Work

Default mode network dynamic functional network connectivity predicts psychotic symptom severity

Year: 2022

Type: conference-paper

Study design: Observational Study

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/embc48229.2022.9871542

Authors: Mohammad S. E. Sendi, Hossein Dini, Luis Emilio Bruni, Vince D. Calhoun

Institutions: Georgia Institute of Technology, Emory University, Georgia State University, Center for Translational Research in Neuroimaging and Data Science, Aalborg University

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Institut National de la Santé et de la Recherche Médicale, National Institute of Mental Health

Awards: Data-driven approaches to identify biomarkers from multimodal imaging big data, ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain Circuit, G2965154831, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity

OpenAlex ID: W4294975667

FWCI: 3.227

Cites: 14

Cited by: 7

Related to: 10