An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning.

ABSTRACT: As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were asses

SUBMITTER: Lu J 

PROVIDER: S-EPMC5653800 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature