Journal Article

FZJ-2015-05243

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques

Rehme, A. (Corresponding author)FZJ* ; Volz, L. J. ; Feis, D.-L. ; Bomilcar-Focke, I. ; Liebig, T. ; Eickhoff, S.FZJ* ; Fink, G. R.FZJ* ; Grefkes, C.FZJ*

2015
Oxford Univ. Press Oxford

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Please use a persistent id in citations: doi:10.1093/cercor/bhu100

Abstract: Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associated with functional impairment in “individual” patients. We investigated whether fMRI allows classification of individual motor impairment after stroke using support vector machines (SVMs). Forty acute stroke patients and 20 control subjects underwent resting-state fMRI. Half of the patients showed significant impairment in hand motor function. Resting-state connectivity was computed by means of whole-brain correlations of seed time-courses in ipsilesional primary motor cortex (M1). Lesion location was identified using diffusion-weighted images. These features were used for linear SVM classification of unseen patients with respect to motor impairment. SVM results were compared with conventional mass-univariate analyses. Resting-state connectivity classified patients with hand motor deficits compared with controls and nonimpaired patients with 82.6–87.6% accuracy. Classification was driven by reduced interhemispheric M1 connectivity and enhanced connectivity between ipsilesional M1 and premotor areas. In contrast, lesion location provided only 50% sensitivity to classify impaired patients. Hence, resting-state fMRI reflects behavioral deficits more accurately than structural MRI. In conclusion, multivariate fMRI analyses offer the potential to serve as markers for endophenotypes of functional impairment.

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Contributing Institute(s):

1. Kognitive Neurowissenschaften (INM-3)
2. Strukturelle und funktionelle Organisation des Gehirns (INM-1)

Research Program(s):

1. 572 - (Dys-)function and Plasticity (POF3-572) (POF3-572)

Appears in the scientific report 2015

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Database coverage:
; BIOSIS Previews ; Current Contents - Life Sciences ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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