1. Beyond 'sex prediction': Estimating and interpreting multivariate sex differences and similarities in the brain
- Author
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Sonia Félix, Carla Sanchis-Segura, Naiara Aguirre, Cristina Forn, and Álvaro Javier Cruz-Gómez
- Subjects
sex differences ,Cerebral Cortex ,Male ,Multivariate statistics ,Sex Characteristics ,Cognitive Neuroscience ,effect size ,Brain ,gray matter ,Magnetic Resonance Imaging ,TIV-adjustment ,Machine Learning ,Neurology ,robust statistics ,Machine learning ,Humans ,Female ,Gray Matter ,Psychology ,sex similarities ,Demography ,MRI - Abstract
Previous studies have shown that machine-learning (ML) algorithms can “predict” sex based on brain anatomical/ functional features. The high classification accuracy achieved by ML algorithms is often interpreted as revealing large differences between the brains of males and females and as confirming the existence of “male/female brains”. However, classification and estimation are quite different concepts, and using classification metrics as surrogate estimates of between-group differences results in major statistical and interpretative distortions. The present study illustrates these distortions and provides a novel and detailed assessment of multivariate sex differences in gray matter volume (GMVOL) that does not rely on classification metrics. Moreover, modeling and clustering techniques and analyses of similarities (ANOSIM) were used to identify the brain areas that contribute the most to these multivariate differences, and to empirically assess whether they assemble into two sex-typical profiles. Results revealed that multivariate sex differences in GMVOL: 1) are “large” if not adjusted for total intracranial volume (TIV) variation, but “small” when controlling for this variable; 2) differ in size between individuals and also depends on the ML algorithm used for their calculation 3) do not stem from two sex-typical profiles, and so describing them in terms of “male/female brains” is misleading.
- Published
- 2022