While we know that IQ is significantly influenced by both genetics and environment (e.g., Plomin et al., 2008
), the results in the current study suggest that the correlation between IQ and brain volume is due substantially to genetic influences. This finding is supported by the few previous behavior genetic studies of brain volume and IQ, which have also found that phenotypic overlap between IQ and brain volume was significant only for genetic factors (Posthuma et al. 2002
; Posthuma et al., 2003
Previous research also has shown that Full Scale IQ is related to both gray-and white-matter brain volumes (Posthuma et al., 2002
). The current results indicate that when IQ is partitioned into Verbal and Performance subscales, however, VIQ and PIQ components of IQ are not related to brain areas in the same way. In the phenotypic correlations, all brain volumes except white matter are more highly related to PIQ than the VIQ. More specifically in the genetic results, PIQ shared significant genetic influence with both gray-matter and white-matter volumes, while VIQ shared significant genetic influence with gray matter but not white matter. While the confidence intervals of these comparisons do overlap, so we cannot say that they are significantly different per se, the pattern of findings is consistent with the hypothesized relation, suggested in the Introduction, between crystallized intelligence and cortical networks. Also, as suggested earlier, PIQ is more strongly related to prefrontal cortex in both the phenotypic correlations and the genetic results, consistent with previous research demonstrating a relation between fluid intelligence and prefrontal cortex. These findings fit well with those of Posthuma et al. (2003)
, who showed that each of four IQ dimensions differed in the pattern of genetic influence with gray and white matter.
Furthermore, we were interested in how more specific measures of cognitive function, processing speed and reading, were related to brain volume. Similar to the findings with PIQ, processing speed also shared significant genetic variance with both gray matter and white matter. Our finding that speed overlaps with the same brain areas as PIQ is reasonable given findings that speed has been found to be more correlated with fluid than crystallized intelligence, both phenotypically and genetically (i.e., Luciano et al., 2004
). This finding is somewhat in contrast to the results of Posthuma et al. (2003)
however, who found that the WAIS processing speed factor was significantly related to white matter but not gray matter. Both studies, however, agree in finding a stronger relation between PS and white matter than with other brain regions. As discussed earlier, this is consistent with prior literature that has shown a strong relation between white matter integrity and PS, but is inconsistent with the findings of van Leeuween and colleagues (2009)
, who found no relation between processing speed and brain volume. The different measures of PS used in the two studies could contribute to this discrepancy in results: our PS score is computed from four separate tests of processing speed, while the PS subscale of the WISC used by van Leeuween and colleagues is a combination of only digit-symbol substitution and symbol search tasks. Since measures of response speed are known to be less reliable in children, and since the WISC PS score is less reliable than other WISC composite scores (Wechsler, 2003
), it is possible that the current study’s older participants, combined with a more robust PS measure, allowed us to find significant relations between PS and brain volume where van Leeuween et al. did not.
In contrast, reading skills shared genetic overlap with Total Brain Volume only. While previous phenotypic results using an overlapping sample to the current study showed some significant relations between brain area volumes and reading skill, these findings were not extremely strong (Phinney et al. 2007
), lending support to the current findings.
The current results present an interesting picture of the relation between specific cognitive functions and different brain structures. The overlap between these factors appears to be entirely due to genetic influences, despite environmental influence on the individual measures. Nonetheless, it is important to realize that this pattern of genetic but not environmental correlations can be interpreted in several ways. Posthuma et al. (2003)
distinguished four possibilities: 1) pleiotropy, 2) unidirectional causation from brain to cognition, 3) unidirectional causation from cognition to brain, and 4) reciprocal causation between brain and cognition. These possibilities relate to the four possible interpretations of any correlation: 1) a third variable influences both A and B, 2) A causes B, 3) B causes A, and 4) reciprocal causation between A and B. The first possibility, pleiotropy, would indicate that the same genes influence both brain volume and cognition, which do not directly influence each other. The second possibility corresponds to a simple innatist model which holds that brain volume directly mediates the relation between genes and cognition. That is, genes determine brain size early in life, perhaps prenatally, and these innate differences in brain size contribute to individual differences in cognitive development in a fairly direct way. The third possibility corresponds to an emergentist model in which cognitive development affects brain development through a protracted postnatal developmental process. This possibility is consistent with a G-E correlation process, in which genetic differences in a neural parameter are correlated with environmental differences that affect postnatal brain developmental processes, like synaptic pruning and myelination. Finally the fourth possibility, reciprocal causation, combines the second and third possibilities. In sum, the overall point is that a genetic correlation between a given brain structure and dimension of cognition, such as those reported here, leaves open the developmental process underlying that correlation. Moreover, the particular developmental process may well vary across different domains of cognition and particular brain structures.
In a more recent paper by van Leeuwen and colleagues (van Leeuwen et al., 2009
, also see De Moor, Boomsma, Stubbe, Willemsen, & De Geus, 2008
), the authors argue that if the causal path runs from cognition to brain, then there should be both environmental and genetic correlations between cognition and brain, since there are significant environmental and genetic effects on cognition, which would then be passed on to brain in the causal chain. Since they found only genetic but not environmental correlations between brain volumes and cognition in this study, they argue that only a causal path from brain to cognition or pleiotropy (possibilities 2and 1above) are consistent with their data. In the current results, since we also found significant genetic but not environmental correlations between brain and cognition, the most likely causal models are either a causal path from brain to cognition or pleiotropy. The same argument could be made from Posthuma et al. (2003)
’s study with adults, which also did not find both environmental and genetic correlations between any cognitive and brain volume measures.
Due to the small sample size in the present study, the power to detect significance was not high; thus some of the non-significant paths between brain volume and cognitive variables could be significant in a larger sample. Furthermore, although we show a differential pattern of significance across bivariate relations, it is important to note that that most of the confidence intervals of these comparisons overlap, so we cannot say that they are significantly different from one another. Thus, the specific differences between measures should be interpreted with caution, and future research with larger samples should be done to replicate these findings. Results also showed that there were almost no additional significant genetic or environmental influences on any of the cognitive measures after that shared with brain volume. There was a significant independent genetic factor for VIQ alone in each model. While the other genetic paths were not significant, estimates for the unique paths for PIQ and reading were moderately high, suggesting that with a larger sample size these might become significant. Interestingly, after the variance shared with brain volume, the additional common genetic path coefficients shared between the cognitive measures were quite low, suggesting that the genetic variation shared with brain volume accounts for most of the overlap between the cognitive measures. There were independent nonshared environmental factors for each cognitive measure; however these estimates include test error. While this could imply that almost all genetic and environmental influences on cognition are shared with brain development, due to the size of the sample in this study, it is possible that we do not have sufficient power to detect those additional influences here. Further studies with larger sample sizes will be needed to explore this further.
Additionally, one factor to consider when generalizing the current results is that our sample was overselected for reading problems. However, although pairs with at least one twin referred for having reading problems do have a lower VIQ than the control pairs, the mean VIQ of the entire sample is actually 101.7, which is very close to the population mean. The standard deviation of the VIQ scores is 12 points, so there is not an issue with restricted range in the sample, either. Therefore, we do not believe that there is a problem with generalizing our findings to an unselected population, but further research is needed to address this question.
Finally, although we did not find significant environmental factors shared between cognition and brain volume, we do not dismiss this possibility in future research. While previous studies support the genetic basis for overlap between brain volume and IQ, based on what we know about brain plasticity, it seems likely that learning would be an environmental influence on cognition that would be mediated by an increase in synaptic density in the cortex, and quite possibly an increase in white matter myelination as well. Thus, while it seems possible that environmental learning could increase brain volume, it may be difficult to observe those differences either in predominantly middle class samples or with the magnet used in the current study. It is possible that in future research with different samples or larger magnets, an environmental correlation between cognition and brain maybe detected.