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

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.

Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy

“…We acknowledge that the definition of the language template used in this study is not universally established. We also acknowledge that although a lateralized resting-state language network has not been consistently demonstrated in prior task functional MR imaging studies, it has been shown that ICA analysis of resting-state functional MR imaging data can produce maps of language-related brain areas at the individual-subject level (4). It has been demonstrated that specific methodologic parameters such as number of ICA components (12) and choice of language template (4) can influence the results of ICAbased functional MR imaging analyses.…”

“…Recent studies have demonstrated the feasibility of defining language networks in both healthy subjects (4,5) and patients with intractable epilepsy (6) by using resting-state functional connectivity. Furthermore, this technique has been used to the study cerebral lateralization of several aspects of brain function, including language, in healthy subjects (7).…”

“…First, each patient's data were reduced to 20 dimensions by using a probabilistic principal component analysis-based method; then, 20 components were estimated by using the FastICA algorithm. We chose this number of components to avoid inconsistently subdividing our broadly defined network of classic language areas (12) and because the feasibility of selecting individual language networks using fixed ICA parameters with resting-state functional MR imaging data has been previously demonstrated (4). These components were then converted into z-score maps and were thresholded at z  1, 2, and 3 for separate analyses.…”

“…These results suggest that an ICA-based approach with resting-state functional MR imaging may be sufficient to determine hemispheric language dominance. This study adds to the growing body of evidence to suggest that resting-state functional MR imaging can be potentially useful for presurgical mapping of eloquent cortices (4,6,(17)(18)(19)(20)(21)(22)(23) but that it is limited by methodologic differences among prior studies, small sample sizes of these studies, and the use of various reference techniques (eg, task-based functional MR imaging, electrocortical stimulation, IAP).…”

Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index

“…The rsfMRI technique extracts the low-frequency fluctuations in the blood oxygen level-dependent (BOLD) signal when subjects are instructed to lie relaxed inside the scanner. 7,8 Tie et al 9 extracted language networks from rsfMRI and evaluated the effectiveness of the automatic identification of language components by using independent component analysis. The study separated the activation patterns corresponding to the language network components from individual rsfMRI data.…”