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Here, a multimodal covariance network (MCN) construction method was proposed to identify inter-regional covariations of the structural skeleton and functional activities by simultaneous magnetic resonance imaging and electroencephalogram (EEG). Results from two independent cohorts confirmed that MCNs could capture cognition-specific hierarchical modules in joint comprehensive multimodal features well, especially when time-resolved EEG was further integrated. The quantitative evaluation further demonstrates significantly larger modularity of MCN integrating fine-grained features from EEG. The application to the discovery cohort identified prominent modular covarying across the default mode and salience networks at rest, while the visual oddball task was accomplished by synchronous structural\u2013functional cooperation within networks associated with attention control and working memory updating. Strikingly, the results of an external validation cohort showed a different covariant pattern corresponding to decision-specific cognitive modules. Overall, the results suggested that multimodal covariance analysis provides a reliable definition of multistate neural cognitive networks, further discloses modular-specific structural and functional co-variation. <\/jats:p>","DOI":"10.1142\/s0129065722500356","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T05:51:15Z","timestamp":1651989075000},"source":"Crossref","is-referenced-by-count":7,"title":["The Task-Dependent Modular Covariance Networks Unveiled by Multiple-Way Fusion-Based Analysis"],"prefix":"10.1142","volume":"32","author":[{"given":"Lin","family":"Jiang","sequence":"first","affiliation":[{"name":"The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. 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