Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features

Abstract

Mental and neurological conditions have been linked to structural brain variations. However, aside from dementia, the value of brain structural characteristics derived from brain scans for prediction is relatively low. One reason for this limitation is the clinical and biological heterogeneity inherent to such conditions. Recent studies have implicated aberrations in the cerebellum, a relatively understudied brain region, in these clinical conditions. Here, we used machine learning to test the value of individual deviations from normative cerebellar development across the lifespan (based on trained data from >27k participants) for prediction of autism spectrum disorder (ASD) (n=317), bipolar disorder (BD) (n=238), schizophrenia (SZ) (n=195), mild cognitive impairment (MCI) (n=122), and Alzheimer's disease (AD) (n=116). We applied several atlases and derived median, variance, and percentages of extreme deviations within each region of interest. Our results show that lobular and voxel-wise cerebellar data can be used to discriminate healthy controls from ASD and SZ with moderate accuracy (the area under the receiver operating characteristic curves ranged from 0.56 to 0.64), The strongest contributions to these predictive models were from posterior regions of the cerebellum, which are more strongly linked to higher cognitive functions than to motor control.