Classification of MRI under the presence of disease heterogeneity using multi-task learning: Application to bipolar disorder

Abstract

Heterogeneity in psychiatric and neurological disorders has undermined our ability to understand the pathophysiology underlying their clinical manifestations. In an effort to better distinguish clinical subtypes, many disorders, such as Bipolar Disorder, have been further sub-categorized into subgroups, albeit with criteria that are not very clear, reproducible and objective. Imaging, along with pattern analysis and classification methods, offers promise for developing objective and quantitative ways for disease subtype categorization. Herein, we develop such a method using learning multiple tasks, assuming that each task corresponds to a disease subtype but that subtypes share some common imaging characteristics, along with having distinct features. In particular, we extend the original SVM method by incorporating the sparsity and the group sparsity techniques to allow simultaneous joint learning for all diagnostic tasks. Experiments on Multi-Task Bipolar Disorder classification demonstrate the advantages of our proposed methods compared to other state-of-art pattern analysis approaches.