Largest‐ever mega‐analysis: Already from age one, males show greater variability of brain structures than females, being more likely to fall in the extremes

Greater male than female variability in regional brain structure across the lifespan. Lara M Wierenga et al. Human Brain Mapping, October 12 2020. https://doi.org/10.1002/hbm.25204

Rolf Degen's take: https://twitter.com/DegenRolf/status/1315991008581169154

Abstract: For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta‐Analysis) Consortium presents the largest‐ever mega‐analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1‐90 years old (47% females). We observed significant patterns of greater male than female between‐subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene‐environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex‐specific vulnerability to disorders.

4 DISCUSSION

In this study, we analyzed a large lifespan sample of neuroimaging data from 16,683 participants spanning nine decades of life starting at birth. Results confirmed the hypothesis of greater male variability in brain structure (Forde et al. 2020; Ritchie et al. 2018; Wierenga et al. 2018, 2019). Variance differences were more pronounced for subcortical volumes and regional cortical surface area than for regional cortical thickness. We also corroborated prior findings of greater male brain structural variance at both upper and lower tails of brain measures (Wierenga et al. 2018). These variance effects seem to describe a unique aspect of sex differences in the brain that does not follow the regional pattern of mean sex differences. A novel finding was that sex differences in variance appear stable across the lifespan for around 50% of subcortical volumes, 70% of cortical surface area measures and almost all cortical thickness measures. Unexpectedly, regions with significant change in variance effects across the age range showed decreasing variance differences between the sexes with increasing age. Finally, we observed greater male inter‐regional homogeneity for cortical thickness, but not for surface area or subcortical volumes, partly replicating prior results of greater within‐subject homogeneity in the male brain (Wierenga et al. 2018). Unexpectedly, subcortical regions showed stronger interregional correlation in females than in males.

Greater male variance was most pronounced in brain regions involved in planning, regulation and inhibition of motor movements (pallidum, right inferior parietal cortex and paracentral region), episodic memory (hippocampus), and multimodal sensory integration (thalamus) (Aron, Robbins, and Poldrack 2004; Burgess, Maguire, and O'Keefe 2002; Grillner et al. 2005). In addition, the early presence of sex differences in brain structural variability may be indicative of genetic effects, in line with findings in a pediatric sample (Wierenga et al. 2018). We also observed that sex differences in structural variation are either stable or may reduce in old age. Longitudinal designs are, however, needed to address the mechanisms underlying this observation.

The expression of greater male variability in both upper and lower tails of the distribution may be related to architectural and geometric constraints that are critical for a delicate balance for effective local‐global communication. For example, neurons only partly regulate their size, and the number of neural connections does not vary strongly with neocortical size across species (Stevens 1989). Although axon size and myelin can compensate firing rates in larger brains by speeding up conduction time, there is a limited energy budget to optimize both volume and conduction time (Buzsáki, Logothetis, and Singer 2013). As such, extreme brain structure (in both directions) may come at a cost. This is in line with recent findings that show that extreme neural activity patterns may induce suboptimal expressions of mental states (Northoff and Tumati 2019). Interestingly, it has been found that individuals with autism spectrum disorder show atypical patterns of brain structure and development in both the upper and lower range (Zabihi et al. 2019), suggesting a possible link between greater male variability and vulnerability for developmental disorders (see also Alnæs et al. 2019)). Together with our findings, this opens up new approaches to understanding sex biased developmental disorders, beyond group‐level mean differences.

Although most results showed stable sex differences with increasing age, half of the subcortical regions and a quarter of the cortical surface area measures showed decreasing sex differences in variance. What stands out is that in all these regions, sex differences in variance were largest in young compared to older age. This is indicative of early mechanisms being involved. Furthermore, for subcortical regions, the patterns showed larger volumetric increases in females then in males. For surface area, interaction effects showed mostly stable variance across age in females, but decreases in variability in males. The observation that there were no significant quadratic interactions makes it unlikely that pubertal hormones may affect greater male variance. Yet, the decrease in male variance in older age, may be indicative of environmental effects later in life. Alternative explanation may be the larger number of clinical or even death rates in males that may lead to some sex difference in survival (Chen et al. 2008; Ryan et al. 1997).

Factors underlying or influencing sex differences in the brain may include sex chromosomes, sex steroids (both perinatal or pubertal), and the neural embedding of social influences during the life span (Dawson, Ashman, and Carver 2000). Although we could not directly test these mechanisms, our findings of greater male variance, that are mostly stable across age, together with the greater male inter‐regional homogeneity for cortical thickness are most in line with the single X‐chromosome expression in males compared to the mosaic pattern of X‐inactivation in females (Arnold 2012). Whereas female brain tissue shows two variants of X‐linked genes, males only show one. This mechanism may lead to increased male vulnerability, as is also seen for a number of rare X‐linked genetic mutations (Chen et al. 2008; Craig, Haworth, and Plomin 2009; Johnson, Carothers, and Deary 2009; Reinhold and Engqvist 2013; Ryan et al. 1997). None of the other sex effects mentioned above predict these specific inter and intra‐individual sex differences in brain patterns. Future studies are, however, needed to directly test these different mechanisms. Furthermore, the observation that greater male homogeneity was only observed in cortical thickness, but not cortical surface area or subcortical volumes, may speculatively indicate that X‐chromosome related genetic mechanisms may have the largest effect on cortical thickness measures.

This paper has several strengths including its sample size, the age range spanning nine decades, the inclusion of different structural measures (subcortical volumes and cortical surface area and thickness) and the investigation of variance effects. These points are important, as most observed mean sex differences in the brain are modest in size (Joel and Fausto‐Sterling 2016). We were able to analyze data from a far larger sample than those included in recent meta‐analyses of mean sex differences (Marwha et al. 2017; Ruigrok et al. 2014; Tan et al. 2016), and a very wide age range covering childhood, adolescence, adulthood and senescence. The results of this study may have important implications for studies on mean sex differences in brain structure, as analyses in such studies typically assume that group variances are equal, which the present study shows might not be tenable. This can be particularly problematic for studies with small sample sizes (Rousselet et al. 2017).

The current study has some limitations. First, the multi‐site sample was heterogeneous and specific samples were recruited in different ways, not always representative of the entire population. Furthermore, although structural measures may be quite stable across different scanners, the large number of sites may increase the variance in observed MRI measures, but this would be unlikely to be systematically biased with respect to age or sex. In addition, variance effects may change in non‐linear ways across the age‐range. This may be particularly apparent for surface area and subcortical volume measures, as these showed pronounced non‐linear developmental patterns through childhood and adolescence (Tamnes et al. 2017; Wierenga et al. 2018). Also, the imbalanced number of subjects across the age range may have diminished variability effects in the older part of the age range. The present study has a cross‐sectional design. Future studies including longitudinal data are warranted to further explore the lifespan dynamics of sex differences in variability in the brain. Last, one caveat may be the effect of movement on data quality and morphometric measures. As males have been shown to move more than females in the scanner (Pardoe, Kucharsky Hiess, and Kuzniecky 2016), this may have resulted in slight under estimations of brain volume and thickness measures for males (Reuter et al. 2015). Although quality control was conducted at each site using the standardized ENIGMA cortical and subcortical quality control protocols (http://enigma.ini.usc.edu/protocols/imaging-protocols/), which involve a combination of statistical outlier detection and visual quality checks and a similar number of males and females had partially missing data (52.4% males), we cannot exclude the possibility that in‐scanner subject movement may have affected the results. Nevertheless, we do not think this can explain our finding of greater male variance in brain morphometry measures, as this was seen at both the upper and lower ends of the distributions.