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

• Published in: Scientific reports Vol. 7; no. 1; pp. 13769 - 16
• Main Authors: Lu, Junfeng, Zhang, Han, Hameed, N. U. Farrukh, Zhang, Jie, Yuan, Shiwen, Qiu, Tianming, Shen, Dinggang, Wu, Jinsong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.10.2017; Nature Publishing Group
• Subjects: 631/378/2649/1594; 692/617; Adult; Algorithms; Automation; Brain - diagnostic imaging; Brain - physiopathology; Brain - surgery; Brain cancer; Brain mapping; Brain Mapping - methods; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - physiopathology; Brain Neoplasms - surgery; Brain tumors; Case-Control Studies; Clinical Decision-Making; Cohort Studies; Feasibility Studies; Female; Functional magnetic resonance imaging; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Language; Magnetic Resonance Imaging - methods; Male; Middle Aged; multidisciplinary; Nerve Net - physiology; Neuroimaging; Preoperative Care; Rest - physiology; Science; Science (multidisciplinary); Signal Processing, Computer-Assisted - instrumentation; Young Adult
• DOI: 10.1038/s41598-017-14248-5
• ISSN: 2045-2322

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.