Defining language networks from resting-state fMRI for surgical planning-a feasibility study

Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision‐making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood‐oxygen‐level dep...

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Published inHuman brain mapping Vol. 35; no. 3; pp. 1018 - 1030
Main Authors Tie, Yanmei, Rigolo, Laura, Norton, Isaiah H., Huang, Raymond Y., Wu, Wentao, Orringer, Daniel, Mukundan Jr, Srinivasan, Golby, Alexandra J.
Format Journal Article
LanguageEnglish
Published New York, NY Blackwell Publishing Ltd 01.03.2014
Wiley-Liss
John Wiley & Sons, Inc
John Wiley and Sons Inc
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Summary:Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision‐making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood‐oxygen‐level dependent (BOLD) signal change while patients perform carefully timed language vs. control tasks. This task‐based fMRI critically depends on task performance, excluding many patients who have difficulty performing language tasks due to neurologic deficits. On the basis of recent discovery of resting‐state fMRI (rs‐fMRI), we propose a “task‐free” paradigm acquiring fMRI data when patients simply are at rest. This paradigm is less demanding for patients to perform and easier for technologists to administer. We investigated the feasibility of this approach in right‐handed healthy control subjects. First, group independent component analysis (ICA) was applied on the training group (14 subjects) to identify group level language components based on expert rating results. Then, four empirically and structurally defined language network templates were assessed for their ability to identify language components from individuals' ICA output of the testing group (18 subjects) based on spatial similarity analysis. Results suggest that it is feasible to extract language activations from rs‐fMRI at the individual subject level, and two empirically defined templates (that focuses on frontal language areas and that incorporates both frontal and temporal language areas) demonstrated the best performance. We propose a semi‐automated language component identification procedure and discuss the practical concerns and suggestions for this approach to be used in clinical fMRI language mapping. Hum Brain Mapp 35:1018–1030, 2014. © 2013 Wiley Periodicals, Inc.
Bibliography:istex:C5D0A1A9908A3364DE9A9486BB0082B7418448C2
The Brain Science Foundation
The Klarman Family Foundation
ark:/67375/WNG-7BVC5C21-Q
ArticleID:HBM22231
The National Institute of Neurological Disorders and Stroke
The National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health - No. R21NS075728; No. P41EB015898; No. P41RR019703; No. P01CA067165
The National Center for Research Resources
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ISSN:1065-9471
1097-0193
1097-0193
DOI:10.1002/hbm.22231