Statistical model-based approaches for functional connectivity analysis of neuroimaging data.


Paul G. Allen School of Computer Science & Engineering and Department of Statistics, University of Washington, United States. Electronic address: [Email]


We present recent literature on model-based approaches to estimating functional connectivity from neuroimaging data. In contrast to the typical focus on a particular scientific question, we reframe a wider literature in terms of the underlying statistical model used. We distinguish between directed versus undirected and static versus time-varying connectivity. There are numerous advantages to a model-based approach, including easily specified inductive bias, handling limited data scenarios, and building complex models from simpler building blocks.

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