Voxelwise Bayesian lesion-deficit analysis

Rong Chen, Argye E. Hillis, Mikolaj Pawlak, Edward H. Herskovits

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1633-1642
Number of pages10
JournalNeuroImage
Volume40
Issue number4
DOIs
StatePublished - May 1 2008

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ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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