title: Multivariate lesion-symptom mapping using support vector regression.
abstract: Lesion analysis is a classic approach to study brain functions. Because brain function is a result of coherent activations of a collection of functionally related voxels, lesion-symptom relations are generally contributed by multiple voxels simultaneously. Although voxel-based lesion-symptom mapping (VLSM) has made substantial contributions to the understanding of brain-behavior relationships, a better understanding of the brain-behavior relationship contributed by multiple brain regions needs a multivariate lesion-symptom mapping (MLSM). The purpose of this artilce was to develop an MLSM using a machine learning-based multivariate regression algorithm: support vector regression (SVR). In the proposed SVR-LSM, the symptom relation to the entire lesion map as opposed to each isolated voxel is modeled using a nonlinear function, so the intervoxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships. To explore the relative merits of VLSM and SVR-LSM we used both approaches in the analysis of a synthetic dataset. SVR-LSM showed much higher sensitivity and specificity for detecting the synthetic lesion-behavior relations than VLSM. When applied to lesion data and language measures from patients with brain damages, SVR-LSM reproduced the essential pattern of previous findings identified by VLSM and showed higher sensitivity than VLSM for identifying the lesion-behavior relations. Our data also showed the possibility of using lesion data to predict continuous behavior scores.
authors.affiliation: Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Zhang
authors.forename: Daniel Y
authors.initials: DY
authors.lastname: Kimberg
authors.forename: H Branch
authors.initials: HB
authors.lastname: Coslett
authors.forename: Myrna F
authors.initials: MF
authors.lastname: Schwartz
authors.forename: Ze
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001037:Aphasia / Q000209:etiology / Q000503:physiopathology; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015999:Multivariate Analysis; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000150:complications / Q000503:physiopathology; D060388:Support Vector Machine
keywords: aphasia; lesion-symptom mapping; support vector regression; total lesion volume control