Lesion-Symptom Mapping Guide

Voxel-based lesion-symptom mapping (VLSM) requires preparing lesion masks, registering them to MNI space with cost-function masking, and selecting covariates and permutation-based statistics to identify brain-behavior relationships.

Disconnection analysis identifies brain-behavior relationships by mapping lesion impacts on white matter tracts using normative connectomes, while network lesion mapping extends this to evaluate how lesions affect broader functional networks across cognitive neuroscience studies.

NiiStat and LESYMAP are recommended tools for chronic stroke lesion studies, supporting voxel-, tract-, and network-level analyses to interpret brain-behavior relationships when combined with proper lesion segmentation and lesion-volume control.

Robust lesion-symptom mapping requires permutation-based statistics to control for multiple comparisons, alongside proper lesion-volume control and covariate selection to ensure valid brain-behavior inference across voxel, tract, and network analyses.

Normalizing lesion masks to MNI space for VLSM workflows requires using cost-function masking during registration to prevent lesion-induced distortions, typically implemented with ANTs or FSL to ensure accurate voxel-level analysis.

Use BCBToolkit for disconnection analysis when your lesion-symptom mapping requires tract-level inference using normative connectomes and tractography resources, whereas standard VLSM is suited for direct voxel-level lesion-behavior relationships.