Analysis of Human Brain Structure Reveals that the Brain “Types” Typical of Males Are Also Typical of Females, and Vice Versa

Abstract

Findings of average differences between females and males in the structure of specific brain regions are often interpreted as indicating that the typical male brain is different from the typical female brain. An alternative interpretation is that the brain types typical of females are also typical of males, and sex differences exist only in the frequency of rare brain types. Here we contrasted the two hypotheses by analyzing the structure of 2176 human brains using three analytical approaches. An anomaly detection analysis showed that brains from females are almost as likely to be classified as “normal male brains,” as brains from males are, and vice versa. Unsupervised clustering algorithms revealed that common brain “types” are similarly common in females and in males and that a male and a female are almost as likely to have the same brain “type” as two females or two males are. Large sex differences were found only in the frequency of some rare brain “types.” Last, supervised clustering algorithms revealed that the brain “type(s)” typical of one sex category in one sample could be typical of the other sex category in another sample. The present findings demonstrate that even when similarity and difference are defined mathematically, ignoring biological or functional relevance, sex category (i.e., whether one is female or male), is not a major predictor of the variability of human brain structure. Rather, the brain types typical of females are also typical of males, and vice versa, and large sex differences are found only in the prevalence of some rare brain types. We discuss the implications of these findings to studies of the structure and function of the human brain.

Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction

“…However, some reports had provided scattered evidence suggesting that the accuracy of sex prediction is reduced when using appropriate TIV-adjustment methods. Thus, Joel and co-workers reported that an anomaly detector algorithm discriminated between the brains of females and males better when brain features were not corrected for TIV-variation than when the same features were corrected with the PCP method 36 . Similarly, in their previously mentioned study 45 , Chekroud and collaborators observed that the sex prediction accuracy of their P LR -EN dropped from 92 to 70% (CI: 65.0-74.2%) when TIV-variation was "regressed out".…”

“…Alternatively, multivariate differences can be investigated through classification/prediction statistical techniques collectively referred to as machine learning or statistical learning [32][33][34] . These techniques are increasingly being used in the study of brain sex differences 31,[35][36][37][38][39][40][41] because they make it possible to estimate the degree of statistical distinctiveness or separateness of the brains of females and males at the multivariate level, with the added conceptual appeal of focusing on individual scores instead of on score summaries such as means. However, to our knowledge, no previous study has specifically analyzed to what extent this multivariate distinctiveness is affected by TIV-adjustment.…”

A comprehensive atlas of white matter tracts in the chimpanzee

“…However, reported sex differences in fasciculi between adult male and female humans are small and limited to the left SLF [55]. Group sex differences found in human brain morphometry have reproducibility problems [56][57][58], and to the degree to which they are reproducible, are not categorical differences but rather differences of mean values, with high degrees of population overlap (e.g., [59]; for reviews, see [60,61]). Crucially, sex differences are often a matter of total volume and not anatomical organization of fascicular projections, and therefore we would not expect our anatomical descriptions to change if male chimpanzee scans were added to the dataset.…”

Generative Critique in Interdisciplinary Collaborations: From Critique in and of the Neurosciences to Socio-Technical Integration Research as a Practice of Critique in R(R)I

“…Critique that goes beyond calls for rethinking scientific routines but questions fundamental epistemological assumptions has been put forward by feminist neuroscientists. Examples are studies by Joel who systematically debunks the assumption that sex exists as a dichotomous, biological variable in human brains [72][73][74]. In addition to neuroscientists' self-critique, humanities scholars and social scientists have engaged critically with the neurosciences.…”