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.
Highlights
Human language function is uniquely complex and critical for quality of life
We first report the results from the training group data analyses, including reliability of the expert rating on the group components and comparison of language networks revealed from different paradigms at the group level
We report the results from the testing group data analyses, including expert rating on the individual subjects’ candidate language components, comparison of the four language network templates (LNTs) for their effectiveness in identification of language components, and comparison of language networks revealed from different paradigms at the individual subject level
Summary
Human language function is uniquely complex and critical for quality of life. surgical approaches to brain lesions within or close to the language functional areas must strongly weigh the risk of causing permanent language deficits against the potential benefits of resection. Task-based fMRI measures cerebral blood flow as a surrogate for neuronal activity by comparing blood-oxygen-level dependent (BOLD) signal changes between the task state and a control or baseline state. This BOLD signal change is very small, on the order of 0.5% to 5% [Bandettini et al, 1992], many (dozens) of trials must be performed by the patient and subjected to statistical analysis to obtain an acceptable signal-to-noise ratio. Patients who cannot perform the task due to aphasias or cognitive deficits are usually excluded from the test, while these are often the patients for whom localization of language areas is most critical for surgical planning. Task-based fMRI requires specially trained personnel for designing and administering task paradigms; and due to high variability in the behavioral task design and scanning parameters, it is difficult to standardize, evaluate, and validate this technique across institutions [Bookheimer, 2007; Tharin and Golby, 2007]
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