Lesion-Symptom Mapping Guide

Purpose

This skill encodes expert methodological knowledge for lesion-symptom mapping in clinical and cognitive neuroscience. A competent programmer without neuropsychology and neuroimaging training will get this wrong because:

• Lesion distributions are not random. Stroke lesions cluster in the middle cerebral artery (MCA) territory, creating systematic collinearity between brain regions. Standard voxelwise methods confound truly causal regions with regions that are simply co-damaged (Sperber, 2020).
• Lesion volume is a massive confound. Larger lesions produce worse behavioral deficits simply because more tissue is damaged. Any analysis that does not control for total lesion volume will attribute behavioral deficits to whichever regions are most often part of large lesions (DeMarco & Turkeltaub, 2018).
• White matter disconnection matters as much as grey matter damage. A focal lesion can disrupt distant regions via white matter pathway disruption (diaschisis). VLSM misses this entirely; disconnection analysis is needed (Foulon et al., 2018).
• Standard multiple comparison correction is insufficient. Permutation-based FWE correction is required because voxelwise tests are massively non-independent (lesion voxels are spatially correlated), violating assumptions of FDR and parametric corrections (Kimberg et al., 2007).
• Small samples produce false localizations. With fewer than 50 patients, VLSM lacks power and produces unreliable maps that do not replicate (Sperber, 2020).

When to Use This Skill

• Planning a voxel-based lesion-symptom mapping (VLSM) study
• Choosing between VLSM, multivariate, and disconnection-based approaches
• Setting statistical thresholds and correction methods for lesion analyses
• Evaluating whether a published lesion study adequately controlled for confounds
• Implementing lesion segmentation and registration pipelines