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.

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

Figure 1

Spatial maps of the two group language components derived from training group rs‐fMRI. Red: “frontal component”; yellow: “temporal component.” Component z‐maps are thresholded at z ≥ 1, and all images are in radiological convention.

Figure 2

Results of training group second‐level random effects analysis (RFX) of task‐based language fMRI. One sample t‐test results were thresholded at t ≥ 3.85 (P < 0.001, uncorrected), and all images are in radiological convention.

Figure 3

Expert rating results on 84 candidate language components of the 18 subjects in the testing group. For each subject, gray column indicates the number of candidate language components. Red, green, and blue columns represent the number of language components agreed upon by five, four, and three experts, respectively (i.e., “consensus language components”).

Figure 4

An example subject's language maps derived from resting‐state and task‐based language fMRIs, overlaid on the structural images. This subject had two consensus language components (first and second language components shown in red and pink respectively). The “RefLC map” is the red map, the “ConLC map” is the union of the red and pink maps.

Figure 5

Dice coefficient results calculated between language maps derived from resting‐state and task‐based language fMRIs. Results of paired t‐test indicate that compared to the whole brain measurement, the putative language ROIs Dice measurement indicates higher overlap between resting‐state language component maps and “task t‐map” (P < 0.001 and P < 0.0001 for “RefLC” and “ConLC” respectively). Compared to “RefLC”, “ConLC” has higher Dice coefficient (within language ROI) with “task t‐map” (P < 0.05).