Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework

• Published in: Journal of neural engineering Vol. 21; no. 1; pp. 16032 - 16047
• Main Authors: Kuang, Li-Dan, Li, He-Qiang, Zhang, Jianming, Gui, Yan, Zhang, Jin
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.02.2024
• Subjects: Brain - diagnostic imaging; Brain Mapping - methods; Canonical polyadic decomposition (CPD); Cerebellum; dynamic functional network connectivity (dFNC); dynamic modules; Humans; low-rank constraint; Magnetic Resonance Imaging - methods; Schizophrenia; Schizophrenia; Canonical polyadic decomposition (CPD); dynamic functional network connectivity (dFNC); dynamic modules; low-rank constraint

Objective. Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic func...