Resting fMRI as an alternative for task-based fMRI for language lateralization in temporal lobe epilepsy patients: a study using independent component analysis

Purpose Our aim is to investigate whether rs-fMRI can be used as an effective technique to study language lateralization. We aim to find out the most appropriate language network among different networks identified using ICA. Methods Fifteen healthy right-handed subjects, sixteen left, and sixteen right temporal lobe epilepsy patients prospectively underwent MR scanning in 3T MRI (GE Discovery™ MR750w), using optimized imaging protocol. We obtained task-fMRI data using a visual-verb generation paradigm. Rs-fMRI and language-fMRI analysis were conducted using FSL software. Independent component analysis (ICA) was used to estimate rs-fMRI networks. Dice coefficient was calculated to examine the similarity in activated voxels of a common language template and the rs-fMRI language networks. Laterality index (LI) was calculated from the task-based language activation and rs-fMRI language network, for a range of LI thresholds at different z scores. Results Measurement of hemispheric language dominance with rs-fMRI was highly concordant with task-fMRI results. Among the evaluated z scores for a range of LI thresholds, rs-fMRI yielded a maximum accuracy of 95%, a sensitivity of 83%, and specificity of 92.8% for z = 2 at 0.05 LI threshold. Conclusion The present study suggests that rs-fMRI networks obtained using ICA technique can be used as an alternative for task-fMRI language laterality. The novel aspect of the work is suggestive of optimal thresholds while applying rs-fMRI, is an important endeavor given that many patients with epilepsy have co-morbid cognitive deficits. Thus, an accurate method to determine language laterality without requiring a patient to complete the language task would be advantageous.