Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.
Keywords: CT, computer tomography; DWI, diffusion weighted imaging; Game-theory; Lesion inference; MAPP, Multi-Area Pattern Prediction; MCA, middle cerebral artery; MRI, magnetic resonance imaging; MSA, Multi-perturbation Shapley value Analysis; MVPA, Multi-Variate Pattern Analysis; Multi-perturbation Shapley value Analysis (MSA); NIHSS; NIHSS, National Institutes of Health Stroke Scale; SVM, support vector machine; VAL, voxel-based analysis of lesions; VBM, voxel-based morphometry; VLSC, VOI-based Lesion Symptom Correlation; VLSM, Volume-based Lesion Symptom Mapping; VOI, volume of interest.