Can a single brain region predict a disorder?

Abstract

We perform prediction of diverse disorders (Cocaine Use, Schizophrenia and Alzheimers disease) in unseen subjects from brain fMRI. First, we show that for multi-subject prediction of simple cognitive states (e.g. motor vs. calculation and reading), voxels-as-features methods produce clusters that are similar for different leave-one-subject-out folds; while for group classification (e.g. cocaine addicted vs. control subjects), voxels are scattered and less stable. Therefore, we chose to use a single region per experimental condition and a majority vote classifier. Interestingly, our method outperforms state-of-the-art techniques. Our method can integrate multiple experimental conditions and successfully predict disorders in unseen subjects (leave-one-subjectout generalization accuracy: 89.3% and 90.9% for Cocaine Use, 96.4% for Schizophrenia and 81.5% for Alzheimers disease). Our experimental results not only span diverse disorders, but also different experimental designs (block design and event related tasks), facilities, magnetic fields (1.5Tesla, 3Tesla, 4Tesla) and speed of acquisition (interscan interval from 1600ms to 3500ms). We further argue that our method produces a meaningful low dimensional representation that retains discriminability.