insula

part of the paralimbic zone, it is critical for perception and modulation of sensory and autonomic data, including pain and visceral sensations. It's also involved in speech.

Trauma changes the brain even in those without PTSD

  • A review of previous research has compared brain activity in those with PTSD who experienced trauma, those who experienced trauma but didn't develop PTSD, and those who never experienced trauma.
  • Those who had PTSD had differential activity in two brain regions.
  • Those who had experienced trauma had diffe

A meta-analysis of studies reporting brain activity in individuals with a diagnosis of PTSD has revealed differences between the brain activity of individuals with PTSD and that of groups of both trauma-exposed (those who had experienced trauma but didn't have a diagnosis of PTSD) and trauma-naïve (those who hadn't experienced trauma) participants.

The critical difference between those who developed PTSD and those who experienced trauma but didn't develop PTSD lay in the basal ganglia. Specifically:

  • PTSD brains compared with trauma-exposed controls showed differentially active regions of the basal ganglia
  • trauma-exposed brains compared with trauma-naïve controls revealed differences in the right anterior insula, precuneus, cingulate and orbitofrontal cortices, all known to be involved in emotional regulation
  • PTSD brains compared with both control groups showed differences in activity in the amygdala and parahippocampal cortex.

The finding is consistent with other new evidence from the researchers, that other neuropsychiatric disorders were also associated with specific imbalances in specific brain networks.

The findings suggest that, while people who have experienced trauma may not meet the threshold for a diagnosis of PTSD, they may have similar changes within the brain, which might make them more vulnerable to PTSD if they experience a subsequent trauma.

The finding also suggests a different perspective on PTSD — that it “may not actually be abnormal or a 'disorder' but the brain's natural reaction to events and experiences that are abnormal”.

http://www.eurekalert.org/pub_releases/2015-08/uoo-tec080315.php

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Cognitive decline in old age related to poorer sleep

February, 2013

A new study confirms the role slow-wave sleep plays in consolidating memories, and reveals that one reason for older adults’ memory problems may be the quality of their sleep.

Recent research has suggested that sleep problems might be a risk factor in developing Alzheimer’s, and in mild cognitive impairment. A new study adds to this gathering evidence by connecting reduced slow-wave sleep in older adults to brain atrophy and poorer learning.

The study involved 18 healthy young adults (mostly in their 20s) and 15 healthy older adults (mostly in their 70s). Participants learned 120 word- nonsense word pairs and were tested for recognition before going to bed. Their brain activity was recorded while they slept. Brain activity was also measured in the morning, when they were tested again on the word pairs.

As has been found previously, older adults showed markedly less slow-wave activity (both over the whole brain and specifically in the prefrontal cortex) than the younger adults. Again, as in previous studies, the biggest difference between young and older adults in terms of gray matter volume was found in the medial prefrontal cortex (mPFC). Moreover, significant differences were also found in the insula and posterior cingulate cortex. These regions, like the mPFC, have also been associated with the generation of slow waves.

When mPFC volume was taken into account, age no longer significantly predicted the extent of the decline in slow-wave activity — in other words, the decline in slow-wave activity appears to be due to the brain atrophy in the medial prefrontal cortex. Atrophy in other regions of the brain (precuneus, hippocampus, temporal lobe) was not associated with the decline in slow-wave activity when age was considered.

Older adults did significantly worse on the delayed recognition test than young adults. Performance on the immediate test did not predict performance on the delayed test. Moreover, the highest performers on the immediate test among the older adults performed at the same level as the lowest young adult performers — nevertheless, these older adults did worse the following day.

Slow-wave activity during sleep was significantly associated with performance on the next day’s test. Moreover, when slow-wave activity was taken into account, neither age nor mPFC atrophy significantly predicted test performance.

In other words, age relates to shrinkage of the prefrontal cortex, this shrinkage relates to a decline in slow-wave activity during sleep, and this decline in slow-wave sleep relates to poorer cognitive performance.

The findings confirm the importance of slow-wave brainwaves for memory consolidation.

All of this suggests that poorer sleep quality contributes significantly to age-related cognitive decline, and that efforts should be made to improve quality of sleep rather than just assuming lighter, more disturbed sleep is ‘natural’ in old age!

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Evidence that IQ is rooted in two main brain networks

January, 2013

A very large online study helps decide between the idea of intelligence as a single factor (‘g’) versus having multiple domains.

An online study open to anyone, that ended up involving over 100,000 people of all ages from around the world, put participants through 12 cognitive tests, as well as questioning them about their background and lifestyle habits. This, together with a small brain-scan data set, provided an immense data set to investigate the long-running issue: is there such a thing as ‘g’ — i.e. is intelligence accounted for by just a single general factor; is it supported by just one brain network? — or are there multiple systems involved?

Brain scans of 16 healthy young adults who underwent the 12 cognitive tests revealed two main brain networks, with all the tasks that needed to be actively maintained in working memory (e.g., Spatial Working Memory, Digit Span, Visuospatial Working Memory) loading heavily on one, and tasks in which information had to transformed according to logical rules (e.g., Deductive Reasoning, Grammatical Reasoning, Spatial Rotation, Color-Word Remapping) loading heavily on the other.

The first of these networks involved the insula/frontal operculum, the superior frontal sulcus, and the ventral part of the anterior cingulate cortex/pre-supplementary motor area. The second involved the inferior frontal sulcus, inferior parietal lobule, and the dorsal part of the ACC/pre-SMA.

Just a reminder of individual differences, however — when analyzed by individual, this pattern was observed in 13 of the 16 participants (who are not a very heterogeneous bunch — I strongly suspect they are college students).

Still, it seems reasonable to conclude, as the researchers do, that at least two functional networks are involved in ‘intelligence’, with all 12 cognitive tasks using both networks but to highly variable extents.

Behavioral data from some 60,000 participants in the internet study who completed all tasks and questionnaires revealed that there was no positive correlation between performance on the working memory tasks and the reasoning tasks. In other words, these two factors are largely independent.

Analysis of this data revealed three, rather than two, broad components to overall cognitive performance: working memory; reasoning; and verbal processing. Re-analysis of the imaging data in search of the substrate underlying this verbal component revealed that the left inferior frontal gyrus and temporal lobes were significantly more active on tasks that loaded on the verbal component.

These three components could also be distinguished when looking at other factors. For example, while age was the most significant predictor of cognitive performance, its effect on the verbal component was much later and milder than it was for the other two components. Level of education was more important for the verbal component than the other two, while the playing of computer games had an effect on working memory and reasoning but not verbal. Chronic anxiety affected working memory but not reasoning or verbal. Smoking affected working memory more than the others. Unsurprisingly, geographical location affected verbal more than the other two components.

A further test, involving 35 healthy young adults, compared performance on the 12 tasks and score on the Cattell Culture Fair test (a classic pen and paper IQ test). The working memory component correlated most with the Cattell score, followed by the reasoning component, with the Verbal component (unsurprisingly, given that this is designed to be a ‘culture-fair’ test) showing the smallest correlation.

All of this is to say that this is decided evidence that what is generally considered ‘intelligence’ is based on the functioning of multiple brain networks rather than a single ‘g’, and that these networks are largely independent. Thus, the need to focus on and maintain task-relevant information maps onto one particular brain network, and is one strand. Another network specializes in transforming information, regardless of source or type. These, it would seem, are the main processes involved in fluid intelligence, while the Verbal component most likely reflects crystallized intelligence. There are also likely to be other networks which are not perhaps typically included in ‘general intelligence’, but are nevertheless critical for task performance (the researchers suggest the ability to adapt plans based on outcomes might be one such function).

The obvious corollary of all this is that similar IQ scores can reflect different abilities for these strands — e.g., even if your working memory capacity is not brilliant, you can develop your reasoning and verbal abilities. All this is consistent with the growing evidence that, although fundamental WMC might be fixed (and I use the word ‘fundamental’ deliberately, because WMC can be measured in a number of different ways, and I do think you can, at the least, effectively increase your WMC), intelligence (because some of its components are trainable) is not.

If you want to participate in this research, a new version of the tests is available at http://www.cambridgebrainsciences.com/theIQchallenge

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How piano tuning changes the brain

September, 2012

In another example of how expertise in a specific area changes the brain, brain scans of piano tuners show which areas grow, and which shrink, with experience — and starting age.

I’ve reported before on how London taxi drivers increase the size of their posterior hippocampus by acquiring and practicing ‘the Knowledge’ (but perhaps at the expense of other functions). A new study in similar vein has looked at the effects of piano tuning expertise on the brain.

The study looked at the brains of 19 professional piano tuners (aged 25-78, average age 51.5 years; 3 female; 6 left-handed) and 19 age-matched controls. Piano tuning requires comparison of two notes that are close in pitch, meaning that the tuner has to accurately perceive the particular frequency difference. Exactly how that is achieved, in terms of brain function, has not been investigated until now.

The brain scans showed that piano tuners had increased grey matter in a number of brain regions. In some areas, the difference between tuners and controls was categorical — that is, tuners as a group showed increased gray matter in right hemisphere regions of the frontal operculum, the planum polare, superior frontal gyrus, and posterior cingulate gyrus, and reduced gray matter in the left hippocampus, parahippocampal gyrus, and superior temporal lobe. Differences in these areas didn’t vary systematically between individual tuners.

However, tuners also showed a marked increase in gray matter volume in several areas that was dose-dependent (that is, varied with years of tuning experience) — the anterior hippocampus, parahippocampal gyrus, right middle temporal and superior temporal gyrus, insula, precuneus, and inferior parietal lobe — as well as an increase in white matter in the posterior hippocampus.

These differences were not affected by actual chronological age, or, interestingly, level of musicality. However, they were affected by starting age, as well as years of tuning experience.

What these findings suggest is that achieving expertise in this area requires an initial development of active listening skills that is underpinned by categorical brain changes in the auditory cortex. These superior active listening skills then set the scene for the development of further skills that involve what the researchers call “expert navigation through a complex soundscape”. This process may, it seems, involve the encoding and consolidating of precise sound “templates” — hence the development of the hippocampal network, and hence the dependence on experience.

The hippocampus, apart from its general role in encoding and consolidating, has a special role in spatial navigation (as shown, for example, in the London cab driver studies, and the ‘parahippocampal place area’). The present findings extend that navigation in physical space to the more metaphoric one of relational organization in conceptual space.

The more general message from this study, of course, is confirmation for the role of expertise in developing specific brain regions, and a reminder that this comes at the expense of other regions. So choose your area of expertise wisely!

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How emotion keeps some memories vivid

September, 2012

Emotionally arousing images that are remembered more vividly were seen more vividly. This may be because the amygdala focuses visual attention rather than more cognitive attention on the image.

We know that emotion affects memory. We know that attention affects perception (see, e.g., Visual perception heightened by meditation training; How mindset can improve vision). Now a new study ties it all together. The study shows that emotionally arousing experiences affect how well we see them, and this in turn affects how vividly we later recall them.

The study used images of positively and negatively arousing scenes and neutral scenes, which were overlaid with varying amounts of “visual noise” (like the ‘snow’ we used to see on old televisions). College students were asked to rate the amount of noise on each picture, relative to a specific image they used as a standard. There were 25 pictures in each category, and three levels of noise (less than standard, equal to standard, and more than standard).

Different groups explored different parameters: color; gray-scale; less noise (10%, 15%, 20% as compared to 35%, 45%, 55%); single exposure (each picture was only presented once, at one of the noise levels).

Regardless of the actual amount of noise, emotionally arousing pictures were consistently rated as significantly less noisy than neutral pictures, indicating that people were seeing them more clearly. This was true in all conditions.

Eye-tracking analysis ruled out the idea that people directed their attention differently for emotionally arousing images, but did show that more eye fixations were associated both with less noisy images and emotionally arousing ones. In other words, people were viewing emotionally important images as if they were less noisy.

One group of 22 students were given a 45-minute spatial working memory task after seeing the images, and then asked to write down all the details they could remember about the pictures they remembered seeing. The amount of detail they recalled was taken to be an indirect measure of vividness.

A second group of 27 students were called back after a week for a recognition test. They were shown 36 new images mixed in with the original 75 images, and asked to rate them as new, familiar, or recollected. They were also asked to rate the vividness of their recollection.

Although, overall, emotionally arousing pictures were not more likely to be remembered than neutral pictures, both experiments found that pictures originally seen as more vivid (less noise) were remembered more vividly and in more detail.

Brain scans from 31 students revealed that the amygdala was more active when looking at images rated as vivid, and this in turn increased activity in the visual cortex and in the posterior insula (which integrates sensations from the body). This suggests that the increased perceptual vividness is not simply a visual phenomenon, but part of a wider sensory activation.

There was another neural response to perceptual vividness: activity in the dorsolateral prefrontal cortex and the posterior parietal cortex was negatively correlated with vividness. This suggests that emotion is not simply increasing our attentional focus, it is instead changing it by reducing effortful attentional and executive processes in favor of more perceptual ones. This, perhaps, gives emotional memories their different ‘flavor’ compared to more neutral memories.

These findings clearly need more exploration before we know exactly what they mean, but the main finding from the study is that the vividness with which we recall some emotional experiences is rooted in the vividness with which we originally perceived it.

The study highlights how emotion can sharpen our attention, building on previous findings that emotional events are more easily detected when visibility is difficult, or attentional demands are high. It is also not inconsistent with a study I reported on last year, which found some information needs no repetition to be remembered because the amygdala decrees it of importance.

I should add, however, that the perceptual effect is not the whole story — the current study found that, although perceptual vividness is part of the reason for memories that are vividly remembered, emotional importance makes its own, independent, contribution. This contribution may occur after the event.

It’s suggested that individual differences in these reactions to emotionally enhanced vividness may underlie an individual’s vulnerability to post-traumatic stress disorder.

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Dealing with math anxiety

November, 2011

A new study shows that some math-anxious students can overcome performance deficits through their ability to control their negative responses. The finding indicates that interventions should focus on anticipatory cognitive control.

Math-anxiety can greatly lower performance on math problems, but just because you suffer from math-anxiety doesn’t mean you’re necessarily going to perform badly. A study involving 28 college students has found that some of the students anxious about math performed better than other math-anxious students, and such performance differences were associated with differences in brain activity.

Math-anxious students who performed well showed increased activity in fronto-parietal regions of the brain prior to doing math problems — that is, in preparation for it. Those students who activated these regions got an average 83% of the problems correct, compared to 88% for students with low math anxiety, and 68% for math-anxious students who didn’t activate these regions. (Students with low anxiety didn’t activate them either.)

The fronto-parietal regions activated included the inferior frontal junction, inferior parietal lobule, and left anterior inferior frontal gyrus — regions involved in cognitive control and reappraisal of negative emotional responses (e.g. task-shifting and inhibiting inappropriate responses). Such anticipatory activity in the fronto-parietal region correlated with activity in the dorsomedial caudate, nucleus accumbens, and left hippocampus during math activity. These sub-cortical regions (regions deep within the brain, beneath the cortex) are important for coordinating task demands and motivational factors during the execution of a task. In particular, the dorsomedial caudate and hippocampus are highly interconnected and thought to form a circuit important for flexible, on-line processing. In contrast, performance was not affected by activity in ‘emotional’ regions, such as the amygdala, insula, and hypothalamus.

In other words, what’s important is not your level of anxiety, but your ability to prepare yourself for it, and control your responses. What this suggests is that the best way of dealing with math anxiety is to learn how to control negative emotional responses to math, rather than trying to get rid of them.

Given that cognitive control and emotional regulation are slow to mature, it also suggests that these effects are greater among younger students.

The findings are consistent with a theory that anxiety hinders cognitive performance by limiting the ability to shift attention and inhibit irrelevant/distracting information.

Note that students in the two groups (high and low anxiety) did not differ in working memory capacity or in general levels of anxiety.

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