Technical considerations of a game-theoretical approach for lesion symptom mapping

BMC Neuroscience

Methodology Article

Claus C. Hilgetag¹  Nils D. Forkert²  Melissa Zavaglia³  Götz Thomalla⁴  Bastian Cheng⁴  Christian Gerloff⁴ 

[1] Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany;Department of Health Sciences, Boston University, 635 Commonwealth Ave., 02215, Boston, MA, USA;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, T2N 4N1, Calgary, AB, Canada;Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany;School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759, Bremen, Germany;Department of Neurology, University Medical Center Eppendorf, Hamburg University, Martinistraße 52, 20246, Hamburg, Germany;

关键词: Brain lesions;    Multi-perturbation Shapley value analysis (MSA);    Game theory;    Lesion inference;    Functional prediction;   

DOI  :  10.1186/s12868-016-0275-6

 received in 2016-01-19, accepted in 2016-06-15,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundVarious 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.ResultsWe 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.ConclusionsMSA 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.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

Fig. 4