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. 2023 Jan 1;180(1):50-64.
doi: 10.1176/appi.ajp.20220194. Epub 2022 Nov 23.

The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression

Dorothea L Floris  1 , Han Peng  1 , Varun Warrier  1 , Michael V Lombardo  1 , Charlotte M Pretzsch  1 , Clara Moreau  1 , Alex Tsompanidis  1 , Weikang Gong  1 , Maarten Mennes  1 , Alberto Llera  1 , Daan van Rooij  1 , Marianne Oldehinkel  1 , Natalie J Forde  1 , Tony Charman  1 , Julian Tillmann  1 , Tobias Banaschewski  1 , Carolin Moessnang  1 , Sarah Durston  1 , Rosemary J Holt  1 , Christine Ecker  1 , Flavio Dell'Acqua  1 , Eva Loth  1 , Thomas Bourgeron  1 , Declan G M Murphy  1 , Andre F Marquand  1 , Meng-Chuan Lai  1 , Jan K Buitelaar  1 , Simon Baron-Cohen  1 , Christian F Beckmann  1 ; APEX Group  1 ; EU-AIMS LEAP Group  1
Affiliations

The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression

Dorothea L Floris et al. Am J Psychiatry. .

Abstract

Objective: The male preponderance in prevalence of autism is among the most pronounced sex ratios across neurodevelopmental conditions. The authors sought to elucidate the relationship between autism and typical sex-differential neuroanatomy, cognition, and related gene expression.

Methods: Using a novel deep learning framework trained to predict biological sex based on T1-weighted structural brain images, the authors compared sex prediction model performance across neurotypical and autistic males and females. Multiple large-scale data sets comprising T1-weighted MRI data were employed at four stages of the analysis pipeline: 1) pretraining, with the UK Biobank sample (>10,000 individuals); 2) transfer learning and validation, with the ABIDE data sets (1,412 individuals, 5-56 years of age); 3) test and discovery, with the EU-AIMS/AIMS-2-TRIALS LEAP data set (681 individuals, 6-30 years of age); and 4) specificity, with the NeuroIMAGE and ADHD200 data sets (887 individuals, 7-26 years of age).

Results: Across both ABIDE and LEAP, features positively predictive of neurotypical males were on average significantly more predictive of autistic males (ABIDE: Cohen's d=0.48; LEAP: Cohen's d=1.34). Features positively predictive of neurotypical females were on average significantly less predictive of autistic females (ABIDE: Cohen's d=1.25; LEAP: Cohen's d=1.29). These differences in sex prediction accuracy in autism were not observed in individuals with ADHD. In autistic females, the male-shifted neurophenotype was further associated with poorer social sensitivity and emotional face processing while also associated with gene expression patterns of midgestational cell types.

Conclusions: The results demonstrate an increased resemblance in both autistic male and female individuals' neuroanatomy with male-characteristic patterns associated with typically sex-differential social cognitive features and related gene expression patterns. The findings hold promise for future research aimed at refining the quest for biological mechanisms underpinning the etiology of autism.

Keywords: Autism Spectrum Disorder; Brain Imaging Techniques; Gender Differences; Machine Learning; Neuroanatomy; Neurodevelopmental Disorders.

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