Abstract Details
Activity Number:
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512
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Type:
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Invited
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Date/Time:
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Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #314424
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Title:
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A Bayesian Approach to the Study of Dynamic Functional Connectivity Networks in fMRI Data
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Author(s):
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Michele Guindani* and Marina Vannucci and Ryan Warnick and Erik Erhardt and Elena Allen and Vince D. Calhoun
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Companies:
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MD Anderson Cancer Center and Rice University and Rice University and University of New Mexico and University of New Mexico and University of New Mexico
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Keywords:
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fMRI data ;
Dynamic Connectivity ;
Bayesian Modeling ;
Bayesian Graphical Models
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Abstract:
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fMRI studies have traditionally assumed stationarity of the connectivity patterns observed in a subject during a fMRI experiment. While the assumption has successfully allowed to study large-scale properties of brain functioning, it is generally recognized that functional connectivity varies with time and tasks performed. In this talk, we describe a novel Bayesian methodological framework for the analysis of temporal dynamics of functional networks in task-based fRMi data collected on a single subject. Our proposed formulation allows joint modeling of the task-related activations in addition to the dynamics of individual functional connectivity. Furthermore, we allow simultaneous learning of the common and differential edges (interactions) in the inferred time-varying functional networks. We illustrate the proposed approach by means of simulation and an analysis on a real fMRI dataset.
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Authors who are presenting talks have a * after their name.
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