Article
Preoperative assessment of language dominance through combined resting-state and task-based functional magnetic resonance imaging
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Published: | June 18, 2018 |
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Objective: Brain lesions in language-related cortical areas remain a challenge in clinical routine. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been shown to be a feasible method for preoperative language assessment. This study examined whether language-related resting-state components obtained by means of an independent data-driven component-based identification algorithm may help determine language dominance in the left or right hemisphere.
Methods: 20 patients with brain lesions close to supposedly language-relevant cortical areas were included in the study. Preoperative language assessment was done with rs-fMRI and task-based MRI (tb-fMRI). tb‑fMRI included different word generations with an appropriate control condition (a syllable-switching task) to decompose language-critical and language-supportive processes. The best fitting independent component analysis (ICA) component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb‑fMRI (including models with and without linguistic control conditions) were subsequently identified using an algorithm based on the Dice index.
Results: RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices (LI) than GLM baseline contrasts. LIs solely derived from GLM contrasts with and without control conditions did not differ significantly.
Conclusion: The results suggest that determining language dominance in the human brain is feasible with both tb-fMRI and rs-fMRI, but the combination of both methods yields higher specificity in preoperative language assessment. Moreover, the choice of the language-mapping paradigm may be considered crucial for the mentioned benefits.