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Multivariate analysis is sufficient for lesion-behaviour mapping

Abstract : Lesion-behaviour mapping aims at predicting individual be-havioural deficits, given a certain pattern of brain lesions. It also brings fundamental insights on brain organization, as lesions can be understood as interventions on normal brain function. We focus here on the case of stroke. The most standard approach to lesion-behaviour mapping is mass-univariate analysis, but it is inaccurate due to correlations between the different brain regions induced by vascularisation. Recently, it has been claimed that multivariate methods are also subject to lesion-anatomical bias, and that a move towards a causal approach is necessary to eliminate that bias. In this paper, we reframe the lesion-behaviour brain mapping problem using classical causal inference tools. We show that, in the absence of additional clinical data and if only one region has an effect on the behavioural scores, suitable multivariate methods are sufficient to address lesion-anatomical bias. This is a commonly encountered situation when working with public datasets, which very often lack general health data. We support our claim with a set of simulated experiments using a publicly available lesion imaging dataset, on which we show that adequate multivariate models provide state-of-the art results.
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Submitted on : Tuesday, February 2, 2021 - 8:48:56 AM
Last modification on : Monday, December 13, 2021 - 9:16:05 AM


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  • HAL Id : hal-02950353, version 2


Lucas Martin, Julie Josse, Bertrand Thirion. Multivariate analysis is sufficient for lesion-behaviour mapping. MICCAI BrainLes 2020 workshop, Oct 2020, Lima, Peru. ⟨hal-02950353v2⟩



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