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An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

ABSTRACT: Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regression-based lesion symptom mapping (SVR-LSM) to map anatomo-behavioural relations. However, this promising method, as well as the multivariate approach per se, still bears many open questions. By using large lesion samples in three simulation experiments, the present study empirically tested the validity of several methodological aspects. We found that (i) correction for multiple comparisons is required in the current implementation of SVR-LSM, (ii) that sample sizes of at least 100-120 subjects are required to optimally model voxel-wise lesion location in SVR-LSM, and (iii) that SVR-LSM is susceptible to misplacement of statistical topographies along the brain's vasculature to a similar extent as mass-univariate analyses.

SUBMITTER: Sperber C

PROVIDER: S-EPMC6865618 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Sperber Christoph C Wiesen Daniel D Karnath Hans-Otto HO

Human brain mapping 20181213 5

Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regression-based lesion symptom mapping (SVR-LSM) to map anatomo-behavioural relations. However, this promising method, as well as the multivariate approach per se, still bears many open questions. By using large lesion samples in three simulation experimen ...[more]

PMID: 30549154

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Project description:PurposeThe aim of this study was to investigate if ischemic stroke final infarction volume and location can be used to predict the associated functional outcome using a multi-class support vector machine (SVM).Material and methodsSixty-eight follow-up MR FLAIR datasets of ischemic stroke patients with known modified Rankin Scale (mRS) functional outcome after 30 days were used. The infarct regions were segmented and used to calculate the percentage of lesioned voxels in the predefined MNI, Harvard-Oxford cortical and subcortical atlas regions as well as using four problem-specific VOIs, which were identified from the database using voxel-based lesion symptom mapping. An overall of 12 SVM classification models for predicting the corresponding mRS score were generated using the lesion overlap values from the different brain region definitions, stroke laterality information, and the optional parameters infarct volume, admission NIHSS, and patient age.ResultsLeave-one-out cross validations revealed that including information about the stroke location in terms of lesion overlap measurements led to improved mRS prediction results compared to classification models not utilizing the stroke location information. Furthermore, integration of the optional features led to improved mRS prediction results in all cases tested. The problem-specific brain regions and additional integration of the optional features led to the best mRS predictions with a precise multi-value mRS prediction accuracy of 56%, sliding window multi-value mRS prediction accuracy (mRS±1) of 82%, and binary mRS (0-2 vs. 3-5) prediction accuracy of 85%.ConclusionTherefore, a graded SVM-based functional stroke outcome prediction using the problem-specific brain regions for lesion overlap quantification leads to promising results but needs to be further validated using an independent database to rule out a potential methodical bias and overfitting effects. The prediction of the graded mRS functional outcome could be a valuable tool if combined with voxel-wise tissue outcome predictions based on multi-parametric datasets acquired at the acute phase.

Dataset Information

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Publications

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

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