Usually I do not set my readers puzzles. It is not seemly. However, the recent coverage of a paper published in Nature has set me a puzzle which I would like you to help me solve. Are the authors of the paper, the reviewers and the editors of Nature Neuroscience aware of what has been left out of this study? Did they spot the gap which calls into question the conclusions, or just choose not to mention it? Let me tell you the story, and then you can judge for yourselves.
Family income, parental education and brain structure in children and adolescents. Nature Neuroscience (2015)doi:10.1038/nn.3983 Published online 30 March 2015
The paper has multiple authors, but I think it kinder not to list all their names. Here is their abstract, which is what most people will read:
Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. We investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1,099 typically developing individuals between 3 and 20 years of age. Income was logarithmically associated with brain surface area. Among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data imply that income relates most strongly to brain structure among the most disadvantaged children.
The two principal authors have given a statement, and these are instructive because they tend to reveal what the authors regard as the real implications of their findings.
Dr Elizabeth Sowell, of the Children’s Hospital Los Angeles (last named author, theory development and interpretation of results), is reported as having said: ‘Our data suggest that wider access to resources likely afforded by the more affluent may lead to differences in a child’s brain structure. Access to higher-quality childcare, more cognitively stimulating materials in the home and opportunities for learning outside the home likely account for some of these effects.’
If correctly reported, she reveals that she thinks that material resources (within the range experienced by US citizens) lead to a difference in the child’s brain, and presumably thereby to intelligence. This is a strong claim.
Dr Kimberley Noble, of Columbia University in New York (first named author who developed the theory, conducted analyses, wrote the introduction, results, discussion and methods, which in my view makes her virtually the sole author) said that despite the clear impact of socio-economic status on the young mind, it would be wrong to think that the changes are fixed. She said: ‘This is the critical point. The brain is the product of both genetics and experience and experience is particularly powerful in moulding brain development in childhood. This suggests that interventions to improve socioeconomic circumstance, family life and/or educational opportunity can make a vast difference.’
Her view is that by improving socioeconomic circumstances brain development can be improved, and thereby intellectual ability. She mentions that the brain is a product of genetics, which leads to the assumption that this has been considered in the paper but, despite that, “experience (my emphasis) is particularly powerful in moulding brain development in childhood”.
Curious about these dramatic claims, I read the paper. Here is a representative part of the introduction:
It is critical to examine socioeconomic factors such as education and income separately, as these correlated factors represent distinct resources that may have different roles in children's development. For example, income may best represent the material resources available to children, whereas parents' educational attainment may be more important in shaping parent-child interactions.
I would have added: parents’ educational attainment is also a proxy measure of their intelligence, and a good indicator of their children’s inherited intelligence, so we need to control for that, ideally by testing parents’ intelligence.
Their main findings (picked out from the paper) were: Parental education was significantly associated with surface area independent of age, scanner, sex and GAF (racial ancestry) (β = 0.141, P = 0.031, F(22, 1076) = 31.67, P < 0.001, R2Adjusted = 0.381). Multiple regression showed that, when adjusting for age, age2, scanner, sex and genetic ancestry, family income was significantly logarithmically associated with children's total cortical surface area, such that the steepest gradient was present at the lower end of the income spectrum (β = −0.19, P = 0.004). We next constructed a model that included both education and income to assess whether these socioeconomic factors uniquely accounted for variance in surface area. Only the income term accounted for unique variance (β = 0.105, P = 0.001, F(22, 1076) = 32.52, P < 0.001, R2Adjusted = 0.387). We next investigated associations between SES factors and cortical thickness. Initial analyses of thickness revealed that models were best fit using a quadratic function for age. When adjusting for age, age2, scanner, sex and GAF, multiple regression analyses indicated that parental education was not associated with cortical thickness, whether considering a linear, logarithmic or quadratic model.
In the discussion section the authors say: We found that parental education was linearly associated with children's total brain surface area, implying that any increase in parental education, whether an extra year of high school or college, was associated with a similar increase in surface area over the course of childhood and adolescence. Family income was logarithmically associated with surface area, implying that, for every dollar in increased income, the increase in children's brain surface area was proportionally greater at the lower end of the family income spectrum. Furthermore, surface area mediated links between income and children's performance on certain executive function tasks.
Notice that it is assumed that an extra year of education might increase the surface area of the brain. In fact the linear slope with parental education is relatively slight, as shown in their figures.
Here is their version of the traditional required “we cannot be absolutely sure” paragraph in the discussion:
Of course, strong conclusions concerning development are limited in a cross-sectional sample. Furthermore, in our correlational, non-experimental results, it is unclear what is driving the links between SES and brain structure. Such associations could stem from ongoing disparities in postnatal experience or exposures, such as family stress, cognitive stimulation, environmental toxins or nutrition, or from corresponding differences in the prenatal environment. If this correlational evidence reflects a possible underlying causal relationship, then policies targeting families at the low end of the income distribution may be most likely to lead to observable differences in children's brain and cognitive development.
You will note that inherited characteristics are not mentioned in this important section. Not a single word. It seems to have escaped notice that the apparent SSE/brain link might both be driven by a common factor of inherited intelligence. Cross-sectional studies are particularly weak when the sample is not randomly drawn from a specific population, so we have, in my view, a sampling issue as well as a cross-sectional issue. However, in the next paragraph genetics makes an appearance, but in a slightly different context, that of race being a confounder of SES.
SES, cultural differences and genetic ancestry are often conflated in our society. To the best of our knowledge, this is the first study of SES and the brain to include as covariates continuously varying measures of degree of genetic ancestry. Notably, our results can only speak to the effects of GAF, a proxy for race. Thus, although the inclusion of genetic ancestry does not preclude the possibility that these findings may reflect, in part, an unmeasured heritable component, it reduces as far as possible the likelihood that apparent SES effects were mediated by genetic ancestry factors associated with SES in the population. Furthermore, associations between SES factors and brain morphometry were invariant across ancestry groups.
Pause a moment here. There is a mention of “an unmeasured heritable component” but it is then dismissed because the SES and brain measure relationships were invariant across racial groups. That is a different matter. SES and brain size can have the same relationship in all racial groups, and yet still be driven by inherited intelligence. What we need to see is means and standard deviations of the brain measures by racial group, so that we can see what has been adjusted in absolute terms. The paper has done well to include a genomic version of race, but that does not cover the major factor of intelligence being heritable in all genetic groups.
The authors conclude: many leading social scientists and neuroscientists believe that policies reducing family poverty may have meaningful effects on children's brain functioning and cognitive development. By elucidating the structural brain differences associated with socioeconomic disparities, we may be better able to identify more precise endophenotypic biomarkers to serve as targets for intervention, with the ultimate goal of reducing socioeconomic disparities in development and achievement.
Does this study allow us to reach that conclusion?
The sample is a bit of a mess from an epidemiological point of view, being composed of volunteers: Participants were recruited through a combination of web-based, word-of-mouth and community advertising at nine university-based data collection sites in (the US). Participants were excluded if they had a history of neurological, psychiatric, medical or developmental disorders. All participants and their parents gave their informed written consent/assent to participate in all study procedures, including whole genome SNP genotype, neuropsychological assessments (NIH Toolbox Cognition Battery).
It seems that the children (and perhaps also the adults) were given the The NIH Toolbox Cognition Battery, but I cannot find any results in the data set. The toolbox includes Vocabulary Comprehension, Reading Recognition and Pattern Comparison (processing speed) task from which an IQ estimate could be drawn and there are 5 other tests which can be looked at for a broader picture.
Income data and educational level were collected not as actual figures but in categories, so are cruder than required, particularly when fine details about lower income effects are being discussed. I have looked at the Excel sheet kindly provided, and Education is measured in years to the nearest two years. That is, all the scores are in even numbers. Years of education is a crude measure anyway (ignores education quality) but this silly restriction in the previous data collection makes it cruder still. It is not a fatal problem, but reduces data quality.
The study makes much of controlling for genetic ancestry, which is a good thing. However, they report none of their results on these differences. They say that the associations are the same in all genetic groups, which is not surprising, but no means or standard deviations for the brain measures by genetic background are given. These differences, if any, could be compared with differences in SES between racial groups, as another test of the hypothesis being examined by the authors. In terms of absolute levels how well do SES, education and race fit the data?
The sample was composed as follows: African 12%, American Indian 5%, Central Asian 2%, East Asian 16%, European 64%, and Oceanic 1%. The authors do not test whether this is representative of the USA. The European figure seems close to the White, non-Hispanic or Latino population which make up 62.6% of the nation's total. African figure is spot on. The national Asian population is given as 4.4% so Asians seem over-represented four times. American Indians are 0.8% of US population so they seem over-represented here 6 times. That oddly American category, Hispanic was either not sampled or otherwise described. Of course, the genetic techniques used in the study need not match perfectly with the US census classifications, but the authors could have sorted this out for us by commenting on the representativeness of their sample.
To put it at its kindest, the authors have missed a trick. They could have given the parents the psychometric test battery for good comparability, or even the very quick Wordsum test as a crude estimate, and then they could have contrasted ability with education and social status. As far as we can deduce from large scale genetic samples, both intelligence and social class have a significant heritable component. To avoid measuring that in parents when all the children have genetic and psychometric measures in place is a great pity. Perhaps all this has been done, and is being held over for another paper, but presented in this way, and described to the Press in the manner the authors have done, is very likely to mislead the average reader about the relative power of genetics and social status in brain development.
The paper and the comments will lead readers to believe that lack of money is stunting the brains of poorer children. This is possible, but not proved by this study because of obvious genetic confounders. The authors should have made it clearer that although they had the opportunity, they did not test the obvious and well established fact that different families have different abilities, and that within families siblings differ in their abilities (by about two thirds of the population variance). These differences in ability, even within families of a particular social class, lead to jobs which are more or less well paid, and thus people of different abilities achieve different social status. We know from proper epidemiological samples (Nettle, 2003) that intelligence at age 11 has more effect on achieved social class than does the original social class of origin (which is what is being measured in this study).
Absent Third world malnutrition (itself increasing rare across the world), brain development, intelligence, health and social status contain a large genetic component. The 1099 brain scans of this study could have told us some very interesting things if coupled to data about the abilities of the parents.
What do you think?