Sex-Informed Neuroanatomy of Autism: Evaluating the Quantitative and Qualitative Models of Sex-Moderation in Cortical Anatomy Using Data from the Pond Network
We proposed two testable models on sex-moderation that underlie the ‘female-protective-effect’ mechanisms contributing to autism etiologies, as a key first step to clarify the sex-differential liability related to the brain. The ‘quantitative sex-moderating model’ (QUAN-model) posits that mechanisms leading to autism are comparable but differ quantitatively across sexes, that females require more brain changes to develop autism. The ‘qualitative sex-moderating model’ (QUAL-model) posits that mechanisms leading to autism operate through different neurobiological/brain changes across sexes.
Testing the QUAN-model and QUAL-model against a null-model of no sex-moderation:
(1) The QUAN-model predicts that across the brain, autism-related changes in females involve similar regions but with larger effect sizes, and/or involve overlapping and additional regions, than those in males.
(2) The QUAL-model predicts that across the brain, autism-related brain changes differ by sex, involving different brain regions and/or with different patterns of changes.
Sample includes 729 participants scanned at the Hospital for Sick Children, Toronto and via the Province of Ontario Neurodevelopmental Disorders (POND) network, using the same scanner: 249 autistic males (age mean=13.3 years, FIQ mean=96.9), 60 autistic females (age mean=14.2, FIQ mean=95.7), 210 neurotypical males (age mean=14.0, FIQ mean=112.7), 210 neurotypical females (age mean=14.2, FIQ mean=113.0). T1-weighted scans were processed with CIVET 2.1. Cortical thickness, area, volume and curvature were estimated across 75,284 vertices. To account for heterogeneity of covariates, data were pre-processed with nonparametric matching – neurotypical controls were matched to autism subjects with full-matching by weighted Euclidean matching for age and whole brain volume (weights 1 and 0.5 respectively), but IQ was not used in matching due to high co-occurrence between autism and intellectual disability (ID). For each metric, a whole-cortex-based model selection determined the best model by Akaike’s Information Criterion.
Testing QUAN-model vs. null by Sign-Concordance: By setting ‘sex’ as male=0 and female=1, autism-effect in males is given as βdiagnosis and autism-effect in females βdiagnosis+βsex*diagnosis. The QUAN-model predicts that autism-effect is stronger in females than in males, therefore βdiagnosis and βsex*diagnosis have the same sign (sign-concordance). This was tested against a randomization distribution of test-statistics from the model with sex-labels permuted.
Testing QUAL-model vs. null by Spatial-Correlation: Evidence for the QUAL-model will be if, across vertices, there is less spatial correlation between autism-effects in females vs. males than by chance. This was tested with the same permutation approach.
Model selection: Thickness, area, volume and smoothed absolute mean curvature were best modeled without sex-covariate interaction terms. Thickness and volume were best modelled by brain volume cubed and a quadratic age term; Area and thickness were best modelled by linear age, and brain volume to the 2/3 power.
QUAN-model: Increased sign-concordance was observed for smoothed absolute mean curvature (but not other metrics) at effect thresholds greater than 50% of maximum observed effect, supporting the QUAN-model; this result was insensitive to whether IQ was included as a covariate.
QUAL-model: All measures were within randomization intervals providing no evidence for the QUAL-model.
These results suggest possible quantitative sex-moderation of cortical curvature in autism. How sex-moderation is associated with autism-ID co-occurrence requires further investigation.