Voxelwise Bayesian lesion-deficit analysis.
Relating cognitive deficits to the presence of lesions has been an important means of delineating structure-function associations in the human brain. We propose a voxel-based Bayesian method for lesion-deficit analysis, which identifies complex linear or nonlinear associations among brain-lesion locations, and neurological status. We validated this method using a simulated data set, and we applied this algorithm to data obtained from an acute-stroke study to identify associations among voxels with infarct or hypoperfusion, and impaired word reading. We found that a distributed region involving Brodmann areas (BA) 22, 37, 39, and 40 was implicated in word reading.
Chen
Rong
R
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. rong.chen@uphs.upenn.edu
Hillis
Argye E
AE
Pawlak
Mikolaj
M
Herskovits
Edward H
EH
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D002545:Brain Ischemia / Q000473:pathology / Q000523:psychology; D002544:Cerebral Infarction / Q000473:pathology / Q000523:psychology; D002560:Cerebrovascular Circulation / Q000502:physiology; D003072:Cognition Disorders / Q000473:pathology*; D003198:Computer Simulation; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging; D004410:Dyslexia / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D020521:Stroke / Q000473:pathology / Q000523:psychology