adolescence

How early environment impacts cognitive development

February, 2012

Follow-up on an early child-care program for low-income children finds long-term benefits for education and employment. A large study pinpoints the advantages children from higher-income families have over those from low-middle families. Norway shows how extending compulsory education is linked to higher IQ.

Benefits of high quality child care persist 30 years later

Back in the 1970s, some 111 infants from low-income families, of whom 98% were African-American, took part in an early childhood education program called the Abecedarian Project. From infancy until they entered kindergarten, the children attended a full-time child care facility that operated year-round. The program provided educational activities designed to support their language, cognitive, social and emotional development.

The latest data from that project, following up the participants at age 30, has found that these people had significantly more years of education than peers who were part of a control group (13.5 years vs 12.3), and were four times more likely to have earned college degrees (23% vs 6%).

They were also significantly more likely to have been consistently employed (75% had worked full time for at least 16 of the previous 24 months, compared to 53% of the control group) and less likely to have used public assistance (only 4% received benefits for at least 10% of the previous seven years, compared to 20% of the control group). However, income-to-needs ratios (income taken into account household size) didn’t vary significantly between the groups (mainly because of the wide variability; on the face of it, the means are very different, but the standard deviation is huge), and neither did criminal involvement (27% vs 28%).

See their website for more about this project.

Evidence that more time at school raises IQ

It would be interesting to see what the IQs of those groups are, particularly given that maternal IQ was around 85 for both treatment and control groups. A recent report analyzed the results of a natural experiment that occurred in Norway when compulsory schooling was increased from seven to nine years in the 1960s, meaning that students couldn’t leave until 16 rather than 14. Because all men eligible for the draft were given an IQ test at age 19, statisticians were able to look back and see what effect the increased schooling had on IQ.

They found that it had a substantial effect, with each additional year raising the average IQ by 3.7 points.

While we can’t be sure how far these results extend to other circumstances, they are clear evidence that it is possible to improve IQ through education.

Why children of higher-income parents start school with an advantage

Of course the driving idea behind improved child-care in the early years is all about the importance of getting off to a good start, and you’d expect that providing such care to children would have a greater long-term effect on IQ than simply extending time at school. Most such interventions have looked at the most deprived strata of society. An overlooked area is that of low to middle income families, who are far from having the risk factors of less fortunate families.

A British study involving 15,000 five-year-olds has found that, at the start of school, children from low to middle income families are five months behind children from higher income families in terms of vocabulary skills and have more behavior problems (they were also 8 months ahead of their lowest income peers in vocabulary).

Low-middle income (LMI) households are defined by the Resolution Foundation (who funded this research) as members of the working-age population in income deciles 2-5 who receive less than one-fifth of their gross household income from means-tested benefits (see their website for more detail on this).

Now the difference in home environment between LMI and higher income households is often not that great — particularly when you consider that it is often a difference rooted in timing. LMI households are more common in this group of families with children under five, because the parents are usually at an early stage of life. So what brings about this measurable difference in language and behavior development?

This is a tricky thing to derive from the data, and the findings must be taken with a grain of salt. And as always, interpretation is even trickier. But with this caveat, let’s see what we have. Let’s look at demographics first.

The first thing is the importance of parental education. Income plus education accounted for some 70-80% of the differences in development, with education more important for language development and income more important for behavior development. Maternal age then accounted for a further 10%. Parents in the higher-income group tended to be older and have better education (e.g., 18% of LMI mothers were under 25 at the child’s birth, compared to 6% of higher-income mothers; 30% of LMI parents had a degree compared to 67% of higher-income parents).

Interestingly, family size was equally important for language development (10%), but much less important for behavior development (in fact this was a little better in larger families). Differences in ethnicity, language, or immigration status accounted for only a small fraction of the vocabulary gap, and none of the behavior gap.

Now for the more interesting but much trickier analysis of environmental variables. The most important factor was home learning environment, accounting for around 20% of the difference. Here the researchers point to higher-income parents providing more stimulation. For example, higher-income parents were more likely to read to their 3-year-olds every day (75% vs 62%; 48% for the lowest-income group), to take them to the library at least once a month (42% vs 35% vs 26%), to take their 5-year-old to a play or concert (86% vs 75% vs 60%), to a museum/gallery (67% vs 48% vs 36%), to a sporting activity at least once a week (76% vs 57% vs 35%). Higher-income parents were also much less likely to allow their 3-year-olds to watch more than 3 hours of TV a day (7% vs 17% vs 25%). (I know the thrust of this research is the comparison between LMI and higher income, but I’ve thrown in the lowest-income figures to help provide context.)

Interestingly, the most important factor for vocabulary learning was being taken to a museum/gallery at age 5 (but remember, these correlations could go either way: it might well be that parents are more likely to take an articulate 5-year-old to such a place), with the second most important factor being reading to 3-year-old every day. These two factors accounted for most of the effects of home environment. For behavior, the most important factor was regular sport, followed by being to a play/concert, and being taken to a museum/gallery. Watching more than 3 hours of TV at age 3 did have a significant effect on both vocabulary and behavior development (a negative effect on vocabulary and a positive effect on behavior), while the same amount of TV at age 5 did not.

Differences in parenting style explained 10% of the vocabulary gap and 14% of the behavior gap, although such differences were generally small. The biggest contributors to the vocabulary gap were mother-child interaction score at age 3 and regular bedtimes at age 3. The biggest contributors to the behavior gap were regular bedtimes at age 5, regular mealtimes at age 3, child smacked at least once a month at age 5 (this factor also had a small but significant negative effect on vocabulary), and child put in timeout at least once a month at age 5.

Maternal well-being accounted for over a quarter of the behavior gap, but only a small proportion of the vocabulary gap (2% — almost all of this relates to social support score at 9 months). Half of the maternal well-being component of the behavior gap was down to psychological distress at age 5 (very much larger than the effect of psychological distress at age 3). Similarly, child and maternal health were important for behavior (18% in total), but not for vocabulary.

Material possessions, on the other hand, accounted for some 9% of the vocabulary gap, but none of the behavior gap. The most important factors here were no internet at home at age 5 (22% of LMIs vs 8% of higher-incomes), and no access to a car at age 3 (5% of LMIs had no car vs 1% of higher incomes).

As I’ve intimated, it’s hard to believe we can disentangle individual variables in the environment in an observational study, but the researchers believe the number of variables in the mix (158) and the different time points (many variables are assessed at two or more points) provided a good base for analysis.

Reference: 

[2676] Campbell, F. A., Pungello E. P., Burchinal M., Kainz K., Pan Y., Wasik B. H., et al.
(2012).  Adult outcomes as a function of an early childhood educational program: An Abecedarian Project follow-up.
Developmental Psychology;Developmental Psychology. No Pagination Specified - No Pagination Specified.

[2675] Brinch, C. N., & Galloway T A.
(2012).  Schooling in adolescence raises IQ scores.
Proceedings of the National Academy of Sciences. 109(2), 425 - 430.

Washbrook, E., & Waldfogel, J. (2011). On your marks : Measuring the school readiness of children in low-to-middle income families. Resolution Foundation, December 2011.

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Myths about gender and math performance

January, 2012

Two new reviews debunk several theories for the reasons for gender gaps in math performance.

Is there, or is there not, a gender gap in mathematics performance? And if there is, is it biological or cultural?

Although the presence of a gender gap in the U.S. tends to be regarded as an obvious truth, evidence is rather more equivocal. One meta-analysis of studies published between 1990 and 2007, for example, found no gender differences in mean performance and nearly equal variability within each gender. Another meta-analysis, using 30 years of SAT and ACT scores, found a very large 13:1 ratio of middle school boys to girls at the highest levels of performance in the early 1980s, which declined to around 4:1 by 1991, where it has remained. A large longitudinal study found that males were doing better in math, across all socioeconomic classes, by the 3rd grade, with the ratio of boys to girls in the top 5% rising to 3:1 by 5th grade.

Regardless of the extent of any gender differences in the U.S., the more fundamental question is whether such differences are biological or cultural. The historical changes mentioned above certainly point to a large cultural component. Happily, because so many more countries now participate in the Trends in International Mathematics and Science Study (TIMSS) and the Programme in International Student Assessment (PISA), much better data is now available to answer this question. In 2007, for example, 4th graders from 38 countries and 8th graders from 52 countries participated in TIMSS. In 2009, 65 countries participated in PISA.

So what does all this new data reveal about the gender gap? Overall, there was no significant gender gap in the 2003 and 2007 TIMSS, with the exception of the 2007 8th graders, where girls outperformed boys.

There were, of course, significant gender gaps on a country basis. Researchers looked at several theories for what might underlie these.

Contradicting one theory, gender gaps did not correlate reliably with gender equity. In fact, both boys and girls tended to do better in math when raised in countries where females have better equality. The primary contributor to this appears to be women’s income and rates of participation in the work force. This is in keeping with the idea that maternal education and employment opportunities have benefits for their children’s learning regardless of gender.

The researchers also looked at the more specific hypothesis put forward by Steven Levitt, that gender inequity doesn’t hurt girls' math performance in Muslim countries, where most students attend single-sex schools. This theory was not borne out by the evidence. There was no consistent link between school type and math performance across countries.

However, math performance in the 29 wealthier countries could be predicted to a very high degree by three factors: economic participation and opportunity; GDP per capita; membership of one of three clusters — Middle Eastern (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia); East Asian (Hong Kong, Japan, South Korea, Singapore, Taiwan); rest (Russia, Hungary, Czech Republic, England, Canada, US, Australia, Sweden, Norway, Scotland, Cyprus, Italy, Malta, Israel, Spain, Lithuania, Malaysia, Slovenia, Dubai). The Middle Eastern cluster scored lowest (note the exception of Dubai), and the East Asian the highest. While there are many cultural factors differentiating these clusters, it’s interesting to note that countries’ average performance tended to be higher when students attribute less importance to mastering math.

The investigators also looked at the male variability hypothesis — the idea that males are more variable in their performance, and their predominance at the top is balanced by their predominance at the bottom. The study found however that greater male variation in math achievement varies widely across countries, and is not found at all in some countries.

In sum, the cross-country variability in performance in regard to gender indicates that the most likely cause of any differences lies in country-specific social factors. These could include perception of abilities as fixed vs malleable, attitude toward math, gender beliefs.

Stereotype threat

A popular theory of women’s underachievement in math concerns stereotype threat (first proposed by Spencer, Steele, and Quinn in a 1999 paper). I have reported on this on several occasions. However, a recent review of this research claims that many of the studies were flawed in their methodology and statistical analysis.

Of the 141 studies that cited the original article and related to mathematics, only 23 met the criteria needed (in the reviewers’ opinion) to replicate the original study:

  • Both genders tested
  • Math test used
  • Subjects recruited regardless of preexisting beliefs about gender stereotypes
  • Subjects randomly assigned to experimental conditions

Of these 23, three involved younger participants (< 18 years) and were excluded. Of the remaining 20 studies, only 11 (55%) replicated the original effect (a significant interaction between gender and stereotype threat, and women performing significantly worse in the threat condition than in the threat condition compared to men).

Moreover, half the studies confounded the results by statistically adjusting preexisting math scores. That is, the researchers tried to adjust for any preexisting differences in math performance by using a previous math assessment measure such as SAT score to ‘tweak’ the baseline score. This practice has been the subject of some debate, and the reviewers come out firmly against it, arguing that “an important assumption of a covariate analysis is that the groups do not differ on the covariate. But that group difference is exactly what stereotype threat theory tries to explain!” Note, too, that the original study didn’t make such an adjustment.

So what happens if we exclude those studies that confounded the results? That leaves ten studies, of which only three found an effect (and one of these found the effect only in a subset of the math test). In other words, overwhelmingly, it was the studies that adjusted the scores that found an effect (8/10), while those that didn’t adjust them didn’t find the effect (7/10).

The power of the adjustment in producing the effect was confirmed in a meta-analysis.

Now these researchers aren’t saying that stereotype threat doesn’t exist, or that it doesn’t have an effect on women in this domain. Their point is that the size of the effect, and the evidence for the effect, has come to be regarded as greater and more robust than the research warrants.

At a practical level, this may have led to too much emphasis on tackling this problem at the expense of investigating other possible causes and designing other useful interventions.

Reference: 

Kane, J. M., & Mertz, J. E. (2012). Debunking Myths about Gender and Mathematics Performance. Notices of the AMS, 59(1), 10-21.

[2698] Stoet, G., & Geary D. C.
(2012).  Can stereotype threat explain the gender gap in mathematics performance and achievement?.
Review of General Psychology;Review of General Psychology. No Pagination Specified - No Pagination Specified.

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For the brain, iron levels may need to be just right

January, 2012

Iron-enriched baby formula improves cognitive development when infants have low iron levels, but harms development when iron levels are already high. Teenage iron levels are linked to white matter integrity in adulthood.

Iron deficiency is the world's single most common nutrient deficiency, and a well-known cause of impaired cognitive, language, and motor development. Many countries therefore routinely supplement infant foods with iron. However, a new study suggests that, while there is no doubt that such fortification has helped reduce iron deficiency, it may be that there is an optimal level of iron for infant development.

In 1992-94, 835 healthy, full-term infants living in urban areas in Chile, took part in a randomized trial to receive iron-fortified formula from 6 months of age to 12 months. A follow-up study has now assessed the cognitive functioning of 473 of these children at 10 years of age. Tests measured IQ, spatial memory, arithmetic achievement, visual-motor integration, visual perception and motor functioning.

Those who had received iron-fortified formula scored significantly lower than the non-fortified group on the spatial memory and visual-motor integration tests. Moreover, their performance on the other tests also tended to be worse, although these didn’t reach statistical significance.

There was no difference in iron level between these two groups (at age 10), and only one child had iron-deficiency anemia.

The crucial point, it seems, lies in the extent to which the infants needed additional iron. Children who had high iron levels at 6 months (5.5%, i.e. 26 infants) had lower scores at 10 years if they had received the iron-fortified formula, but those with low 6-month iron levels (18.4%; 87 infants) had higher scores at 10 years.

Further research is needed to confirm these findings, but the findings are not inconsistent with the idea that iron overload promotes neurodegenerative diseases.

In another longitudinal study, brain scans have revealed that teenage iron levels are associated with white matter fiber integrity.

The study first measured iron levels in 615 adolescent twins and siblings, and then scanned their brains when they were in their early twenties. Myelin (white matter) contains a lot of iron, so the strong correlation between teenage iron level and white matter integrity in young adulthood is not unexpected.

The correlation was stronger between identical twins that non-identical twins, suggesting a genetic contribution. Again, not unexpected — the transport of iron around the body is affected by several genes. One particular gene variant, in a gene that governs cellular absorption of transferrin-bound iron, was associated with both high iron levels and improved white matter integrity. This gene variant is found in about 12-15% of Caucasians.

The vital missing bit of information (because it wasn’t investigated) is whether this gene variant is associated with better cognitive performance. Further research will hopefully also investigate whether, while it might be better to have this variant earlier in life, it is detrimental in old age, given the suggestions that iron accumulation contributes to some neurodegenerative disorders (including Alzheimer’s).

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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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IQ can rise or fall significantly during adolescence

November, 2011

A small study of adolescents shows marked variability in IQ over a four-year period for many of them. This variability correlated with specific changes in the brain.

IQ has long been considered to be a fixed attribute, stable across our lifetimes. But in recent years, this assumption has come under fire, with evidence of the positive and negative effects education and experiences can have on people’s performance. Now a new (small) study provides a more direct challenge.

In 2004, 33 adolescents (aged 12-16) took IQ tests and had their brains scanned. These tests were repeated four years later. The teenagers varied considerably in their levels of ability (77-135 in 2004; 87-143 in 2008). While the average IQ score remained the same (112; 113), there were significant changes in the two IQ scores for some individuals, with some participants gaining as much as 21 points, and others falling as much as 18 points. Clear change in IQ occurred for a third of the participants, and there was no obvious connection to specific attributes (e.g., low performers didn’t get better while high performers got worse).

These changes in performance correlated with structural changes in the brain. An increase in verbal IQ score correlated with an increase in the density of grey matter in an area of the left motor cortex of the brain that is activated when articulating speech. An increase in non-verbal IQ score correlated with an increase in the density of grey matter in the anterior cerebellum, which is associated with movements of the hand. Changes in verbal IQ and changes in non-verbal IQ were independent.

While I’d really like to see this study repeated with a much larger sample, the findings are entirely consistent with research showing increases in grey matter density in specific brain regions subsequent to specific training. The novel part of this is the correlation with such large changes in IQ.

The findings add to growing evidence that teachers shouldn’t be locked into beliefs about a student’s future academic success on the basis of past performance.

Postscript: I should perhaps clarify that IQ performance at each of these time points was age-normed - this is not a case of children just becoming 'smarter with age'.

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Ability to remember memories' origin develops slowly

October, 2011

A study comparing the brains of children, adolescents, and young adults has found that the ability to remember the origin of memories is slow to mature. As with older adults, impaired source memory increases susceptibility to false memories.

In the study, 18 children (aged 7-8), 20 adolescents (13-14), and 20 young adults (20-29) were shown pictures and asked to decide whether it was a new picture or one they had seen earlier. Some of the pictures were of known objects and others were fanciful figures (this was in order to measure the effects of novelty in general). After a 10-minute break, they resumed the task — with the twist that any pictures that had appeared in the first session should be judged “new” if that was the first appearance in the second session. EEG measurements (event-related potentials — ERPs) were taken during the sessions.

ERPs at the onset of a test stimulus (each picture) are different for new and old (repeated) stimuli. Previous studies have established various old/new effects that reflect item and source memory in adults. In the case of item memory, recognition is thought to be based on two processes — familiarity and recollection — which are reflected in ERPs of different timings and location (familiarity: mid-frontal at 300-500 msec; recollection: parietal at 400-70 msec). Familiarity is seen as a fast assessment of similarity, while recollection varies according to the amount of retrieved information.

Source memory appears to require control processes that involve the prefrontal cortex. Given that this region is the slowest to mature, it would not be surprising if source memory is a problematic memory task for the young. And indeed, previous research has found that children do have particular difficulty in sourcing memories when the sources are highly similar.

In the present study, children performed more poorly than adolescents and adults on both item memory and source memory. Adolescents performed more poorly than adults on item memory but not on source memory. Children performed more poorly on source memory than item memory, but adolescents and adults showed no difference between the two tasks.

All groups responded faster to new items than old, and ERP responses to general novelty were similar across the groups — although children showed a left-frontal focus that may reflect the transition from analytic to a more holistic processing approach.

ERPs to old items, however, showed a difference: for adults, they were especially pronounced at frontal sites, and occurred at around 350-450 msec; for children and adolescents they were most pronounced at posterior sites, occurring at 600-800 msec for children and 400-600 msec for adolescents. Only adults showed the early midfrontal response that is assumed to reflect familiarity processing. On the other hand, the late old/new effect occurring at parietal sites and thought to reflect recollection, was similar across all age groups. The early old/new effect seen in children and adolescents at central and parietal regions is thought to reflect early recollection.

In other words, only adults showed the brain responses typical of familiarity as well as recollection. Now, some research has found evidence of familiarity processing in children, so this shouldn’t be taken as proof against familiarity processing in the young. What seems most likely is that children are less likely to use such processing. Clearly the next step is to find out the factors that affect this.

Another interesting point is the early recollective response shown by children and adolescents. It’s speculated that these groups may have used more retrieval cues — conceptual as well as perceptual — that facilitated recollection. I’m reminded of a couple of studies I reported on some years ago, that found that young children were better than adults on a recognition task in some circumstances — because children were using a similarity-based process and adults a categorization-based one. In these cases, it had more to do with knowledge than development.

It’s also worth noting that, in adults, the recollective response was accentuated in the right-frontal area. This suggests that recollection was overlapping with post-retrieval monitoring. It’s speculated that adults’ greater use of familiarity produces a greater need for monitoring, because of the greater uncertainty.

What all this suggests is that preadolescent children are less able to strategically recollect source information, and that strategic recollection undergoes an important step in early adolescence that is probably related to improvements in cognitive control. But this process is still being refined in adolescents, in particular as regards monitoring and coping with uncertainty.

Interestingly, source memory is also one of the areas affected early in old age.

Failure to remember the source of a memory has many practical implications, in particular in the way it renders people more vulnerable to false memories.

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Brain continues to develop well into our 20s

October, 2011

A new study shows that the wiring that connects the frontal lobes to other parts of the cerebral cortex continues to develop well into young adulthood — except for a small minority that show degradation.

Brain imaging data from 103 healthy people aged 5-32, each of whom was scanned at least twice, has demonstrated that wiring to the frontal lobe continues to develop after adolescence.

The brain scans focused on 10 major white matter tracts. Significant changes in white matter tracts occurred in the vast majority of children and early adolescents, and these changes were mostly complete by late adolescence for projection and commissural tracts (projection tracts project from the cortex to non-cortical areas, such as the senses and the muscles, or from the thalamus to the cortex; commissural tracts cross from one hemisphere to the other). But association tracts (which connect regions within the same hemisphere) kept developing after adolescence.

This was particularly so for the inferior and superior longitudinal and fronto-occipital fascicule (the inferior longitudinal fasciculus connects the temporal and occipital lobes; the superior longitudinal fasciculus connects the frontal lobe to the occipital lobe and parts of the temporal and parietal lobes). These frontal connections are needed for complex cognitive tasks such as inhibition, executive functioning, and attention.

The researchers speculated that this continuing development may be due to the many life experiences in young adulthood, such as pursing post-secondary education, starting a career, independence and developing new social and family relationships.

But this continuing development wasn’t seen in everyone. Indeed, in some people, there was evidence of reductions, rather than growth, in white matter integrity. It may be that this is connected with the development of psychiatric disorders that typically develop in adolescence or young adulthood — perhaps directly, or because such degradation increases vulnerability to other factors (e.g., to drug use). This is speculative at the moment, but it opens up a new avenue to research.

Reference: 

[2528] Lebel, C., & Beaulieu C.
(2011).  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood.
The Journal of Neuroscience. 31(30), 10937 - 10947.

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Adolescent binge drinking can damage spatial working memory

August, 2011

This study finds that adolescent females are particularly vulnerable to the effects of binge drinking, and points to specific changes in brain activation patterns seen in binge drinkers.

Binge drinking occurs most frequently among young people, and there has been concern that consequences will be especially severe if the brain is still developing, as it is in adolescence. Because of the fact that it is only some parts of the brain — most crucially the prefrontal cortex and the hippocampus — that are still developing, it makes sense that only some functions will be affected.

I recently reported on a finding that binge drinking university students, performed more poorly on tests of verbal memory, but not on a test of visual memory. A new study looks at another function: spatial working memory. This task involves the hippocampus, and animal research has indicated that this region may be especially vulnerable to binge drinking. Spatial working memory is involved in such activities as driving, figural reasoning, sports, and navigation.

The study involved 95 adolescents (aged 16-19) from San Diego-area public schools: 40 binge drinking (27 males, 13 females) and 55 control (31 males, 24 females). Brain scans while performing a spatial working memory task revealed that there were significant gender differences in brain activation patterns for those who engaged in binge drinking. Specifically, in eight regions spanning the frontal cortex, anterior cingulate, temporal cortex, and cerebellum, female binge drinkers showed less activation than female controls, while male bingers exhibited greater activation than male controls. For female binge drinkers, less activation was associated with poorer sustained attention and working memory performances, while for male binge drinkers, greater activation was linked to better spatial performance.

The differences between male binge drinkers and controls were smaller than that seen in the female groups, suggesting that female teens may be particularly vulnerable. This is not the first study to find a gender difference in the brains’ response to excess alcohol. In this case it may have to do, at least partly, with differences in maturity — female brains mature earlier than males’.

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Folic acid tied to better grades in Swedish teens

August, 2011

More evidence of the importance of adequate folate consumption for cognitive functioning at all ages.

Most research into the importance of folate and B12 levels has centered on seniors, and it does seem clear now that having adequate levels of these vitamins is important for maintaining cognitive functioning as you get older. Folic acid levels are of course also regarded as crucial when the brain is developing, which is why pregnant women are urged to take supplements, and why some countries fortify their bread with it. There is less research in the extensive ground between these two end-points.

A Swedish study involving 386 15-year-olds has now found that those in the top third of folic acid intake (more than 253 micrograms per day for girls and 335 for boys) performed significantly better on their school grades compared to those in the bottom third (less than 173 micrograms folic acid per day for girls and 227 for boys).

Interestingly, while homocysteine levels in the blood were initially significant, this association disappeared after other significant predictors (gender, smoking, and SES) were controlled for. Neither was a genotype linked to higher homocysteine levels (MTHFR 677 TT homozygosity) significantly related to academic achievement. Low folate and B12 levels are associated with higher homocysteine levels in the blood, and there is evidence that it is this increase in homocysteine that is the reason for the cognitive impairment seen in age-related cognitive decline. This finding, then, suggests that this is only one part of the story.

Sweden does not fortify flour with folic acid as the United States, Canada and Australia do. Folate is a B vitamin found particularly in citrus fruit, green leafy vegetables, whole-wheat bread, and dried beans and peas; however, they are often destroyed by cooking or processing.

The sum of school grades in 10 core subjects obtained in the final semester of compulsory 9 years of schooling was used as the measure of academic achievement

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Young binge drinkers less able to learn new verbal information

July, 2011

Binge drinking university students, regardless of gender, performed more poorly on tests of verbal memory, but not on a test of visual memory.

Following animal research indicating that binge drinking damages the hippocampus, and other research showing that this learning and memory center is still developing during adolescence, a new study has investigated the effects of binge drinking on learning in university students. The study, involving 122 Spanish university students (aged 18-20), of whom half engaged in binge drinking, found a clear association between binge drinking and a lower ability to learn new verbal information.

Specifically, binge drinkers were more affected by interference in the Rey Auditory Verbal Learning Test, and remembered fewer words; they also performed worse on the Weschler Memory Scale-3rd ed. (WMS-III) Logical Memory subtest, both on immediate and delayed recall. However, there were no differences between the two groups on the WMS-III Family Pictures subtest (measuring visual declarative memory).

These results persisted even after controlling for other possible confounding variables such as intellectual levels, history of neurological or psychopathological disorders, other drug use, or family history of alcoholism.

The genders were evenly represented in both groups. Interestingly, and in contradiction of some other research, women were not found to be more vulnerable to the neurotoxic effects of binge drinking.

Reference: 

[2298] Parada, M., Corral M., Caamaño‐Isorna F., Mota N., Crego A., Holguín S R., et al.
(Submitted).  Binge Drinking and Declarative Memory in University Students.
Alcoholism: Clinical and Experimental Research.

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