Abstract
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years, the resting-state fMRI (RS-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Twenty patients suffering from brain lesions close to supposed language-relevant cortical areas were included. RS-fMRI and task-based (TB-fMRI) were performed for the purpose of preoperative language assessment. TB-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language-critical and language-supportive processes. Subsequently, the best fitting 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) was identified using an algorithm based on the Dice index. Thereby, the RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. In general, the results suggest that determining language dominance in the human brain is feasible both with TB-fMRI and RS-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
Highlights
Our study aimed to examine whether a method using an identification algorithm for the resting-state language network (RSLN) based on the individual TB-fMRI results could lead to significant changes in language lateralization measurements
Twenty patients suffering from lesions close to the language-relevant cortical areas were included (12 men and 8 women). fMRI was conducted for preoperative language assessment
Results of the verb generation and antonym task are condensed in Figures 1 and 2, which show second-level group statistics for both general linear models (GLMs) and resting-state fMRI (RS-fMRI) analysis
Summary
Tumors in language-related areas remain challenging in neurosurgery. Brain-mapping measures are a balancing act between maximal tumor resection and enhancing patient survival [1]. The preservation of eloquent areas improves health-related quality of life [2] by reducing the risk of neurological impairment [1,3,4]. Because of interindividual anatomic variability, preoperative assessment and intraoperative cortical mapping are often required to optimize clinical outcome [4,5,6]. This is especially important in presurgical language mapping and identification of language laterality
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