Bayesian Methods in Brain Networks

Abstract

The recent developments in resonance imaging technologies have allowed growing access to a wide variety of complex information on brain functioning. Regardless of the several types of data modalities which are now available, a fundamental interest in the neurosciences is in conducting inference on brain networks. In this article, we discuss statistical methods for brain networks with a specific focus on the Bayesian approach. Due to its ability to allow careful uncertainty quantification, borrowing of information, and inclusion of experts knowledge, the Bayesian paradigm can provide a particularly effective tool in the neuroscience studies.

Bayesian Time-Varying Tensor Vector Autoregressive Models for Dynamic Effective Connectivity

“…A primary goal of many functional magnetic resonance imaging (fMRI) experiments is to investigate the integration among different areas of the brain in order to explain how cognitive information is distributed and processed. Neuroscientists typically distinguish between functional connectivity, which measures the undirected associations, or temporal correlation, between the fMRI time series observed at different locations, and effective connectivity, which estimates the directed influences that one brain region exerts onto other regions (Friston, 2011;Zhang et al, 2015;Durante and Guindani, 2020). One way to model effective connectivity is via a vector auto-regression (VAR) model, a widely-employed framework for estimating temporal (Granger) casual dependence in fMRI experiments (see, e.g.…”