Jin Huaqing, Verma Parul, Jiang Fei, Nagarajan Srikantan, Raj Ashish
Department of Radiology and Biomedical Imaging, University of California San Francisco, USA San Francisco, CA.
Department of Epidemiology and Biostatistics, University of California San Francisco, USA San Francisco, CA.
bioRxiv. 2023 Mar 11:2023.03.01.530704. doi: 10.1101/2023.03.01.530704.
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 can not 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.
脑功能连接与结构连接之间的关系已引起神经科学界的广泛关注，通常使用数学建模进行推断。在众多建模方法中，频谱图模型（SGM）独具特色，因为它具有脑振荡宽带频谱的闭式解，仅需全局生物物理可解释参数。虽然SGM在参数方面较为简洁，但SGM参数的确定并非易事。先前关于SGM的工作通过计算密集型退火算法确定参数，该算法仅提供点估计，且参数估计无置信区间。为填补这一空白，我们纳入基于模拟的推断（SBI）算法，并开发了一种用于推断SGM参数后验分布的贝叶斯程序。此外，使用SBI显著降低了推断SGM参数的计算负担。我们在健康受试者的静息态脑磁图记录上评估了所提出的SBI-SGM框架，结果表明所提出的程序在恢复功率谱和α频段空间分布方面与退火算法具有相似的性能。此外，我们还分析了参数之间的相关性及其与后验分布的不确定性，而退火推断无法做到这一点。这些分析为更深入理解SGM生物物理参数之间的相互作用提供了帮助。总体而言，基于模拟的贝叶斯推断的使用能够对生成模型参数的不确定性进行稳健且高效的计算，并可能为生成模型在临床转化应用中的使用铺平道路。
