Identification of Essential Language Areas by Combination of fMRI from Different Tasks using Probabilistic Independent Component Analysis

Functional magnetic resonance imaging (fMRI) has been used to lateralize and localize language areas for preoperative planning purposes. To identify the essential language areas from this kind of observation method, we propose an analysis strategy to combine fMRI data from two different tasks using probabilistic independent component analysis (PICA). The assumption is that the independent components separated by PICA identify the networks activated by both tasks. The results on twelve normal subjects' data show that a language-specific component was consistently identified, with the participating networks separated into different components. Compared with a model-based method, PICA'S ability to capture the neural networks whose temporal activity may deviate from the task timing suggests that PICA may be more appropriate for analyzing language fMRI data with complex event-related paradigms, and may be particularly helpful for patient studies. This proposed strategy has the potential to improve the correlation between fMRI and invasive techniques which can demonstrate essential areas and which remain the clinical standard.