part of the medial section of the posterior parietal cortex
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:
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”.
 Stark, E. A., Parsons C. E., Van Hartevelt T. J., Charquero-Ballester M., McManners H., Ehlers A., et al.
(2015). Post-traumatic stress influences the brain even in the absence of symptoms: A systematic, quantitative meta-analysis of neuroimaging studies.
Neuroscience & Biobehavioral Reviews. 56, 207 - 221.
A pilot study involving 17 older adults with mild cognitive impairment and 18 controls (aged 60-88; average age 78) has found that a 12-week exercise program significantly improved performance on a semantic memory task, and also significantly improved brain efficiency, for both groups.
The program involved treadmill walking at a moderate intensity. The semantic memory tasks involved correctly recognizing names of celebrities well known to adults born in the 1930s and 40s (difficulty in remembering familiar names is one of the first tasks affected in Alzheimer’s), and recalling words presented in a list. Brain efficiency was demonstrated by a decrease in the activation intensity in the 11 brain regions involved in the memory task. The brain regions with improved efficiency corresponded to those involved in Alzheimer's disease, including the precuneus region, the temporal lobe, and the parahippocampal gyrus.
Participants also improved their cardiovascular fitness, by about 10%.
Smith, J.C. et al. 2013. Semantic Memory Functional MRI and Cognitive Function After Exercise Intervention in Mild Cognitive Impairment. Journal of Alzheimer’s Disease, 37 (1), 197-215.
Brain scans have revealed that those who regularly practiced yoga had larger brain volume in the
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!
 Mander, B. A., Rao V., Lu B., Saletin J. M., Lindquist J. R., Ancoli-Israel S., et al.
(2013). Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging.
Two recent conference presentations add to the evidence for the benefits of ‘brain training’, and of mental stimulation, for holding back age-related cognitive decline.
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)
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!
 Teki, S., Kumar S., von Kriegstein K., Stewart L., Lyness R. C., Moore B. C. J., et al.
(2012). Navigating the Auditory Scene: An Expert Role for the Hippocampus.
The Journal of Neuroscience. 32(35), 12251 - 12257.
Full text available at http://www.jneurosci.org/content/32/35/12251.abstract?sid=2d788914-e53c-...
A study involving those with a strong genetic risk of developing Alzheimer’s has found that the first signs of the disease can be detected 25 years before symptoms are evident. Whether this is also true of those who develop the disease without having such a strong genetic predisposition is not yet known.
The study involved 128 individuals with a 50% chance of inheriting one of three mutations that are certain to cause Alzheimer’s, often at an unusually young age. On the basis of participants’ parents’ medical history, an estimate of age of onset was calculated.
The first observable brain marker was a drop in cerebrospinal fluid levels of amyloid-beta proteins, and this could be detected 25 years before the anticipated age of onset. Amyloid plaques in the precuneus became visible on brain scans 15-20 years before memory problems become apparent; elevated cerebrospinal fluid levels of the tau protein 10-15 years, and brain atrophy in the hippocampus 15 years. Ten years before symptoms, the precuneus showed reduced use of glucose, and slight impairments in episodic memory (as measured in the delayed-recall part of the Wechsler’s Logical Memory subtest) were detectable. Global cognitive impairment (measured by the MMSE and the Clinical Dementia Rating scale) was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset.
Family members without the risky genes showed none of these changes.
The risky genes are PSEN1 (present in 70 participants), PSEN2 (11), and APP (7) — note that together these account for 30-50% of early-onset familial Alzheimer’s, although only 0.5% of Alzheimer’s in general. The ‘Alzheimer’s gene’ APOe4 (which is a risk factor for sporadic, not familial, Alzheimer’s), was no more likely to be present in these carriers (25%) than noncarriers (22%), and there were no gender differences. The average parental age of symptom onset was 46 (note that this pushes back the first biomarker to 21! Can we speculate a connection to noncarriers having significantly more education than carriers — 15 years vs 13.9?).
The results paint a clear picture of how Alzheimer’s progresses, at least in this particular pathway. First come increases in the amyloid-beta protein, followed by amyloid pathology, tau pathology, brain atrophy, and decreased glucose metabolism. Following this biological cascade, cognitive impairment ensues.
The degree to which these findings apply to the far more common sporadic Alzheimer’s is not known, but evidence from other research is consistent with this progression.
It must be noted, however, that the findings are based on cross-sectional data — that is, pieced together from individuals at different ages and stages. A longitudinal study is needed to confirm.
The findings do suggest the importance of targeting the first step in the cascade — the over-production of amyloid-beta — at a very early stage.
Researchers encourage people with a family history of multiple generations of Alzheimer’s diagnosed before age 55 to register at http://www.DIANXR.org/, if they would like to be considered for inclusion in any research.
 Bateman, R. J., Xiong C., Benzinger T. L. S., Fagan A. M., Goate A., Fox N. C., et al.
(2012). Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease.
New England Journal of Medicine. 120723122607004 - 120723122607004.
The protein associated with Alzheimer's disease appears to impair cognitive function many years before symptoms manifest. Higher levels of this protein are more likely in carriers of the Alzheimer’s gene, and such carriers may be more affected by the protein’s presence.
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.
 Rodrigue, K. M., Kennedy K. M., Devous M. D., Rieck J. R., Hebrank A. C., Diaz-Arrastia R., et al.
(2012). Β-Amyloid Burden in Healthy Aging Regional Distribution and Cognitive Consequences.
Neurology. 78(6), 387 - 395.
 Kantarci, K., Lowe V., Przybelski S. a, Weigand S. d, Senjem M. l, Ivnik R. J., et al.
(2012). APOE modifies the association between Aβ load and cognition in cognitively normal older adults.
Neurology. 78(4), 232 - 240.
A study involving 159 older adults (average age 76) has confirmed that the amount of brain tissue in specific regions is a predictor of Alzheimer’s disease development. Of the 159 people, 19 were classified as at high risk on the basis of the smaller size of nine small regions previously shown to be vulnerable to Alzheimer's), and 24 as low risk. The regions, in order of importance, are the medial temporal, inferior temporal, temporal pole, angular gyrus, superior parietal, superior frontal, inferior frontal cortex, supramarginal gyrus, precuneus.
There was no difference between the three risk groups at the beginning of the study on global cognitive measures (MMSE; Alzheimer’s Disease Assessment Scale—cognitive subscale; Clinical Dementia Rating—sum of boxes), or in episodic memory. The high-risk group did perform significantly more slowly on the Trail-making test part B, with similar trends on the Digit Symbol and Verbal Fluency tests.
After three years, 125 participants were re-tested. Nine met the criteria for cognitive decline. Of these, 21% were from the small high-risk group (3/14) and 7% from the much larger average-risk group (6/90). None were from the low-risk group.
The results were even more marked when less stringent criteria were used. On the basis of an increase on the Clinical Dementia Rating, 28.5% of the high-risk group and 9.7% of the average-risk group showed decline. On the basis of declining at least one standard deviation on any one of the three neuropsychological tests, half the high-risk group, 35% of the average risk group, and 14% (3/21) of the low-risk group showed decline. (The composite criteria required both of these criteria.)
Analysis estimated that every standard deviation of cortical thinning (reduced brain tissue) was associated with a nearly tripled risk of cognitive decline.
The 84 individuals for whom amyloid-beta levels in the cerebrospinal fluid were available also revealed that 60% of the high-risk group had levels consistent with the presence of Alzheimer's pathology, compared to 36% of those at average risk and 19% of those at low risk.
The findings extend and confirm the evidence that brain atrophy in specific regions is a biomarker for developing Alzheimer’s.
 Dickerson, B. C., & Wolk D. A.
(2012). MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults.
Neurology. 78(2), 84 - 90.
Dickerson BC, Bakkour A, Salat DH, et al. 2009. The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloidpositive individuals. Cereb Cortex;19:497–510.
A new imaging study reveals what’s going on in the brains of expert shogi players that’s different from those of amateurs. It’s all about developing instincts.
The mental differences between a novice and an expert are only beginning to be understood, but two factors thought to be of importance are automaticity (the process by which a procedure becomes so practiced that it no longer requires conscious thought) and chunking (the unitizing of related bits of information into one tightly integrated unit — see my recent blog post on working memory). A new study adds to our understanding of this process by taking images of the brains of professional and amateur players of the Japanese chess-like game of shogi.
Eleven professional, 9 high- and 8 low-rank amateur players of shogi were presented with patterns of different types (opening shogi patterns, endgame shogi patterns, random shogi patterns, chess, Chinese chess, as well as completely different stimuli — scenes, faces, other objects, scrambled patterns).
It was found that the board game patterns, but not the other patterns, stimulated activity in the posterior precuneus of all shogi players. This activity, for the professional players, was particularly strong for shogi opening and endgame patterns, and activity in the precuneus was the only regional activity that showed a difference between these patterns and the other board game patterns. For the amateurs however, there was no differential activity for the endgame patterns, and only the high-rank amateurs showed differential activity for the opening shogi patterns. Opening patterns tend to be more stereotyped than endgame patterns (i.e., endgame patterns are better reflections of expertise).
The players were then asked for the best next-move in a series of shogi problems (a) when they only had one second to study the pattern, and (b) when they had eight seconds. When professional players had only a second to study the problem, the caudate nucleus was active. When they had 8 seconds, activity was confined to the cerebral cortex, as it was for the amateurs in both conditions. This activity in the caudate, which is part of the basal ganglia, deep within the brain, is thought to reflect the development of an intuitive response.
The researchers therefore suggest that this type of intuition, an instinct achieved through training and experience, is what marks an expert. Making part of the process unconscious not only makes it faster, but frees up valuable space in working memory for aspects that need conscious thought.
The posterior precuneus directly connects with the dorsolateral prefrontal cortex, which in turn connects to the caudate. There is also a direct connection between the precuneus and the caudate. This precuneus-caudate circuit is therefore suggested as a key part of what makes a board-game expert an expert.
 Wan, X., Nakatani H., Ueno K., Asamizuya T., Cheng K., & Tanaka K.
(2011). The Neural Basis of Intuitive Best Next-Move Generation in Board Game Experts.
Science. 331(6015), 341 - 346.
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