working memory

The importance of cognitive control for intelligence

October, 2012

Brain imaging points to the importance of cognitive control, mediated by the connectivity of one particular brain region, for fluid intelligence.

What underlies differences in fluid intelligence? How are smart brains different from those that are merely ‘average’?

Brain imaging studies have pointed to several aspects. One is brain size. Although the history of simplistic comparisons of brain size has been turbulent (you cannot, for example, directly compare brain size without taking into account the size of the body it’s part of), nevertheless, overall brain size does count for something — 6.7% of individual variation in intelligence, it’s estimated. So, something, but not a huge amount.

Activity levels in the prefrontal cortex, research also suggests, account for another 5% of variation in individual intelligence. (Do keep in mind that these figures are not saying that, for example, prefrontal activity explains 5% of intelligence. We are talking about differences between individuals.)

A new study points to a third important factor — one that, indeed, accounts for more than either of these other factors. The strength of the connections from the left prefrontal cortex to other areas is estimated to account for 10% of individual differences in intelligence.

These findings suggest a new perspective on what intelligence is. They suggest that part of intelligence rests on the functioning of the prefrontal cortex and its ability to communicate with the rest of the brain — what researchers are calling ‘global connectivity’. This may reflect cognitive control and, in particular, goal maintenance. The left prefrontal cortex is thought to be involved in (among other things) remembering your goals and any instructions you need for accomplishing those goals.

The study involved 93 adults (average age 23; range 18-40), whose brains were monitored while they were doing nothing and when they were engaged in the cognitively challenging N-back working memory task.

Brain activity patterns revealed three regions within the frontoparietal network that were significantly involved in this task: the left lateral prefrontal cortex, right premotor cortex, and right medial posterior parietal cortex. All three of these regions also showed signs of being global hubs — that is, they were highly connected to other regions across the brain.

Of these, however, only the left lateral prefrontal cortex showed a significant association between its connectivity and individual’s fluid intelligence. This was confirmed by a second independent measure — working memory capacity — which was also correlated with this region’s connectivity, and only this region.

In other words, those with greater connectivity in the left LPFC had greater cognitive control, which is reflected in higher working memory capacity and higher fluid intelligence. There was no correlation between connectivity and crystallized intelligence.

Interestingly, although other global hubs (such as the anterior prefrontal cortex and anterior cingulate cortex) also have strong relationships with intelligence and high levels of global connectivity, they did not show correlations between their levels of connectivity and fluid intelligence. That is, although the activity within these regions may be important for intelligence, their connections to other brain regions are not.

So what’s so important about the connections the LPFC has with the rest of the brain? It appears that, although it connects widely to sensory and motor areas, it is primarily the connections within the frontoparietal control network that are most important — as well as the deactivation of connections with the default network (the network active during rest).

This is not to say that the LPFC is the ‘seat of intelligence’! Research has made it clear that a number of brain regions support intelligence, as do other areas of connectivity. The finding is important because it shows that the left LPFC supports cognitive control and intelligence through a mechanism involving global connectivity and some other as-yet-unknown property. One possibility is that this region is a ‘flexible’ hub — able to shift its connectivity with a number of different brain regions as the task demands.

In other words, what may count is how many different connectivity patterns the left LPFC has in its repertoire, and how good it is at switching to them.

An association between negative connections with the default network and fluid intelligence also adds to evidence for the importance of inhibiting task-irrelevant processing.

All this emphasizes the role of cognitive control in intelligence, and perhaps goes some way to explaining why self-regulation in children is so predictive of later success, apart from the obvious.

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Regular cocoa drinking helps those with MCI

September, 2012

Daily consumption of a high level of cocoa was found to improve cognitive scores, insulin resistance and blood pressure, in older adults with mild cognitive impairment.

Back in 2009, I reported briefly on a large Norwegian study that found that older adults who consumed chocolate, wine, and tea performed significantly better on cognitive tests. The association was assumed to be linked to the flavanols in these products. A new study confirms this finding, and extends it to older adults with mild cognitive impairment.

The study involved 90 older adults with MCI, who consumed either 990 milligrams, 520 mg, or 45 mg of a dairy-based cocoa drink daily for eight weeks. Their diet was restricted to eliminate other sources of flavanols (such as tea, red wine, apples and grapes).

Cognitive assessment at the end of this period revealed that, although scores on the MMSE were similar across all groups, those consuming higher levels of flavanol cocoa took significantly less time to complete Trail Making Tests A and B, and scored significantly higher on the verbal fluency test. Insulin resistance and blood pressure was also lower.

Those with the highest levels of flavanols did better than those on intermediate levels on the cognitive tests. Both did better than those on the lowest levels.

Changes in insulin resistance explained part, but not all, of the cognitive improvement.

One caveat: the group were generally in good health without known cardiovascular disease — thus, not completely representative of all those with MCI.

 

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Each memory experience biases how you approach the next one

September, 2012

A new study provides evidence that our decision to encode information as new or try and retrieve it from long-term memory is affected by how we treated the last bit of information processed.

Our life-experiences contain a wealth of new and old information. The relative proportions of these change, of course, as we age. But how do we know whether we should be encoding new information or retrieving old information? It’s easy if the information is readily accessible, but what if it’s not? Bear in mind that (especially as we get older) most information / experiences we meet share some similarity to information we already have.

This question is made even more meaningful when you consider that it is the same brain region — the hippocampus — that’s involved in both encoding and retrieval, and these two processes depend (it is thought) on two quite opposite processes. While encoding is thought to rely on pattern separation (looking for differences), retrieval is thought to depend on pattern completion.

A recent study looked at what happens in the brain when people rapidly switch between encoding new objects and retrieving recently presented ones. Participants were shown 676 pictures of objects and asked to identify each one as being shown for the first time (‘new’), being repeated (‘old’), or as a modified version of something shown earlier (‘similar’). Recognizing the similar items as similar was the question of interest, as these items contain both old and new information and so the brain’s choice between encoding and retrieval is more difficult.

What they found was that participants were more likely to recognize similar items as similar (rather than old) if they had viewed a new item on the preceding trial. In other words, the experience of a new item primed them to notice novelty. Or to put it in another way: context biases the hippocampus toward either pattern completion or pattern separation.

This was supported by a further experiment, in which participants were shown both the object pictures, and also learned associations between faces and scenes. Critically, each scene was associated with two different faces. In the next learning phase, participants were taught a new scene association for one face from each pair. Each face-scene learning trial was preceded by an object recognition trial (new and old objects were shown and participants had to identify them as old or new) — critically, either a new or old object was consistently placed before a specific face-scene association. In the final test phase, participants were tested on the new face-scene associations they had just learned, as well as the indirect associations they had not been taught (that is, between the face of each pair that had not been presented during the preceding phase, and the scene associated with its partnered face).

What this found was that participants were more likely to pair indirectly related faces if those faces had been consistently preceded by old objects, rather than new ones. Moreover, they did so more quickly when the faces had been preceded by old objects rather than new ones.

This was interpreted as indicating that the preceding experience affects how well related information is integrated during encoding.

What all this suggests is that the memory activities you’ve just engaged in bias your brain toward the same sort of activities — so whether or not you notice changes to a café or instead nostalgically recall a previous meal, may depend on whether you noticed anyone you knew as you walked down the street!

An interesting speculation by the researchers is that such a memory bias (which only lasts a very brief time) might be an adaptive mechanism, reflecting the usefulness of being more sensitive to changes in new environments and less sensitive to irregularities in familiar environments.

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Review of working memory training programs finds no broader benefit

July, 2012

A meta-analysis of 23 studies has found no evidence that working memory training has wider cognitive benefits for normally developing children and healthy adults.

I have said before that there is little evidence that working memory training has any wider benefits than to the skills being practiced. Occasionally a study arises that gets everyone all excited, but by and large training only benefits the skill being practiced — despite the fact that working memory underlies so many cognitive tasks, and limited working memory capacity is thought to negatively affect performance on so many tasks. However, one area that does seem to have had some success is working memory training for those with ADHD, and researchers have certainly not given hope of finding evidence for wider transfer among other groups (such as older adults).

A recent review of the research to date has, sadly, concluded that the benefits of working memory training programs are limited. But this is not to say there are no benefits.

For a start, the meta-analysis (analyzing data across studies) found that working memory training produced large immediate benefits for verbal working memory. These benefits were greatest for children below the age of 10.

These benefits, however, were not maintained long-term (at an average of 9 months after training, there were no significant benefits) — although benefits were found in one study in which the verbal working memory task was very similar to the training task (indicating that the specific skill practiced did maintain some improvement long-term).

Visuospatial working memory also showed immediate benefits, and these did not vary across age groups. One factor that did make a difference was type of training: the CogMed training program produced greater improvement than the researcher-developed programs (the studies included 7 that used CogMed, 2 that used Jungle Memory, 2 Cognifit, 4 n-back, 1 Memory Booster, and 7 researcher-developed programs).

Interestingly, visuospatial working memory did show some long-term benefits, although it should be noted that the average follow-up was distinctly shorter than that for verbal working memory tasks (an average of 5 months post-training).

The burning question, of course, is how well this training transferred to dissimilar tasks. Here the evidence seems sadly clear — those using untreated control groups tended to find such transfer; those using treated control groups never did. Similarly, nonrandomized studies tended to find far transfer, but randomized studies did not.

In other words, when studies were properly designed (randomized trials with a control group that is given alternative treatment rather than no treatment), there was no evidence of transfer effects from working memory training to nonverbal ability. Moreover, even when found, these effects were only present immediately and not on follow-up.

Neither was there any evidence of transfer effects, either immediate or delayed, on verbal ability, word reading, or arithmetic. There was a small to moderate effect on training on attention (as measured by the Stroop test), but this only occurred immediately, and not on follow-up.

It seems clear from this review that there are few good, methodologically sound studies on this subject. But three very important caveats should be noted in connection with the researchers’ dispiriting conclusion.

First of all, because this is an analysis across all data, important differences between groups or individuals may be concealed. This is a common criticism of meta-analysis, and the researchers do try and answer it. Nevertheless, I think it is still a very real issue, especially in light of evidence that the benefit of training may depend on whether the challenge of the training is at the right level for the individual.

On the other hand, another recent study, that compared young adults who received 20 sessions of training on a dual n-back task or a visual search program, or received no training at all, did look for an individual-differences effect, and failed to find it. Participants were tested repeatedly on their fluid intelligence, multitasking ability, working memory capacity, crystallized intelligence, and perceptual speed. Although those taking part in the training programs improved their performance on the tasks they practiced, there was no transfer to any of the cognitive measures. When participants were analyzed separately on the basis of their improvement during training, there was still no evidence of transfer to broader cognitive abilities.

The second important challenge comes from the lack of skill consolidation — having a short training program followed by months of not practicing the skill is not something any of us would expect to produce long-term benefits.

The third point concerns a recent finding that multi-domain cognitive training produces longer-lasting benefits than single-domain training (the same study also showed the benefit of booster training). It seems quite likely that working memory training is a valuable part of a training program that also includes practice in real-world tasks that incorporate working memory.

I should emphasize that these results only apply to ‘normal’ children and adults. The question of training benefits for those with attention difficulties or early Alzheimer’s is a completely different issue. But for these healthy individuals, it has to be said that the weight of the evidence is against working memory training producing more general cognitive improvement. Nevertheless, I think it’s probably an important part of a cognitive training program — as long as the emphasis is on part.

Reference: 

Melby-Lervåg, M., & Hulme, C. (2012). Is Working Memory Training Effective? A Meta-Analytic Review. Developmental psychology. doi:10.1037/a0028228
Full text available at http://www.apa.org/pubs/journals/releases/dev-ofp-melby-lervag.pdf

[3012] Redick, T. S., Shipstead Z., Harrison T. L., Hicks K. L., Fried D. E., Hambrick D. Z., et al.
(2012).  No Evidence of Intelligence Improvement After Working Memory Training: A Randomized, Placebo-Controlled Study..
Journal of Experimental Psychology: General.
Full text available at http://psychology.gatech.edu/renglelab/publications/2012/RedicketalJEPG.pdf
 

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Gender differences in effects of anxiety on performance

July, 2012

Two studies indicate that, while anxiety is present in both sexes, it only impairs performance in females.

A British study looking at possible gender differences in the effects of math anxiety involved 433 secondary school children (11-16 years old) completing customized (year appropriate) mental mathematics tests as well as questionnaires designed to assess math anxiety and (separately) test anxiety. These sources of anxiety are often confounded in research studies (and in real life!), and while they are indeed related, reported correlations are moderate, ranging from .30 to .50.

Previous research has been inconsistent as regards gender differences in math anxiety. While many studies have found significantly greater levels of math anxiety in females, many studies have found no difference, and some have even found higher levels in males. These inconsistencies may stem from differences in how math anxiety is defined or measured.

The present study looked at a rather more subtle question: does the connection between math anxiety and math performance differ by gender? Again, previous research has produced inconsistent findings.

Findings in this study were very clear: while there was no difference between boys and girls in math performance, there were marked differences in both math and test anxiety. Girls showed significantly greater levels of both. Both boys and girls showed a positive correlation between math anxiety and test anxiety, and a negative correlation between math anxiety and math performance, and test anxiety and performance. However, these relationships between anxiety and performance were stronger for girls than boys, with the correlation between test anxiety and performance being only marginally significant for boys (p<0.07), and the correlation between math anxiety and performance disappearing once test anxiety was controlled for.

In other words, greater math anxiety was linked to poorer math performance, but it was significant only for girls. Moreover, anxiety experienced by boys may simply reflect test anxiety, rather than specific math anxiety.

It is worth emphasizing that there was no gender difference in performance — that is, despite laboring under the burden of greater levels of anxiety, the girls did just as well as boys. This suggests that girls might do better than boys if they were free of anxiety. It is possible, however, that levels of anxiety didn’t actually differ between boys and girls — that the apparent difference stems from girls feeling more free to express their anxiety.

However, the finding that anxiety is greater in girls than boys is in line with evidence that anxiety (and worry in particular) is twice as prevalent in women as men, and more support for the idea that the girls are under-performing because of their anxiety comes from another recent study.

In this study, 149 college students performed a relatively simple task while their brain activity was measured. Specifically, they had to identify the middle letter in a series of five-letter groups. Sometimes the middle letter was the same as the other four ("FFFFF") while sometimes it was different ("EEFEE"). Afterward the students completed questionnaires about their anxiety and how much they worry (Penn State Worry Questionnaire and the Anxious Arousal subscale of the Mood and Anxiety Symptom Questionnaire).

Anxiety scores were significantly negatively correlated with accuracy on the task; worry scores were unrelated to performance.

Only girls who identified themselves as particularly anxious or big worriers recorded high brain activity when they made mistakes during the task (reflecting greater performance-monitoring). Although these women performed about the same as others on simple portions of the task, their brains had to work harder at it. Then, as the test became more difficult, the anxious females performed worse, suggesting worrying got in the way of completing the task.

Greater performance monitoring was not evident among anxious men.

[A reminder: these are group differences, and don't mean that all men or all women react in these ways.]

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Effect of blood pressure on the aging brain depends on genetics

July, 2012
  • For those with the Alzheimer’s gene, higher blood pressure, even though within the normal range, is linked to greater brain shrinkage and reduced cognitive ability.

I’ve reported before on the evidence suggesting that carriers of the ‘Alzheimer’s gene’, APOE4, tend to have smaller brain volumes and perform worse on cognitive tests, despite being cognitively ‘normal’. However, the research hasn’t been consistent, and now a new study suggests the reason.

The e4 variant of the apolipoprotein (APOE) gene not only increases the risk of dementia, but also of cardiovascular disease. These effects are not unrelated. Apoliproprotein is involved in the transportation of cholesterol. In older adults, it has been shown that other vascular risk factors (such as elevated cholesterol, hypertension or diabetes) worsen the cognitive effects of having this gene variant.

This new study extends the finding, by looking at 72 healthy adults from a wide age range (19-77).

Participants were tested on various cognitive abilities known to be sensitive to aging and the effects of the e4 allele. Those abilities include speed of information processing, working memory and episodic memory. Blood pressure, brain scans, and of course genetic tests, were also performed.

There are a number of interesting findings:

  • The relationship between age and hippocampal volume was stronger for those carrying the e4 allele (shrinkage of this brain region occurs with age, and is significantly greater in those with MCI or dementia).
  • Higher systolic blood pressure was significantly associated with greater atrophy (i.e., smaller volumes), slower processing speed, and reduced working memory capacity — but only for those with the e4 variant.
  • Among those with the better and more common e3 variant, working memory was associated with lateral prefrontal cortex volume and with processing speed. Greater age was associated with higher systolic blood pressure, smaller volumes of the prefrontal cortex and prefrontal white matter, and slower processing. However, blood pressure was not itself associated with either brain atrophy or slower cognition.
  • For those with the Alzheimer’s variant (e4), older adults with higher blood pressure had smaller volumes of prefrontal white matter, and this in turn was associated with slower speed, which in turn linked to reduced working memory.

In other words, for those with the Alzheimer’s gene, age differences in working memory (which underpin so much of age-related cognitive impairment) were produced by higher blood pressure, reduced prefrontal white matter, and slower processing. For those without the gene, age differences in working memory were produced by reduced prefrontal cortex and prefrontal white matter.

Most importantly, these increases in blood pressure that we are talking about are well within the normal range (although at the higher end).

The researchers make an interesting point: that these findings are in line with “growing evidence that ‘normal’ should be viewed in the context of individual’s genetic predisposition”.

What it comes down to is this: those with the Alzheimer’s gene variant (and no doubt other genetic variants) have a greater vulnerability to some of the risk factors that commonly increase as we age. Those with a family history of dementia or serious cognitive impairment should therefore pay particular attention to controlling vascular risk factors, such as hypertension and diabetes.

This doesn’t mean that those without such a family history can safely ignore such conditions! When they get to the point of being clinically diagnosed as problems, then they are assuredly problems for your brain regardless of your genetics. What this study tells us is that these vascular issues appear to be problematic for Alzheimer’s gene carriers before they get to that point of clinical diagnosis.

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Menopause ‘brain fog’ a product of poor sleep and depression?

May, 2012

A smallish study of women approaching and in menopause found that some experienced poorer working memory and attention, and these were more likely to have poorer sleep, depression, and anxiety.

A study involving 75 perimenopausal women aged 40 to 60 has found that those with memory complaints tended to show impairments in working memory and attention. Complaints were not, however, associated with verbal learning or memory.

Complaints were also associated with depression, anxiety, somatic complaints, and sleep disturbance. But they weren’t linked to hormone levels (although estrogen is an important hormone for learning and memory).

What this suggests to me is that a primary cause of these cognitive impairments may be poor sleep, and anxiety/depression. A few years ago, I reported on a study that found that, although women’s reports of how many hot flashes they had didn’t correlate with memory impairment, an objective measure of the number of flashes they experienced during sleep did. Sleep, as I know from personal experience, is of sufficient importance that my rule-of-thumb is: don’t bother looking for any other causes of attention and memory deficits until you have sorted out your sleep!

Having said that, depressive symptoms showed greater relationship to memory complaints than sleep disturbance.

It’s no big surprise to hear that it is working memory in particular that is affected, because what many women at this time of life complain of is ‘brain fog’ — the feeling that your brain is full of cotton-wool. This doesn’t mean that you can’t learn new information, or remember old information. But it does mean that these tasks will be impeded to the extent that you need to hold on to too many bits of information. So mental arithmetic might be more difficult, or understanding complex sentences, or coping with unexpected disruptions to your routine, or concentrating on a task for a long time.

These sorts of problems are typical of those produced by on-going sleep deprivation, stress, and depression.

One caveat to the findings is that the study participants tended to be of above-average intelligence and education. This would protect them to a certain extent from cognitive decline — those with less cognitive reserve might display wider impairment. Other studies have found verbal memory, and processing speed, impaired during menopause.

Note, too, that a long-running, large population study has found no evidence for a decline in working memory, or processing speed, in women as they pass through perimenopause and menopause.

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How marijuana impairs working memory

April, 2012

A mouse study indicates that the psychoactive component of marijuana, TCP, impairs working memory by initiating a process that ends with neural connections being weakened.

A new study explains how marijuana impairs working memory. The component THC removes AMPA receptors for the neurotransmitter glutamate in the hippocampus. This means that there are fewer receivers for the information crossing between neurons.

The research is also significant because it adds to the growing evidence for the role of astrocytes in neural transmission of information.

This is shown by the finding that genetically-engineered mice who lack type-1 cannabinoid receptors in their astroglia do not show impaired working memory when exposed to THC, while those who instead lacked the receptors in their neurons do. The activation of the cannabinoid receptor expressed by astroglia sends a signal to the neurons to begin the process that removes AMPA receptors, leading to long-term depression (a type of synaptic plasticity that weakens, rather than strengthens, neural connections).

See the Guardian and Scientific American articles for more detail on the study and the processes involved.

For more on the effects of marijuana on memory

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Support for link between physical activity & academic success

March, 2012

A review supports the benefits of physical activity for children’s and adolescent’s scholastic performance, but points to the need for better studies. A recent study looks at the effects on attention of different types of physical activity.

A review of 10 observational and four intervention studies as said to provide strong evidence for a positive relationship between physical activity and academic performance in young people (6-18). While only three of the four intervention studies and three of the 10 observational studies found a positive correlation, that included the two studies (one intervention and one observational) that researchers described as “high-quality”.

An important feature of the high-quality studies was that they used objective measures of physical activity, rather than students' or teachers' reports. More high-quality studies are clearly needed. Note that the quality score of the 14 studies ranged from 22%! to 75%.

Interestingly, a recent media report (NOT, I hasten to add, a peer-reviewed study appearing in an academic journal) spoke of data from public schools in Lincoln, Nebraska, which apparently has a district-wide physical-fitness test, which found that those were passed the fitness test were significantly more likely to also pass state reading and math tests.

Specifically, data from the last two years apparently shows that 80% of the students who passed the fitness test either met or exceeded state standards in math, compared to 66% of those who didn't pass the fitness test, and 84% of those who passed the fitness test met or exceeded state standards in reading, compared to 71% of those who failed the fitness test.

Another recent study looks at a different aspect of this association between physical exercise and academic performance.

The Italian study involved138 normally-developing children aged 8-11, whose attention was tested before and after three different types of class: a normal academic class; a PE class focused on cardiovascular endurance and involving continuous aerobic circuit training followed by a shuttle run exercise; a PE class combining both physical and mental activity by involving novel use of basketballs in varying mini-games that were designed to develop coordination and movement-based problem-solving. These two types of physical activity offered the same exercise intensity, but very different skill demands.

The attention test was a short (5-minute) paper-and-pencil task in which the children had to mark each occurrence of “d” with double quotation marks either above or below in 14 lines of randomly mixed p and d letters with one to four single and/or double quotation marks either over and/or under each letter.

Processing speed increased 9% after mental exercise (normal academic class) and 10% after physical exercise. These were both significantly better than the increase of 4% found after the combined physical and mental exertion.

Similarly, scores on the test improved 13% after the academic class, 10% after the standard physical exercise, and only 2% after the class combining physical and mental exertion.

Now it’s important to note is that this is of course an investigation of the immediate arousal benefits of exercise, rather than an investigation of the long-term benefits of being fit, which is a completely different question.

But the findings do bear on the use of PE classes in the school setting, and the different effects that different types of exercise might have.

First of all, there’s the somewhat surprising finding that attention was at least as great, if not better, after an academic class than the PE class. It would not have been surprising if attention had flagged. It seems likely that what we are seeing here is a reflection of being in the right head-space — that is, the advantage of continuing with the same sort of activity.

But the main finding is the, also somewhat unexpected, relative drop in attention after the PE class that combined mental and physical exertion.

It seems plausible that the reason for this lies in the cognitive demands of the novel activity, which is, I think, the main message we should take away from this study, rather than any comparison between physical and mental activity. However, it would not be surprising if novel activities that combine physical and mental skills tend to be more demanding than skills that are “purely” (few things are truly pure I know) one or the other.

Of course, it shouldn’t be overlooked that attention wasn’t hampered by any of these activities!

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Pycnogenol improves cognition in college students in small trial

March, 2012

Another small study indicates that the plant extract Pycnogenol may improve working memory.

Back in 2008, I reported on a small study that found that daily doses of Pycnogenol® for three months improved working memory in older adults, and noted research indicating that the extract from the bark of the French maritime pine tree had reduced symptoms in children with ADHD. Now another study, involving 53 Italian university students, has found that cognitive performance improved in those taking 100 mg of Pycnogenol every day for eight weeks.

Students taking the supplement had higher scores on university exams than the control group, and they were apparently happier, less anxious, and more alert. It seems plausible that the improvement in academic performance results from working memory benefits.

The plant extract is an antioxidant, and benefits may have something to do with improved vascular function and blood flow in the brain.

However, the control group was apparently not given a placebo (I’m relying on the abstract and press release here, as this journal is not one to which I have access), they were simply “a group of equivalent students”. I cannot fathom why a double-blind, placebo procedure wasn’t followed, and it greatly lessens the conclusions of this study. Indeed, I wouldn’t ordinarily report on it, except that I have previously reported on this dietary supplement, and I am in hopes that a better study will come along. In the meantime, this is another small step, to which I wouldn’t give undue weight.

Reference: 

Luzzi R., Belcaro G., Zulli C., Cesarone M. R., Cornelli U., Dugall M., Hosoi M., Feragalli B. 2011. Pycnogenol® supplementation improves cognitive function, attention and mental performance in students. Panminerva Medica, 53(3 Suppl 1), 75-82.

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