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”.
My recent reports on brain training for older adults (see, e.g., Review of working memory training programs finds no broader benefit; Cognitive training shown to help healthy older adults; Video game training benefits cognition in some older adults) converge on the idea that cognitive training can indeed be beneficial for older adults’ cognition, but there’s little wider transfer beyond the skills being practiced. That in itself can be valuable, but it does reinforce the idea that the best cognitive training covers a number of different domains or skill-sets. A new study adds little to this evidence, but does perhaps emphasize the importance of persistence and regularity in training.
The study involved 59 older adults (average age 84), of whom 33 used a brain fitness program 5 days a week for 30 minutes a day for at least 8 weeks, while the other group of 26 were put on a waiting list for the program. After two months, both groups were given access to the program, and both were encouraged to use it as much or as little as they wanted. Cognitive testing occurred before the program started, at two months, and at six months.
The first group to use the program used the program on average for 80 sessions, compared to an average 44 sessions for the wait-list group.
The higher use group showed significantly higher cognitive scores (delayed memory test; Boston Naming test) at both two and six months, while the lower (and later) use group showed improvement at the end of the six month period, but not as much as the higher use group.
I’m afraid I don’t have any more details (some details of the training program would be nice) because it was a conference presentation, so I only have access to the press release and the abstract. Because we don’t know exactly what the training entailed, we don’t know the extent to which it practiced the same skills that were tested. But we may at least add it to the evidence that you can improve cognitive skills by regular training, and that the length/amount of training (and perhaps regularity, since the average number of sessions for the wait-list group implies an average engagement of some three times a week, while the high-use group seem to have maintained their five-times-a-week habit) matters.
Another interesting presentation at the conference was an investigation into mental stimulating activities and brain activity in older adults.
In this study, 151 older adults (average age 82) from the Rush Memory and Aging Project answered questions about present and past cognitive activities, before undergoing brain scans. The questions concerned how frequently they engaged in mentally stimulating activities (such as reading books, writing letters, visiting a library, playing games) and the availability of cognitive resources (such as books, dictionaries, encyclopedias) in their home, during their lifetime (specifically, at ages 6, 12, 18, 40, and now).
Higher levels of cognitive activity and cognitive resources were also associated with better cognitive performance. Moreover, after controlling for education and total brain size, it was found that frequent cognitive activity in late life was associated with greater functional connectivity between the posterior cingulate cortex and several other regions (right orbital and middle frontal gyrus, left inferior frontal gyrus, hippocampus, right cerebellum, left inferior parietal cortex). More cognitive resources throughout life was associated with greater functional connectivity between the posterior cingulate cortex and several other regions (left superior occipital gyrus, left precuneus, left cuneus, right anterior cingulate, right middle frontal gyrus, and left inferior frontal gyrus).
Previous research has implicated a decline in connectivity with the posterior cingulate cortex in mild cognitive impairment and Alzheimer’s disease.
Cognitive activity earlier in life was not associated with differences in connectivity.
The findings provide further support for the idea “Use it or lose it!”, and suggests that mental activity protects against cognitive decline by maintaining functional connectivity in important neural networks.
Miller, K.J. et al. 2012. Memory Improves With Extended Use of Computerized Brain Fitness Program Among Older Adults. Presented August 3 at the 2012 convention of the American Psychological Association.
Han, S.D. et al. 2012. Cognitive Activity and Resources Are Associated With PCC Functional Connectivity in Older Adults. Presented August 3 at the 2012 convention of the American Psychological Association.
http://www.sciencedaily.com/releases/2012/08/120803193555.htm (first study only)
Another study adds to the evidence that changes in the brain that may lead eventually to Alzheimer’s begin many years before Alzheimer’s is diagnosed. The findings also add to the evidence that what we regard as “normal” age-related cognitive decline is really one end of a continuum of which the other end is dementia.
In the study, brain scans were taken of 137 highly educated people aged 30-89 (participants in the Dallas Lifespan Brain Study). The amount of amyloid-beta (characteristic of Alzheimer’s) was found to increase with age, and around a fifth of those over 60 had significantly elevated levels of the protein. These higher amounts were linked with worse performance on tests of working memory, reasoning and processing speed.
More specifically, across the whole sample, amyloid-beta levels affected processing speed and fluid intelligence (in a dose-dependent relationship — that is, as levels increased, these functions became more impaired), but not working memory, episodic memory, or crystallized intelligence. Among the elevated-levels group, increased amyloid-beta was significantly associated with poorer performance for processing speed, working memory, and fluid intelligence, but not episodic memory or crystallized intelligence. Among the group without elevated levels of the protein, increasing amyloid-beta only affected fluid intelligence.
These task differences aren’t surprising: processing speed, working memory, and fluid intelligence are the domains that show the most decline in normal aging.
Those with the Alzheimer’s gene APOE4 were significantly more likely to have elevated levels of amyloid-beta. While 38% of the group with high levels of the protein had the risky gene variant, only 15% of those who didn’t have high levels carried the gene.
Note that, while the prevalence of carriers of the gene variant matched population estimates (24%), the proportion was higher among those in the younger age group — 33% of those under 60, compared to 19.5% of those aged 60 or older. It seems likely that many older carriers have already developed MCI or Alzheimer’s, and thus been ineligible for the study.
The average age of the participants was 64, and the average years of education 16.4.
Amyloid deposits varied as a function of age and region: the precuneus, temporal cortex, anterior cingulate and posterior cingulate showed the greatest increase with age, while the dorsolateral prefrontal cortex, orbitofrontal cortex, parietal and occipital cortices showed smaller increases with age. However, when only those aged 60+ were analyzed, the effect of age was no longer significant. This is consistent with previous research, and adds to evidence that age-related cognitive impairment, including Alzheimer’s, has its roots in damage occurring earlier in life.
In another study, brain scans of 408 participants in the Mayo Clinic Study of Aging also found that higher levels of amyloid-beta were associated with poorer cognitive performance — but that this interacted with APOE status. Specifically, carriers of the Alzheimer’s gene variant were significantly more affected by having higher levels of the protein.
This may explain the inconsistent findings of previous research concerning whether or not amyloid-beta has significant effects on cognition in normal adults.
As the researchers of the first study point out, what’s needed is information on the long-term course of these brain changes, and they are planning to follow these participants.
In the meantime, all in all, the findings do provide more strength to the argument that your lifestyle in mid-life (and perhaps even younger) may have long-term consequences for your brain in old age — particularly for those with a genetic susceptibility to Alzheimer’s.
Following on from research showing that long-term meditation is associated with gray matter increases across the brain, an imaging study involving 27 long-term meditators (average age 52) and 27 controls (matched by age and sex) has revealed pronounced differences in white-matter connectivity between their brains.
The differences reflect white-matter tracts in the meditators’ brains being more numerous, more dense, more myelinated, or more coherent in orientation (unfortunately the technology does not yet allow us to disentangle these) — thus, better able to quickly relay electrical signals.
While the differences were evident among major pathways throughout the brain, the greatest differences were seen within the temporal part of the superior longitudinal fasciculus (bundles of neurons connecting the front and the back of the cerebrum) in the left hemisphere; the corticospinal tract (a collection of axons that travel between the cerebral cortex of the brain and the spinal cord), and the uncinate fasciculus (connecting parts of the limbic system, such as the hippocampus and amygdala, with the frontal cortex) in both hemispheres.
These findings are consistent with the regions in which gray matter increases have been found. For example, the tSLF connects with the caudal area of the temporal lobe, the inferior temporal gyrus, and the superior temporal gyrus; the UNC connects the orbitofrontal cortex with the amygdala and hippocampal gyrus
It’s possible, of course, that those who are drawn to meditation, or who are likely to engage in it long term, have fundamentally different brains from other people. However, it is more likely (and more consistent with research showing the short-term effects of meditation) that the practice of meditation changes the brain.
The precise mechanism whereby meditation might have these effects can only be speculated. However, more broadly, we can say that meditation might induce physical changes in the brain, or it might be protecting against age-related reduction. Most likely of all, perhaps, both processes might be going on, perhaps in different regions or networks.
Regardless of the mechanism, the evidence that meditation has cognitive benefits is steadily accumulating.
The number of years the meditators had practiced ranged from 5 to 46. They reported a number of different meditation styles, including Shamatha, Vipassana and Zazen.
Older news items (pre-2010) brought over from the old website
Meditation may increase gray matter
Adding to the increasing evidence for the cognitive benefits of meditation, a new imaging study of 22 experienced meditators and 22 controls has revealed that meditators showed significantly larger volumes of the right hippocampus and the right orbitofrontal cortex, and to a lesser extent the right thalamus and the left inferior temporal gyrus. There were no regions where controls had significantly more gray matter than meditators. These areas of the brain are all closely linked to emotion, and may explain meditators' improved ability in regulating their emotions.
Luders, E. et al. 2009. The underlying anatomical correlates of long-term meditation: Larger hippocampal and frontal volumes of gray matter. NeuroImage, 45 (3), 672-678.
Gene predicts better outcome as cortex normalizes in teens with ADHD
Recent research found that thickening of brain areas that control attention in the right cortex (right orbitofrontal/inferior prefrontal and posterior parietal cortex ) was associated with better clinical outcomes in ADHD. A new study has found that these brain areas are thinnest in those who carry a particular variant of a gene. The version of the dopamine D4 receptor gene, called the 7-repeat variant, was found in nearly a quarter of youth with ADHD and about one-sixth of the healthy controls. Although this particular gene version increased risk for ADHD, it also made it more likely that the areas would thicken during adolescence, with consequent improvement in behaviour and performance.
Shaw, P. et al. 2007. Polymorphisms of the Dopamine D4 Receptor, Clinical Outcome, and Cortical Structure in Attention-Deficit/Hyperactivity Disorder. Archives of General Psychiatry, 64, 921-931.
Key brain link in associative learning directly observed
Rat studies have now shown that the amygdala supports the formation of new associations by changing nerve cell firing patterns in a different but connected part of the brain. In earlier studies, the researchers had demonstrated that nerve cells in the amygdala and the orbitofrontal cortex changed their firing patterns to reflect new associations between cues and outcomes. In this later study, they examined how changes in neural activity in amygdala might be supporting changes in the orbitofrontal cortex. Rats were first deprived of water, then repeatedly given either desirable drinking water, laced with sugar, or undesirable drinking water, laced with quinine. The associations then learned would show up in the orbitofrontal cortex when the rats smelled the odor cue. The same activation patterns did not however, show up in those rats who had their amygdala chemically lesioned (although these rats still learned to avoid the undesirable drinking water). Specifically, although lesioned rats had neurons in the orbitofrontal cortex that were responsive to the odor cues, they did not have neurons that were responsive in anticipation of the predicted outcome. The responsive neurons were also less associative, more responsive to the identity of the cue rather than the association betwen odor and consequence.
Schoenbaum, G., Setlow, B., Saddoris, M.P. & Gallagher, M. 2003. Encoding Predicted Outcome and Acquired Value in Orbitofrontal Cortex during Cue Sampling Depends upon Input from Basolateral Amygdala. Neuron, 39, 855-867.