Eine interessante Studie beschäftigt sich mit Unterschieden im Gehirn:
Sex differences in human brain structure and function are of substantial scientific interest because of sex-differential susceptibility to psychiatric disorders and because of the potential to explain sex differences in psychological traits. Males are known to have larger brain volumes, though the patterns of differences across brain subregions have typically only been examined in small, inconsistent studies. In addition, despite common findings of greater male variability in traits like intelligence, personality, and physical performance, variance differences in the brain have received little attention. Here we report the largest single-sample study of structural and functional sex differences in the human brain to date (2,750 female and 2,466 male participants aged 44-77 years). Males had higher cortical and sub-cortical volumes, cortical surface areas, and white matter diffusion directionality; females had thicker cortices and higher white matter tract complexity. Considerable overlap between the distributions for males and females was common, and subregional differences were smaller after accounting for global differences. There was generally greater male variance across structural measures. The modestly higher male score on two cognitive tests was partly mediated via structural differences. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale characterisation of neurobiological sex differences provides a foundation for attempts to understand the causes of sex differences in brain structure and function, and their associated psychological and psychiatric consequences.
Aus der Studie:
In a single-scanner sample of over 5,000 participants from UK Biobank, we mapped sex differences in brain volume, surface area, cortical thickness, diffusion parameters, and functional connectivity. One main theme of the neurostructural results was that associations with sex were global. Males generally had larger volumes and surface areas, whereas females had thicker cortices. The differences were substantial: in some cases, such as total brain volume, more than a standard deviation. We also found that volume and surface area mediated nearly all of the small sex difference in reasoning ability, but far less of the difference in reaction time. For white matter microstructure, females showed lower directionality (FA) and higher tract complexity (OD); white matter microstructure was a poor mediator of the cognitive sex difference. Resting-state fMRI analyses also revealed a global effect: around 54% of connections showed a sex difference. These differences clustered around specific networks, with stronger connectivity in females in the default mode network and stronger connectivity in males between unimodal sensory and motor cortices as well as high-level cortical areas in the rostral lateral prefrontal cortex. Overall, for every brain measure that showed even large sex differences, there was always overlap between males and females (see Figure 1 and ).
The principal strengths of the present study are its sample size (providing sensitivity for the identification of small effects with high statistical power), the wide range of MRI modalities, and the consideration of both mean and variance differences. Given the surfeit of small-n studies in neuroscience , it is of great importance to test hypotheses in large, wellpowered
samples, especially given that many neural sex differences are small . Here, we had excellent statistical power to find small effects in brain subregions, providing a robust,
definitive, and detailed analysis. For our subregional analysis, we had a far larger sample size than the most recent meta-analysis . In contrast to that meta-analysis—which found greater volume for females in areas such as the thalamus, the anterior cingulate gyrus, and the lateral occipital cortex—our study found no brain subregions where females had a larger volume than males. The reason for this may be the more restricted age range of the participants in our study (sex may have different effects at different ages), or, more likely, study size and heterogeneity: the data for that part of the meta-analysis came from many separate studies, on separate scanners, with small sample sizes (many with n < 100), whereas our contrasts were based on a very large, single-scanner study.
The higher male volume in our study appeared largest in some regions involved in emotion and decision-making, such as the bilateral orbitofrontal cortex, the bilateral insula, and the left isthmus of the cingulate gyrus [22-25], but also areas such as the right fusiform gyrus. For surface area, which showed an even larger difference favouring males, the regions that showed the largest effects were broadly areas involved in the hypothesized intelligencerelated circuit in the “P-FIT” model : for example, the bilateral superior frontal gyri, the bilateral precentral gyri, the left supramarginal gyrus, and the bilateral rostral middle frontal areas. However, some of the regions involved in this theorized circuit were also larger, in terms of thickness, for females. For instance, the bilateral inferior parietal regions were the regions with numerically the largest difference favouring females in cortical thickness. Our finding that the cortex was thicker for females is consistent with a number of previous, smaller studies (e.g. [27-29]), though our greater statistical power allowed us to find smaller differences in thickness across the cortex.
Whereas previous work has found some white matter regions where fractional anisotropy was higher for females , we found that males showed higher FA in 18 of the 22 tracts we examined. FA also generally showed greater variance in males. On the other hand, higher orientation dispersion was found for females in all tracts. Unexpectedly, higher OD was found to be related to lower cognitive performance on the two tests examined here. Since OD is a relatively new measure of white matter microstructure , further work should aim to
clarify its behavioural correlates.
The issue of adjusting for overall brain size in analyses of sex differences (e.g. ) was addressed in each of our macrostructural analyses. As can be seen comparing Figures 2 and 3, after this adjustment, the higher male volume and surface area was substantially reduced, often to non-significance. For those latter brain regions, this implies that the sex difference was general: their larger volume or surface area was a by-product of the overall larger male brain. However, for some regions, especially for surface area (particularly in areas such as theleft isthmus of the cingulate gyrus and the right precentral gyrus), males still showed a significantly higher measurement, indicating specific sex differences in the proportional configuration of the cortex, holding brain size equal. Most interestingly, for some areas (for example the right insula, the right fusiform gyrus, and the left isthmus of the cingulate gyrus), the difference was reversed, with females showing significantly larger brain volume.(…)
Our analysis also focused on sex differences in variability. The best-studied human phenotype in this context has been cognitive ability: almost universally, studies have found that males show greater variance in this trait ([6,18,39], though see ). This has also been found to be the case for academic achievement test results (a potential consequence of intelligence differences [8,41,42]), other psychological characteristics such as personality , and a range of physical traits such as athletic performance , and both birth and adult weight . Here, for the first time, we directly tested sex differences in the variance of several brain measures, finding greater male variance across almost the entire brain for volume, surface area, and white matter fractional anisotropy, but only patchy and inconsistent variance differences for cortical thickness and white matter orientation dispersion
One potential candidate to explain greater male variability across multiple phenotypes is the hypothesized ‘female-protective’ mechanism involving effects of the X chromosome [44,45], or other protective factors that “buffer” females from potential deleterious consequences of rare genetic mutations. For instance, if deleterious genetic variants are found on one X chromosome in (heterozygous) females, they may be buffered by the presence of the opposite allele on the other X chromosome. Since males carry only one X chromosome, this effect
cannot occur, increasing the likelihood of the allele being expressed in males, and thus increasing the variation in the phenotype linked to that allele [44,46]. In sex-biased phenotypes like autism (ASD), female protective effects are also frequently discussed. It is known that ASD females typically require a higher burden of rare, deleterious de novo mutations compared to males with ASD , and this effect extends into the general population when examining autistic traits in typically-developing individuals . It is possible that higher male variability could be linked to genetic mechanisms that inherently buffer females against deleterious genetic influences, but may have much a more variable and significant effect on average in males. As studies like UK Biobank release even larger amounts of data on individuals who have both neurostructural and genotype data, researchers will be able to perform well-powered tests of these hypotheses.
Using the (limited) data on cognitive abilities available in our sample, we tested whether the data were consistent with any consequences of brain structural differences in terms of ability differences. There were very small correlations between brain variables and the cognitive tests, and these associations did not differ by sex (consistent with a prior meta-analysis on the link between brain volume and intelligence ). Mediation modelling suggested that, for verbal-numerical reasoning, a very large portion (up to 99%) of the modest sex difference was mediated by brain volumetric and surface area measures. Smaller fractions (up to 38%) of the modest link between sex and reaction time could be explained by volume or surface area. Perhaps unexpectedly, given evidence and theory linking white matter microstructure to cognitive processing speed [50,51], white matter microstructural measures only mediated a small proportion of the sex difference in reaction time (this may have been due to weaknesses in this cognitive measure; see below). Cortical thickness had trivial mediating effects compared to volume and surface area: no more than 7.1% of the sex-cognitive relation was mediated by thickness in any analysis. Thus, the data are consistent with higher volume and cortical surface area (but not cortical thickness or microstructural characteristics) being of particular relevance to sex differences in reasoning abilities (but not particularly reaction time). Sex differences in intrinsic functional connectome organization also revealed results that corroborate and extend prior work. Notably, the original study of the 1,000 Functional Connectomes dataset reported sex differences similar to those we identified – that is, Female>Male connectivity within the default mode network and some evidence for a Male>Female effect in sensorimotor and visual cortices . The higher female connectivity within circuits like the DMN may be particularly important, given that DMN regions are typically considered as the core part of the “social brain” . Whether such an effect can help explain higher average female ability in domains like social cognition , and whether such functional differences can be integrated with differences in the structural connectome , remains to be seen. Finally, recent work has shown that intrinsic functional connectome organization can be parsimoniously described as a small number of connectivity gradients The most prominent connectivity gradient has at one pole the DMN and at the other unimodal sensory and motor cortices. The observed pattern of sex differences in functional connectome organization observed here recapitulates the two main poles of that principal connectivity gradient ; see Figure S12. One potential way of describing the biological significance of these functional sex differences is that mechanisms involved in shaping sex differences (biological, cultural, or developmental) may influence this principal connectivity gradient; the result may be the multiple network differences we discovered.