Utilizing brain measures for large-scale classification of Autism applying EPIC

摘要

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with atypical cortical maturation leading to a deficiency in social cognition and language. Numerous studies have attempted to classify ASD using brain measurements such as cortical thickness, surface area, or volume with promising results. However, the underpowered sample sizes of these studies limit external validity and generalizability at the population level. Large scale collaborations such as Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) or the Autism Brain Imaging Data Exchange (ABIDE) aim to bring together like-minded scientists to further improve investigations into brain disorders. To the best of our knowledge, this study represents the largest classification analysis for detection of ASD vs. healthy age and sex matched controls using cortical thickness brain parcellations and intracranial volume normalized surface area and subcortical volumes. We were able to increase classification accuracy overall from 56% to 60% and for females only by 6%. These novel findings using Evolving Partitions to Improve Connectomics (EPIC) underscore the importance of large-scale data-driven approaches and collaborations in the discovery of brain disorders.