Predicting poststroke depression from brain connectivity

J. Mitra*, K.-K. Shen, S. Ghose, P. Bourgeat, J. Fripp, O. Salvado, B. Campbell, S. Palmer, L. Carey, S. Rose

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationComputational Diffusion MRI
Subtitle of host publicationMICCAI Workshop 2014
EditorsLauren O’Donnell, Gemma Nedjati-Gilani, Yogesh Rathi, Marco Reisert, Torben Schneider
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages89-99
Number of pages11
ISBN (Electronic)9783319111827
ISBN (Print)9783319111810
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventMICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014 - Boston, United States
Duration: 18 Sep 201418 Sep 2014

Publication series

NameMathematics and Visualization
Volume39
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

Conference

ConferenceMICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston
Period18/09/1418/09/14

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