Bayesian inference of a spectral graph model for brain oscillations

• Published in: NeuroImage (Orlando, Fla.) Vol. 279; p. 120278
• Main Authors: Jin, Huaqing, Verma, Parul, Jiang, Fei, Nagarajan, Srikantan S, Raj, Ashish
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2023; Elsevier Limited; Elsevier
• Subjects: Algorithms; Attention; Bayes Theorem; Bayesian; Bayesian analysis; Brain; Computational neuroscience; Computer Simulation; Connectomes; Humans; Magnetoencephalography; Mathematical models; Models, Theoretical; Nervous system; Neural networks; Oscillations; Simulation-based inference; Spatial distribution; Spectral graph theory; Structure-function relationships; Connectomes; Simulation-based inference; Magnetoencephalography; Spectral graph theory; Bayesian
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120278

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 Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications. •The work employs the parsimonious spectral graph model (SGM) for brain dynamics.•This work adopts a neural network for SBI of SGM, avoiding complex computations..•The novel SGM-SBI integration could be a pivotal advance in SC-FC mapping’s practical utility.