Predicting poststroke depression from brain connectivity

Abstract

Depression is a common neuropsychological consequence of stroke. The ability to predict patients at high risk of developing depressive disorders using noninvasive neuroimaging strategies has the potential to help guide treatment programs aimed to enhance functional and cognitive recovery. In this study we hypothesize that modeling the disconnection of key cortical and subcortical brain networks due to ischemic brain injury may be used to predict poststroke depression. The loss in structural connectivity was measured using diffusion-weighted MRI (dMRI) and white matter fiber tracking for 25 stroke patients (acquired 12 months after stroke) and 41 age-matched control participant. Two connectivity matrices were generated for each control participant, one with and one without the use of a manually delineated stroke lesion of a patient as an exclusion mask. A paired t -test using network-based statistics (NBS) was then performed on these connectivity matrices to determine the neural networks affected by the ischemic injury. This procedure was repeated for all stroke patients, in an independent fashion, to generate 25 disconnectivity matrices that were subsequently used in regression forest to provide a probabilistic prediction of depression. The probabilistic scores obtained from regression forests (in a leave-one-out manner) and the clinical depression scores for 25 stroke patients achieved a high positive Pearson’s correlation with p D 0:78 (p < 0:00001). This methodology shows promise as a predictive tool of poststroke depression that maybe useful for optimizing rehabilitation strategies.