Voxelwise Bayesian lesion-deficit analysis

被引：27

作者：

Chen, Rong ^{[1
]}

Hillis, Argye E. ^{[2
]}

Pawlak, Mikolaj ^{[1
]}

Herskovits, Edward H. ^{[1
]}

机构：

[1] Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA

[2] Dept Neurol, Baltimore, MD USA

来源：

NEUROIMAGE | 2008年 / 40卷 / 04期

关键词：

D O I：

10.1016/j.neuroimage.2008.01.014

中图分类号：

Q189 [神经科学];

学科分类号：

071006 [神经生物学];

摘要：

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. (c) 2008 Elsevier Inc. All rights reserved.

引用

页码：1633 / 1642

页数：10

共 25 条

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[3]

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