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000203262 1001_ $$0P:(DE-Juel1)165784$$aRehme, Anne$$b0$$eCorresponding author
000203262 245__ $$aIdentifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques
000203262 260__ $$aOxford$$bOxford Univ. Press$$c2015
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000203262 520__ $$aConventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associated with functional impairment in “individual” patients. We investigated whether fMRI allows classification of individual motor impairment after stroke using support vector machines (SVMs). Forty acute stroke patients and 20 control subjects underwent resting-state fMRI. Half of the patients showed significant impairment in hand motor function. Resting-state connectivity was computed by means of whole-brain correlations of seed time-courses in ipsilesional primary motor cortex (M1). Lesion location was identified using diffusion-weighted images. These features were used for linear SVM classification of unseen patients with respect to motor impairment. SVM results were compared with conventional mass-univariate analyses. Resting-state connectivity classified patients with hand motor deficits compared with controls and nonimpaired patients with 82.6–87.6% accuracy. Classification was driven by reduced interhemispheric M1 connectivity and enhanced connectivity between ipsilesional M1 and premotor areas. In contrast, lesion location provided only 50% sensitivity to classify impaired patients. Hence, resting-state fMRI reflects behavioral deficits more accurately than structural MRI. In conclusion, multivariate fMRI analyses offer the potential to serve as markers for endophenotypes of functional impairment. 
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000203262 7001_ $$0P:(DE-HGF)0$$aVolz, L. J.$$b1
000203262 7001_ $$0P:(DE-HGF)0$$aFeis, D.-L.$$b2
000203262 7001_ $$0P:(DE-HGF)0$$aBomilcar-Focke, I.$$b3
000203262 7001_ $$0P:(DE-HGF)0$$aLiebig, T.$$b4
000203262 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5
000203262 7001_ $$0P:(DE-Juel1)131720$$aFink, G. R.$$b6
000203262 7001_ $$0P:(DE-Juel1)161406$$aGrefkes, C.$$b7
000203262 773__ $$0PERI:(DE-600)1483485-6$$a10.1093/cercor/bhu100$$gVol. 25, no. 9, p. 3046 - 3056$$n9$$p3046 - 3056$$tCerebral cortex$$v25$$x1460-2199$$y2015
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