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

• Published in: Human brain mapping Vol. 40; no. 5; pp. 1381 - 1390
• Main Authors: Sperber, Christoph, Wiesen, Daniel, Karnath, Hans‐Otto
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2019
• Subjects: Algorithms; Behavior; Bias; Brain; Brain Diseases - pathology; Brain Diseases - psychology; Brain Mapping - methods; Cerebrovascular Circulation; Cognitive ability; Computer simulation; Empirical analysis; Humans; Image Processing, Computer-Assisted; Learning algorithms; Machine learning; Magnetic Resonance Imaging; Mapping; Models, Neurological; Multivariate analysis; Nervous system; Regression analysis; Statistical analysis; Statistical methods; Stroke - pathology; Stroke - psychology; Support Vector Machine; Support vector machines; support vector regression; SVR‐LSM; VLSM; voxel‐based lesion behaviour mapping; voxel‐based lesion symptom mapping; support vector regression; voxel-based lesion symptom mapping; VLSM; voxel-based lesion behaviour mapping; machine learning; SVR-LSM

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...