Reading Time: 17 minutes

First, let me apologise for the delay in this post. When I volunteered to help Jonathan with content, I opted to take a Friday afternoon slot, as that suits my work week. The sheer volume of sources that I pulled together for this piece has made this somewhat more than a 4-5 hour job (indeed, this might be the first of two or three posts).



Following my post a couple of weeks ago on Race and IQ, there have been a number of posts by a contributor called Rob – no surname, no avatar, no ability to view his profile (a sure sign he believes what he says, without reservation).

Rob was kind enough to bullet point his primary contentions as follows:

Compared to Blacks, Whites’ brains:

  • are 7% larger (1438cc versus 1343cc)
  • are 100 grams heavier
  • have deeper fissuration in the frontal and occipital regions
  • have more complex convolutions and larger frontal lobes
  • have more pyramidal neurons
  • have 16% thicker supra-grandular layer
  • react faster on mental chronometry tests
  • have 600 million more neurons (each carries about 600 billion synapses, which each carry one bit of cortical information)

So, my post today will be given over to illustrating how these metrics are not as clear cut as he makes out, and that most if not all of these effects can be laid squarely at the foot of poverty.

(I will re-quote the bullet points above as a numbered list throughout this post to make it easier to cross-reference and/or comment.)



Because the brain is the organ from which all cognition and emotion originates, healthy human brain development represents the foundation of our civilization. Accordingly, there is perhaps nothing more important that a society must do than foster and protect the brain development of our children (Luby, 2015[i]).

Poverty has been known to be a major risk factor for adverse life outcomes for decades. However, these issues were generally seen as more issues of ‘state’ than ‘trait’ and thus presumed to be transient in nature. This gave rise to the idea that lifting someone out of poverty or giving them the appropriate moralistic prod, would be sufficient to solve the problem. The persistence of poverty, therefore, has been cast, by some, as indicative of moral deficiency… or racial inferiority. However, work this century is increasingly pointing to the long lasting impacts on the size and shape (morphology) of the brain, of being raised in poverty (Morris, 2007[ii]).

According to Morris (ibid.) around 200 million children under 5 years of age in the developing world are cognitively delayed due to the impacts of stress, nutrition, and access to education. However, in rich, developed countries, particularly those where wealth disparities are high (e.g. the US and UK), higher proportions of children may be cognitively delayed. The Avon Longitudinal Study of Parents and Children (ALSPAC) found that outcomes such as verbal IQ, visual stereo-acuity, motor control, communication, and social development scores were negatively impacted by their mother’s diet during pregnancy, specifically their intake of oily fish, and even more specifically, their intake of docosahexaenoic acid (Carlson, 2008[iii]). This dietary choice correlates with low Socio-Economic Status, though the impact is somewhat mediated by breastfeeding (docosahexaenoic acid is the DHA that the baby formula adverts mention, because it is a key ingredient of breast milk).

The following, from Santiago, et al. (2011[iv]) is pretty much the TL;DR equivalent of my entire post. If you’re finding yourself “time poor” read this, and then skip on down to ‘So it all boils down to brain size’:

There is clear evidence that low SES and income are linked to poor psychological and physical health outcomes, showing a clear gradient whereby more health problems are experienced with each step down the SES ladder. Explanations involving social selection and differences in life-style risk factors are limited in their ability to explain this SES-health gradient. The incidence of smoking, drinking, obesity, poor diet, and sedentary lifestyles do increase among lower SES individuals. However, these explanations only account for a small portion of the SES-health gradient (Sapolsky, 2004[v]). In addition, access to healthcare is a real problem faced by many low-income families, but this explanation has also failed to fully explain the SES- health gradient. The gradient exists even in countries with socialized healthcare, and for diseases that are not affected by preventative health care (Sapolsky, 2004). Social causation theory posits that poor people develop psychological and physical health problems as a result of living with poverty-related hardship. Indeed, the SES-health gradient is strongest for diseases with sensitivity to stress such as heart disease, diabetes, metabolic disorders, and psychological disorders (Sapolsky, 2004). Studies comparing social causation of psychological disorders with alternative models such as social selection generally find strong support for the social causation of psychological disorders such as depression and anxiety (e.g., Wadsworth & Achenbach, 2005[vi]). Poverty is chronic and toxic, taxing mental and physical resources, ultimately resulting in higher mortality rates for those in poverty (e.g., Rehkopf et al., 2006[vii]). Low SES also takes its toll on children and adolescents, with familial SES predicting anxiety at age 15 (Miech et al., 1999[viii]). Furthermore, increases in income, or emergence out of poverty, have been linked to declines in psychological problems such as aggression (Costello, Compton, Keeler, & Angold, 2003[ix]). Poverty’s damage occurs at multiple levels. Poor families are exposed to more dangerous and deteriorating neighborhoods, more crowded and noisier homes, more conflict and instability in the family, and more polluted air and water (Evans, 2004[x]). These multiple risks in turn affect children and adults leading to an array of psychological and physical morbidity (Evans, 2004).

Compared to European-Americans, more than twice as many African-American households earn less than $15,000, and nearly 1.5 times as many earns between $15,000 and $24,999. In fact, the first income bracket where European-Americans dominate, proportionately, is $50,000-$74,999. Below that level is 42.6% of European-American households, as compared to 61.4% of African American households (US Census, 2015[xi]). However, the term ‘household’ conceals the number of dependent children. The fertility rate for European-Americans is 1.8, for African-Americans, it’s 2.1 (Pew, 2012[xii]).

So, having laid out the general case for looking at poverty as a causal factor in reduced flourishing, let’s look at the specifics.


Brain size

1) [whites’ brains] are 7% larger (1438cc versus 1343cc)

2) [whites’ brains] are 100 grams heavier

Hair, et al. (2015[xiii]) demonstrated that children living significantly below the federal poverty level had less grey matter, smaller frontal and temporal lobes, and less hippocampal volume. These are regions critical for cognitive and academic performance. Whilst this had previously been demonstrated, in general terms (e.g. Duncan, Brooks-Gunn, 1997/1999[xiv]; Haveman & Wolfe, 1995[xv]), this was the first time it was linked to specific areas. The reduced volume in the frontal and temporal lobes explained 15-20% of the achievement deficits found. Hippocampal volume increased with caregiving support and decreased with hostility (Luby, et al. 2013[xvi]; Perkins, et al., 2013[xvii]). Luby (2015[xviii]) also suggests that because children with other risk factors were screened out, and because poverty normally acts as an amplifier for these risk factors, that the results are probably an underestimation.

Noble et al. (2015[xix]) note that even small differences in income among low-SES children are associated with relatively large differences in cortical area. Similar incremental differences among high-SES children make markedly less difference. As such, there are substantial differences between even low- and middle-SES children, by the time they get to kindergarten. The difference, when measured by language abilities, is about one standard deviation (SD), and about two-thirds of an SD when measuring Executive Function (Farah, 2009[xx]). Recall, that in a prior post I pointed to similar impacts due to highly punitive environments. But of course punitive environments and child abuse are highly correlated with low Socio-Economic Status (see also Cortical Thickness, below).


Brain Complexity

3) [whites’ brains] have deeper fissuration in the frontal and occipital regions

4) [whites’ brains] have more complex convolutions and larger frontal lobes

You’ll note that point four is really a restatement of points one and two, in the prior section, as well as point three, listed here. There is some reason to point to the frontal lobes in particular, but it does seem a little like padding out the list with unnecessary bullet-points, not least because much of the size difference being discussed is in the frontal lobes.

One study found increased cortical folding (fissuration) in the temporal lobe in preterm births, which was found to lead to decreased language ability and reading (Kesler, et al., 2006[xxi]). While in another study looking specifically at children with a history of maltreatment, cortical thickness was reduced in the anterior cingulate, superior frontal gyrus, and orbitofrontal cortex, amongst others (Kelly, et al., 2013[xxii]). Lack of cortical folding of frontal regions of the cortex among low-SES children suggests not just a lag in volume, but a lag in complexity (Jednoróg, et al., 2012 [xxiii]). This indicates a link between Socio-Economic stressors (including maltreatment) and cognitive development.

In addition to this, there is work suggesting that schizophrenics have reduced cortical folding, likewise with Bipolar disorder, but to a lesser extent (Palaniyappan & Liddle, 2012[xxiv];2014[xxv]). Furthermore, schizophrenics demonstrate significant increases in cortical folding in some areas, with significant decreases in others. As schizophrenics age, the regions with decreased cortical folding also show accelerated reduction (Palaniyappan, et al., 2011[xxvi]). Of course, what this means for samples of low-SES minorities is that older test subjects may be symptomatic of schizophrenia at the level of brain structure, but never hospitalised or diagnosed, and thus seen as a “healthy participant.” This creates a downward drag on the average volume of actually healthy minority participants due to the inclusion of non-healthy participants. African-Americans have an incidence of schizophrenia that is around three times that of whites (Bresnahan, et al., 2007[xxvii]; Schwartz & Blankenship, 2014[xxviii]). And that is just those that are diagnosed with some form of psychosis – others are struggling through life, or failing and being imprisoned.

These differences are noticeable in clinical populations, however, as the morphological differences predict the impact on the individual better than standard diagnostic information (Palaniyappan & Liddle, 2014[xxix]). It is reasonable to assume that these morphological differences will be evident in subclinical populations (though presumably to a lesser extent). In fact, there is a problem with the very idea of “subclinical” in African-American and/or poor populations in the US. The term implicitly assumes that individuals who are sick seek medical attention, but with a health system that is bordering on non-existent for those in or near poverty, this assumption is not warranted.

Whilst not relating to schizophrenia, Dr. Peter Hotez (2014[xxx]) states:

A group of neglected infections are emerging as important causes of psychiatric and mental illness among vulnerable populations living in extreme poverty in the United States. These chronic infections may partially account for the achievement gap noted among socioeconomically disadvantaged students.

The neglected tropical diseases (NTDs) are a group of chronic parasitic and related infections that can last decades or even the lifetime of an individual. During this time, they produce long-lasting and debilitating effects that impair productive capacity and child development. Indeed, the NTDs have actually been shown to trap people in poverty through these adverse effects

Another effect of poverty that is somewhat orthogonal to poverty, but that has a great bearing on the matter at hand.


Cortical thickness

5) [whites’ brains] have more pyramidal neurons

6) [whites’ brains] have 16% thicker supra-grandular [sic] layer

Both of these points are, in part, repetitions of point four, relating to the frontal lobes. The pyramidal neurons are concentrated around the frontal cortex – and the hippocampus and amygdala – these latter areas being more affected by poverty. The supragranular layer is the outermost layer of the cortex, and the claim is that the bulk of the difference in brain size is in the frontal lobes, and especially the cortex thereof.

However, cortical thickness is nowhere near the simple and illustrative measure that Rob would have it be:

At 10 years of age, more intelligent children have a slightly thinner cortex than children with a lower IQ. This relationship becomes more pronounced with increasing age: with higher IQ, a faster thinning of the cortex is found over time. In the more intelligent young adults, this relationship reverses so that by the age of 42 a thicker cortex is associated with higher intelligence. In contrast, cortical surface is larger in more intelligent children at the age of 10. The cortical surface is still expanding, reaching its maximum area during adolescence. With higher IQ, cortical expansion is completed at a younger age; and once completed, surface area decreases at a higher rate” (Schnack, et al., 2015[xxxi]).


Processing speed

7) [whites’ brains] react faster on mental chronometry tests

These ‘mental chronometry’ tests are indicative of mental efficiency. Mental efficiency comes down to two things: 1) the way in which one has learned about the relationships between things in one’s environment, and thus whether or not related items are stored more closely to one another; and, 2) the physiological efficiency of sending signals from one part of the brain to the other. Whilst they are inter-related, the former relates to education, the latter to myelination. Myelination being the process of forming myelin (fatty tissue) around the axons, as insulation, thereby improving the efficiency of signal transmission.

I have no sources that look at mental chronometry measures on low-SES individuals, specifically, but I can discuss education and myelination. First, as mentioned previously, the Avon Longitudinal Study of Parents and Children (ALSPAC) found that outcomes such as visual stereo-acuity and motor control were negatively impacted by the quite literal poverty of their mother’s diet during pregnancy (Morris, 2007[xxxii]).

With respect to myelination:

Cortical thickness decreases rapidly in childhood and early adolescence, followed by a more gradual thinning and ultimately plateauing in early adulthood[xxxiii],[xxxiv][xxxv][xxxvi]. This cortical thinning is thought to relate to synaptic pruning and increases in myelination expanding into the neuropil, both of which would appear as decreases in gray matter on magnetic resonance imaging (MRI)[xxxvii]. It is thought that experience-related synaptic pruning influences surface area, as well as pressure from increased myelination expanding the brain surface outward. In contrast to thickness, surface area expands through early adolescence and then shrinks through middle adulthood[xxxviii]. These maturational changes, in concert, result in the mature human brain, and are influenced by both genetic programming and experience” (Noble, et al., 2015[xxxix]). [emphasis mine]

Here we see that education – really just exposure to more stimuli – causes expansion of brain volume, through increased myelination. Access to education is reduced by poverty, and so the pressure on the brain to be plastic (to adapt to “new” stimuli) reduces.

Note that intelligence is related to the thickness of the grey matter (cortex, pyramidal and supragranular cells) in different ways at different times in the life course, which does more damage to points five and six.


The racialist conclusion

8) [whites’ brains] have 600 million more neurons (each carries about 600 billion synapses, which each carry one bit of cortical information)

This is really just a restatement of points one, two, five, and six, in effect – so, more padding of the list. So the response really is just to look at all of the above points.

Also, if the implication of that point is meant to be that each neuron “carries about 600 billion synapses,” it is incorrect. If we’re talking about each brain having 600 billion synapses due to 600 million extra neurons, then that is correct.


So it all boils down to the brain size

To quote Rob more fully on this:

Weighing brains at autopsy, Whites averaged heavier brains than Blacks and had more complex convolutions and larger frontal lobes. Subsequent studies have found an average Black–White difference of about 100 g. Studies have found that the more White admixture (judged independently from skin color), the greater the average brain weight in Blacks. In a study of 1,261 American adults, Ho et al. (1980) found that 811 White Americans averaged 1,323 g and 450 Black Americans averaged 1,223 g. Since the Blacks and Whites were similar in body size, differences in body size cannot explain away the differences in brain weight.

On the admixture point, I have shown in previous posts that darker-skinned individuals are treated differently than lighter skinned individuals. This effect persists even where no personal history is involved in the interaction (i.e. experiments involving photos of unknown individuals). As such darker-skinned African-Americans are more likely to suffer from effects related to prejudice. But of course, the darkness of an individual’s skin is not a direct corollary for the amount of African DNA.

Rob goes on to say:

The same three-way pattern of race differences has been found using the simplest culture-free cognitive measures such as reaction time tasks, which 9- to 12-year-old children perform in less than 1 s. Lynn (2006) found that East Asian children from Hong Kong and Japan were faster than European children from Britain and Ireland, who in turn were faster than African children from South Africa. Using similar tasks, this pattern of racial differences was also found in California (Jensen, 1998; Rushton & Jensen, 2005). Within each group, the children with higher IQ scores perform faster those with lower scores.

Now, if the history of slavery, segregation, and systemic racism in the US were to be condensed down into a quarter of the time, and thus to have a significantly more intense effect, which country would you choose to illustrate this?

Thanks, Alex. I’ll take ‘South Africa’ for 100.

Note that the Lynn paper looked at children aged 9-12, in 2006. In other words, the participants were the first cohort of black South African children to be born after apartheid (which came to an end in 1994).

I have illustrated that the impact of poverty is such that it negatively impacts on the size and shape of brains, and thus on an array of abilities (executive function, memory processing, fear processing), all of which impact behavioural control. Clearly, the adults raised under apartheid (or under Jim Crow, and the subsequent systemic racism) are not going to suddenly stop acting as destitute people. They have destitute (fearful and stressed) brains. Indeed, they will teach their children how to behave in a destitute fashion, even were it not the case that poverty was widespread in post-Apartheid South Africa (or the post-Jim Crow African-American population)… but it is the case. The repercussions of South African racialist policy (and its US equivalent) is, and will be, evident in the behaviour of both black and white South Africans for decades, just as it has been and continues to be for both African- and European-Americans.


My conclusion

Am I denying that there are differences between races?

For such a simple-sounding question the answer is complicated. The short answer is: No, I’m not denying that.

Are some of those differences evident in brain morphology?

I would say, yes, but as with the ever-changing thickness of the cortex through the normal maturation process, we don’t know what types of thought, what mental talents are inherent in that morphology. Until such time as we can separate variations in brain morphology from poverty, this is impossible, no matter how much we try and statistically correct for it.

What I am denying, therefore, is the excessively simplistic relationship between these differences, IQ (itself a problematic measure), and the (almost exclusively) Western European definitions of success that are put forth as objective outcome measures. The massive rate of anti-depressants being taken by Americans (and everyone else in Western civilisation, albeit to a lesser extent), illustrates the problem with any claim about the “success” of Western Civilisation in general, and the American experiment in particular.

Almost all of the claimed differences between European- and African-Americans boil down to the effects of poverty on the brain. African-Americans are more than twice as likely to be in poverty than whites. Unlike other poor people in the US, African-Americans also have the compounding genetic and epigenetic effects from slavery, Jim Crow, and systemic racism to contend with (and being the subjects of a distinctly different kind of racism to Hispanics). In other words, the social environment has been a strong selective pressure on African-Americans for 400 years, and African-Americans with a tendency towards intelligence or willfulness (aka being “uppity”) have tended not to fare well until more recently, and even now, the US is certainly not “post-racial.”

We have only been testing IQ, in anything like an objective fashion, for about 100 years, and have only started to recognise the neurological underpinnings of these various differences for half of that time, at best. At no time in that 100 years have we been able to separate the testing of IQ from the impacts of English colonialism and American interventionism on world populations, not to mention the pillaging of natural resources from Africa (resulting in a third world continent). We haven’t been able to test African-Americans who have benefitted from a lifetime of excellent nutrition and educational opportunities in significant numbers. We certainly haven’t been able to test the children and grandchildren of such individuals.

Until such time as we can study the differences between the races without the impact of race being instantiated in the brains we’re trying to study, any claim about racial difference is fraught with problems.




Further reading

For those that are interested, here are some interesting articles that I picked up along the way, in no particular order: – It’s Not Just About Bad Choices

PDF from ResearchGate – Perkins, et al. (2013). Poverty and Language Development: Roles of Parenting and Stress Innovations in Clinical Neuroscience 10(4): 10–19.

Yoshikawa, Aber & Beardslee (2012). The Effects of Poverty on the Mental, Emotional, and Behavioral Health of Children and Youth: Implications for Prevention. American Psychologist 67(4); 272–284

PDF from JAMA – Luby, J. L. (2015). Poverty’s Most Insidious Damage – The Developing Brain. JAMA Pediatrics.



[i] Luby, J. L. (2015). Poverty’s most insidious damage: the developing brain. JAMA Pediatrics, 169(9); 810-811.


[iii] Carlson, S. E. (2009). Docosahexaenoic acid supplementation in pregnancy and lactation. The American Journal of Clinical Nutrition, 89(2), 678S-684S.

[iv] Santiago, C. D., Wadsworth, M. E. & Stump, J. (2011). Socioeconomic status, neighborhood disadvantage, and poverty-related stress: Prospective effects on psychological syndromes among diverse low-income families. Journal of Economic Psychology 32; 218–230.,%20and%20Poverty-related%20stress%20Longitudinal%20Effects%20on%20Psychological%20Syndromes%20among%20Diverse%20Low-income%20Families.pdf

[v] Sapolsky, R. M. (2004). Social status and health in humans and other animals. Annu. Rev. Anthropol., 33, 393-418.

[vi] Wadsworth, M. E., & Achenbach, T. M. (2005). Explaining the link between low socioeconomic status and psychopathology: testing two mechanisms of the social causation hypothesis. Journal of consulting and clinical psychology, 73(6), 1146.

[vii] Rehkopf, D. H., Haughton, L. T., Chen, J. T., Waterman, P. D., Subramanian, S. V., & Krieger, N. (2006). Monitoring socioeconomic disparities in death: comparing individual-level education and area-based socioeconomic measures. American Journal of Public Health, 96(12), 2135-2138.

[viii] Miech, R. A., Caspi, A., Moffitt, T. E., Wright, B. R. E., & Silva, P. A. (1999). Low socioeconomic status and mental disorders: a longitudinal study of selection and causation during young adulthood 1. American journal of Sociology, 104(4), 1096-1131.

[ix] Costello, E. J., Compton, S. N., Keeler, G., & Angold, A. (2003). Relationships between poverty and psychopathology: A natural experiment. Journal of the

American Medical Association, 290, 2023–2029.

[x] Evans, G. W. (2004). The environment of childhood poverty. American psychologist, 59(2), 77.



[xiii] Hair, N. L., Hanson, J. L., Wolfe, B. L. & Pollak, S. D. (2015). Association of Child Poverty, Brain Development, and Academic Achievement. JAMA Pediatrics 169(9), 822-829.


[xiv] Duncan, G. J., & Brooks-Gunn, J. (Eds.). (1999). Consequences of growing up poor. New York: Russell Sage Foundation.

[xv] Haveman, R. & Wolfe, B. (1995). The determinants of children’s attainments: A review of methods and findings. Journal of Economic Literature, 33(4); 1829-1878.

[xvi] Luby, et al. (2013). The Effects of Poverty on Childhood Brain Development

The Mediating Effect of Caregiving and Stressful Life Events. JAMA Pediatrics 167(12): 1135-1142. doi:10.1001/jamapediatrics.2013.3139


[xvii] Perkins, et al. (2013). Poverty and Language Development: Roles of Parenting and Stress Innovations in Clinical Neuroscience 10(4): 10–19.

[xviii] Luby, J. L. (2015). Poverty’s Most Insidious Damage – The Developing Brain. JAMA Pediatrics.

[xix] Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., … & Schork, N. J. (2015). Family income, parental education and brain structure in children and adolescents. Nature neuroscience, 18(5), 773-778.

[xx] Farah, M.J.(2009). Mind, Brain, and Education in Socioeconomic Context. In M. Ferrari and L. Vuletic (Eds.), The Developmental Relations between Mind, Brain and Education. Dordrecht: Springer Science + Business.

[xxi] Kesler, S. R., Vohr, B., Schneider, K. C., Katz, K. H., Makuch, R. W., Reiss, A. L., & Ment, L. R. (2006). Increased temporal lobe cortical folding in preterm children. Neuropsychologia, 44(3), 445-453.

[xxii] Kelly, P. A., Viding, E., Wallace, G. L., Schaer, M., De Brito, S. A., Robustelli, B., & McCrory, E. J. (2013). Cortical thickness, surface area, and cortical folding abnormalities in children exposed to maltreatment: neural markers of vulnerability?. Biological psychiatry, 74(11), 845-852.

[xxiii] Jednoróg, K., Altarelli, I., Monzalvo, K., Fluss, J., Dubois, J., Billard, C., … & Ramus, F. (2012). The influence of socioeconomic status on children’s brain structure. PloS one, 7(8), e42486.

[xxiv] Palaniyappan, L., & Liddle, P. F. (2012). Aberrant cortical cortical folding in schizophrenia: a surface-based morphometry study. Journal of psychiatry & neuroscience: JPN, 37(6); 399-405.

[xxv] Palaniyappan, K. & Liddle, P. F. (2014). Diagnostic Discontinuity in Psychosis: A Combined Study of Cortical Cortical folding and Functional Connectivity. Schizophrenia Bulletin 40(3), pp. 675–684


[xxvi] Palaniyappan, L., Mallikarjun, P., Joseph, V., White, T. P., & Liddle, P. F. (2011). Folding of the prefrontal cortex in schizophrenia: regional differences in cortical folding. Biological psychiatry, 69(10), 974-979.

[xxvii] Bresnahan, M., Begg, M. D., Brown, A., Schaefer, C., Sohler, N., Insel, B., Vella, L. & Susser, E. (2007). Race and risk of schizophrenia in a US birth cohort: another example of health disparity? International Journal of Epidemiology 36(4); 751-758. doi: 10.1093/ije/dym041

[xxviii] Schwartz, R. C., & Blankenship, D. M. (2014). Racial disparities in psychotic disorder diagnosis: A review of empirical literature. World journal of psychiatry, 4(4), 133.

[xxix] Palaniyappan, K. & Liddle, P. F. (2014). Diagnostic Discontinuity in Psychosis: A Combined Study of Cortical Cortical folding and Functional Connectivity. Schizophrenia Bulletin 40(3), pp. 675–684


[xxx] Hotez, P. J. (2014). Neglected infections of poverty in the United States and their effects on the brain. JAMA psychiatry, 71(10), 1099-1100.

[xxxi] Schnack, H. G., Van Haren, N. E., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … & Pol, H. E. H. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral cortex, 25(6), 1608-1617.


[xxxiii] Sowell ER, et al. (2003). Mapping cortical change across the human life span. Nature Neuroscience 6:309–315. [PubMed: 12548289])

[xxxiv] Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., … & Giedd, J. N. (2011). How does your cortex grow?. Journal of Neuroscience, 31(19), 7174-7177.

[xxxv] Sowell, E. R., Peterson, B. S., Kan, E., Woods, R. P., Yoshii, J., Bansal, R., … & Toga, A. W. (2007). Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cerebral cortex, 17(7), 1550-1560.

[xxxvi] Schnack, H. G., Van Haren, N. E., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … & Pol, H. E. H. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral cortex, 25(6), 1608-1617.

[xxxvii] Sowell ER, et al. (2003). Mapping cortical change across the human life span. Nature Neuroscience 6:309–315. [PubMed: 12548289])

[xxxviii] Schnack, H. G., Van Haren, N. E., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … & Pol, H. E. H. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral cortex, 25(6), 1608-1617.

[xxxix] Noble, et al. (2015). Family Income, Parental Education and Brain Structure in Children and Adolescents. Nature: Neuroscience 18(5): 773–778.