title: Technical considerations of a game-theoretical approach for lesion symptom mapping.
abstract: Various strategies have been used for inferring brain functions from stroke lesions. We explored a new mathematical approach based on game theory, the so-called multi-perturbation Shapley value analysis (MSA), to assess causal function localizations and interactions from multiple perturbation data. We applied MSA to a dataset composed of lesion patterns of 148 acute stroke patients and their National Institutes of Health Stroke Scale (NIHSS) scores, to systematically investigate the influence of different parameter settings on the outcomes of the approach. Specifically, we investigated aspects of MSA methodology including the choice of the predictor algorithm (typology and kernel functions), training dataset (original versus binary), as well as the influence of lesion thresholds. We assessed the suitability of MSA for processing real clinical lesion data and established the central parameters for this analysis.
  
  
  
  We derived general recommendations for the analysis of clinical datasets by MSA and showed that, for the studied dataset, the best approach was to use a linear-kernel support vector machine predictor, trained with a binary training dataset, where the binarization was implemented through a median threshold of lesion size for each region. We demonstrated that the results obtained with different MSA variants lead to almost identical results as the basic MSA.
  
  
  
  MSA is a feasible approach for the multivariate lesion analysis of clinical stroke data. Informed choices need to be made to set parameters that may affect the analysis outcome.
authors.affiliation: Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany. m.zavaglia@uke.de.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759, Bremen, Germany. m.zavaglia@uke.de.
authors.forename: Melissa
authors.initials: M
authors.lastname: Zavaglia
authors.affiliation: Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany.|Department of Radiology and Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
authors.forename: Nils D
authors.initials: ND
authors.lastname: Forkert
authors.affiliation: Department of Neurology, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany.
authors.forename: Bastian
authors.initials: B
authors.lastname: Cheng
authors.affiliation: Department of Neurology, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Gerloff
authors.affiliation: Department of Neurology, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany.
authors.forename: Götz
authors.initials: G
authors.lastname: Thomalla
authors.affiliation: Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany.|Department of Health Sciences, Boston University, 635 Commonwealth Ave., Boston, MA, 02215, USA.
authors.forename: Claus C
authors.initials: CC
authors.lastname: Hilgetag
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D066264:Datasets as Topic; D005716:Game Theory; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D015999:Multivariate Analysis; D012720:Severity of Illness Index; D020521:Stroke / Q000000981:diagnostic imaging
keywords: Brain lesions; Functional prediction; Game theory; Lesion inference; Multi-perturbation Shapley value analysis (MSA)