choice changes connectivity between prefrontal cortex and striatum

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


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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Since we`re talking about the topic,

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

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

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

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

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

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


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