title: Individual prediction of chronic motor outcome in the acute post-stroke stage: Behavioral parameters versus functional imaging.
abstract: Several neurobiological factors have been found to correlate with functional recovery after brain lesions. However, predicting the individual potential of recovery remains difficult. Here we used multivariate support vector machine (SVM) classification to explore the prognostic value of functional magnetic resonance imaging (fMRI) to predict individual motor outcome at 4-6 months post-stroke. To this end, 21 first-ever stroke patients with hand motor deficits participated in an fMRI hand motor task in the first few days post-stroke. Motor impairment was quantified assessing grip force and the Action Research Arm Test. Linear SVM classifiers were trained to predict good versus poor motor outcome of unseen new patients. We found that fMRI activity acquired in the first week post-stroke correctly predicted the outcome for 86% of all patients. In contrast, the concurrent assessment of motor function provided 76% accuracy with low sensitivity (<60%). Furthermore, the outcome of patients with initially moderate impairment and high outcome variability could not be predicted based on motor tests. In contrast, fMRI provided 87.5% prediction accuracy in these patients. Classifications were driven by activity in ipsilesional motor areas and contralesional cerebellum. The accuracy of subacute fMRI data (two weeks post-stroke), age, time post-stroke, lesion volume, and location were at 50%-chance-level. In conclusion, multivariate decoding of fMRI data with SVM early after stroke enables a robust prediction of motor recovery. The potential for recovery is influenced by the initial dysfunction of the active motor system, particularly in those patients whose outcome cannot be predicted by behavioral tests.
authors.affiliation: Department of Neurology, University of Cologne, Cologne, Germany.|Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Jülich, Jülich, Germany.
authors.forename: Anne K
authors.initials: AK
authors.lastname: Rehme
authors.affiliation: Department of Neurology, University of Cologne, Cologne, Germany.|Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA.
authors.forename: Lukas J
authors.initials: LJ
authors.lastname: Volz
authors.affiliation: Max Planck Institute for Metabolism Research, Cologne, Germany.
authors.forename: Delia-Lisa
authors.initials: DL
authors.lastname: Feis
authors.affiliation: Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Jülich, Jülich, Germany.|Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany.
authors.forename: Simon B
authors.initials: SB
authors.lastname: Eickhoff
authors.affiliation: Department of Neurology, University of Cologne, Cologne, Germany.|Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Jülich, Jülich, Germany.
authors.forename: Gereon R
authors.initials: GR
authors.lastname: Fink
authors.affiliation: Department of Neurology, University of Cologne, Cologne, Germany.|Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Jülich, Jülich, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Grefkes
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002531:Cerebellum / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007206:Individuality; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000503:physiopathology*; D009069:Movement Disorders / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology*; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D020127:Recovery of Function / Q000502:physiology*; D020521:Stroke / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology*; D060388:Support Vector Machine
keywords: functional magnetic resonance imaging (fMRI); motor impairment; motor recovery; multivariate pattern classification; support vector machine