Leveraging edge-centric networks complements existing network-level inference for functional connectomes.

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Bibliographic Details
Title: Leveraging edge-centric networks complements existing network-level inference for functional connectomes.
Authors: Rodriguez RX; Interdepartmental Neuroscience Program, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA. Electronic address: raimundo.rodriguez@yale.edu., Noble S; Department of Radiology and Biomedical Imaging, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA., Tejavibulya L; Interdepartmental Neuroscience Program, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA., Scheinost D; Interdepartmental Neuroscience Program, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale School of Engineering and Applied Science, 17 Hillhouse Avenue, New Haven, CT 06511, USA; Department of Statistics and Data Science, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511, USA; Child Study Center, Yale School of Medicine, 230 South Frontage Road, New Haven, CT 06519, USA; Wu Tsai Institute, Yale University, 100 College Street, New Haven, CT 06510, USA.
Source: NeuroImage [Neuroimage] 2022 Dec 01; Vol. 264, pp. 119742. Date of Electronic Publication: 2022 Nov 08.
Publication Type: Journal Article; Research Support, N.I.H., Extramural
Journal Info: Publisher: Academic Press Country of Publication: United States NLM ID: 9215515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9572 (Electronic) Linking ISSN: 10538119 NLM ISO Abbreviation: Neuroimage Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1095-9572
DOI:10.1016/j.neuroimage.2022.119742