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

Christoph Sperber1, Daniel Wiesen1, Hans-Otto Karnath1,2

  • 1Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.

Human Brain Mapping
|December 15, 2018
PubMed
Summary

Support vector regression-based lesion symptom mapping (SVR-LSM) requires multiple comparison correction and large sample sizes (100-120 subjects) for accurate lesion behavior mapping. This machine learning method, like mass-univariate analyses, can misplace statistical topographies along brain vasculature.

Keywords:
SVR-LSMVLSMmachine learningsupport vector regressionvoxel-based lesion behaviour mappingvoxel-based lesion symptom mapping

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Area of Science:

  • Cognitive Neuroscience
  • Machine Learning
  • Neuroimaging

Background:

  • Multivariate lesion-behavior mapping using machine learning complements traditional anatomo-behavioral approaches.
  • Support vector regression-based lesion symptom mapping (SVR-LSM) is a validated technique for mapping these relations.
  • Open questions remain regarding the optimal implementation and validity of SVR-LSM.

Purpose of the Study:

  • To empirically test the validity of methodological aspects of SVR-LSM.
  • To investigate the impact of sample size and statistical corrections on SVR-LSM accuracy.
  • To compare the susceptibility of SVR-LSM to anatomical misplacement with mass-univariate analyses.

Main Methods:

  • Three simulation experiments using large lesion samples.
  • Application and validation of support vector regression-based lesion symptom mapping (SVR-LSM).
  • Voxel-wise lesion location modeling within SVR-LSM.

Main Results:

  • Multiple comparison correction is necessary for the current SVR-LSM implementation.
  • Optimal modeling of voxel-wise lesion location in SVR-LSM requires sample sizes of 100-120 subjects.
  • SVR-LSM exhibits similar susceptibility to statistical topography misplacement along brain vasculature as mass-univariate analyses.

Conclusions:

  • Methodological refinements, including multiple comparison correction and adequate sample sizes, are crucial for robust SVR-LSM.
  • SVR-LSM, while promising, shares limitations with traditional methods regarding anatomical localization accuracy.
  • Further research is needed to optimize multivariate lesion-behavior mapping techniques in cognitive neuroscience.