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Activity Number: 512
Type: Invited
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #314446 View Presentation
Title: Bayesian Approaches for Simultaneous Inference of Brain Activation and Connectivity in Multi-Subject Studies
Author(s): Raquel Prado* and Hernando Ombao and Zhe Yu
Companies: UC Santa Cruz and UC Irvine and UC Irvine
Keywords: fMRI ; state-space models ; brain connectivity ; Bayesian inference ; hierarchical models
Abstract:

We consider hierarchical Bayesian state-space models for jointly inferring activation and connectivity from brain signals. We present models for fMRI data from multiple regions of interest (ROIs) that allow us to obtain simultaneous estimation of connectivity networks and hemodynamic response functions that are region, task and subject-specific, while taking into account variations across subjects and experimental conditions. In the context of behavioral fMRI experiments these models allow us to discover possible associations between behavioral and neural domains. We also discuss state-space models for inferring brain connectivity from electrophysiological physiological signals such as local field potentials (LFP) and electroencephalograms. Finally, we present spectral domain methods for brain signal analysis and explore connections between these and time domain approaches based on state-space models.


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