TA3a.4

Causality Driven Disentanglement Learning for Dynamic Brain Networks

Sema Athamnah, Michigan State University, United States; Saba Nasiri, Dorina Thanou, EPFL, Switzerland; Selin Aviyente, Michigan State University, United States
Session:
TA3a: Networked Neuroscience Lecture
Track:
Networks and Graphs
Location:
Room 3
Presentation Time:
Tue, 27 Oct, 09:30 - 09:55 PT (UTC -7)
Session Chair:
Selin Aviyente, Michigan State University, United Staes
Presentation
Discussion
Resources
No resources available.
Session TA3a
TA3a.1: Transferability of spatio-temporal covariance neural networks for multiscale fMRI analysis
Saurabh Sihag, SUNY at Albany, United States; Andrea Cavallo, Delft University of Technology, Netherlands; Yucheng Zhu, University of Maryland, United States; Elvin Isufi, Delft University of Technology, Netherlands; Gonzalo Mateos, University of Rochester, United States; Alejandro Ribeiro, University of Pennsylvania, United States
TA3a.2: Contributions of lateral versus long-range structural connectivity in explaining brain activity: a graph-signal-processing approach
Maria Giulia Preti, Dimitri Van De Ville, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
TA3a.3: Graph-based variational representation learning and reconstruction for multimodal neuroimaging data
Meenu Ajith, Ishaan Batta, Vince Calhoun, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), United States
TA3a.4: Causality Driven Disentanglement Learning for Dynamic Brain Networks
Sema Athamnah, Michigan State University, United States; Saba Nasiri, Dorina Thanou, EPFL, Switzerland; Selin Aviyente, Michigan State University, United States
Contacts