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Cereb Cortex. 2015 Sep;25(9):3046-56. doi: 10.1093/cercor/bhu100. Epub 2014 May 16.

Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.

Author information

1
Max Planck Institute for Neurological Research, Cologne, Germany Department of Neurology, University of Cologne, Cologne, Germany Institute of Neuroscience and Medicine (INM-2, INM-3), Research Centre Juelich, Juelich, Germany.
2
Max Planck Institute for Neurological Research, Cologne, Germany Department of Neurology, University of Cologne, Cologne, Germany.
3
Max Planck Institute for Neurological Research, Cologne, Germany.
4
Department of Radiology and Neuroradiology, University of Cologne, Cologne, Germany.
5
Institute of Neuroscience and Medicine (INM-2, INM-3), Research Centre Juelich, Juelich, Germany Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany.
6
Department of Neurology, University of Cologne, Cologne, Germany Institute of Neuroscience and Medicine (INM-2, INM-3), Research Centre Juelich, Juelich, Germany.

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.

KEYWORDS:

diagnostic imaging; diffusion imaging; ischemia; motor impairment; support vector machine

PMID:
24836690
DOI:
10.1093/cercor/bhu100
[Indexed for MEDLINE]

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