In The American journal of psychiatry
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.
Floris Dorothea L, Peng Han, Warrier Varun, Lombardo Michael V, Pretzsch Charlotte M, Moreau Clara, Tsompanidis Alex, Gong Weikang, Mennes Maarten, Llera Alberto, van Rooij Daan, Oldehinkel Marianne, Forde Natalie J, Charman Tony, Tillmann Julian, Banaschewski Tobias, Moessnang Carolin, Durston Sarah, Holt Rosemary J, Ecker Christine, Dell’Acqua Flavio, Loth Eva, Bourgeron Thomas, Murphy Declan G M, Marquand Andre F, Lai Meng-Chuan, Buitelaar Jan K, Baron-Cohen Simon, Beckmann Christian F
2022-Nov-23
Autism Spectrum Disorder, Brain Imaging Techniques, Gender Differences, Machine Learning, Neuroanatomy, Neurodevelopmental Disorders
