Lesion-Symptom Mapping Guide

Lesion-symptom mapping requires a confound-aware planning checklist that controls for lesion volume, time post-onset, hemisphere, and sample size before running permutation-based statistical tests to keep voxelwise analyses valid and replicable.

VLSM maps behavior to focal lesion voxels, while disconnection analysis captures remote white matter tract damage. A multi-method decision tree helps choose between them based on whether your chronic stroke study needs voxel, tract, or network-level behavioral attribution.

SVR-LSM permutation testing requires setting statistical thresholds and hierarchical regression parameters that compare voxel, tract, and network contributions. You must plan permutation correction with explicit covariate control before launching the processing pipeline to ensure validity.

Cost-function-masked registration is needed during lesion segmentation when structural damage distorts standard brain normalization. It prevents inaccurate spatial transformation of lesions in stroke and tumor cohorts, ensuring disconnection mapping via tools like BCBToolkit remains anatomically valid.

Lesion network mapping supports both stroke and tumor cohorts. The planning workflow accommodates different lesion segmentation strategies and registration protocols to handle diverse neuropathological anatomies while maintaining rigorous disconnection and network mapping methodology.

Voxelwise lesion mapping without hierarchical regression risks mislocalizing behavior by ignoring remote disconnection effects. Failing to compare voxel, tract, and network contributions separately leads to confounded results from non-random vascular patterns and uncontrolled lesion volume.