Striatum

We know that stress has a complicated relationship with learning, but in general its effect is negative, and part of that is due to stress producing anxious thoughts that clog up working memory. A new study adds another perspective to that.

The brain scanning study involved 60 young adults, of whom half were put under stress by having a hand immersed in ice-cold water for three minutes under the supervision of a somewhat unfriendly examiner, while the other group immersed their hand in warm water without such supervision (cortisol and blood pressure tests confirmed the stress difference).

About 25 minutes after this (cortisol reaches peak levels around 25 minutes after stress), participants’ brains were scanned while participants alternated between a classification task and a visual-motor control task. The classification task required them to look at cards with different symbols and learn to predict which combinations of cards announced rain and which sunshine. Afterward, they were given a short questionnaire to determine their knowledge of the task. The control task was similar but there were no learning demands (they looked at cards on the screen and made a simple perceptual decision).

In order to determine the strategy individuals used to do the classification task, ‘ideal’ performance was modeled for four possible strategies, of which two were ‘simple’ (based on single cues) and two ‘complex’ (based on multiple cues).

Here’s the interesting thing: while both groups were successful in learning the task, the two groups learned to do it in different ways. Far more of the non-stressed group activated the hippocampus to pursue a simple and deliberate strategy, focusing on individual symbols rather than combinations of symbols. The stressed group, on the other hand, were far more likely to use the striatum only, in a more complex and subconscious processing of symbol combinations.

The stressed group also remembered significantly fewer details of the classification task.

There was no difference between the groups on the (simple, perceptual) control task.

In other words, it seems that stress interferes with conscious, purposeful learning, causing the brain to fall back on more ‘primitive’ mechanisms that involve procedural learning. Striatum-based procedural learning is less flexible than hippocampus-based declarative learning.

Why should this happen? Well, the non-conscious procedural learning going on in the striatum is much less demanding of cognitive resources, freeing up your working memory to do something important — like worrying about the source of the stress.

Unfortunately, such learning will not become part of your more flexible declarative knowledge base.

The finding may have implications for stress disorders such as depression, addiction, and PTSD. It may also have relevance for a memory phenomenon known as “forgotten baby syndrome”, in which parents forget their babies in the car. This may be related to the use of non-declarative memory, because of the stress they are experiencing.

[3071] Schwabe L, Wolf OT. Stress Modulates the Engagement of Multiple Memory Systems in Classification Learning. The Journal of Neuroscience [Internet]. 2012 ;32(32):11042 - 11049. Available from: http://www.jneurosci.org/content/32/32/11042

Genetic comparisons have pinpointed a specific protein as crucial for brain size, both between and within species. Another shows how genetic regulation in the frontal lobes distinguishes the human brain from that of closely related species, and points to two genes in particular as critical.

The protein determining brain size

Comparison of genome sequences from humans and other animals has revealed what may be a crucial protein in the development of the human brain. The analysis found that humans have more than 270 copies of a protein called DUF1220 — more than any other animal studied — and that the number of copies in a species seems to match how close they are to us. Chimpanzees, for example, have 125, and gorillas 99, while marmosets have only 30, and mice just one.

Moreover, comparison of humans with microcephaly and macrocephaly reveals that those with microcephaly (“small brain”) have lower numbers of this protein than normal for humans, and those with macrocephaly (“large brain”) have higher numbers. Copy numbers of the protein were also correlated with gray matter volume in humans without these brain disorders.

In other words, evidence from three lines of inquiry converge on DUF1220 copy number being associated with brain size.

Differences in gene expression and connectivity

But the development of the human brain is not only about size. The human brain is more complex, more connected, than the brains of most other animals. Another genetic analysis has been comparing gene activity in humans, chimpanzees and rhesus macaques, using post-mortem brain tissue of three regions in particular – the frontal cortex, hippocampus and striatum.

Gene expression in the frontal lobe of humans showed a striking increase in molecular complexity, with much more elaborate regulation and connection. The biggest differences occurred in the expression of human genes involved in plasticity.

One gene in particular stood out as behaving differently in the human brain. This gene — called CLOCK, for obvious reasons — is thought to be the master regulator of our body’s clocks. The finding suggests it has influence beyond this role. Interestingly, this gene is often disrupted in mood disorders such as depression and bipolar syndrome.

A second important distinction was how many more connections there were in human brains among networks that included the language genes FOXP1 and FOXP2.

In comparison to all this, gene expression in the caudate nucleus was very similar across all three species.

The findings point to the role of learning (the genes involved in plasticity) and language in driving human brain evolution. They also highlight the need to find out more about the CLOCK gene.

Research has shown that younger adults are better decision makers than older adults — a curious result. A new study tried to capture more ‘real-world’ decision-making, by requiring participants to evaluate each result in order to strategize the next choice.

This time (whew!), the older adults did better.

In the first experiment, groups of older (60-early 80s) and younger (college-age) adults received points each time they chose from one of four options and tried to maximize the points they earned.  For this task, the younger adults were more efficient at selecting the options that yielded more points.

In the second experiment, the rewards received depended on the choices made previously.  The “decreasing option” gave a larger number of points on each trial, but caused rewards on future trials to be lower. The “increasing option” gave a smaller reward on each trial but caused rewards on future trials to increase.  In one version of the test, the increasing option led to more points earned over the course of the experiment; in another, chasing the increasing option couldn’t make up for the points that could be accrued grabbing the bigger bite on each trial.

The older adults did better on every permutation.

Understanding more complex scenarios is where experience tells. The difference in performance also may reflect the different ways younger and older adults use their brains. Decision-making can involve two different reward learning systems, according to recent thinking. In the model-based system, a cognitive model is constructed that shows how various actions and their rewards are connected to each other. Decisions are made by simulating how one decision will affect future decisions. In the model-free system, on the other hand, only values associated with each choice are considered.

These systems are rooted in different parts of the brain. The model-based system uses the intraparietal sulcus and lateral prefrontal cortex, while the model-free system uses the ventral striatum. There is some evidence that younger adults use the ventral striatum (involved in habitual, reflexive learning and immediate reward) for decision-making more than older adults, and older adults use the dorsolateral prefrontal cortex (involved in more rational, deliberative thinking) more than younger adults.

We learn from what we read and what people tell us, and we learn from our own experience. Although you would think that personal experience would easily trump other people’s advice, we in fact tend to favor abstract information against our own experience. This is seen in the way we commonly distort what we experience in ways that match what we already believe. But there is probably good reason for this tendency (reflected in confirmation bias), even if it sometimes goes wrong.

But of course individuals vary in the extent to which they persist with bad advice. A new study points to genes as a critical reason. Different brain regions are involved in the processing of these two information sources (advice vs experience): the prefrontal cortex and the striatum. Variants in the genes DARPP-32 and DRD2 affect the response to dopamine in the striatum. Variation in the gene COMT, on the other hand, affects dopamine response in the prefrontal cortex.

In the study, over 70 people performed a computerized learning task in which they had to pick the "correct" symbol, which they learned through trial and error. For some symbols, subjects were given advice, and sometimes that advice was wrong.

COMT gene variants were predictive of the degree to which participants persisted in responding in accordance with prior instructions even as evidence against their correctness grew. Variants in DARPP-32 and DRD2 predicted learning from positive and negative outcomes, and the degree to which such learning was overly inflated or neglected when outcomes were consistent or inconsistent with prior instructions.

Older news items (pre-2010) brought over from the old website

October 2008

Why it’s so hard to disrupt your routine

New research has added to our understanding of why we find it so hard to break a routine or overcome bad habits. The problem lies in the competition between the striatum and the hippocampus. The striatum is involved with habits and routines, for example, it records cues or landmarks that lead to a familiar destination. It’s the striatum that enables you to drive familiar routes without much conscious awareness. If you’re travelling an unfamiliar route however, you need the hippocampus, which is much ‘smarter’. The mouse study found that when the striatum was disrupted, the mice had trouble navigating using landmarks, but they were actually better at spatial learning. When the hippocampus was disrupted, the converse was true. This may help us understand, and treat, certain mental illnesses in which patients have destructive, habit-like patterns of behavior or thought. Obsessive-compulsive disorder, Tourette syndrome, and drug addiction all involve abnormal function of the striatum. Cognitive-behavioral therapy may be thought of as trying to learn to use one of these systems to overcome and, ultimately, to re-train the other.

Lee, A.S. et al. 2008. A double dissociation revealing bidirectional competition between striatum and hippocampus during learning. Proceedings of the National Academy of Sciences, 105 (44), 17163-17168.

http://www.eurekalert.org/pub_releases/2008-10/yu-ce102008.php

August 2008

One sleepless night increases dopamine

A study has found that sleep deprivation increases the level of the hormone dopamine in two brain structures: the striatum, which is involved in motivation and reward, and the thalamus, which is involved in alertness. The rise in dopamine following sleep deprivation may promote wakefulness to compensate for sleep loss. However, since the amount of dopamine correlated with feelings of fatigue and impaired performance on cognitive tasks, it appears that the adaptation is not sufficient to overcome the cognitive deterioration induced by sleep deprivation and may even contribute to it. Amphetamines increase dopamine levels.

Volkow, N.D. et al. 2008. Sleep Deprivation Decreases Binding of [11C]Raclopride to Dopamine D2/D3 Receptors in the Human Brain. Journal of Neuroscience, 28, 8454-8461.

http://www.eurekalert.org/pub_releases/2008-08/sfn-osn081808.php

April 2008

How chronic exposure to solvents can impair the brain

Chronic occupational exposure to organic solvents, found in materials such as paints, printing and dry cleaning agents, has been linked to long-term cognitive impairment, but chronic solvent-induced encephalopathy (CSE) is still a controversial diagnosis. An imaging study of 10 CSE patients who had been exposed to solvents and had mild to severe cognitive impairment, 10 participants who had been exposed to solvents but had no CSE symptoms, and 11 participants who were not exposed to solvents and had no symptoms, has now found impairment in the frontal-striatal-thalamic (FST) circuitry of CSE patients. The disturbances are predictive of the clinical findings — impaired psychomotor speed and attention — and were also linked to exposure severity.

Visser, I. et al. 2008. Cerebral impairment in chronic solvent-induced encephalopathy (p NA). Annals of Neurology, Published online April 15 2008

http://www.eurekalert.org/pub_releases/2008-04/w-dib041508.php

February 2007

Common gene version optimizes thinking but carries a risk

On the same subject, another study has found that the most common version of DARPP-32, a gene that shapes and controls a circuit between the striatum and prefrontal cortex, optimizes information filtering by the prefrontal cortex, thus improving working memory capacity and executive control (and thus, intelligence). However, the same version was also more prevalent among people who developed schizophrenia, suggesting that a beneficial gene variant may translate into a disadvantage if the prefrontal cortex is impaired. In other words, one of the things that make humans more intelligent as a species may also make us more vulnerable to schizophrenia.

Meyer-Lindenberg,A. et al. 2007. Genetic evidence implicating DARPP-32 in human frontostriatal structure, function, and cognition. Journal of Clinical Investigation, 117 (3), 672-682.

http://www.sciencedaily.com/releases/2007/02/070208230059.htm
http://www.eurekalert.org/pub_releases/2007-02/niom-cgv020707.php

July 2006

How multitasking impedes learning

A number of studies have come out in recent years demonstrating that the human brain can’t really do two things at once, and that when we do attempt to do so, performance is impaired. A new imaging study provides evidence that we tend to use a less efficient means of learning when distracted by another task. In the study, 14 younger adults (in their twenties) learned a simple classification task by trial-and-error. For one set of the cards, they also had to keep a running mental count of high tones that they heard while learning the classification task. Imaging revealed that different brain regions were used for learning depending on whether the participants were distracted by the other task or not — the hippocampus was involved in the single-task learning, but not in the dual-task, when the striatum (a region implicated in procedural and habit learning) was active. Although the ability of the participants to learn didn’t appear to be affected at the time, the distraction did reduce the participants' subsequent knowledge about the task during a follow-up session. In particular, on the task learned with the distraction, participants could not extrapolate from what they had learned.

Foerde, K., Knowlton, B.J. & Poldrack, R.A. Modulation of competing memory systems by distraction. Proceedings of the National Academy of Sciences, 103, 11778-11783.

http://www.sciencedaily.com/releases/2006/07/060726083302.htm