Abstract
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.
Highlights
Various strategies have been used for inferring brain functions from stroke lesions
We focused on the support vector machines (SVM) variants with the lowest root mean squared error (RMSE) for both types of training
The first, simple and straightforward assessment of the data is by the relative Lesion Overlap, shown in the second row on the MNI brain atlas. This representation shows that all volume of interest (VOI) involved in the study are damaged to different extent
Summary
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. Lesion inferences have made an enormous contribution to understanding the human brain and have laid the basis for attributing mental functions to specific brain regions [e.g. 1], they have several drawbacks, such as the difficulty to infer function on the basis of the behavior of individual patients, the principal assumption of the localization of function, as well as the plasticity of the injured brain [2]. A broad range of additional techniques exist to investigate the functions of the living brain through the correlation of behavior and cognition with brain activity, as shown by electrophysiology and functional imaging. In this context, lesion inferences, which link behavioral functions directly and causally to a neural substrate, still have an important role in modern neuroscience [2]. Young et al [3]
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