Objectives: This project investigated the potential of machine learning algorithms to distinguish between individuals with autism and neurotypical individuals based on their brain structure, activation, and synchronization.
Methods: fMRI and MRI data were collected for 43 high-functioning individuals with autism and 43 neurotypical control participants. During the fMRI scan, participants read sentences and decided if they were true or false, interspersed with a fixation condition. The data submitted to the machine learning algorithms included activated voxel counts within several regions of interest (ROIs) during fixation, functional connectivity (synchronization) measures between pairs of ROIs obtained during the task performance, and white matter measurements from MRI scans. The classification algorithms that were used to predict a diagnosis of autism included Gaussian Naïve Bayes, logistic regression, and support vector machines.
Results: Our classifiers were able to distinguish between individuals with autism and neurotypical controls with an accuracy of 66%, where the group membership was established using ADOS scores and expert clinical diagnosis.
Conclusions: These findings suggest that brain imaging data has potential to play a role in diagnosing autism. Using several different machine learning algorithms, we were able to distinguish between individuals with autism and neurotypical controls with an accuracy well above chance based on brain imaging data concerning structure, activation, and synchronization.