tau pathology effects on neuronal firing

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


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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

Since we`re talking about the topic……

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

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

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

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

the structure and function of the cerebellum

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


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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

Self-awareness and the mirror self-recognition test

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


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

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

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

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

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

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


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

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


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

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

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

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

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

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

Since we`re talking about the topic………

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

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

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

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


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

nucleus accumbens activity predicts group behaviour in crowdfunding task

Posted comment on ´When brain beats behaviour: neuroforecasting crowdfunding outcomes` by A. Genevsky, C. Yoon and B. Knutson and published in Journal of Neuroscience vol 37(36) 2017 p. 8625, doi.org/10.1523/JNEUROSCI.1633-16.2017


Genevsky, Yoon and Knutson investigated whether neural activity could forecast market level crowdfunding outcomes for individuals as well as aggregate funding outcomes occurring weeks later. They claimed that existing studies showed neural activity could be used to reliably predict individual choices including those regarding purchasing and financial risk taking. Existing studies also showed that activity of the nucleus accumbens (NAC) area in particular could forecast aggregate choice eg. song download frequency, and activity of the medial prefrontal cortex (mPFC) area could forecast aggregate choice, eg. call volume in response to health related adverts. However, existing studies had not yet explicitly identified which neural indicator linked to individual choice generalises to forecasting aggregate choice.

For their experiments, Genevsky, Yoon and Knutson took 30 subjects (14 female, average age approx. 23yrs) who had to decide whether to fund projects relating to documentary film production that were proposed on the Internet crowdfunding website, Kickstarter. Whilst going through the decision-making process, each subject was subjected to fMRI scanning particularly of the decision-making areas. The subjects were paid $20 an hour each for participating in the test plus given $5 cash for the crowd funding task. The subjects were then sent pictures (viewing time allowed –  2 secs) and associated text (viewed for 6secs) for 36 crowdfunding appeals and had to say on 7 point scales whether they liked the project (valence – affective response and arousal) and whether they thought the project would reach its funding threshold (termed project campaign success). The participants indicated affective response based on how they previously felt when presented with the project. Arousal correlated with approach and avoidance motivation so valence and arousal ratings were converted to mean deviated positive-arousal and negative arousal scores onto axes rotated through 45 degrees. The subjects demonstrated their choice of funding or not by pressing yes or no (left or right) buttons (decision time allowed –  4 secs) and were told that one appeal would be chosen at random after the session.  If the subjects had agreed to fund that particular appeal the funding amount would be removed from their additional $5 payment and the contribution would be made online to the appropriate project. If the appeal had not been selected, then the subjects retained the full $5 additional amount. (As additional ´reward` if the selected project was subsequently funded on closing of the internet appeal the participants would be allowed to view the associated film once it had been completed.)

Subjective ratings of each appeal were collected immediately, but funding outcomes were only available after all the window of opportunities for the total 36 projects were closed online. 18 projects were chosen for funding whereas the other 18 did not reach their funding threshold before the end of the session. (A replication study was carried out and here 14 were funded with 22 not. This study differed slightly to the initial one since it consisted of projects of 2 types eg. face or place whereas the initial one had 3 types, eg face, place and text.)

FMRI scanning occurred simultaneously to the decision-making process. The whole brain was scanned as well as regions of interest (ROI) such as the midbrain (amygdala and anterior insula – AI), the NAC and the orbitofrontal cortex (OFC) as well as sensory input areas implicated in processing faces (fusiform gyrus – FG), places (parahippocampal gyrus – PG) and text (frontal gyrus). Aggregate forecasting analyses were carried out including group averaged choice, rating and fMRI ROI and the data classified with forecasting eventual internet funding for each appeal or not. Individual choice prediction analyses of the fMRI data were pre-processed and extracted from volumes of interest (VOIs) for comparison with behavioural choice and subjective rating indicators. The analysis of individual funding choices compared projects which subjects choose to fund versus those that they did not. For group funding choices, a comparison of subjects was made in which projects that were viewed as potentially receiving full funding were pitched against those that did not. Psychophysiological interaction analyses (PPI) were also carried out to identify context-dependent modulation of functional connectivity between brain regions responsible for sensory input and anticipatory effect.

Various classification analyses were carried out, but before these were calculated the trials were divided into training sets (80% of the total) and testing sets (20%). Individual choice tests were classified after down-sampling at a 50%-50% split then conducted on a trial-to-trial basis including self-reported ratings of likes, likelihood of success, positive arousal, negative arousal and brain activity of the ROIs. For whole brain classification analyses fMRI activity was extracted and features selected. Each subject was classified using the model from the training session of the remaining subjects.

In their experiments investigating individual choice, Genevsky, Yoon and Knutson showed that on average their subjects chose on average to fund around 14 projects. The ratings of self-reported liking of the project and perceived likelihood of success associated with the individual choices to fund. Positive arousal also strongly associated with individual choice to fund, but negative arousal did not. The authors also found that individual funding choices did not significantly differ as a function of the type of project eg. face, place or text projects received the same level of favouritism for funding.

The whole brain fMRI results relating to predicting individual choice showed that brain activity was different during project presentation for trials where subjects subsequently chose to fund compared to trials where they did not. Significant clusters in the bilateral NAC and mPFC as well as the bilateral striatum were observed. Further investigation showed that the NAC activity was higher in the initial part of the presentation compared to the latter part, but vice versa for the activity of the mPFC region. Genevsky, Yoon and Knutson also observed that FG and PG activity did not correlate to funding, but activity of the left IFG region did. The PPIs carried out for functional connectivity showed activity from the FG, PG, IFG, and NAC VOIs differentially predicted choice for the various projects with different image content eg face, place and text. The authors showed correlated activity between the NAC and FG areas associated with individual choice to fund only projects with the face stimuli and NAC and PG areas only in place conditions. The activity of the NAC and left IFG was not significantly associated with individual choices to fund any condition.

Therefore to summarise the results regarding individual choice, the results were classified according to prediction of individual funding choices. A first classifier included behavioural self-reported ratings of liking, likelihood of success and affect and classified individual funding choices at a 86% accuracy level. A second classifier using neural imaging data alone also significantly predicted individual funding choices (approx. 58% accuracy). A third classifier combined behavioural and neural predicted individual funding choices with approx. 86% accuracy and a fourth classifier using whole brain neural activity during project  presentation stage significantly predicted individual funding choices with approx. 59% accuracy. The largest clusters of activity were in the NAC and mPFC areas preceding choice and the observation that the NAC features predicted choice before the mPFC was consistent with other existing research that proposes that anticipatory effect precedes value integration in decision-making.

The aim of Genevsky, Yoon and Knutson`s experiments were to investigate whether individual choice predictions could be transferred to forecast aggregate choices. Since the aggregate funding outcomes were received weeks after the experiment, this study looked to see if the behavioural and neural correlates obtained in their experiment could forecast outcomes that occurred much later. The authors found that average ratings of project likeability and perceived likelihood of success were not associated with internet funding outcomes under these conditions. They also found that neither average funding outcomes were associated with internet funding outcomes nor were positive/negative arousal ratings. Differences in image category with level of aggregate funding were however observed with projects associated with faces receiving more funding (83%) than place (17%) or text (50%). When the level of neural activity was investigated relative to the funding of projects it was found that after all data manipulation had been carried out only the NAC activity significantly differed for projects that were aggregately funded on the internet compared to NAC, mPFC and left IFG activity that predicted individual choices in the experiment. Therefore, the authors concluded that the neural model fitted better aggregate funding choice than either the behavioural or affect models.

Genevsky, Yoon and Knutson continued with a classification of aggregate funding outcomes. They said that their behavioural model, including average ratings of likings, perceived likelihood of success, affect and funding choices classified funding outcomes with approx. 53% accuracy which did not exceed chance and hence, was not valid to forecast internet funding outcomes. Their neural model performed slightly better with an average VOI activity classifying internet outcomes with approx. 59% accuracy. The whole brain neural model where activity was measured during project presentation classified internet funding outcomes with 67% accuracy. Again the largest cluster of activity was found in the NAC area during the period before the choice was made.

In conclusion, Genevsky, Yoon and Knutson said their experiments showed what they expected. They wanted to look at whether neural correlates could predict individual choices and aggregate choices occurring at a later date as in the case of internet crowd-funding projects. They hypothesised that the NAC (linked to positive arousal) and the mPFC (linked to value integration) would predict individual choices to fund on a trial-by-trial basis and these views were supported by their experiments. They also showed that NAC activity occurred before mPFC activity and this was consistent with existing research that proposed that affective evaluation precedes value integration in individual decision-making. The authors wanted to investigate whether conclusions made from individual choice evaluations were also valid for scaled situations. This follows on from traditional psychologist theory that the results of a representative sample should be transferable to a larger group. Therefore, Genevsky, Yoon and Knutson investigated whether in their experimental set-up of crowd-funding with individual choosing to fund particular projects whether these results would transfer to the aggregate choice level. They found that their behavioural measures such as self-reported ratings of likings, perceived likelihood of success did not forecast funding outcomes, but the neural activity of a circuit consistent with anticipatory effect (that of the NAC) did and performance of this circuit was better at forecasting market outcomes than the circuit involved in value integration (that of the mPFC). The authors suggested that the difference in NAC and mPFC predictive value may indicate the difference in roles these areas play in choices with the NAC primarily involved in choices involving ´good` features whereas the mPFC and other areas play roles in choices involving mixtures of ´good` features and ´bads`, time or probability.

The authors also investigated whether choice reflected project type eg. face, place and text. They found that visual content increased activity in relevant sensory regions as expected eg. face in FG, place in PG but these did not correlate to funding choices. However the functional connectivity between these areas and NAC did and hence, Genevsky, Yoon and Knutson suggested that increased activity in the NAC could have provided the impetus for funding. They also suggested that affective activity could then flexibly incorporate diverse types of sensory input or motor input when supporting choice, but could not in itself have an effect.

Therefore, the results of their experiments proved to Genevsky, Yoon and Knutson that certain neural features that predict individual choice could be scaled to forecast aggregate market level outcomes and this supported existing research (eg. NAC activity during passive exposure to novel songs can forecast internet downloads 2 years later) and they extended their discussion by indicating the implications of such a finding. They suggested that although neuroforecasting could be used in situations relevant to business and policy-making, it was necessary to investigate further to establish the full potential (eg. see which markets/areas are suitable for such a technique) and its limits.


What makes this article interesting is that it looks at one modern example of decision-making from psychological and neurochemical perspectives. The article makes the claim that a snapshot of neural functioning of a particular brain region linked to decision-making (the nucleus accumbens – NAC) can forecast group behavior ahead of time, which of course if correct and possible to carry out, would give an obvious advantage to certain people in certain situations eg. advertisers and those wishing to sway public opinion. The question is, is this claim correct or over-ambitious?

The experimental set-up used by the authors, Genevsky, Yoon and Knutson has some advantages and some disadvantages regarding decision-making research. It uses as its basis a crowdfunding internet website which provides a number of projects relating to a particular familiar and perceivable topic, that of film production. The decision the participants have to make relates to whether he (or she) likes the project and judges the value of that project to be one worthy of giving money from a restricted amount to. The fate of one project selected by the experimenters is compared to that forecast by the test individual, the limited and known group of test individuals and the group as a whole of unknown subscribers of that particular website. The outcomes of the first two are achievable immediately after all data manipulation has been carried out and the latter, at a much later date when the website deadline is reached. The advantage of such a test set-up is that it appeals to a computer-capable generation and internet crowdfunding is modern although still based on decisions made by comparing risk and reward evaluations just like those made using other more standard psychological tests. However, it takes advantage of real-time imaging capability with finer measurements of particular brain area activity and is capable of accurate timing between physical action (button-pressing) and mental thought processing (decision-making).

However, the experimental set-up also does have some disadvantages from a research perspective. The experimental set-up involves neurochemical mechanisms relating to only short-term cognitive processing and biochemical mechanisms (eg. no long-term memory formation) and there is limited scope for condition manipulation since there is no immediate feedback to the participants and no influence on project content. There is also a problem with the choice of test subjects with relation to the characteristics of the internet group as a whole. It is assumed that all individuals have computer knowledge and experience of crowd funding activities, but whereas the individuals of the test group can be chosen to reflect a balance of values, opinions and personal characteristics, the internet group is unknown and may in fact be biased or lean towards particular directions. The only thing the participants share is that it is assumed that every one of them wants to win the ´reward` whether it is the monetary return on investment, or non-monetary earlier availability of the film and this reward only comes from them agreeing with the decision of the majority. This latter disadvantage is important since it could put in doubt the claim of the authors that the results of their participant test group can be extrapolated to the wider audience. Instead of the participant assessing the project according to his own values, he is assessing them as to whether other`s value the project and therefore, investigating NAC activity would reflect ability to perceive the wish of a majority and not a personal opinion.

If we assume that the author`s results are true, ie.  that the NAC is a neural indicator of choice then we should first look at what role the NAC plays in decision-making and why activity of this particular area could show individual and group characteristics. The task set before each participant whether part of the test group of not requires System 2 decision-making which is slow, sequential and requiring cognitive processing involving right inferior prefrontal cortex (PFC) activity. The first stage requires the individual to acknowledge that ´he/she` (from now on for the sake of literary convenience given as ´he` only) knows what he needs to do`. From the sensory stimuli, he knows that there is no ´magic answer` indicating whether to fund of not and intuitive actions may result in loss of stake money and therefore, he needs to assess the worth of the project before him to see whether it would be worth investing in. (The ´worth` relates to the later reward whether return on investment or seeing the film before the scheduled release date.) Since the participant knows he has a limited amount of money available, then he must go for a project with high value (meaning likely to be chosen by others).

The second-stage of the decision-making process regarding this crowdfunding task is the setting of the goal or in biochemical terms, the setting of the purpose transitional neuronal cell assembly (purpose tNCA) representing this goal in neural firing activity. This allows the incoming information from the presented project to be compared to the ideal set by the participant. Since it is assumed that participants have prior experience of this type of funding, it is therefore likely they have pre-established criteria on which to base their judgement of project suitability eg. purely what others might think, or what I think because I am normally in agreement with other people`s thinking, or favouritism for people, lots of pictures, CGI etc.  As said before, the mythical high value worth given to the ideal project is dependent on what others think as well as what the individual thinks so the high value worth project will be something that reflects current thinking at the time. In this case the age of participants may play a role which leads us back to the point considered above that the sample group may be biased towards particular opinions or values. From a biochemical point of view this second stage where the ideal is set is essentially an unreal situation akin to prospective memory scenarios and assessment is a future projection of value only and not of details. Therefore, neural activity follows that of prospective memory tasks with activation shown in the right dorsolateral PFC, ventrolateral PFC and medial PFC.

The third and fourth stages of the decision-making rely on individual characteristics and biasness. These are the stages when the processing strategy is set and the input is compared to the ideal already established in Stage 2. There is a reliance at this point on experience at decision-making to establish the optimum choice and make the decision. Possible strategies that could be used are ´aims, goals, objectives`, ´consequence and sequel`, ´emotional values`, ´other people`s views`, ´consider all factors` and ´plus and minus points`. It is likely that the ideal comparison strategy in the case of this crowdfunding experiment would be sensory input from the project event versus the ´aim/objective` neural representation given by the purpose tNCA established in the second stage. The biochemical mechanism would involve matching the similarity of the neural representation of the incoming information against the active neural representation of the ideal with the strength of neuronal firing increased by overlapping event characteristics. The strength of firing would be cast as the biochemical indicator for the optimal choice and therefore, strong firing would mean that the value of the project meets the decision criteria set previously. Weak firing means that the project does not reach the ideal and hence, would be considered not worthy of funding. Once the decision is made then the action is carried out, for example either pressing the button for funding, or moving straight away onto the next project. Either way, the whole process is carried out again from Stage 2 with new input representing the next project to be considered, but still the original purpose tNCA representing the decision sort criteria.

Although there is no official feedback from the experimenters to the participants in this decision-making stage, that does not mean that adjustments to the process are not made. These alterations are however, determined by the individuals and are dependent on personal characteristics to some extent and habit. For example, if the individual feels that he may be funding too many projects or not enough, then biochemically the anterior cingulate cortex (ACC) area may be stimulated and connectivity of this area to the dorsal lateral PFC may mean that a change in the process would occur. It is likely that there would be a re-establishment of the purpose tNCA and sorting criteria increased or adjusted eg. more detail included. Or, for example if the individual feels that he is funding too many projects of one type and that he should be more diverse in his investments then again biochemically the anterior cingulate cortex (ACC) area would be stimulated and connectivity of this area to the dorsal lateral PFC would occur leading to re-establishment of the purpose tNCA, but in this case with the instruction to exclude characteristics of one type.

Although we can see where the prefrontal cortex plays a role in the establishment of process and strategy, the authors indicated that the nucleus accumbens (NAC) was the key indicator for positive decision-making in their crowdfunding task. Therefore, what does NAC do in this experimental set-up? The NAC is known for its role in the assessment of personal reward value. It is a part of the basal ganglia group of brain areas with the striatum and substantia nigra and has a physiological structure consisting of a core and shell. Neural input, which is dopaminergic and causes dopamine release, comes from the ventral tegmental area (part of the reward mesolimbic pathway and can give rise to addiction if overactivity of the NAC leads to the release of excess dopamine), the PFC (via part of the globus pallidus and medial dorsal nucleus of thalamus and already given above as part of the decision-making pathway amongst other functions) and the amygdala (known for its role in the fear pathway and error monitoring).

In the experimental set-up described here, the NAC is involved in the personal value of outcomes and reward. Therefore, the activity in the NAC observed would be the result of personal value encoding of the project being observed and used in the decision making process at Stages 3 and 4. (The assumption is that high personal value is the spur to funding which as we will see later may not be the case for all individuals, but is if we assume that they are all wanting to achieve monetary or non-monetary gain.) The release of dopamine in the NAC encodes features of the stimulus and action selection associated with rewards. Activity is necessary for using information about expected outcome (ie. anticipated rewards) to guide behaviour and West and colleagues showed that the core and shell regions of the NAC played different roles in guiding this motivated behaviour. It was also even shown that different dopamine receptor populations were responsible with dopamine-mediated responses after the cue involving the DA2 R and responses before the action carried out by both DA1 R and DA2 R (Owesson-Wright and colleagues). It is also known that the connectivity of the NAC core with the anterior insular cortex (IC) is important in the selection and performance of actions based on the assessment of outcome value (Parkes and colleagues).  Therefore, the interconnectivity of the areas responsible for value encoding (BLA) and retrieving (IC and NAC) is related to the decisions made.

Therefore, in the experimental set-up used by Genevsky, Yoon and Knutson, the value of each project in achieving the common goal (that of reward) would be assessed by a mechanism that involved the activity of the NAC and this was observed. This activity is assumed to relate to all crowdfunding participants since the neurochemical mechanism is common to all even if the level of activity varies between individuals. However, there are other factors to consider in the determination of values and this particular experimental set-up did not show the role of other key areas some of which are more important than the NAC in the determination of value, particularly personal value. This includes the posterior medial PFC (which is important in the modulation of tactic information to action), the striatum (which is functionally coupled to the mPFC and integrates output, attributed to the planning and selection of action), the ACC (which is essential for the learning of value with the determination of value to oneself the responsibility of the NAC and the value to others the responsibility of the ACC) and the orbitofrontal cortex (OFC). The OFC is well known as an area linked to the encoding of value signals and assigns goal-value codes that are category dependent whereas recording of the value in the mPFC is independent of category. Therefore, activity of this area gives computational values and comparison of those values in order that decisions are made.

The experimental set-up also could not show how the retrieval of outcomes could influence future decisions. We have already mentioned that the test individual could alter strategy during the trial according to whether he felt that his strategy for choosing projects to fund did not lead to enough diversity, but the test did not allow feedback since there was a delayed outcome. Therefore, the individual could not use previous experience to shape reward/risk evaluation, could not update since only a snapshot of neural activity was observed, and had no awareness of any inaccuracy in the decisions made. The experimental set-up also did not allow investigation into how the participant dealt with risk. Although the experiment could result in the participant walking away with a short-term loss of money (ie. because he has invested in a project that was not chosen), the amount was small at only 20% of the total day`s monetary gain. Since feedback was not given, the test individuals could not employ reappraisal strategies in order to reduce their short-term loss. From a neurochemical basis, this function would not be reflected by the NAC measurements with choice of riskier options demonstrating increased PCC activity and a trade-off represented by competition between striatum and PFC activity.

Therefore, the use of NAC, even if it is part of the common decision-making mechanism, as an indicator for value assessment may not be ideal. This is further supported by considering some of the factors that may affect activity level of the NAC itself which could lead to spurious or inaccurate conclusions if results of test individuals are given as indicators of group decisions. One such cause is the connectivity of the NAC itself with other areas. (We can assume that the well-known factor effecting NAC activity that of administration of drugs such as cocaine and amphetamine and the results of addiction is unlikely since participants of any test situation would be screened.) For example, it is known that age has an effect on NAC activity. Chowdhury and colleagues showed that older adults had an abnormal signature of expected value in their experiments linked to the NAC signal and reflecting the dopaminergic input from the substantia nigra and ventral tegmental areas. It was found that structural connectivity between these areas and the striatum was indicative of the expected reward values observed for individuals and the abnormal reward processing in older individuals could be positively influenced by the administration of the dopamine precursor, L-DOPA. If this is the case, sample testing has to be on a mixed-aged group of individuals if it is to be used to indicate preferences of a larger audience.

Another factor to be considered is the level of motivation and mood of the individuals used in the test sample. We can assume that individuals of the test sample would have high motivation for success at forecasting the crowdfunding case as reward would lead to monetary and non-monetary gain. However, a larger group may have mixed motivational levels and we have already talked about personal value not being a reason for funding and motivation is the same.  For example some crowdfunding participants may be interested in crowdfunding as a hobby, others altruistic and this may change how they value projects, the reward and what decisions they make. This has a neurochemical basis and for example, it has been shown that low value rewards produce decreased ventromedial PFC signals and higher dorsolateral PFC signals in relation to ACC activity. Since these areas are linked to decision-making and NAC activity, an effect on firing in this area is likely to occur as a result of motivational level which would affect the decision made whether to fund a project or not. Mood of the individual can also effect value judgements and this can occur whether the mood is explicitly expressed or implicitly processed (Cohen and colleagues). Even the emotional impact of the experiment itself could produce spurious value assessments since anxiety can lead to activation changes in the PFC and striatum leading to decreased encoding of risky options. Also tiredness may have an effect on value judgements since Lui and colleagues found that there was an observable decrease in ratio of excitatory synaptic input to inhibitory synaptic inputs into the NAC after acute sleep deprivation. This was the result of a reduction in glutamate release in the medial PFC causing decreased activity in NAC excitation, but causing an increase in motivation for reward. Therefore, using a wide ranging and extensive number of individuals for the test sample is important if results are to be used as indicator for the decision activity of a larger group of people.

Therefore, have we answered the question of whether the claim of the authors that the activity of the NAC of a ´small` sample group could be used as an indicator of decision-making for a much larger one or not? (Timing is irrelevant since the participants perform their decision-making processing and action in real-time only once. It is the data manipulation and deadlines that introduce the time factor.) We have shown that the NAC is an important part of the decision-making process by its role in the encoding and retrieval of personal value relating to reward which determines whether a project is considered fund-worthy or not when compared to personal ideals. This means that NAC activity is not a reflection of personal values relating to content, but a reflection of the value dictated by the projected success of the project in terms of how many other crowdfunders rate it as worthy of receiving investment. Therefore, if the experimenter wants to look at personal value relating to the project content itself, the NAC may not be the best area to use. We have also shown that activity of this area is subject to neurochemical change and is influenced by a number of psychological and biological factors. Therefore, its worth as a neural indicator may be subject not only to test sample size, but also to the personal characteristics of the subjects themselves eg. age, personal values, motivations. This may mean that the ´noise` of neural activity dictated by standard deviation measurements is such that no definitive conclusions can be made as to whether the test sample reflects what a larger group may give. There is also the added disadvantage that the accompanying neural imaging itself is costly and requires laboratory equipment, supervision and expertise. An alternative may be matching visual fixations to comprehensive psychological evaluations relating to personal values and motivations. Rippert and colleagues found that neural correlates of value vary with visual fixations such as saccades with numbers greater for preferred options. It may be possible therefore, to correlate visual stimulus and performance which would be easier to measure, is non-invasive and could mean that the subjects do not have to be in a laboratory to choice. This could mean that greater numbers of subjects could be used so that the test sample size would rule out inter-individual differences. Therefore, in answer to the question whether the NAC activity is suitable as an indicator of decision-making for a large number of individuals, then we have to say it is possible only by indirectly indicating personal value of a project, but there should be further investigation into this idea and there may be more suitable alternatives.

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

……it is known that administration of sodium lactate can induce a panic attack which affects judgement since the fear pathway is stimulated and changes in BLA and VTA/NAC connectivity occurs. Or, administration of the antidepressant citalopram can lead to increased medial PFC activity and changes in value assessment and encoding. Therefore, can we assume that the administration of these mood changers before the test would cause changes in value assessment and hence, decisions whether to fund a project, or not?

…would a change in experimental set-up so that the ´reward` (either the monetary gain or the earlier access to the funded film) was received by a person other than the participant lead to a change in NAC activity since ACC encodes values for others?

…can we assume that priming or meditation would lead to changes in NAC activity and decision-making since both are said to affect the neurochemical status of the individual and hence would change value encoding and assessment of rewards?

Posted in decision-making, neuronal firing, nucleus accumbens, Uncategorized, values | Tagged , , ,

investigation of default mode network role in automatic decision making

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


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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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feature binding in human visual working memory

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


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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

interbrain cortical synchronisation in motor areas encodes aspects of social interaction

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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