Bayesian Inference of a Spectral Graph Model for Brain Oscillations.

ABSTRACT: The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Baye

SUBMITTER: Jin H 

PROVIDER: S-EPMC10002745 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature