A Bayesian model for dynamic functional connectivity estimation in the human brain with structural priors
Manchanda, Sameer
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https://hdl.handle.net/2142/105843
Description
Title
A Bayesian model for dynamic functional connectivity estimation in the human brain with structural priors
Author(s)
Manchanda, Sameer
Issue Date
2019-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2019-11-26T20:49:36Z
Keyword(s)
Bayesian inference
Functional Connectivity
Abstract
Studies of dynamic functional connectivity have demonstrated that anatomical linkage is related to persistent functional connectivity. Bayesian models can leverage this connection by regularizing estimates of functional connectivity according to the strength of the corresponding structural connectivity. We proposed and evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal transformations and an ability to recover simulated data generated according to both discrete and continuous temporal dynamics. Finally, it outperformed sliding window baselines and anatomically un-informed baselines on estimating instantaneous covariances according to out-of-sample log likelihood on two task datasets.
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