Deep learning and cross-species analysis identify the influences of sex and cannabinoid signaling on cerebellar vermis morphology

Abstract

The cerebellum is the only folded cortical structure present in both rodents and humans, potentially allowing mechanistic studies across species. We trained a deep convolutional neural network to predict biological sex from midline cerebellar MRI images, and identified anterior midvermis lobules and white matter as morphological determinants of sex by heatmap backpropagation. Our comparative cross species analysis shows that in mice, like in humans, the extent of anterior vermis folding and the shapes of white matter exhibit sex-dependent differences. In both species, sex and anterior vermis folding patterns influence hand/forelimb dexterity, nevertheless, neither sex nor the extent of folding are good predictors of an individual′s behavior, because the variability in performance between individuals is far greater than the differences between conditions. Finally, utilizing mice constitutively lacking CB1 cannabinoid receptors, we identify developmental cannabinoid signaling as a novel molecular mechanism limiting secondary fissure formation.