Format

Send to

Choose Destination
Hum Brain Mapp. 2014 Mar;35(3):1018-30. doi: 10.1002/hbm.22231. Epub 2013 Jan 3.

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

Author information

1
Harvard Medical School, Boston, Massachusetts, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.

Abstract

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.

KEYWORDS:

functional connectivity; independent component analysis (ICA); language mapping; resting-state networks (RSNs); task-based fMRI; “task-free” paradigm

PMID:
23288627
PMCID:
PMC3683367
DOI:
10.1002/hbm.22231
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center