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 assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.
Bibliographical noteFunding Information:
The authors would like to thank our neuropsychologist (Yan Zhou) for the language assessment; the nurses (Qiuyue Wu, Chunmei Li, Ye Wang) for their contribution; the MRI technician (Zhong Yang) for collecting the data, and Jianbing Shi, Geng Xu for technical support in neuronavigation and electrophysiological monitoring. This work was supported by the National Natural Science Foundation of China (No. 81401395) and the National Key Technology R&D Program of China (No. 2014BAI04B05). This work was supported in part by National Institutes of Health (NIH) grants (EB022880, AG041721, AG049371, AG042599).
© 2017 The Author(s).
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