Universität zu Köln

Rehme, A. K., Volz, L. J., Feis, D. -L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S. B., Fink, G. R. and Grefkes, C. (2015). Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques. Cereb. Cortex, 25 (9). S. 3046 - 3057. CARY: OXFORD UNIV PRESS INC. ISSN 1460-2199

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Rehme, A. K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Volz, L. J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Feis, D. -L. UNSPECIFIED UNSPECIFIED UNSPECIFIED Bomilcar-Focke, I. UNSPECIFIED UNSPECIFIED UNSPECIFIED Liebig, T. UNSPECIFIED UNSPECIFIED UNSPECIFIED Eickhoff, S. B. UNSPECIFIED UNSPECIFIED UNSPECIFIED Fink, G. R. UNSPECIFIED UNSPECIFIED UNSPECIFIED Grefkes, C. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-395269
DOI:	10.1093/cercor/bhu100
Journal or Publication Title:	Cereb. Cortex
Volume:	25
Number:	9
Page Range:	S. 3046 - 3057
Date:	2015
Publisher:	OXFORD UNIV PRESS INC
Place of Publication:	CARY
ISSN:	1460-2199
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language STATE FUNCTIONAL CONNECTIVITY; ALZHEIMERS-DISEASE; LONGITUDINAL FMRI; RECOVERY; NETWORKS; CORTEX; REORGANIZATION; MRI; CLASSIFICATION; ACTIVATION Multiple languages Neurosciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/39526