude motor movements and learning of these movements rely on the activity of certain brain areas in a particular pattern. Motor memory just like episodic memory is dependent on learning and recall processes and the consolidation of memories can occur during sleep with replaying of the activation patterns specific to the movements during the SWS sleep phase. Vahdat and colleagues have shown that in the case of motor memories there is a shift of neuronal firing strength from the cortical areas to the putamen and cerebellum during sleep and hence, consolidation is linked to some extent to these shifts. This can be explained by the role of the putamen and cerebellum in the motor loop, the reduction of involvement of the higher areas in the formation of strategy and tactics of the motor movements required for the task in the recall phase compared to the learning phase and the decreased levels of conscious awareness and relaxed emotional status exhibited during the retesting phase. Learning impairments said to occur due to the lack of sleep can be caused by the inhibition of the replay process observed in the SWS and sleep spindle phases and are likely to affect both episodic and motor memories. They are likely to be linked to failed changes in area activation strength from the higher cortical areas to the subcortical as observed by Vahdat and colleagues in their experiments.

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

…..it is said that new information can be acquired by an individual during sleep. Would a repetition of the above experiment when new information is presented during the sleep phases (eg. introduction of a conditioning sound or smell) have an effect on the observed shift of activation pattern strength from the higher cortical areas to the putamen and cerebellum as observed by Vahdat and colleagues?

…caffeine is said to enhance memory consolidation, but has an adverse effect on sleep. Can we assume that the connectivity changes observed in this experiment would still occur, but there may be increased performance levels due to better memory retention?

…would an intervening task have an effect on the activation patterns observed by Vahdat and colleagues and the shift of activity strength to the putamen would still be present since stimuli are said to be more familiar after an intervening task?

…it is said that mindful meditation leads to less reliable memory recall. Does it have an effect on motor memory and would mindful mediation cause the same shift in activation strength of the cortical and basal ganglia areas observed by Vahdat and colleagues in their experiments?

Posted in neuronal connectivity, neuronal firing, procedural memory, recall, sleep, Uncategorized | Tagged , , , ,

dorsolateral prefrontal cortex role in decision-making

Posted comment on ´Stimulation of dorsolateral prefrontal cortex enhances adaptive cognitive control: a high-definition transcranial direct current stimulation study` by O. Gbadeyan, K. McMahon, M. Steinhauser and M. Meinzer and published in Journal of Neuroscience 2016 36 (50) p.12530; doi.org/10.1523/JNEUROSCI.2450-16.2016

SUMMARY

Gbadeyan and colleagues investigated in their experiments the role of the dorsolateral prefrontal cortex (DLPFC) in the adjustment of strategic control to real-time conflict caused by previously experiences of conflict. The authors support conflict monitoring theory where the anterior cingulate cortex (ACC) adjusts attentional resources to goal-directed information and ignores irrelevant information – a process believed to involve the lateral prefrontal cortex (LPFC). Their research centred on the role of the dorsolateral prefrontal cortex in strategic control in particular since most research using human subjects was said to concentrate on the activity of the ACC and any findings on the role of the DLPFC appears to be contradictory, eg. different views on the contributions made by the two hemispheres.

In their experiments, Gbadeyan and colleagues used 120 healthy participants and performed high-definition transcranial direct current stimulation (HD-tDCS) under 4 conditions – left or right DLPFC, or left or right primary motor cortex (M1). The M1 area was used to assess the specificity of the HD-tDCS performed since the area is thought to play no part in the response to conflict. Active and sham HD-tDCS were performed on each group in crossover and double blind tests. The adaptation to conflict was assessed during a visual flanker test ie. assessment of modulation of the flanker effect as a function of previous response to conflict. The participants were required to respond to a centrally presented target (a centre row of arrows) for 100ms after being presented with first a fixation cross (300ms) and then the distracting material. This was 4 flanker arrows which were presented (100ms) as either pointing in the same direction as the target stimulus (congruent), or in the opposite direction (incongruent). The participants were required to ignore these flanker arrows and to respond by indicating the direction of the target row arrows by pressing either the left or right response key. Each subject performed 5 blocks of trials.  The HD-tDCS used was a concentric set up with a 20 min long administration before ramping down. Nine potential side effects were recorded by all participants. Data analysis involved the mean response times (RTs) and varying ANOVA. Adverse effects between the active and sham trials were compared using paired t tests.

The experiments were performed to measure the conflict adaptation effect. The authors expected that there would be a flanker effect because responses are slower for incongruent trials than congruent. The responses were also expected to be slower due to the modulation of the response from the directly preceding trials and this is the so-called conflict adaptation effect to anticipated levels of conflict. On analysis of the mean RTs results of their experiments, Gbadeyan and colleagues found that there was a sizeable conflict adaptation effect in all groups and in all conditions (greater than 30%). ANOVA showed a significant 2 way interaction between current congruency and previous congruency indicating a strong conflict adaptation effect. There were also significant results for an ANOVA 4 way interaction between region, stimulation, current congruency and previous congruency. Separate ANOVAs between stimulation, current congruency and previous congruency found the largest conflict adaptation effect in the active condition than in the sham group, but the effect was not further modulated by laterality ie. both DLPFC groups had the same influence. With the M1 directed HD-tDCS then the conflict adaptation effect was reflected by significant interaction between current congruency and previous congruency, but was not further influenced by stimulation.

Gbadeyan and colleagues reanalysed the DLPFC with additional variable response repetition (repetition, switch) to see if the effects of active stimulation on conflict adaptation were larger for, or were restricted to response repetitions. This would indicate a priming effect. The authors found that the conflict adaptation effects were larger on response repetition trials than on response switches, but did not further interact with stimulation. This indicated that the effects did not rely on priming. To see if the effect of stimulation significantly changed the course of the experiment the researchers used an additional variable Block. No significant results were obtained under these conditions.

The authors also investigated whether the conflict adaptation effect demonstrated regional specificity (left vs right hemisphere) and motor cortex involvement. A 5 way ANOVA showed that there was significant interaction between region and laterality for DLPFC. It was found that the overall mean RTs were all higher when stimulation was applied to the right hemisphere as compared to left, whereas the opposite was observed for the M1 brain area.

Gbadeyan and authors concluded from their investigations that the DLPFC has a causal role in adaptive cognitive control. Their observations support previous evidence obtained from BOLD responses and also reports about DLPFC activity of current trials matching the activity of ACC in previous trials.  The activity appeared not to be restricted to either the left or right hemisphere which was contrary to reports from other researchers who had linked adaptive control to activity in one hemisphere or another. The authors concluded their article by continuing to describe the beneficial effects of tDCS stimulation on cognition generally and how HD-tDCS in particular could modulate cognition in a regionally specific manner.

COMMENT

What makes this article interesting is it further describes the subtleties of the decision-making process and the various roles that the brain area, the prefrontal cortex (PFC), plays in it. In the last Neurochitchat Blog post, we described the ventral medial prefrontal cortex (VMPFC – or orbitofrontal area, OFC) area and its multiple roles in various cognitive processes such as memory, conditioning, attention, emotions and consciousness. That blog post also described the PFC`s roles in general and the OFC roles in particular in the decision- making System 1 mechanism (the rapid decision-making mechanism) with a link to the striatum and also in System 2 (the slow mechanism) relating to values, conscious choice and the effect that values play on that choice. Research demonstrating these various roles include for example: the orbitofrontal area-basal amygdala pathway and the personal assessment of values given to events and the later recording of updates to that value; comparison of values as in choosing which action to be followed (Rich); the alternating process between two options, also responsibility of the frontal cortex-basal amygdala pathway (Rich); the switching of attention;  the link to reward with encoding predictions of reward values (Zhou); the allocation of rewards, the responsibility of the orbitofrontal cortex-anterior cingulate cortex pathway (Chang); the awareness of choice, involving the VMPFC and DLPFC (De Martino); and the calculation of delay in respect to expected reward/risk (Rudebeck) with feedback of the event responsibility of the VMPFC and rostrolateral prefrontal cortex connectivity.

In this article, Gbadeyan and colleagues discuss the role of a further part of the PFC brain area, that of the dorsolateral prefrontal cortex (DLPFC) and its role in particular in decision-making. This area (designated Brodmann areas 9 and 46) is connected to the post-parietal cortex and is known to be involved in updating information and maintaining the working memory state. Research has shown its activity in working memory is dependent on connectivity with the frontoparietal area (Ekman) and that activity is NMDA dependent. However, the DLPFC area is also known for its effects on decision-making – a function predominantly known to be linked to the VMPFC – with activity not vital to decision-making since it can be carried out without it, but influential.

Therefore, the question is ´what roles does the DLPFC play then in decision-making?` We suggest here that in this context DLPFC acts as a neuronal ´signal amplifier` that allows differences in event values to be more easily distinguished by the sender region, the VMPFC. What do we mean by that? Imagine hitting a ball against a wall where the wall doubles the force of that ball so that when it rebounds back it comes back at double the speed. If we hit several balls with different forces at the wall then the returning balls would be more easily distinguished if the wall can alter the return speed. Doubling numbers increases the difference between consecutive numbers more than between consecutive ones eg. 2 squared is 4, 3 squared is 9 so the difference between the two is 5 compared to just 1 if consecutive numbers are only considered. If a brain area receives a neuronal firing signal and amplifies it to double or triple its original firing strength and then returns it back to its sender region then distinguishing between competing signals by that sender is improved. An alternative option is that the ´amplifying cell` could dismiss the weakest signals and only return the stronger ones. If the signals sent are the values of the various options in the decision-making task and the sender is the ventromedial prefrontal cortex then by amplifying the received signals the dorsolateral prefrontal cortex would have aided the ventromedial prefrontal cortex in its choice of the optimal course of action. The DLPFC area is itself not responsible for the formation of event values or their assessment, both of which are jobs of the VMPFC and other brain areas, it just makes the task of the next stage in the decision-making physiological mechanism, that of choice, by these other decision-making areas easier.

There are several areas of support for such an action of the DLPFC and these are:

  • It is known that there is reciprocal connectivity between the VMPFC and DLPFC areas. Therefore, the VMPFC could send its signal to the DLPFC and the DLPFC could return it – the strength of the signal is not measurable so it does not directly prove that the DLPFC acts as an amplifier. This could explain why low motivational decisions lead to earlier activity in the DLPFC – the DLPFC strengthens the signal even though it is not warranted by the normal valuation process since it is of low motivation. It could also explain why high rewards are favoured since the signal is strong when it is sent to the DLPFC so amplifying would mean further strengthening which is then received and re-registered by the VMPFC.
  • It is also known that there is increased activity in this DLPFC area when actions are selected and initiated (Spence). Therefore, the DLPFC is involved in decision-making, but the exact stage is not determined. It is also known that the DLPFC is involved in reasoning tasks (D`Espito) with the right hemisphere thought to be involved in plan generation and the left in plan execution. A role in strategic control is also reported with the VMPFC known for comparing values as in choosing which action to be followed (Rich), but the awareness of choice the responsibility of the VMPFC and linked DLPFC (De Martino) and feedback of the event the responsibility of the VMPFC and rostrolateral PFC connectivity. Therefore, it would seem that DLPFC plays a role in strategic control.
  • Support also comes from the observations that the activity of the DLPFC leads to irrelevant information being ignored. The lateral intraparietal area (LIP) is also involved in ignoring distracting information, but this could relate to the functioning of the attentional system only. The role of the DLPFC in this function could relate to the other informational situations, eg. sensory input and memory formation. Repetition leads to a shift away from task relevance. In the case of DLPFC, then distracting information is part of the decision-making process since it can be considered as what forms the other options before the choice is made. Since we know that DLPFC activity suppresses distracting information (Gbadeyan) then this implies that the suggestion that the DLPFC only sends back the strongest option is likely.
  • It is also known that activity of this area is linked to conscious awareness. If this suggestion is valid then the incoming signal is amplified and strengthened by the DLPFC so that the information reaches conscious awareness. This information takes priority over the distracting, irrelevant information which according to point (3) above could be suppressed by the DLPFC.

Therefore, the suggested role of signal amplification by the DLPFC implies that this area is part of the decision-making process and does not act just as assurance or verification that the process carried out by other brain areas particularly the VMPFC is correct. It implies that the DLPFC can play a role in both the slow decision-making System 2 (here, strengthens the best value option) and the fast System 1 ´magic answer` decision-making process (here, strengthens the strongest firing option). It can also explain the disputed role of DLPFC in spatial working memory (eg. Platke – yes, Mackay – no with roles in accuracy only and a requirement for precentral sulcus activity). If we look at spatial working memory as being an example of part of a decision-making process undertaken when the route is followed and a decision is required as to the next step, if the above hypothesis is true then the options (ie. turn left or turn right) require the retrieved memory of the correct option to be a stronger firing assembly compared to the unreal option. Amplification of the firing of the possible options by the DLPFC would mean that the correct route learnt from previous experiences would be chosen. This could explain why Mackay said the DLPFC is involved in accuracy only. If the DLPFC is not involved then previous experience would still present the option with the highest value, but it would be more difficult to recognise it from other options presented that represent previous unsuccessful and successful attempts carried out during the learning process. This supports the idea that DLPFC is part of the strategic control mechanism.

This proposed signal amplification role of the DLPFC means that the area is fully functioning for all aspects of decision-making, but at different levels for different tasks. It is therefore, necessary to investigate the relationship between the two for optimal efficiency in all circumstances. The rebound nature of the area may mean that its efficiency can be affected by factors such as tiredness or stress which may imply that only the simple ´magic answer` decision-making mechanism can be used and not the more complicated option assessment and choice mechanism. Therefore, factors influencing the activity of the area may be important and certainly it highlights the need for care in interpreting brain area connectivity and cognitive functioning.

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

……it is said that mindful meditation increases awareness of unexpected distractors. If the role of the DLPFC in amplification of neuronal signals is valid, would an effect on activity of this area in decision-making be observed if mindful meditation is carried out during or before problem solving?

….tDCS is known to decrease stress, but has a negative effect on working memory. What happens to DLPFC activity in an anxiety situation? Would more errors be observed as the ability to suppress distractors cannot be overcome?

…..spatial memory performance is said to be impaired in a rat model of neuropathic pain and this is associated with a reduced hippocampus-prefrontal cortex connectivity? What would happen to DLPFC activity in this situation? Does the pain cause the DLPFC to preferentially strengthen and send back signals relevant to the removal of the pain stimulus whether they are relevant to the decision-making task at hand or not?

…….the administration of a drug that temporarily prevents the opening of K+ channels in the PFC leads to a restoration of working memory activity in ageing (Laubach). Also, administration of estrogen is known to restore multi-synaptic boutons in the DLPFC area in ageing and hence, an increase in working memory is observed. Do these factors have an effect on DLPFC functioning in decision-making?

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

choice changes connectivity between prefrontal cortex and striatum

Posted comment on ´Human choice strategy varies with anatomical projections from ventromedial prefrontal cortex to medial striatum` by P. Piray, I. Toni and R. Cools and published in Journal of Neuroscience 2016, 36 (10) p. 2857; doi.org/10.1523/JNEUROSCI.2033-15.2016

SUMMARY

Piray, Toni and Cools` article begins with a description of the two decision-making systems: one goal directed (model-based, flexible, and cognitively expensive) and the other habitual (model-free, rigid and more rapid). They state that these systems could arise from two computational mechanisms representing reinforcement learning and associated with firing in the frontostriatal circuits which are believed to be responsible for learning and behaviour. The authors investigated how the two systems relate to the specific connectivity of neural circuits particularly of the dorsomedial striatum and the ventromedial prefrontal cortex (vmpfc) for the model-based system and dorsolateral striatum and vmpfc for the model-free system and how structural differences in these circuits could account for the individual differences observed in choosing which decision-making system would be used in any situation.

In their experiments over 30 healthy volunteers underwent probabilistic tractography diffusion tensor imaging (DTI), connectivity based parcellation of the frontal lobe and  computational explicit learning associated with a multistep decision task.  In each trial of the task the subject had to make a choice between two fractal stimuli (70% chance) leading to one of two different second-stage sets represented by different colours. Then the subjects had to make another choice between two stimuli presented in the second-stage set (30%). Each stimulus at this second stage was associated with a specific probability of delivering a monetary reward and the probabilities were changed in order to keep the motivation to participate high. In this way, the authors could distinguish between model-based and model-free choices.

In all 120 trials were performed and the results were analysed by logistic regression using MATLAB. Piray, Toni and Cools used a 2 by 2 factorial design with transition (common or rare) against reward delivery on the previous trial (rewarded or unrewarded). The degree of model-free and model-based decision-making used was quantified as the main effect of the delivery of the reward and the interaction effect between reward delivery and transition respectively. They also fitted the results to reinforcement learning models for model-based, model-free learning plus a hybrid account. Structural and diffusion images were recorded using MRI and DTI and a connectivity matrix was computed between the striatum and frontal cortex after striatum-based parcellation of the frontal cortex had been completed. The authors associated 2-8 clusters for each group and demonstrated the stability of the clusters and as a result identified 5 clusters as targets. Also, as part of their experiments any influence from bottom-up systems was discounted by lesioning the anterior limb of the internal capsule thus destroying all the fibres going from the striatum to the frontal lobe along the striatal-thalamocortical pathway.

The premise of the authors in their experiments was that the probability of transition from the first to the second stage set was different. Each first stage choice led 70% of the time to one of the two second state sets (termed by the authors as common transition) or for the remaining 30% to the other choice (termed rare transition). Therefore, in the model-based system then the first stage choice associated with a rewarded second-stage choice was reinforced. Hence, the probability of choosing a first stage action that was ultimately rewarded after a rare transition decreased. When the model-free system applied then the transition probabilities were not observed and the first stage choice was reinforced regardedless of the second stage. The probability of repeating the actions in the subsequent trials (termed stay probability) was a function of the current trial. If the model-free system was applied then repeating the first stage choice in a subsequent trial became a function of reward delivery regardless of transition occurring. Therefore, according to Piray, Toni and Cools model-free and model-based behaviour was quantified to 2 key effects: the main effect of the reward and the interaction effect of the  reward and transition.

When the stay probability was analysed from the test results, the experiment showed a significant main effect of reward delivery ie. the model-free system was employed as well as an interaction between reward delivery and transition (ie. model-based system). Piray, Toni and Cools also showed that there were large differences in the degree of model-based control between individuals. In half of the subjects, the characteristics of this type of control were clearly observed whereas in the other half there was no evidence of a reliance on it. The authors also looked at a hybrid model combining the learned values of model-based and model-free strategies according to the task. They found that at an individual level then the hybrid model outperformed the model-based results (all 31 participants) whereas comparing it to the model-free situation then its use was low (only 6 out of the 31). It was therefore, suggested that in analysis of reward-by-transition then the model-based decision-making strategy was not evident in half of subjects, but was evident in the other half. This confirmed that participants consistently used model-free control whereas the use of a model-based strategy depended on the subject.

Through parcellation of the striatum and cortex and DTI, the structural interconnectivity of the striatum was investigated to see if the differences in model-based strategy use were dependent on structural differences. In this investigation, the authors looked at the 5 clusters common to all participants which overlapped with other brain areas areas (precentral cluster – with frontal lobe motor areas; posterior prefrontal – with presupplementary motor area and frontal gyrus; dorsal prefrontal – with anterior cingulate gyrus and para-cingulate gyrus; anterior prefrontal – para-cingulate gyrus and anterior cingulate cortex plus dorsal parts of frontal pole;  and vmpfc – including frontal orbital cortex and ventral parts of the frontal lobe). A large variability was observed with 3 out of the 5 clusters, but not the precentral area and the vmpfc. It was found that only individuals relying on the model-based strategy had stronger interconnectivity between the vmpfc and medial striatum and this was also observed with the hybrid model. Piray, Toni and Cools also found using lesioning the bottom-up tracts that the effect was top-down and that these afferences went from the vmpfc to the striatum. A similar analysis with the model-free strategy found no significant correlation between strength of connectivity and frontal clusters with striatum. However, lower structural connectivity between the right dorsal prefrontal and right medial caudate nucleus was observed.

Therefore, Piray, Toni and Cools concluded that the connectivity between the frontal cortex and striatum predicted differences in the use of the model-based strategy between individuals. Other researchers also implicated areas such as amygdala, hippocampus, lateral prefrontal cortex and the default mode network (DMN). They also tested other areas and found that there were marginal effects between the left posterior cingulate cortex (the socalled DMN hub) and the vmpfc so the authors positively associated this observed connectivity to model-based strategy control.

Using a tensor model and fractional anisotrophy, activity was investigated relating to white matter microstructure integrity. The investigation was carried out on the use of the white matter tract and model-based strategy. The authors looked at the strength of the vmpfc tract which consists of 4 major bundles arising from it (the uncinate fascicle, the corpus callosum, the superior longitudinal fascicle and the cingulus bundle).  A significant correlation between tract integrity and model-based use was observed only in the cingulum bundle and it reflected the degree of model-based control employed. This supported observations made by other researchers. Piray, Toni and Cools also found that the dorsomedial striatal/vmpfc tracts went through the cingulate cortex bundle. No change in grey matter was observed between individuals in these areas and therefore, it was concluded that the correlation between model-based control and vmpfc-striatum tract strength was not accompanied by changes in grey matter.

Therefore, the Piray, Toni and Cools concluded in their article that it appears that there is a mechanism for the instrumental action control through which the medial striatum determines at least partly the relative contribution of model-based and model-free systems during decision-making according to the top-down model-based information from the vmpfc. This was described as being important in the understanding of neural connectivity that could be influenced in impulsive or compulsive psychiatric disorders.

COMMENT

What makes this article interesting is that not only does it provide more proof that the physiology of the brain is adaptable and responds to the demands placed on it from the side of information, but also that neuronal physiology can be adapted in response to the use of particular cognitive methods. The authors of the article commented upon here, Piray, Toni and Cools showed with their experiments that the neuronal connectivity between the ventral medial prefrontal cortex (vmpfc) and the basal ganglial striatum was thicker (hence, implying greater efficiency) when a certain decision-making strategy had been employed frequently. The cognitive decision-making method employed in this case required informational input from the beginning to the reward (the end-point of the process) as well as processing relating to optimising strategy and assessment of the overall process according to the success of reward attainment. Therefore, the authors looked at particular brain areas known to be associated with decision-making, real-time information processing and the emotional system relating to reward and value.

In general, decision-making can be said to be a multiple stage neurochemical system leading to a specified action or outcome. There are many circumstances when decision-making can be employed, eg. when there is a lack of stored information to interpret the real-time stimulus, or the stimulus provides no clear course of action dictated by previous experience but provides equal ranking options, or the stimulus is so far removed from the task (the end-point) that multiple cognitive steps are needed to bring the two together (such as that seen in problem solving), or the stimulus and task (the endpoint) are ´unreal` and any stages inbetween are based on conjecture and/or reality (such as that seen in forward planning). What we can say is that: it is highly individual; occurs in real-time; the process matches the task at hand; and it requires the attentional, emotional and working memory systems.

The general decision-making process can be said to consist of 7 cognitive and neurochemical stages (Salt, 2012). Stage 1 involves defining the purpose of the task, ie. I know where I am, is not where I want to be. In Piray, Toni and Cools experiments, the definition of the purpose is based on instructions, but it can in other situations be elicited by ´cues` in the stimulus. Therefore, both rely on informational input and requires the formation of an ´unreal` image representing the sought after goal. Stage 2, the input stage, also occurs in real-time and is again related to the task demands or is stimulated by the same cues used in defining the purpose. This stage is the acknowledgement that there could be more than one option and, hence points of access ie. the cues set to define the decision-making task are set. Stage 3 can be termed the questions or solutions stage and occurs if the so-called ´magic answer` (ie. the answer that just ´pops into the head` after presentation of certain cues and thoughts) fails to appear. This stage requires ´reframing` of the information so that a decision can be made in the future based on presented information and therefore, it requires a certain amount of previous experience as well as possibly the application of creativity to be successful. This stage is also likely to involve the attentional system in its monitoring role for conflict and acceptable process timing, the emotional system which will respond to unacceptable timing (dictated by activity of the cingulate cortex) as well as possibly conscious awareness. The following stage, Stage 4, is where the various options available for making the decision are constructed. Strategies are applied and these strategies reflect invidividual experience and favourites, eg. Consequence and Sequel, Other Peoples Views, Consider All Factors, Plus and Minus Points.  It is clear that the better this stage is the more likely that the resulting decision is the most optimal. This stage is also where construction errors can occur, for example because of the use of false facts or biasness of facts, a misunderstanding of the problem, habit, dominance of particular emotional values and constricting time factors.

The theoretical end-stage for the processing of information before the action is taken is Stage 5 where a decision is made from the options constructed in Stage 4. According to Salt 2012 the decision making stage means taking either the ´simplest path` or where more complex assessment is made. The ´simplest path` is the easiest solution and involves taking the option which shows the greatest firing strength or has the highest rating for self-interest which means taking into consideration emotional values and/or priorities. Emotional values mean the value of the event to the individual from the point of the two defining emotions, pleasure and pain with pleasure being a graded value. Priorities on the other hand are entirely personal with some perhaps reflecting an individual`s physiological needs such as hunger or sleep and others more cognitive and subjective such as personal long-term goals or sense of morality.

The other decision-making path in Stage 5 is much more complex and can be further split into two defining criteria based on either emotional factors ie. ´heart` or logic and facts  ie. ´head`. As indicated by the name the ´heart` decision-making stage requires emotional values of the options to take priority to determine which decision is taken. The biochemical mechanism probably involves the comparison of emotional values for each of the options described in Stage 4 and the option which demonstrates the strongest firing of the emotional value is the path followed. This type of mechanism involves the brain areas associated with emotional values and storage of emotional values eg. the prefrontal cortex, striatum, nucleus accumbens. The other decision-making method termed ´head` involves making decisions based on facts and logic and instead of comparing the emotional worth of the available options then a comparison of the factual ´worth` of the different options is made. Hence, there is a ´mathematical-type` basis to this type of decision-making process and this may not be so visible or so instantaneous as decisions based on emotional values. Salt (2012) suggests that the psychologists` views on decision-making can be divided ultimately into three neurochemical techniques for distinguishing the firing patterns of the different options and these are based on frequency, utility and risk with the ideal solution demonstrating high probability, high utility and low risk/high reward.

In the case of decisions being made as a result of looking at ´frequency`, Salt (2012) suggested that this is based on the strength of neuronal activation within the neuronal cell assembly representing that option. This means that the choice of option will be that neuronal cell assembly that demonstrates the strongest connections between its firing cells and overall firing and will be caused by the neuronal cell assembly representing the largest amount of relevant detail or will be that most strongly consolidated achieved by frequent activation. It should be noted that firing strength here means in all cases not exact mathematical numbers, but more rough approximations like, for example a show of hands, degree of lighting, or an overall impression. Therefore, in the case of decision-making made according to ´frequency` the firing neuronal cell assembly demonstrating the strongest connections between its participating cells will be chosen when comparing the different options constructed in Stage 4.

This is different to the second ´head` method that could be used that of ´utility` which represents the strength of the similarity of the characteristics of the various options and hence, the degree of usefulness in obtaining the purpose defined in Stage 1. This method involves the matching of  ´characteristics` between the reactivated stored neuronal cell assemblies of each option (a process requiring the attentional system) with the ´unreal` image stored of the purpose (Stage 1) so that the option with the greatest number of characteristics shared with the goal is considered the most likely to be useful. Some strategies for the construction of options favour this criterion eg. Consequence and Sequel.

The Prospect theory (Kahneman and Tversky, 1979) combined two types of decision-making mechanism, that of utility (the quality of characteristics described above) and risk and according to Salt (2012) risk forms the third ´head` type mathematical strategy suggested here. Risk can be calculated using the strength of emotional response and can be re-defined as the assessment of the chance of reward or loss being received. Natural decision-making seeks to maximise reward (happiness) and minimise loss (stress, pain) and therefore, the emotional strength of each option is calculated according to personal values and priorities. The values obtained from each option are compared and that option producing the highest value (reward or loss) indicates the most ideal solution.

Therefore, Stage 5 of the decision-making process determines the ´strongest` option chosen to be followed to the end-stage of the mechanism whatever this is and this is carried out in Stage 6 (the Action stage). Completion of this stage leads to shift downwards of the cognitive system and attention is switched again to external events if not taken up with the monitoring task. The emotional system also shifts to a relaxed status as the decided action takes place. The final stage of the decision-making process is the acknowledgment of the outcome relative to what is expected and for this the measure of conflict between the two is recorded by the cingulate cortex in its monitoring role. Neurochemically, this feedback requires the incoming information to be matched against the firing neuronal cell assembly representing the purpose (as defined in Stage 1). The attentional system which probably monitors the comparison ´relaxes` the cognitive systems if the two match, but if unexpected results are obtained it will shift the brain areas activities to a fear state to answer why.

Now that we have described the basic decision-making mechanism we can see how Piray, Toni an Cools`s two types of decision-making can relate to the suggested neurochemical mechanism. The first type, the model-free scheme was described as rapid, rigid, and conforms essentially to the ´magic answer` decision-making mechanism given above. This is where the decision made is based on tried and tested previous experience and there is a strong reliance on taking the established route. The authors` second type, the model-based scheme, is here still a multiple stage decision-making process, but demands less time and less cognitive resources than other tasks because the experimental method used by Piray, Toni and Cools essentially indicates a ´magic answer` type mechanism. The subject knows instinctively the correct decision because repetition and practice quickens the decision-making process to a point of automaticity. Therefore, how does this affect the various stages of decision-making process? Both Stages 1 (defining purpose) and Stage 2 (input stage) are unaltered, but Stage 3 (questions or solutions stage) probably becomes easier or less complicated because of the previous experimental experience ie. the subject knows where the points of access are and what to look for. Stage 4, the construction of options, is also likely to be easier since the subject would know which strategy to apply. Stage 5 (the information gathering end-stage) is likely in Piray, Toni and Cools experiments to begin more slowly with the use of the ´head-based` decision-making strategy because the subject would be learning to apply knowledge of the experiment and its demands and would be busy assessing for high probability, high utility and low risk to maximise high reward frequency. The method chosen is likely to be based on strength of activation (high probability of route choice most frequently giving reward) with assessment of the reward and its value occurring through the emotional system response. The strength of similarity of characteristics method would not be applicable since the experimental conditions were not changed during the trials only certain features. Stage 7, the feedback stage, would be important because the monitoring of the decision and result (reward) would lead to learning of strategy so that the choice of path next time could be positively manipulated. As the subject participates in more and more trials the speed at which the decisions could be made would increase and strategy and feedback would simplify the decision-making process.

As seen by the complexity of the decision-making neurochemical mechanism, multiple physiological systems are in play during the whole process (eg. attention, working memory, visual pathways, motor pathways as well as the emotional system) and hence, activation can be observed in multiple brain areas. No single area is defined as the seat of decision-making, seat of recall, seat of emotions etc. with cognitive demands relying on simultaneous activity in multiple areas. Piray, Toni and Cools showed that their experiments frequent use of their so-called model-based strategy ie. the ´head-based`  decision-making system given above had an effect on the connectivity of two brain areas, the ventralmedial prefrontal cortex (vmpfc) and the striatum. This corresponded to the increased cognitive demand when more involved ´thinking and analysis` was required, more strategies employed and feedback was taken into account in comparison to the model-free system when decision-making was quicker, simpler in terms of that the answer appears more readily (the so-called ´magic answer`) without serious, multi-staged analysis. The resulting effects of such computation implies that the connectivity between the vmpfc and striatum is needed in this type of decision-making and this is supported by other researchers who have found that effective decision-making requires the strength of this pathway (Chung-Chuan and colleagues) – a process requiring the dopamine system. It has been described that a decision requires and brings about ´value` with value described in terms of reward/loss. This supports the views that the value of goal-directed actions are the responsibility of the striatum and prefrontal cortex in the emotional pathway and access to emotional tag ie. the pfc stored memory of the emotional value placed on the stored information. Piray, Toni and Cools found that the vmpfc to striatum tract strengthened with the model-based decision-making of their experiments and that there was a correlation between the model-based control and individual differences in strength of this pfc/striatum tract. Both brain areas have roles in real-time processing and in emotional values in decision-making.

Therefore, does this observation support what we know about neurochemistry of decision-making and the emotional system? We know that there are 2 ways in which the emotional system is involved in the decision-making process. The first is that the real-time emotional status affects the performance of the mechanism at that time and secondly, that neurochemical representations of personal emotional values are part of the decision-making process. In the first case, the current status of the emotional system at the time of the decision-making process means that activation of the striatum is linked with the other brain areas playing a role in the emotional pathway particularly that of pleasure. The striatum may not have as strong links as other basal ganglia areas such as nucleus accumbens, but it is involved. A signal is sent from the prefrontal cortex to the hippocampus and cingulus and also a signal is sent to the globus pallidus of the basal ganglia which spreads to the putamen, caudate and striatum with activation of these areas having an inhibitory effect on the thalamus. Therefore, in terms of pleasure, the full extent of the neuronal capability of the thalamus is not used. The prefrontal cortex also sends an inhibitory signal direct to the amygdala so that firing of this area is effectively ´switched off`. This real-time emotional status of the subject having an effect on decision-making, however, is not likely to be the main reason here for the observed stronger connectivity between the vmpfc and striatum associated with the frequently-used model-based decision-making strategy since both strategies are likely to have the same emotional pathway demands during the performance of the experiment.

Therefore, the strengthened vmpfc to striatum connectivity must be part of the decision-making process itself. This can be explained by the use of values to determine decision options, ie. this type of decision-making process requires the comparison of emotional tags/values and these are hypothesised as stored in the prefrontal cortex. Each option constructed in Stage 3 of the decision-making process from each point of access (the cue) activates the appropriate stored neuronal cell assembly and these contain the relevant information relative to the purpose of the process as well as a connection to the ´emotional tag` which is the memory store of the emotional value of that option and neurochemically stored at its point of origin possibly in the prefrontal cortex. Therefore, when the stored neuronal assembly is fired the informational content is activated and the value of that information is also registered and here in Piray, Toni and Cools` experiments that is the amount of the reward received in past experiments.

Supporting evidence for this role of the prefrontal cortex and striatum in the value part of the decision-making mechanism is abundant. For example, Bechara in 2005 described two separate, but interacting neural systems that control decision-making: an impulsive amygdala system for signalling pain or pleasure of immediate prospects and a reflective prefrontal cortex system for signalling pain or pleasure of future prospects. At the neurotransmitter level then Bechara proposed that drugs which can alter neurotransmitter levels, can modulate, bias or even hijack the goal-driven cognitive resources that are needed for the normal operation of the reflective system, thus providing support for the drug induced influences on brain memory input. Morris, Brandford and Franks`s 1977 study showed that the activity of dopamine neurons in a decision-making situation was modulated according to the value of the upcoming action and that the activity determined behaviour and not the reward. They concluded that the decisions were generated elsewhere and conveyed to the dopamine neurons, which then played their role in decision-making by affecting the basal ganglia efficacy. The exact connectivity demonstrated here between the vmpfc and striatum was described by Daw, Niv and Dayan in 2005 who showed that competition between dorsolateral striatal areas and the prefrontal cortex system affected decision-making.

Even though both the vmpfc and striatum are involved therefore, in model-based decision-making their roles in the process are not necessarily the same. The prefrontal cortex is linked to various cognitive functions eg. it has roles in processing (Baddeley`s central executive), the emotional system (sliding switch location and emotional tag site) and the attentional system (perceptual load capacity and task relevancy). Both Stanovich and West (2000) and Evans (2003) identified two decision-making systems with different functions and different locations involving the prefrontal cortex. System 1 was described as having belief-based processes, which are rapid, parallel and automatic in nature and involve the activation of the ventral medial prefrontal cortex and System 2 which involves the activation of the right inferior prefrontal cortex and is linked to slow and sequential thinking and uses the central working memory system (here model-based). It should be noted that the terminology of these systems do not agree with those of Piray, Toni and Cools who described the System 1 as the model-free system and System 2 as model-based, but was the one which showed activation of the vmpfc. The observation could be explained by the nature of the experiment itself because its repetitive nature meant that the experimental process is learnt, becomes more automatic and mathematical analysis of the best route to get maximum reward is carried out early on. Also, the experiment means that emotional system activation is not associated to just one or the other decision-making system eg. a gut reaction of the model-free system involves the emotional system as well as its involvement in the model-based system with its value comparisons for the various strategies.

The role of the ventromedial prefrontal cortex area in model-based decision-making as proposed by the authors is supported by others who have also shown its link to emotional values. The vmpfc (anatomically synonymous with orbitofrontal cortex) is thought to be responsible for subjective value of outcome (work by Winecott, Kringelbach and Wallis and teams), increased activity in encoding goal values (Hare and colleagues) and with activation in predicted success ie. value (Kao and colleagues). However, the wmpfc was considered by some (eg.Wunderlich) to be unlikely to be responsible for the comparison of emotional values since activity did not depend on what options were available at any one time, but instead depending on true values (Paduo-Schiappa and Assad). Since it is thought that some comparison has to be made in decision-making and self-evidence shows that one selects by choosing which one is ´liked` the most, it is likely that a comparison of emotional values stored in the emotional tag located likely in the vmpfc, then this makes it a more likely that this is the area assigned this function. The choosing of the option based on emotional value will then initiate the running through of the information linked to the stored memory and neuronal cell assembly so that the appropriate action could be made. This would explain the observation that areas involved in decision-making are the parietal cortex, basal ganglia and motor structures (Kaini, Hanks and Shadlen, 2006).

Whereas the role of the vmpfc is not only linked to option values, but also information processing, the role of the striatum is likely to be purely related to the emotional system involvement. This particular area is part of the emotional system relating to the emotion pleasure and is reliant primarily on the action of the dopamine neurotransmitter. Two effects are seen in the model-based system, which indicate the area involved is the vmpfc. One is in real-time and is the response to the cingulate cortex in its assessment of the situation and strategies. For example, relating to experimental timing issues, the subject is happy if the timing of the process is as expected and hence, the emotional system relating to the expressed emotion, pleasure is dominant. However, if the subject considers the process to be too slow then there is a shift to activate the amygdala and the fear system responds with anxiousness and fear emotions expressed. The other role is in the decision-making mechanism itself as described above and the striatum has been found to be responsible for the trade-off of strategies. Therefore, experience and computational simplicity is traded-off against flexibility. The strategies are computed in the caudate and posterior putamen and the vmpfc integrates these computations. The value of the goal directed actions also plays a role in feedback which directs future decision-making and activity of the striatum appears to be important in this final stage of the decision-making process. Anticipatory signals in the striatum was found to lead to encoding success in the hippocampus (Wang and colleagues) and the striatum is responsible for reward prediction error, an effect that is weaker when the episodic memory ie. the stored past experience, is stronger (Wimmer and colleagues).

Therefore, the observation of strengthened connectivity between the vmpfc and striatum with frequent model-based decision-making observed by Piray, Toni and Cools has a valid explanation. However, there are two notes of caution attached to the interpretation of their results. The first relates to the process of decision-making itself. We have already discussed in the comment on this article how activity of the emotional system is important to decision-making not only through the provision of values and their use with the various strategies, but also through the emotional status of the subject at the time of the process. However, it has also been shown that not all decision-making is performed using conscious strategies and this system is described by the authors as ´model-free` or by others as the ´magic answer`. In this case, conscious application of values does not occur even though automatic application does. Therefore, the increased connectivity between vmpfc and striatum may apply to both systems relating to values, but only one in relation to conscious awareness.

The second note of caution should be attached to Piray, Toni and Cools` suggestion that this adaptation of the brain could be used as a link to the causes and possible therapeutic routes of certain psychiatric disorders such as impulse control. The importance of brain functioning and cognition means that fail-safe physiological mechanisms are in place so that even if brain areas are negatively affected, the brain can adapt to a certain extent to allow functioning to continue if possible. Means of adaptation are for example physiological in the form of changes in brain area function via mass action, equipotentiality and distributed control capability, the benefits of cognitive reserve, the presence of right and left hemispheres, and via processing safeguards such as shifting of focus, changing perception, changing levels associated with arousal and sleep states and even the capability to shut-down under extreme conditions such as that seen in coma. Therefore, any observation connected with a psychiatric disorder may not be the direct cause of that disorder, but instead a mere adaptation to it and rebalancing that may not be cognitively beneficial in the end.

Since we`re talking about the topic,

…..factors such as stress and anxiety are known to have an effect on decision-making. Anxiety is known to lead to decreased neural coding of the subjective value of risky options in the vmpfc and the striatum and a decreased prediction of observed choices and decreased functional coupling of other areas. Can we assume that if anxiety is induced in the subject by perhaps linking reward to performance expectations would we see the same strengthening of connectivity of the vmpfc and striatum if the experiment was repeated?

……is it possible that the effect of more changes in reward or a sudden change from reward to risk would see a shift from strengthening of connectivity from the vmpfc to the striatum to strengthening the connectivity between the vmpfc and the amygdala in line with the activation of the fear emotional system instead?

……it is known that the administration of L-Dopa increases the concentration of dopamine in the prefrontal cortex and other dopaminergic pathways and results in an increase in the number of risky options when the reward is a gain, but not in the case of losses (Routledge and colleagues). Therefore, would the administration of L-Dopa in a repeat of the experiments described in this article increase the number of attempts at rare transition in order to increase the overall performance of the experiment?

….since an age-related decline in attention and executive function (eg. choice accuracy, increase in omissions, increased response latency) is reported due to decreased glutaminergic transmission in the medial prefrontal cortex (Guidi and colleagues), if the experiment is repeated with elderly subjects would the increased strengthening between the vmpfc and striatum still be observed?

….lesions of the vmpfc means that there is a decreased sensitivity to fairness and an increased likelihood of acceptance of unfair offers (Gu and colleagues). Would vmpfc lesions in an animal model cause a difference in vmpfc and striatum connectivity and increase the number of errors in a mouse model replica of the experiment?

…. it is known that cognitive training can increase attention and working memory. Would such training affect the performance of the subject in the experiment and would the effect on connectivity between the vmpfc and striatum be observed earlier?

 

Posted in decision-making, neuronal connectivity, prefrontal cortex, striatum, Uncategorized | Tagged , , ,

event related potentials show primary visual consciousness

Posted comment on ´Cortical Neural Synchronisation Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials` by C.Babiloni, N. Marzona, A. Soricelli, S. Cordone, J.C. Millan-Calenti, C. Del Percio and A. Bujan and published in Front.Hum. Neurosci. 30th June 2016, doi.org/10.3389/fnhum.2016.00310

SUMMARY

Babiloni and colleagues discuss in their article how primary visual consciousness (PVC) is linked to increased cortical neural synchronization in the case of three different types of visual stimuli. Their article begins with a general summary of views on consciousness and includes: a definition of PVC;  a description of the neural correlates where neuronal activity represents the mental representation of the visual features being experienced; the two opposing views of consciousness, that of ´globalism` and ´localization`; the binding problem of the conscious experience; and the link between brain rhythms and consciousness.

Babiloni and colleagues also describe in their article the limitations of some consciousness studies where only a few selected brain areas are investigated and the lack of technical capability to look at the real-time development of PVC from a temporal and spatial information perspective. This is why they used high resolution electroencephalography (EEG) and recorded event-related potentials (ERPs). ERPs represented here the coordinated neural activity of excitatory cortical pyramidal neurons and inhibitory interneurons in response to specific stimuli and Babiloni and colleagues developed a specific scientific program to investigate these in relation to PVC. They proposed that the ERP could synchronize millisecond by millisecond with the qualia of the PVC.

In their experiments, Babiloni and colleagues used three basic visual features (visuospatial, facial emotions and written words) and their stimulus paradigm was based on the following sequence of visual stimuli: the background masking stimulus (forward masking hiding the cue); the cue stimulus (the stimulus to self-report at the end of the trial); the background masking stimulus (backward masking hiding the cue); and  the target stimulus (the ´go` stimulus causing the hand motor response of pressing a mouse button). The durations of the cue stimuli were determined for each subject by a short preliminary test where he/she received the cue stimuli for different lengths of time on a computer monitor and then had to respond by pressing a computer button if the cue had been observed (ie. the correct ´seen` response). A verbal report followed after this hand response. The sequence of cue stimuli was planned to mix the cue stimuli with different durations in a random order to avoid the effect of learning. The ERP results were then analysed by a computer software program so that the computed percentages of ´seen` cue stimuli could be calculated and the cue stimulus duration of 50% ´seen` was taken as the test comparison point. In later experiments, EEG recordings were always carried out using this optimal duration time. The cue stimuli used by Babiloni and team were all visual based and the threshold times for the different experiments was varied, eg. 101ms in the visuospatial experiments, around 65 ms for the facial expression experiments and 37.2 ms for the written words experiments. They looked at the ERP peak latency and sources for the ´seen` and ´not seen` experiments and related to this either the presence or absence of PVC.

The results obtained showed that visual ERP showed a typical pattern independent of whether ´seen` or ´not seen`, but the brain area sources of firing were different. In the emotional face expressions experiment greater activity was observed in the parietal and frontal sources at about 180ms post stimulus, whereas in the written words/letters experiment higher activity occurred in the occipital, parietal and temporal areas at 230ms post stimulus. In the case of the visuospatial stimuli, higher activity was measured in the dorsal occipital and parietal areas later still at about 400ms post stimulus. The hypothesis advocated by the authors to explain their observations was that PVC is associated with greater cortical synchronicity of neuronal firing, but the sources were different and correlated to the cortical regions associated with PVC of that particular stimulus feature.

In the case of the results relating to ERP peak latency, no differences were found for the three different types of visual stimuli. Since a difference would indicate that there is specific timing of the neural correlates of the PVC for the features, the lack of difference indicated that there is no specific timing of neural correlates of the PVC. The authors therefore suggested similar timing of the cortical neural synchronization regardless of the PVC experienced.

The results of the visuospatial experiment showed a lack of latency in the three main components of the ERP (ie. the N1, P2 and P3) between the ´seen` and ´not seen` conditions. This indicated to the authors that timing and stages of cortical neural synchronization was the same whether PVC was present or not. However, a difference in reaction time of the physical response was observed with the response faster in the ´seen` trial. The authors explained this by suggesting enhanced information processing independent of PVC. Cortical neural synchronization also showed some difference in relation to PVC in the intraparietal P3 component peaking around 400 ms post-stimulus and this again was higher in the ´seen` trial compared to the ´not seen` trial. The sources appeared to be the extra-striate occipital and posterior parietal areas. Therefore, the authors suggested that the brain processes simple visuospatial stimuli with enhanced cortical neural synchronization around 400 ms post-stimulus in association with PVC. The effect was not related to the stimuli features themselves since the results of both trials were identical.

Therefore, Babiloni and colleagues concluded that in the case of visuospatial stimuli then the PVC and responses were linked to activity in the parietal and occipital areas which supported evidence from other research. The time of the ERP at 400ms confirmed that the PVC of visuospatial qualia occurs at the later stages of information processing although other experimenters had reported a P1 component at around 100ms or 120ms which they explained could have been spatial attentional processes instead. An experiment where subjects had to press on the opposite side of the screen to the presented cue also gave differences between ´seen` and ´not seen` trials in the P3 component at between 100ms and 400ms. The ERP activity in this case was located to the occipital, parietal and prefrontal cortical areas.

Babiloni and colleagues also discussed in their article cases of visuospatial PVC in subjects suffering from visuospatial neglect and visual extinction (ie. where there are deficits in spatial awareness for stimuli on the opposite side to the brain lesion, but the information of the extinguished stimuli is still processed). The authors found that reaction times were affected and that the extinguished stimuli were still processed by the same occipital and parietal areas of the dorsal stream exhibiting the same enhanced activity as in the ´seen` trials of healthy subjects. The authors said their findings that occipital and parietal ERP components were higher in ´seen` than ´not seen` trials and in the P3 supported work by others.

In the experiments on PVC and facial expressions, three emotional conditions (neutral,  happy and sad) were studied with tests based on emoticon recognition. The results obtained gave post-stimulus ERP waveforms with the highest amplitudes at the parietal midline electrodes and consisted of four main components, eg. the N100, N170, P200, and P300. No statistical difference was observed in latency between the ´seen` and ´not seen` trials. Therefore, the timing of the cortical neural synchronization was the same regardless of the presence or absence of PVC. The reaction time in this case was faster for the happy faces in the ´seen` trial and so the authors concluded that PVC is associated with enhanced information processing. Some differences in ERP components were observed between the two trials eg.the ERP component at the parietal N170 component peak at 180-200ms was higher. Therefore, Babiloni and colleagues concluded that the brain processes emotional face expressions with enhanced cortical neural synchronization around 200ms post-stimulus possibly in association with PVC.  The N170 peak latency occurred earlier with facial expression than with visuospatial information and the authors explained this by facial expression being processed faster as a result of biological and social salience. Source analysis showed that the N170 component had higher activity in the prefrontal, premotor, and posterior parietal areas for the sad face. Therefore, the authors concluded that the brain processes emotional face expressions giving PVC at an early 200ms post stimulus and enhanced cortical neural synchronization is observed in the parietal, temporal and frontal brain areas.

For the experiments on written words, the experimenters used 2 Italian words and 2 English words. ERP waveforms were found to be at their highest activity in the parietal and temporal electrodes and the ERP waveforms observed had 4 main components, the P1, N1, P2 and P3. Again there was no latency between peaks of the ´seen` and ´not seen` trials. Therefore, like the other visual stimuli neural cortical synchronization exhibits the same timing regardless of the presence or not of PVC. Also again reaction time was quicker in the ´seen` trials indicating that PVC is associated with enhanced information processing of visual stimuli. However, in this case the N1 peaked at 230 ms post-stimulus which was the one component with the highest difference between the ´seen` and ´not seen` trials and suggesting that the brain processes words around 230ms post-stimulus. Source analysis showed that brain activity associated with the N1 component was higher in the left parietal, occipital and temporal areas demonstrating that enhanced cortical neural synchronization occurs in the processing of words in the left dorsal and left ventral streams formed by the occipital, parietal and temporal areas. Different networks for the PVC for visuospatial information and facial expressions was hence, observed.

The authors discussed in their article stimulus expectancy in relation to written words. The ERP difference was observed in P3 when there was little stimulus expectancy and in P2 when expectancy was high. The P2 amplitude decreased as awareness of the stimulus increased. These observations were not expected, but the authors explained that the P2 component reflects the comparison of sensory inputs and stored memory. Therefore, a high amplitude of P2 is not observed due to a mismatch between high expectancy of the stimulus appearance and the missed stimulus detection. This effect was not observed in the written words experiment probably due to the negligible effect of learning (the experiment was so designed that learning was not an influence), stimulus expectancy and cognitive load (there was no fatigue and physical stimulus features remained fixed during the whole EEG session).

Therefore, in conclusion, Babiloni and colleagues state in the article that there are no differences in the ERP peak latencies between the ´seen` and ´not seen` trials which suggests that cortical neural synchronization timing is the same regardless of PVC. Analysis of the source of firing for the ERP show however differences between ´seen` and ´not seen` trials. For visuospatial stimuli, the PVC was related to higher activity in the dorsal occipital and parietal sources at about 400ms post-stimulus. For the emotional face expressions, greater activity was reported in the parietal and frontal sources at about 180 ms post-stimulus and for the written letters, there was higher activity in the left occipital, parietal, and temporal sources at about 230 ms post-stimulus. Therefore, Babiloni and colleagues suggested that PVC is associated with an increased cortical neural synchronization having entirely different spatiotemporal characteristics for different features of the visual stimuli studied ie. visuospatial, emotional facial expression and written words and letters and possibly, the corresponding qualia. Brain areas activated were specific with the dorsal visual stream synchronized in association with the PVC of visuospatial and emotional facial expression and both dorsal and ventral visual streams synchronized with the PVC of written words. The authors concluded that their cortical neural synchronization observations support the ´localisationist` theories of consciousness and that the cortical neural synchronization within specialized networks leads to PVC by what they termed a ´multidimensional palette` of each given feature and quale. Each element was shown to have its own specific timing. They also state that the PVC should not be considered as an instantaneous mental experience to be related to one peak of local neural responses, but should be regarded as a progressive ´build up` phenomenon. This explains PVC`s emergence over a period of time and the temporal synchronization of many different brain regions. The authors also state that the reliability of data from experiments could be subject to unknown factors and therefore, it would be better to use another procedure in for example the rating of the visibility of stimuli. The low spatial resolution of LORETA was also given as a limitation as well as that the analyses are limited to one process of consciousness, ie. Visual stimuli recognition. Babiloni and colleagues end their article by indicating future research areas which could include the influence of top-down processing on the PVC and ERPs, or the use of tDCS.

COMMENT

This article is interesting because it continues the exploration of the neural correlates of the conscious experience. The results confirm that the various elements of an event contribute to the overall conscious experience of that event with different post-stimulus timings. From an earlier article by Fairhill, Albi and Melcher discussed in this Blog in March 2015 the entire sensory integration of the final conscious experience relating to visual sequences appears to take place within 2-3 seconds of the stimulus and here Babiloni and colleagues show that individual visual processes, ie. those relating to facial expressions, written language and visuospatial events achieve awareness at different times within that overall period.

In the set of experiments described in this article visual processing is investigated until the point of conscious awareness at the level of primary visual consciousness (PVC) occurs. This means that the higher order consciousness associated with language, applied reasoning and decision-making is not considered. The level of Self relating to this lower level conscious experience in these experiments according to the definition of Damasio is that of photo Self and core Self, but not autobiographical Self since memories, views or reasoning are not needed for the task at hand. (However, it could be said that autobiographical Self actually does play a role since assessment of the incoming information is based on the memories stored of previous experiences. The Self allows perception to take place, but this analysis is carried out subconsciously and therefore, autobiographical Self in its truest sense of conscious application of personal knowledge is not involved.)

In their experiments on the timing of conscious awareness, Babiloni and colleagues measured the membrane potentials for three types of visual stimulus studied from the time of stimulus to the time of conscious awareness. Conscious awareness was assessed through a physical action, ie by the pressing of a button and by the common test for consciousness that of verbal report. Therefore, the neuronal firing occurring on stimulus does so unconsciously (ie termed here as ´not seen` by the subject) until the point when the stimulus evokes awareness (ie termed here ´seen` by the subject). Analysis of the ERPs recorded gave Babiloni and colleagues an average ERP when the stimulus was 50% of the time ´seen` by the subject. Since it is said that there are two types of information processing in the brain (fast, which is automatic, inflexible, effortless and dependent on context and slow, which is effortful, controlled flexible, requiring working memory and independent of context), the nature of the experiments means that fast unconscious processing occurs until the point of the ERP peak when PVC is said to be achieved. Therefore, Cleerman`s view of automatic behaviour in his Radical Plasticity Theory should be re-considered. Cleerman said that automatic behaviour is not truly unconscious behaviour rather behaviour where awareness is optional. Babiloni and colleagues experiments show that behaviour ie. the pressing of the button in this case is not instigated until PVC occurs. Therefore, unconscious processing must reach a certain level before the action can occur so that awareness is not ´optional` until after this point. This fits in with Franklin and Baars 2010 ´preconscious` and ´never conscious` descriptions of events. In this case, the time before the ERP represents the ´preconscious` processing of the stimulus and these reach conscious awareness at the point of PVC. However, there are definitely event characteristics that never reach consciousness (eg. the sound of the computer fan or the chair creaking) since the task demands that attention is focused on the stimuli required to complete the test. Therefore, these characteristics although processed to some extent never reach awareness and eventually fade.

The experimental results are said to support the´localisationist` hypotheses of consciousness rather than the ´globalist` ones. The authors explain that the  ´globalist` hypotheses like Baars original Global Workspace Theory then would provide no difference in timing with all features reaching conscious awareness at same time. They state that the fact that different times are observed for the three different types of visual stimuli means that the ´localisationist` theories are more likely to be appropriate. The theories given as examples are the Reentrant Dynamic Core Theory of Edelman and Tononi and Zeki`s Microconsciousness Theory and these indicate that consciousness arises from multiple neuronal groups firing with the mechanism of re-entry amongst distinct and distant neuron groups within the dynamic core of the thalamo-cortical connections binding the features together. The observations made here support the view of Marcel and slippage which he related to blink and tap elements of a sensory experience being separated. In the experiments described in this article firing of visual pathways (the dorsal WHAT and ventral WHERE pathways) represent the visual input, maintenance and recognition. The resulting PVC is seen through the brain areas firing at that time and these represent the areas critical for consciousness (Bor and Seth hypothesis) such as the cingulate cortex, prefrontal cortex and parietal cortex and the first modality areas such as the visual cortex V1 and secondly, the prefrontal parietal network (PPN) responsible for attention, working memory and the central executive. This is supported by work by Nog who said that visuoperceptual consciousness demands local activity in the visual cortex and global frontal parietal workspace activity with a 300 ms delay and strong temporal firing.

The firing observed by Babiloni and colleagues in their experiments is dependent on the type of stimuli used. Emotional face expression provoked high activity in the parietal and frontal brain areas, written letters in the occipital, parietal and temporal areas and visuospatial stimuli in the dorsal, occipital, and parietal areas. These areas represent different cognitive demands  ie visual and emotional memory for emotional face expression; visual pathways plus pathways for language and meaning for written letters and visual pathways and pathways for object recognition and location assessment for visuospatial stimuli. In all cases, the firing pathways associated with attention are activated. Although Bor and Seth maintain that attention is not consciousness and that the Baars original Global Workspace Theory did not address the matter of attention, attention plays an important role in conscious awareness. It is highly selective for task relevant visual events (Jacobs said attention in consciousness is top-down modulation to stop incoming visual information via inhibition at the early visual cortex level) and is sensitive to temporal order (Eimer) and is an important factor in any sensory input. That is why it is included in the later ´globalist` models and features in ´localisationist` consciousness models. Therefore, the experiments of Babiloni and colleagues explore the firing and connectivity of brain areas relating to the input and processing of visual stimuli with relation to time and the emergence of conscious awareness.

Three things can be said about the timing of the emergence of the conscious awareness observed by Babiloni and colleagues. The first concerns the nature of sensory experience. Fairhill, Albi and Melcher found that sensory information integrated over 2-3 seconds post-stimulus to form the visual experience. This was described by Zmigod as the temporal binding window (TBW). In this article, Babiloni and colleagues found that the three visual capabilities they examined reached consciousness at different times, but within this 2-3 second window. Owing to the types investigated, it is unlikely that the three capabilities are experienced within one event eg. facial expression and written words cannot be observed together and therefore, each stimulus has to be treated separately. Therefore, the unity of consciousness and how elements of sensory experiences come together cannot be demonstrated or investigated here. By changing and expanding the experiment eg. facial expression plus spoken word from a different location the unity of the conscious awareness could be looked at. It could be assumed that the presentation of the combined sensory event would lead to firing of the appropriate pathways and the emergence of conscious awareness of one ´draft` of the event if Dennett`s Multiple Draft theory of consciousness is the accepted tenet. The ´draft` experienced is the one fleeting version of the events occurring at the time and is reliant on the sensory neuronal cell firing and assembly formation and brain rhythms adapt accordingly. Alpha rhythms through the post-parietal and lateral occipital areas are required for event characteristic maintenance and gamma rhythms for assembly formation, feature and binding and holding. These would demonstrate the unity of the conscious experience even though the different features show Marcel`s slippage.

The purity or simplicity of the nature of the characteristics and the tasks demanded in the experiments also mean that there is no ´filling in` of event features occurring. For example, the subject is asked to judge whether a face is happy or sad. This is not open to interpretation since most people can judge such clear basic emotions without question or reflection. No assessment of questionable facial expressions such is showing regret or those requiring subjective opinions are asked of the subject and therefore, the exploration of the extent of the quale of the sensory event is relatively basic. Neither can the effect of processing on the event characteristics be explored. A number of researchers (eg. Windey, Gevers and Cleermans) support the Level of Processing Hypothesis which states that the transition of unconscious to conscious perception is influenced by the level of processing imposed by the task requirements. Subthreshold stimuli have bottom-up processing and a forward sweep of firing terminating in the somatosensory cortex preventing access to the conscious experience. This interruption is due to a predominance of inhibitory processing in this area. The increase in alpha rhythms and a disconnection from the somatosensory cortex area from the frontoparietal area are likely to correlate to the increased perception and are thought to serve as a gating mechanism for access to the conscious experience. In Babiloni and colleagues` experiments a high level of processing is not required and the experiment ends at the point of PVC. The forward sweep of the neuronal firing occurs in the areas observed by the researchers for each type of stimuli and these continue until the time when the ERP is measured. According to the Level of Processing Theory, the somatosensory cortex area inhibits access to the frontal parietal cortex area until the point when PVC occurs and this is likely to be related to the quantity of firing cells, not the quality ie. the threshold of conscious awareness is reached (ie quantified as phi). The speed at which this occurs may reflect the synchronicity of the relevant areas and the quantity of firing. For example, the inclusion of emotions as in emotional facial recognition means that the firing of the cerebellum and basal ganglia areas join the ´dynamic core` thalamo-cortical areas. The question as to why the conscious awareness of the written word comes before visuospatial information may be that visual and spatial firing and processing may require more coordinating pathways and information processing before phi is achieved.

The second observation about timing relates to the advantageous effect of priming of the subject due to the repeated experimental conditions. Priming through knowledge of the experiment means that the ERPs observed may be faster than normally expected since the subject knows what he is looking for and repetition gives him the practice. Prediction also leads to visual events achieving access to consciousness faster (Acer) and this too is reflected here by the repetition of the experimental condition. The effect of priming can be seen through the lack of latency in the three main components of the ERP (ie the N1, P2 and P3) between the ´seen` and ´not seen` trials. This means that there is the same timing and stages of cortical neural synchronization whether PVC is achieved or not. We know that the first 270 ms of neuronal activity for any stimulus is the same independent of later consciousness state, and effect increased to 750 ms for children. However, there is a difference in reaction time of the physical response and this is faster in the ´seen` than ´not seen` trials. The authors of the article explain this by enhanced information processing independent of conscious awareness.

The third point relating to timing is however aided by the experimental set-up. It has been reported that it is impossible to report the precise time of the conscious experience (Gray) and on looking back the timing always appears to be wrong with timing later than when it actually occurs (Paulignan). Therefore, the experimental set-up used by Babiloni and colleagues in their experiments which included the physical action of manually pushing a button and verbal reporting means that the time of conscious awareness is more accurate than those experiments relying on verbal report only. Through conditioning ie. repetition of the experiment, the physical actions become automatic and this unconscious processing means that the physical action can be started before the language capability kicks in. However, it does introduce an interesting question as to whether the unconscious processing which sets off the physical action simultaneously initiates the verbal response or whether the initiation of the verbal response actually is a result of subconscious beginnings of the hand movement.

Therefore, to summarise, Babiloni and colleagues experiments support the view that the conscious ´draft` for a visual event emerges over a period of time relying on firing of appropriate brain areas and binding. The speed at which this PVC occurs is dependent on the elements that make up the experience and also to some extent the subject`s own capabilities. The continuing development of faster and more accurate equipment and computer capability can only help the research into this very important area.

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

….as stated above Babiloni and colleagues` experiments involve simple visual stimuli and therefore, would the use of more complicated stimuli using a combination of senses, or  using stimuli that provoke errors by including irrelevant material shift the awareness level from PVC to higher order consciousness so that a more in-depth observation of brain area firing and ERPs measurements can be made?

….can we assume that the use of more emotionally relevant material would shift the consciousness level from PVC to the higher order of consciousness and involve the autobiographical Self and hence a comparison of the two would confirm where conscious awareness of  more complicated emotional responses is?

…..the administration of ketamine increases the level of irrelevant information inputted because of the subject`s inability to ignore it. Would such an administration increase the level of errors in pushing the button and slow the verbal reporting in a repeat of Babiloni and colleagues` experiments?

…..since people with split personalities are reported to have two conscious awareness  with both hemispheres having special awareness (eg. right side – facial recognition, left side – language – Gazzaniga), what would happen to the ERPs if Babiloni and colleagues` experiments were repeated?

…..children are said to demonstrate a 750 ms delay in conscious awareness. Would they also demonstrate the same differences in ERP in the order of the stimuli? (The experiments may need to be adapted to take in the age of the child.)

Posted in consciousness, neuronal firing, Uncategorized, visual input | Tagged , ,

adenosine receptors and neuronal firing

Posted comment on ´Adenosine receptors: expression, function and regulation` by S. Sheth, R. Brito, D. Mukherjea, L.P: Rybak and V. Ramkumar and published in International Journal of Molecular Science 2014 15(2) 2024-2052 doi.org/10.3390/ijms15022024 pmcid:pmc3958836

SUMMARY

Adenosine receptors (ARs) are G-protein coupled receptors (GPCR) that mediate the actions of the natural cellular modulator, adenosine. Although Sheth and colleagues outline the properties of the receptors occurring in the peripheral system as well, this summary will only concentrate on the information about those receptors occurring in the brain or in brain cell cultures or slices. In their article, Sheth and colleagues describe the 4 subtypes of adenosine receptors found in the brain and detail their different localizations. The subtype A1R, which exhibits a high affinity for the natural agonist adenosine, is widely distributed on neurons in the cortical, hippocampal and cerebellar areas and can also be found on the glial cell populations such as astrocytes, oligodendrocytes and microglia. In neurons, subtype A1R is localized to the synaptic regions where it modulates the release of neurotransmitters such as glutamate, acetylcholine, serotonin and GABA. The other subtype also showing a high affinity for adenosine is the A2AR. Sheth and colleagues describe this subtype as having a lower distribution than the A1R and as being only localized in the striatal and olfactory bulb areas (on neurons and on glial cells such as microglia and oligodendrocytes and possibly astrocytes) and the hippocampus (at presynaptic areas). The adenosine receptor subtype here modulates the release of the neurotransmitters glutamate, acetylcholine, GABA and noradrenaline. The two other subtypes, A2BR and A3R have a lower affinity for adenosine and the expression of the A2BR is shown to be at low levels on neuronal and glial cells in a wider selection of areas such as the cortex, hippocampus, cerebellum and striatum.

Sheth and colleagues describe the traditional classification of the subtypes of these G-protein coupled receptors by their differing coupling to the adenylyl cyclase (AC) enzyme at the membrane surface. The A1R and A3R are coupled to inhibitory G-proteins (Gi) hence agonist activation leads to a decrease in cyclic adenosine monophosphate (cAMP) levels in the cells. However, A2AR and A2BR are coupled to stimulatory G-proteins (Gs) and hence, agonist activation of these subtypes leads to an increase in cAMP resulting in protein kinase A (PKA) activation and the series of secondary effects attributed to a raised cAMP level. The differing levels of receptors in the brain areas mean that the effect of adenosine can be either stimulatory or inhibitory dependent on the type of receptor present.

Another property of the receptor leading to distinguishing the different subtype populations is, according to Sheth and colleagues, how the receptor population desensitizes on prolonged agonist exposure. In their article, the authors describe a general desensitization mechanism typical for G-protein coupled receptors. Desensitisation in this case involves the phosphorylation of the receptors by G-protein coupled receptor kinases (GPK) which leads to preferential binding of arrestin molecules. This leads to uncoupling of the receptor from the G-protein and an internalization of the arrestin-receptor complex by a clathrin-coated pit dependent endocytosis process. Within the vesicle the receptor undergoes a dephosphorylation process and is re-inserted into the cell membrane to restore agonist sensitivity. In the case of prolonged agonist activation, the internalized receptors are transferred to lysosomes and are degraded thus resulting in a down-regulation of the receptor at the cell surface and a general decreased agonist sensitivity. According to Sheth and colleagues, the adenosine receptor subtypes demonstrate different desensitization process characteristics. In the case of the A1R, this receptor subtype is phosphorylated and internalized slowly (has a half-life of several hours), whereas the A2AR and A2BR undergo the same mechanism, but more rapidly (about an hour) and A3R within minutes. Prolonged agonist at the A1R leads to increased AC activity and a reported desensitization of the insulin dependent glucose transport system which may explain some neuropathological effects seen under these conditions. A high increase in mRNA for the A1R was also observed indicating to the authors that the arrestin binding as a result of the prolonged exposure primes the cell for recovery once the exposure is stopped.

In the case of the A2AR, the authors describe the desensitization process as dependent on the length of time the cell is exposed to agonists. In short-term exposure, there is rapid desensitization of the A2AR-stimulated AC activity associated with decreased receptor-Gs coupling and agonist stimulated phosphorylation of the receptor itself. However, longer exposure to the agonist causes a down-regulation of the total receptor number and an up-regulation of alpha subunits of the Gi protein. The authors describe an effective G-protein coupled receptor kinase subtype (GPK2) and found that the mechanism is inhibited by Tumour Necrosis Factor type alpha (TNF-alpha), a pro-inflammatory cytokine. They explained this observation by saying that there is ´novel cross-talk` between the TNK-alpha receptor and the A2AR. It was found that treatment with TNF-alpha led to reduced translocation of GPK2 to the plasma membrane and reduced GPK2 association with the plasma membrane, thus preventing A2AR activity. The A2BR was found to demonstrate the same desensitization and internalization mechanisms as for A2AR (ie. GPK2 and arrestin dependent). Here, the TNF-alpha reduced the agonist-dependent receptor phosphorylation and attenuated the agonist-mediated A2BR desensitization. The authors explained that this action may contribute to the excessive astrocytic activation that occurs in neurodegenerative diseases. The subtype A3R was found by the authors to undergo the same processes as the other subtypes as a result of long-term agonist exposure.

Another property of ARs described by the authors in their article is their ability to form homodimers (ie. with each other) and heterodimers with different adenosine subtypes or receptors of other neurotransmitters. Sheth and colleagues describe the existence of A1R homodimers in the cortex, hippocampal pyramidal cells and cerebellar Purkinje cells. They also said that homodimers present in the cortex could explain the diphasic nature of the effect of small and large doses of caffeine on motor activity. The authors also describe the situations where ARs can form heterodimers with other subtypes. In the case of the A1R/A2AR heterodimer, Sheth and colleagues found that activation of the A2AR by its specific agonist reduced the affinity of the participating partner in the heterodimer ie A1AR for its specific agonist. This was observed by looking at the intracellular calcium ion levels (reduced on pretreatment with A2AR agonist), abolishment of K+ evoked glutamate release and increased GABA uptake. However, this effect was found not to be reciprocal. A cross antagonism and/or physical interaction between the two adenosine receptor subtypes was demonstrated by the A1AR effect being blocked not only by the selective antagonist DPCPX for the A1AR, but also by the SCH 58261 (selective A2AR antagonist) and the effect of A2AR on GABA uptake was blocked by both SCH 58261 and DPCPX. The receptors were shown to be internalized together when exposed to both sets of agonists. The differential responses of the A1R and A2AR agonists on GABA uptake involved the activation of the Gi and Gs proteins respectively and the authors concluded that the presence of these A1R-A2AR heterodimers could increase the complexity by which the two receptors regulate neuronal action at the cell surface. The heterodimer complex of A2AR-A2BR was also found and appears to exist for receptor trafficking and hence, have an effect on regulation of cellular function.

The authors also described in their article the cases of adenosine receptor heterodimers formed with other neurotransmitter receptors. An interaction of adenosine receptors with ATP receptors (classed P2X, P2Y, P2U and P2Z) was demonstrated with the A1AR subtype.  P type receptors are also G-protein coupled and P2Y1 can form a heterodimer with A1AR. It was found co-localised in the cell body and dendritic regions in rat cortical neurons, but in the soma and dendrites of slices of cortical, hippocampal and cerebellar neurons. The heterodimer was found to be less effective in inhibiting cAMP formation than with the native A1AR alone.

Other heterodimers formed between adenosine receptors and receptors of other neurotransmitters were found in the striatum. In this area there is also an interaction between A2AR and CB1 receptors (cannabinoid and endocannabinoid as agonists, coupled to Gi proteins). This type of heterodimer when activated by a CB1 agonist leads to reduced agonist induced cAMP accumulation. The effective coupling of the CB1 receptor to the Gi protein requires prior or simultaneous activation of the A2AR. Interaction between ARs and dopamine receptors was also found in this brain area, eg a A2AR-D2R in striatal cultures and further dopamine receptor heterodimers were found in fibroblast cultures and cortical neurons such as in the nucleus accumbens (both A1R-D1R). In the case of the former, it was found that the receptors of this particular type of heterodimer internalized together on activation – an action dependent on the presence of beta-arrestin2 and the gene Akt. Pre-administration with cocaine (an activator of D1R) led to the dissolution of the heterodimeric complex. The heterodimer could also associate with CB1 R to form a trimeric complex.

The authors suggested that the role of adenosine receptors in neuropathological diseases such as Parkinson`s disease and Huntingdon`s Chorea could be attributed to the presence of the homodimeric and heterodimeric forms of the receptor. Another factor contributing to their neuropathological influence is their association with transcription factors such as nuclear factors which regulate the expression of proteins in the neurons. The authors proposed that certain forms of the adenosine receptor complex could act as sensors of cellular oxidative stress which is known to be indicated by the activity certain transcription factors such as NF-kB. This particular transcription factor regulates the expression of the ARs particularly A1R and A2AR, ie. those that have a high affinity for the natural agonist. A1 R was found to be positively regulated by oxidative stress brought about by the excessive level of reactive oxygen species (ROS) within the cell.  Treatment of the cell with the nerve growth factor (and others) led to a three-fold decrease in A2AR expression within 3 days and this observation was explained by the location of the NF-kB consensus sites on the A2AR gene promotor. In the case of the A1R then NF-kB not only regulated the expression of the receptor, but also caused a deficit in A1R/Gi protein – an observation associated with increased neuronal apoptosis. The authors also described the cellular situation in hypoxia regarding adenosine activity. In this case, hypoxia was found to be associated with increased adenosine levels and an up-regulation of the A2AR, but also a desensitisation of A1R which was linked to a decreased density of the A1R (an observation under dispute).

Sheth and colleagues also discussed the involvement of adenosine receptors and some normal and abnormal physiological processes such as sleep, the development of certain cancers and in the protection against hearing loss. Adenosine has been shown to be involved in the sleep-wake cycle in differing ways. Adenosine is known to promote sleep, but an increase in the forebrain level is linked to prolonged wakefulness. Increasing adenosine via decreasing adenosine deaminase function leads to a deeper sleep and higher slow-wave activity within sleep. Inhibition of the adenosine intracellular uptake by inhibiting the transporter protein also leads to symptoms similar to sleep deprivation. The actions of adenosine in sleep were attributed to the A1R where A1R agonist administration leads to increased sleep and antagonists to increased wakefulness. Sleep deprivation was shown to be linked to an increase in A1R density in the basal forebrain which the authors suggested could be responsible for the subsequent sleep re-bound. The increased levels of adenosine observed in wakefulness however were attributed to the astrocytes being the source of the agonist. Adenosine in this case was released by a SNARE-dependent exocytosis. An interesting observation according to the authors was that experiments using a dominant negative SNARE protein led to a lower level of memory deficit induced by sleep deprivation compared to controls. This suggested to them that adenosine plays a role in memory deficits observed with sleep deprivation. The conflicting roles of adenosine in sleep is further shown by the studies using A1R and A2AR knock-out mice who show that both types of receptor are involved in both mediating the sleep suppressing role and arousal action of caffeine. Further studies indicate that A1R expression and normal sleep patterns should be regarded as dissociated and that they mediate a physiological drive following sleep deprivation.

The role of adenosine receptors and hearing loss prove more conclusive of an adenosine role. The authors explained that the cochlea expresses 3 subtypes of ARs in different cells and that A1R confers protection against hearing loss whereas A2AR activation exacerbates cisplatin induced ototoxicity. Both agonists of the A1R and antagonists of the A2AR are used to treat ototoxicity. The mechanism by which adenosine acts involves a reduction in adenosine levels and adenosine uptake inhibitors, while theophylline and adenosine deaminase is increased. Application of adenosine to inner hair cells causes an increase in intracellular calcium ion levels demonstrating an A1R link. Oxidative stress by activation of NF-kB was shown to lead to enhanced transcriptional activity of the A1R gene. The authors suggested that feedback regulation could increase a cyto-protective activity of the A1R in response to oxidative stress caused by noise exposure or therapeutic agents. ROS was also found to increase inflammatory processes in the cochlea by activating NF-kB. The oxidative stress in the cochlea was said to contribute to the inflammatory process by activating signal transducers and the activator of transcription 1 (STAT1) transcription factor which can couple the activation of transient receptor potential vanilloid receptor (TRPV)-1 to the induction of inflammation. Down-regulation of STAT1 ameliorates cisplatin-induced ototoxicity in rats and therefore, it was suggested that the otoprotective actions of the A1R against cisplatin ototoxicity possibly involves inhibition of both NF-kB and STAT1 transcription factors.

Sheth and colleagues also described the role of adenosine in some cancers. Although these are not related to brain function, they will form part of this summary because of their importance and link to general adenosine functioning in the brain. Studies on the link between adenosine and cancer show differing results, eg. debatable differences are observed in the expression and function of A1R in breast cancer, with high A1R expression being seen in human colorectal adenocarcinoma and human leukemia. The source of the increased expression was said to be activated astrocytes and microglia. However, other studies found that A1R demonstrates anti-tumour effects, eg. A1R activation increases apoptosis by activating caspases in human colon cancer cells. Over-expression of the A2AR was observed in several cancer cell lines. It was found to stimulate cell proliferation, migration and tube formation, but again was also found to inhibit tumour growth and angiogenesis in other studies by activating caspases to induce apoptosis. Reports of the action of A2BR in cancer appear more consistent with it being pro-angiogenic with receptor activation leading to neovascularization through the production of vascular endothelial growth factor (VEGF) and the release of the pro-angiogenic growth factor, interleukin-8. The receptor A3AR like the A1R also demonstrates differing observations with over-expression being shown in different types of cancer cells, eg. prostate and breast carcinoma, but again demonstrating anti-tumour actions in others, eg. growth of melanomas and prostate cancer cells inhibited by A3AR agonist administration. This activity is explained by an increase in the natural killer (NK) cell activity which promotes killing of the tumour cells. In prostate cancer cells, activation of A3AR leads to a suppression of high levels of ROS generated by these cells – an action involving the inhibition of NADPH oxidases.

Sheth and colleagues concluded their article by saying that because of its demonstrated modulatory cellular roles a better understanding of the physiology and functioning of adenosine and adenosine receptors is necessary. Such knowledge could aid the development of new therapies for treatment of some neuropathological diseases.

COMMENT

This article is interesting because it again describes how a little known molecule can have a significant effect on neuronal functioning and synaptic performance. Neuroscience has spent many years concentrating on the popular neurotransmitters such as acetylcholine and glutamate, but as more and more is known then it is becoming clearer that the absolute workings observed at the neuronal and synaptic level are extremely complex and consist of thousands of different elements all of which have their own structure, mechanisms and influencing factors. This article attempts to describe one such small element and here we discuss how this element can influence the neuronal and synaptic workings as a whole. Although the authors in this article give evidence of adenosine`s roles in the human body we will discuss in this blog comment only those observations relating to the brain and here, the complexity of the effect of adenosine is dependent on different mechanisms.

One of the fundamental differences in its activity is adenosine`s link to the level of cAMP within the cell. Such a link is important because of the well-known prolific effects of cAMP as a secondary messenger. A rise in cellular cAMP can lead to protein kinase activation resulting in the general effects of phosphorylation of serine/threonine residues and effects on for example, glycogen metabolism, stimulation of the expression of specific genes (relating to phosphorylation of the transcriptional activitator cAMP-response element binding protein, CREB) and the closing of potassium channels.. In the case of adenosine, this neuromodulation takes the form of a rise in cAMP which is linked to neurotransmitter release. Therefore, the cAMP response is dependent on the structure of the adenosine receptor itself. Adenosine receptors are known to be G-protein receptors and therefore, the observation of adenosine effects on cAMP means that these G-proteins are linked to adenylyl cyclase function. In the case of adenosine, certain subtypes of receptor (eg. A2AR and A2BR) are linked to stimulatory G-proteins (Gs) and result in excitatory responses and increased cellular cAMP. However, other subtypes (A1R and A3R) are linked to inhibitory G-proteins (Gi) and agonist activation here leads to synaptic inhibition and a decrease in cAMP. This decrease in cAMP means that neurotransmitter release is inhibited or lower. Therefore, the effect of the agonist adenosine in each brain area eg. on neurotransmitter release can be dependent on the type and number of adenosine receptor subtypes present on the neurons or glial cells within that area. And this is observed since certain areas such as the cortex, hippocampus and cerebellum have high distribution of the inhibitory A1R and other areas eg. the striatum and olfactory bulb a high distribution of the excitatory A2AR.

Since G-proteins can also be linked to ion gated channels, the excitatory or inhibitory modulating effect of adenosine can depend on the link between receptor subtype present and associated ion channel. Adenosine receptors have been reported to be linked to potassium ion and calcium ion channels and hence, stimulatory action (ie. through the A2AR and A2BR) is likely to be linked to potassium ion channel shut-down or non-functioning and the inhibitory action (ie. through the A1R and A3R) with potassium ion channel opening. Again the overall effect of adenosine at the neuron or glial cell will depend on the subtype of receptor present and its attached G-protein.

The level of naturally occurring adenosine can also play a part in the effect of adenosine on cellular function and this can be seen by looking at the affinity of adenosine for each receptor subtype. Both A1R and A2AR exhibit high affinity for the agonist which means that lower levels of agonist are necessary for action to occur than the other two subtypes (A2BR and A3R). Therefore, this means that one subtype needs a low level of agonist for its inhibitory effect (A1R) and the other a low level of agonist for its excitatory effect (A2AR). The subtypes A2BR and A3R both demonstrate low affinity for the agonist and therefore, need to be in higher cellular levels for an effect on the cAMP action to occur. Therefore, not only does location and distribution of the receptor subtypes matter, but also the level of naturally occurring agonist.

Another factor that can affect the action of the adenosine receptor subtypes is their ability to interact with other receptors (either adenosine receptors itself or receptors of other neurotransmitters) to form monodimers, homodimers or heterodimers. Since each form has different characteristics, the level of each within a brain area or even more specifically the neuronal synapse can lead to a different action and sensitivity to adenosine. For example, the heterodimeric A1R-A2AR responds to A2AR activation by decreasing the affinity of adenosine for the A1R, although the effect is not reciprocal. This means that the overall effect of a high level of adenosine is excitatory – the presence of the A1R has been essentially ´neutered`. The heterodimer forms with A2AR and CB1R (Gs and Gi proteins) leads to decreased cAMP production (ie. the excitatory effect of  the A2AR is essentially ´neutered`) and A2AR with DA receptors with an effect linked to Parkinson`s disease. The effect of these homodimeric and heterodimeric forms can be explained by, for example, the changes to the phospholipid membrane fluidity affecting the binding and action of its components and the influences of the interaction of the two receptors on the quartenary protein structures of the ´complex` that could favour one binding over another.

Therefore, bearing in mind the structure of the adenosine receptor and its affinity for agonist we can see that adenosine can have an excitatory or inhibitory effect at the cell level. In the case of the excitatory effect, adenosine is likely to exert its action through the high affinity receptor, A2AR. One possible version of the mechanism employed here is that both the neuronal synaptic receptors and the astrocytes are involved since both are present and contain functional AR. The neuronal action potential leads to increased calcium ion concentration as normal and a release of synthesised adenosine (or ATP) either via adenosine transporters (passive or Na+ dependent – a mechanism that fits in with sodium ion changes occurring with depolarisation) or from the attachment of vesicles containing ATP and other neurotransmitters in the normal exocytotic mechanisms associated with the action potential and neurotransmitter release. Since extracellular adenosine is related to intracellular adenosine and ATP concentration, therefore its release reflects the metabolic demand of cell.

In this version of neuronal excitation, the released adenosine (or ATP) then binds to either presynaptic neuronal A2ARs or astroglial A2ARs, but in both cases the binding leads to the activation of G-protein related adenylyl cyclase (Gs protein) and cAMP production and increased neurotransmitter release. In the case of presynaptic receptors, the raised cAMP produces the same effects as other neurotransmitters (eg. increased neurotransmitter release) so that the released neurotransmitter can then bind to the post-synaptic receptors along with the neurotransmitter released directly from the action of depolarisation. Neurotransmitters can also bind to neurotransmitter receptors on astroglial cells  leading to gliotransmission and neurotransmitter release that can subsequently bind post-synaptically. The effects can be antagonised by the action of caffeine and theophylline which are both A2AR dependent. Hence, the excitatory response of such a set of neurons is enhanced by the presence of adenosine since the effect does not directly cause nerve transmission. Therefore, the mechanism is ideal for areas where frequency of firing is not enough to cause transmission of the signal directly.

 

However, adenosine can also have an inhibitory effect. This effect is elicited through the A1R, where the adenylyl cyclase effect is inhibitory due to the presence of the Gi protein. In this case, the mechanism of adenosine action can be hypothesised as beginning just like in the case of its excitatory action with the production of adenosine and its release via transporters or vesicle endocytosis. However, in the case of adenosine having an inhibitory effect, the adenosine released binds to high affinity A1R on presynaptic neurons or astroglial cells. In the case of the presynaptic binding, ion gated channels could be opened and potassium ions enter the cell as in the normal stages of cell transmission dependent on depolarisation. The presence of the Gi protein means that cAMP production is decreased. Therefore, the overall effect of the action of adenosine is that neurotransmitter release is decreased (eg. as observed with glutamate, 5HT, Ach and GABA). This results in decreased neurotransmitter binding to pre-synaptic neuronal cells and decreased neurotransmitter release. Adenosine binding to the A1R on astroglial cells has the same effect.

However, it is interesting to note that in areas where GABA is released that the inhibitory effect of GABA is enhanced by the presence of adenosine receptors. This can be explained by the action of GABA itself which switches on chloride ion channels and leads to the hyperpolarisation of cells, whereas adenosine merely switches the cell off by removing the membrane potential differences caused by the ion concentration differences. This inhibitory process can also be seen in the SC-CA1 hippocampal area, but here a different mechanism is employed. In this case, astrocytic calcium ions trigger the release of ATP and subsequent presynaptic binding. Therefore in general, inhibition occurs through potassium ion gating into the presynaptic neuron leading to the removal of the membrane potential difference caused by ion concentration differences and resulting in the dissolution of firing, or it by adenosine inhibiting adenylyl cyclase action which decreases cAMP concentration and its secondary effects resulting in decreased neurotransmitter release.

These excitatory and inhibitory actions of adenosine and its receptor subtypes can be put to good effect and sleep is a good example of the balancing act of these two effects. Neural activity leads to increased levels of adenosine in the awake state and in the prolonged awake state the increased level comes from astrocytes. It is known that the activity of the A2 antagonists, caffeine and theophylline, lead to maintaining the awake state, but adenosine itself can promote sleep and also its level decreases during sleep. This ´conflicting` action of adenosine can be explained by looking at the location and distribution of adenosine receptor subtypes. In the case of wakefulness, some areas are needed to be excitatory and therefore adenosine A1Rs are blocked and A2ARs are active. Some areas though need to exhibit inhibited adenosine activity. Here, the A1Rs are active with A2ARs blocked or non-existent. In the case of sleep, the reverse occurs ie. adenosine requirements mean that A2AR are inhibited (or blocked – supported by the action of caffeine and theophylline which are both A2AR antagonists) and the inhibitory action of the A1R dominates.

Therefore, the action of the naturally occurring adenosine in brain areas can be explained by the location and distribution of the various subtypes of its own receptor on the neurons and astroglial cells present and the mechanisms involved relating to the G-proteins associated with them. Since adenosine has a modulatory role in the neuronal synapse, then anywhere where the capability to produce and release adenosine exists and its receptors occur then this area can be affected by it. The presence of A1R will enhance inhibition or decrease excitation in an excitatory neuron and the presence of A2AR will enhance excitation or decrease inhibition in an inhibitory neuron and sleep is a natural example of when these types of mechanisms are brought into play. The authors of the article, Sheth and colleagues, also showed how adenosine receptors are involved in other brain functions and neuropathological diseases. However, can we say that adenosine could be used to exert a therapeutic effect through its modulating activity? The answer is it might be possible to aid the action of one brain area or another by enhancing or inhibiting its natural level, but administration of adenosine or another agonist would have to be very location specific and an intensive understanding of the interplay between brain areas would be required. However, with the increasing accuracy of selective drug administration and the improving knowledge and imaging of interconnectivity between brain areas then this might be a possibility for the future and adenosine agonists and antagonists may become important in the treatment of some neuropathological diseases such as Parkinson`s disease.

Since we`re talking about the topic………

…it is believed that there is an over-excitability of certain hippocampal areas in Alzheimer`s disease. Would an exploration of A1R and A2AR populations and an upregulation of the A1R in these areas have a positive effect in combating this disease effect?

….it is known that in Parkinson`s disease the brain area substantia nigra shows decreased activity due to a decreased density of dopamine receptors. It has been shown that dopamine receptors can be linked to adenosine receptors in heterodimeric complexes and therefore, could the activity of the dopamine receptor be enhanced not just be administering dopamine agonist, but also by administering adenosine agonists? Again, is selectivity of administration to this area alone important or could the knowledge of interconnectivity between the substantia nigra and other brain areas be used to administer adenosine agonists to these other areas which may be easier to administer to?

 

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GABA B receptor related synaptic inhibition mechanism in the hippocampus

Posted comment on ´Neuronal chloride regulation via KCC2 is modulated through a GABA B receptor protein complex`  by R. Wright, S.E. Newey, A. Ilie, W. Wefelmeyer, J.V. Raimondo, R. Ginham, R.A. J. Mcllhinney and C.J. Akerman and published in Journal of Neuroscience 31st May 2017 37(22) p. 5447, doi.org/10.1523/JNEUROSCI.2164-16.2017

SUMMARY

It is known that synaptic inhibition can occur through the activity of ionotropic gamma aminobutyric acid A receptors (GABA A Rs) which produce fast inhibitory synaptic currents involving transmembrane chloride gradients and also through gamma aminobutyric acid B receptors (GABA B Rs) which produce slower inhibitory actions and are G-protein related (metabotropic receptors). Wright and colleagues investigated the GABA B Rs of the rat hippocampus which are physically associated with the potassium chloride cotransporter protein, KCC2 (solute carrier family 12, member 5 protein – SLC12A5).

In their experiments, Wright and colleagues used cortical membrane samples from 5 Sprague Dawley rats. The peptide mixtures were analyzed by liquid chromatography tandem mass spectrometry (MS/MS) and reference was made to a GlaxoSmithKline non-redundant protein database. Coimmunoprecipitation, biotinylation of cell surface receptors, immunofluorescence and electrophysiological experiments were conducted on organotypic hippocampal brain slices from P7 male Wistar rats (cultured for 7–14 DIV before experimentation), or on CHO cell cultures (or transfected CHO cells cultures) which expressed the rat GABA B R1b and GABA B R2 (termed CHO GABABR1b/R2) . The advantages of using the organotypic hippocampal brain slices were that they allowed tests to be performed on the same sample and also that the method was shown to produce mature and stable chloride homeostatic mechanisms as required. Intracellular chloride concentrations were measured using cyan and yellow fluorescent protein (CFP-YFP) based chloride sensor proteins with the organotypic hippocampal CA3 cells excited at 850nm and 510nm respectively for the two separate ionic channels. The ratio of the two was calculated and used in the results.

Wright and colleagues found in their coimmunoprecipitation and mass spectrometry experiments that the GABA B R complexes of the cortical preparations used contained multiple peptide components, eg. G protein subunits, KCC2, potassium channel tetramerization proteins, NEM sensitive fusion proteins, and 14-3-3 signaling proteins. KCC2 was found in three isolates of the neuronal cell membranes associated with GABA B R1 in samples of the cortex and hippocampus. Western Blot analysis gave two distinct bands at about 130 and 270 kd representing the receptor proteins existing in monomeric and dimeric forms. This occurred with both GABA B R1a and GABA B R1b forms. The protein complexes were found to be associated with the somatic and dendritic plasma membranes of the organotypic hippocampal pyramidal cells. Cortical samples also showed KCC2 co-localised with both the GABA B R 1a and 1b forms and this association was additionally confirmed using the CHO cell line. KCC2 was predicted to consist of a cytoplasmic amino acid domain and a cytoplasmic carboxyl domain existing either side of the transmembrane domain and consisting of twelve transmembrane helices. Biotynlation experiments showed that the fusion products of the KCC2 that contained the transmembrane domain were trafficked to the cell surface. The authors also found that GABA B R can form a complex with KCC2 that does not contain intracellular terminal domains, but it cannot form a complex that does not contain the transmembrane domain, which indicated that KCC2 associates with GABA B R via the transmembrane domain.

In investigating the effect of GABA B R on transmembrane chloride gradients, Wright and colleagues looked at the reversal potential of ionotropic GABA A R (EGABAA) and CA3 pyramidal cells using the GABA A receptor agonist, muscimol. They found that CA3 pyramidal cells demonstrated a hyperpolarizing EGABAA state of -82.8 mV with a resting potential of – 71.5mV with a shift to -70mV on application of furosemide (a GABA A R antagonist). This was said to demonstrate an active KCC2. When the GABA B R agonist, SKF97541, was used there was a depolarizing shift from -82mV to – 78mV demonstrating increased intracellular chloride. This change was prevented by using a GABA B R antagonist. The GABA B R effect was shown to be related to G protein signaling since it was disrupted by using the Gi/Go protein antagonist, PTX. This also blocked the SKF97541 effect. Using SCH23390 which blocks downstream GIRK channels (G protein coupled inwardly rectifying potassium channels), the SKF97541 change was found not to be prevented showing that GIRK channels are not involved in the mechanism. The EGABAA was also not affected by activation of post-synaptic adenosine receptors, which are also G protein coupled and linked to potassium channel activity. This confirmed the hypothesis that activation of GABA B R with SKF97451 involved an intracellular concentration increase of chloride ions.

The authors also investigated if GABA B R activation could regulate KCC2 at the plasma membrane. They showed that the shift in EGABAA occurred because of reduced KCC2 function. Furosemide was used that blocks KCC2 activity and cells were found to have higher depolarizing resting EGABAA (-70mV compared to -83mV of the controls). This occurred within 5 minutes which demonstrated that KCC2 functions continuously to maintain the effect. The effect of the sodium-potassium-chloride cotransporter protein NKCC1, which can also regulate chloride concentrations in hippocampal pyramidal cells, was discounted by an experiment using its selective blocker, bumetanide. The results were the same as the controls and the effect of the GABA B R agonist was not changed. The effects of other manipulations that alter GABA B R activation were also tested. The authors used zero Mg2+ ACSF that reduces KCC2 levels. In this case, they found a depolarizing shift in EGABAA (to -64mV).

Biotinylation experiments were also used to quantify changes in the plasmalemmal level of chloride transporter proteins in neuronal tissue. Here, the authors found that after SKF97451 activation, there was a reduction of KCC2 at the cell surface with both monomeric and dimeric forms reduced (80, 83%). The level of GABA B R1 was also found to be reduced at the cell membrane with a concomitant decrease in electrophysiological recordings (-88mV went to -79mV). The reduced level of KCC2 was found not to be linked to degradation changes, but to the amount of surface protein trafficking (ie. endocytosis and recycling) taking place. Application of SKF97451 to the CHO cells produced the same results as the organotypic slices. Therefore, the authors suggested that the KCC2 chloride transport mechanism is sensitive to KCC2 expression levels, post-translational modifications or that intermediate proteins are involved in regulating the surface expression in neurons.

In order to investigate whether GABAB R regulation of KCC2 involved clathrin mediated endocytosis, the authors performed pretreatment with the blocker dansylcadaverine (DC) and found no change in EGABAA in CA3 pyramidal cells in the presence or absence of SKF97451 ie. the normal EGABAA shift was prevented by the pretreatment. The reduction of surface levels of KCC2 following GABA B R activation reduced with pretreatment with DC indicating that disruption of clathrin-mediated endocytosis prevented the GABA B R mediated change in surface KCC2. Surface KCC2 levels were not altered in the presence of DC. Treating cells with a combination of calcium ion channel blockers, selective protein kinase C inhibitors, general kinase blockers, tyrosine phosphatase inhibitors, or protein phosphatase 1 and 2 inhibitors had no effect on the SKF97451 induced shift in EGABAA, nor on the levels of surface KCC2. This indicated that calcium signaling was not involved. Therefore, it was concluded that GABA B R regulation of KCC2 involves clathrin-mediated endocytosis and is not linked to calcium signaling.

In order to investigate whether synaptically-driven GABA B R activity affects intracellular chloride regulation (ie. that the GABA B mediated effect occurred at the inhibitory synaptic connections at presynaptic GABAergic interneurons) or not, the authors evoked monosynaptic GABA B R responses and measured synaptic EGABAA of -76mV similar to the muscimol evoked responses. GABA B R are thought to be located predominantly extra-synaptically in hippocampal pyramidal cells and are thought to be activated under robust GABA release occurring during periods of high frequency presynaptic firing. Wright and colleagues found a single presynaptic stimulus generated a pure GABA B R response in CA3 pyramidal neurons which was blocked by SR95531 (a selective GABA A R antagonist). A high frequency train of stimuli (6 at 20HZ) produced a postsynaptic response comprising of a large GABA A R conductance (found over a range of frequencies) and a smaller GABA B R conductance that could be blocked by CGP55845 (a selective GABA B R antagonist). They concluded that the optimal presynaptic frequency for activating a GABA B R response was close to 20HZ. Blocking KCC2 with the selective antagonist VU0240551 led to a change in the EGABAA reduced shift due to GABA B R stimulation. Therefore, Wright and colleagues experiments showed that the GABA B R mediated effect occurred via KCC2 and was evoked by the agonist effect and by synaptically evoked GABA release.

Therefore, Wright and colleagues concluded that they had identified an association between GABA B R activity and KCC2 at the cell surface. Agonist binding leads to GABA B R activation and G protein activation and chloride entry (increase in intracellular chloride of about 1.2mm) as part of the signaling mechanism. KCC2 reduced function was involved, ie. agonist activation modulates proteins with which KCC2 is physically associated with the GABA B R. The authors concluded that the GABA B R effect on KCC2 was different to other activity dependent mechanisms that can regulate KCC2. Post-translational regulation linked to calcium signaling events and associated enzymatic modifications (KCC2 function is associated with its phosphorylation state since its turnover is rapid as a function of phosphorylation) was not shown since there were no effects from calcium ion signaling blockers, phosphatases and phosphokinases etc. The reduced function of KCC2 was also not caused by increased degradation since the total level of KCC2 present was not altered. Instead the authors concluded that the effect occurred at the cell surface level. Relating to this there were conflicting reports about the GABA B R level at surface. Some researchers claim that the receptor is stable whether active or not whilst others state that the receptor is mobile and rapidly internalized in a clathrin-dependent manner relating to activation. Wright and colleagues` experiments showed that GABA B R activation led to down-regulation of the receptor and KCC2 surface expression. The discrepancy between the reports was explained on the dimerization state of the receptor complex or the experimental system used. The authors claim that the effect is observed only due to a subset of proteins since protein proportion is less than 25% for both surface proteins. The effect occurred over a similar, but not identical timescale of down-regulation of NCC2 function which could reflect the sensitivity of the experimental method or functional changes in the KCC2 resulting from recycling to the membrane, or changes in membrane domain, cellular location or molecular interactions. Since the authors found that blocking clathrin-mediated endocytosis prevented the GABA B R down-regulation of KCC2 function and expression at membrane their findings supported the observations from other researchers that internalized GABA B R are associated with the clathrin binding adaptor protein 2 complex and that KCC2 also undergoes fast clathrin-mediated endocytosis.

Therefore, Wright and colleagues concluded in their article that GABA B R modulates its effect on KCC2 function via a mechanism involving clathrin-mediated endocytosis. Since their experiments produced a reduction of only 20% in the level of surface KCC2 and a smaller GABA B R mediated shift in EGABAA with furosemide they concluded that different pools of KCC2 must exist in the membrane. This supports observations from others that KCC2 is also localised at glutamergic postsynaptic structures (perhaps NMDAR), functionally associated with kainite receptors and implicated in glutamatergic transmission. This supports the evidence that GABA B R activation could affect NMDA R activation. Therefore, Wright and colleagues` experiments were said to demonstrate an interaction of GABA R systems within the hippocampus and shows how GABA B R can regulate one type of inhibitory synaptic transmission.

COMMENT

What makes this article interesting is that it discusses the topic of synaptic inhibition instead of the more popular topic of neuronal stimulation. The type of synaptic inhibition discussed here is not the normal shutting off of stimulation of a firing cell by hyperpolarization through potassium ion channel opening and potassium ions flooding in and the subsequent readjustment of the cellular electric signal and induction of mechanisms in place regarding the endocytosis, enzyme and protein phosphorylation and dephosphorylation mechanisms for example that naturally end the firing of the cell in question, but instead this article describes the action of another mechanism which actually prevents cellular firing. GABA binding and GABA receptor action take the membrane potential away from its firing threshold and prevents the next cell in the neuronal pathway from initiating an action potential and depolarizing. This means that firing of cells further down in the neuronal pathway is prevented and earns the GABA synapse the name of inhibitory synapses. Inhibitory synapses normally involve post-synaptic receptors that are mainly transmitter gated ion channels and this paper describes one such case, that of the amino acid neurotransmitter, GABA, the GABA receptor and its functional mechanisms. GABA is known to have an inhibiting effect on firing and physiological conditions can arise from extremes in this mechanism, ie. too much inhibition leads to coma; too little leads to seizures and epilepsy for example.

It is known that GABA released from presynaptic neurons in the course of firing of that neuron binds to post-synaptic GABA receptors and synaptic inhibition occurs through either the ionotropic GABA A R (relies on transmembrane chloride channels and chloride ion gradients to generate fast inhibitory synaptic currents), and/or the metabotropic GABA B R which produces a slower inhibitory effect. Two mechanisms are linked to GABA B R activation: G protein signaling which generates cAMP and initiates a cascade mechanism involving protein kinase activation and phosphorylation of proteins; and inhibition via the opening of chloride channels so that the firing threshold of the cell is not reached. There is normally low chloride permeability in cells since chloride ions are linked to several cell functions such as the regulation of cell pH which is tightly regulated.

In the case of the GABA A R this receptor is linked directly to the opening of the ionic chloride channel and causes fast synaptic inhibitory action (80% IPSP from this type). Its agonist is muscimol (used in the experiments described above to generate the EGABAA)  and its antagonist is bicuculline. This receptor is particularly known for its binding to certain common substances: it binds benzodiazepines (released on anxiety) leading to increased frequency of opening of the chloride channel; it binds barbiturates which increase the length of time the channel is open; and it binds ethanol, although for this it needs a specific subunit structure. Binding of GABA leads to synaptic inhibition, but it is not always associated with big responses, eg. in CA1 there could be a shunting inhibition. This is where the synapse acts as an electrical shunt preventing the current from flowing from one side to another because the membrane potential at the site of the inhibitory synapse is at the time equal to the chloride equilibrium potential (ECl – about -65mV). Opening of the chloride channel allows chloride ions to cross the membrane in a direction that brings the membrane potential towards this chloride equilibrium potential. If the membrane potential is less negative than -65mV when the neurotransmitter is  released then activation of these channels would cause a hyperpolarizing IPSP, but if the membrane potential is -65mV then no IPSP is visible after chloride channel activation because the value of the membrane potential is the same as the ECl. This is called the reversal potential. In this case the positive current therefore flows outwards across the membrane at this site to bring the membrane potential to -65mV and there is formally an equivalent inward movement of negatively charged chloride ions. Tominaga found that theta burst brain wave stimulation could induce spike firing and LTP in CA1 cells. When theta burst stimulation was paired with a NMDA R blocker then enhanced GABA A R spike firing was observed. This enhanced excitatory postsynaptic potential was blocked with a GABA A R antagonist. They suggested therefore that pulsed burst stimulation activated the GABA A R system to cause short term spike firing increases without increasing postsynaptic excitability, thus establishing a link between post synaptic firing in CA1 with GABA shunting inhibition. However, in Wright and colleagues experiments only chloride ion channels and the inhibitory effect of chloride ion movement by GABA was investigated.

The work by Wright and colleagues looked at the other form of GABA receptor that of the  GABA B R which exhibits different properties to its companion GABA A receptor. GABA B R has auxiliary subunit proteins that modulate agonist response plus the kinetics of the G protein signaling. G protein activation leads to the formation of cAMP via the conversion of ATP to cAMP free in the cytosol. Free cAMP activates protein kinases which catalyse phosphorylation (ie. the transfer from ATP of a phosphoryl group to serine or threonine amino acid residues of proteins). In some neurons one protein phosphorylated when cAMP rises is a type of potassium channel causing it to close and hence reducing the membrane conductance of potassium so that the cell becomes more excitable. It is also reported in some cells that the rise in cAMP concentration is linked to changes in cellular processes such as the degradation of storage fuels and as in this case the induction of opening of chloride channels.

The GABA B R also has proteins for the control of dimerization or desensitization of the receptor and also has molecular partners for associations that enable the GABA B subunits to regulate gene transcription and intracellular trafficking of other membrane proteins. The recycling of the GABA B R at the cell surface is dynamic and modulated through receptor activation, composition, phosphorylation, or degradation. GABA B R are also associated with chloride channel functioning as seen above with GABA A R and also linked to synaptic inhibition. However, in this case the receptor is associated with KCC2, a potassium-chloride cotransporter protein, where receptor activation leads to down-functioning of the KCC2 function. Rapid changes in KCC2 function have been shown to be elicited in an activity-dependent fashion and involve different post-translational regulation mechanisms of the transporter protein, including its phosphorylation state and regulation at the cell surface. The mechanism could be that cAMP is formed from G-protein activation on GABA binding to the post-synaptic receptor. The cAMP leads to activation of protein kinase which phosphorylates the KCC2`s serine/threonine residues causing a conformational change that reduces the KCC2 activity, but opens the chloride channel. Wright and colleagues found that the level of KCC2 at the membrane surface reduced 20 minutes after stimulus. This indicates that the phosphorylated form of KCC2 is inactive and clathrin induced endocytosis of KCC2 and the receptor complex occurs. In this case, KCC2 in its normal form is associated with the receptor inhibiting the opening of the chloride channel and in its phosphorylated form causes the chloride channel to open. Opening of channels on amino acid phosphorylation is seen with the  opening of non-specific cation channels in olfactory epithelial cells. Here, phosphorylation with cAMP protein kinase action allows calcium ions and other cations into the cell with the flow of cations causing depolarization of the neuronal membrane and initiating the action potential. In the case of KCC2 and the GABA B R then either the binding of the agonist on the postsynaptic receptor will cause a conformational change that results in opening of chloride channel directly or the binding of the agonist to the GABA B R itself will cause activation of protein kinase by the cAMP formed which will then phosphorylate the serine or threonine residues of the KCC2. This GABA binding will result in down-functioning of the KCC2 protein and cause conformational changes in the chloride ion channel resulting in it opening and chloride ions to flow inward. The difference to GABA A R binding is that GABA B  R binding occurs when there are repetitive stimuli ie. firing is more sustained hence, long term stimuli. Therefore, the KCC2 association with the GABA receptor changes the way in which it is associated with the chloride channel.

Therefore, there are two types of chloride inhibition associated with GABA receptor binding: one fast through the GABA A R and a slower inhibition through GABA B R. We cans ask why there is a need for two systems of inhibition brought about by the same GABA neurotransmitter. It should be pointed out first of all that just because there are two types of receptor this does not mean that one area has one type of receptor and another one has the other. It is known that an area can have both, for example the globus pallidus. In this area, GABA B R are intracellular and presynaptic and GABA A R are on plasma membranes. Therefore, their localization is then probably linked with the two types of inhibition that the receptors are associated with. Fast inhibition is likely to be associated with a strong stimulus with multiple neuronal cells firing and connectivity between neurons and neuronal pathways meaning that the signal is transmitted quickly and efficiently and that neuronal cell assemblies symbolizing the electrical representation of the stimulus are formed. At the cellular level this  means that the firing of neurons of a particular pathway occur from sensory level upwards to the higher cortical levels with action potentials, depolarization and release of neurotransmitter into the synaptic cleft occurring at each level. When the neurotransmitter GABA is released it binds to the postsynaptic neuronal membrane and results in the connecting cell being unlikely to reach its firing threshold since it is effectively hyperpolarized by causing a change in the ionic balance of the cell. Therefore, the transmitting signal stops at that cell. However, if the signal is transmitted via other neurotransmitters being released or firing of other cells not containing GABA synthesizing enzymes and substrates then the transmission of the signal continues. The overall pattern of firing results in the electrical representation of the stimuli being established.

The result of GABA B R action is the same as GABA A R apart from it being slower due the associated KCC2 involvement. Why then does any cell or brain area require a mechanism of slower inhibition? The difference between GABA A R inhibition and GABA B R inhibition is that GABA B R inhibition occurs in the case of repeated or sustained stimuli. Repeated or sustained stimuli are required for long-term memory and the appropriate physiological changes of the neuron. If an external stimuli is repeated but then stops then there is no problem. The signal stops as described above and no inhibition of cellular firing is required. However, repetition of cellular firing can be elicited by internal stimulation only and cells have been shown in the hippocampus and PFC to exhibit these characteristics. We also know that many long-term memories are formed where there is no deliberate repetition of stimuli so the conditions for long-term physiological changes associated with long-term memory have to be achieved by internal means. For example in the case of the hippocampus, this area plays a critical role in long-term memory, spatial memory, object and location (timing and order) for example. Activation of the cells occurs along the sensory pathways from the entorhinal cortex to the hippocampal dentate gyrus to the CA3 region and then to the CA1 which can then activate the deep entorhinal cortex again. The firing signal goes from the hippocampus then to the PFC and other areas. After a particular time the cells are then inhibited from firing by activation of the GABAergic cells that release GABA and bind to the postsynaptic membrane receptors, opening the postsynaptic chloride channels to prevent the threshold of firing being reached in the next cell of the pathway. This stops the repeated firing condition of these reverberating firing cells so that the cells are free to experience other stimuli. It is also known that that repetition of firing by internal stimulation can cause hyperexcitability of some cells, which can lead to epilepsy. Therefore, the internal method of stopping the transmitting signal is beneficial if the firing is carried on for too long. Another reason is to support desensitization of receptors through multiple stimuli. Desensitisation is a natural method for preventing the hyperexcitability described above. Repeated stimuli already cause the electrical stimulation for long term physical changes to be put in play, but desensitization of the receptors allow neuron firing to be switched off without exhausting the cell ie. before the absolute refractory period. Hence, the cells are more capable of responding to other stimuli within a short period of recovery time. The activation of chloride channels and the failure of the post-synaptic cell to reach firing threshold complements the decrease in sensitivity of the receptor to the agonist.

Therefore, what does inhibition mean to the overall pattern of firing and the electrical signal? Inhibitory synapses contribute to the overall synaptic integration of the cellular system in which they exist. IPSPs can be subtracted from ESPS making the postsynaptic neuron less likely to fire and elicit an action potential. Also, shunting inhibition acts to drastically reduce membrane resistance and consequently dendritic length constant (depolarization is 37% of at the origin) thus allowing positive current to flow out across the membrane instead of internally down the dendrite toward the spike initiation zone. The reaction of GABA R functioning is that the firing signal is stopped and since there is phase locking of firing and non-firing cells and neuronal cell assembly formation then maybe it is better to think of neuronal cell assemblies not just in terms of firing cells, but also of those non-firing at the same time. For example, cortical firing described by Deneve says that the area tightly balances excitation. Inhibitory currents not only match the excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. This suggested that a tight excitatory/inhibitory balance may be a signature of a highly cooperative code with the precise, tight balance providing a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs. This tight balance of excitatory/inhibitory balance may be critical for correct functioning and this supports the views of others where it is known that neural dynamics are poised at criticality (Zhigelor) and that neural avalanches and long range temporal correlations are hallmarks of critical dynamics in neuronal activity and occur at fast and slow timescales. If there is synaptic integration then it is likely that inhibition occurs at the rich nodes (Nigam) where any excitation or inhibition is likely to have its greatest effect. Also, it is recognized that key to correct functioning of the brain is its capability to reconfigure its network structure to respond to its demands (Cohen). This could mean local, within-network communication (ie. critical for motor execution) or integrative, between-network communication (ie. critical for working memory) and involve excitation or inhibition of cells. Therefore, this balance of inhibitory/excitatory firing is essential for correct brain functioning.

Such a balance has been shown to be important in the development of neurons and reinforces why GABAergic neurons play an important role in connectivity during this time. Restivo showed that new neurons are generated continuously in the subgranular zone of the hippocampus and integrate into existing hippocampal circuits throughout adulthood. Although the addition of these new neurons may facilitate the formation of new memories, as they integrate, they provide additional excitatory drive to CA3 pyramidal neurons. During development, to maintain homeostasis, new neurons form preferential contacts with local inhibitory circuits. During adulthood, new neurons form connections with inhibitory cells in the dentate gyrus and CA3 regions as they integrate into hippocampal circuits. In particular, en passant bouton and filopodia connections with CA3 interneurons peak when adult-generated dentate granule cells are approx. 4 weeks of age, a time point when these cells are at their most excitable. Restivo found that CA3 interneurons were activated robustly during learning and that their activity was strongly coupled with activity of 4-week-old (but not older) adult-generated DGCs. Hence, this indicated that as adult-generated neurons integrate into hippocampal circuits, they transiently form strong anatomical, effective, and functional connections with local inhibitory circuits in the CA3.

The balance of excitatory and inhibitory synapses and synaptic integration may also be important for the brain at rest. We know that that brain commonly exhibits spontaneous (ie. in the absence of a task) fluctuations in neural activity that are correlated across brain regions (van der Brink). The topography of these intrinsic correlations is in part determined by the fixed anatomical connectivity between regions, but it is not clear which factors dynamically sculpt this topography. Potential candidates are given as the subcortical catecholaminergic neuromodulatory systems, such as the locus coeruleus-norepinephrine system which sends diffuse projections to most parts of the forebrain. Here, it was found that catecholamines reduce the strength of the functional interactions during rest and this decrease showed an anterior–posterior gradient in the cortex, with strongest connections between regions belonging to distinct resting-state networks. In this case, the firing noradrenalinergic neurons have NE receptors that are G-protein linked and could provide an increased potassium channel phosphorylation and activity. Therefore, the system is dampened by inhibition of firing. Therefore, it is possible that the GABAergic system could instigate the same effect on the excitatory/inhibitory system via its chloride ion channel opening when the brain is at rest.

Therefore, the study of neuronal firing inhibition is important to the workings of the brain as a whole. This is clear when we see that imbalances of these inhibitory systems produce marked physiological effects eg. hyperexcitability or coma and development problems. Hence, it could be that not only should we be looking at firing for explanations of cognitive defects, but also be looking at the contribution that non-firing cells makes to the overall picture of synaptic integration and investigate why certain cells and systems are not firing. For example, although Alzheimer disease is linked to amyloid deficiency and endocytosis disruption, chloride channel function may provide another reason why hyperexcitability of the hippocampal areas exist and may give another mechanism by which manipulation could have a beneficial effect. Therefore, cellular firing inhibition may be as important as its complementary stimulation action.

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

….the first direct evidence of synaptic connections between interneurons came from  paired recording of experiments combined with biocytin labeling and anatomical reconstruction of recorded neurons. Neuronal firing in artificial conditions may not be representative of what is actually going on in the brain with long distance inhibition and excitation coming into play. Therefore, should organotypic slices always be used to confirm results achieved in patch clamp experiments on synaptic integration?

….when the GABA A agonist is tested with the calcium ion dependent protein kinase inhibitor then there is no change in EGABAA effect. Since GABA B R causes phosphorylation of chloride channels, should the experiments be repeated with pyruvate kinase not linked to calcium ions? Would a change in results be observed and if no change is seen does this rule out a phosphorylation effect?

…..it is known that kainite receptors depress GABA mediated inhibition and increase the firing rate of interneurons. This is shown to also require KCC2. What happens in this mechanism? Are kainite receptors presynaptic so GABA is not released and therefore the post-synaptic effect of GABA does not occur? If  kainite receptors are blocked will the same effect on EGABAA be observed?

……what would be the effect on signaling of influencing the lipid raft of the GABA B R G-protein complex? Would a ´hardening` of the lipid raft prevent the chloride channel functioning and hyperpolarizing effect?

 

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