Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland.
Department of Computer Science, Aalto University, Finland.
Neuroimage. 2023 Oct 1;279:120318. doi: 10.1016/j.neuroimage.2023.120318. Epub 2023 Aug 11.
Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.
大规模的相位同步网络被认为调节着大脑区域之间的通讯，而这些区域对认知功能至关重要，但它们与结构基础（即结构-功能关系）的映射仍然知之甚少。生物物理网络模型（BNM）已经证明了局部振荡活动和区域间解剖连接在产生 EEG 和 MEG 观察到的 alpha 波段（8-12 Hz）相位同步网络方面的影响。然而，区域间传导延迟的影响仍然未知。在这项研究中，我们将一个具有标准“距离依赖延迟”的 BNM 与另外两个考虑空间变化传导速度的替代方法（“等时延迟”和“混合延迟”）指定延迟的 BNM 进行了比较。我们遵循近似贝叶斯计算（ABC）工作流程，i）指定 BNM 参数的神经生理信息先验分布，ii）通过先验预测检查验证先验分布的适用性，iii）使用无似然推理的贝叶斯优化（BOLFI）将每个 BNM 拟合到 alpha 波段 MEG 静息态数据（N=75），iv）使用 ABC 模型比较在单独的 MEG 数据集（N=30）上对拟合的 BNM 进行选择。先验预测检查表明，每个 BNM 生成的动力学范围都包含了在 MEG 数据中看到的那些，这表明了先验分布的适用性。将模型拟合到 MEG 数据产生了每个 BNM 参数的可靠后验分布。最后，模型比较表明，具有“距离依赖延迟”的 BNM 最有可能描述 MEG 中看到的 alpha 波段相位同步网络的生成。这些发现表明，距离依赖延迟可能有助于人类 alpha 波段相位同步网络的新皮层结构。因此，我们的研究阐明了区域间延迟在产生可能支持对认知至关重要的区域之间通讯的大规模相位同步网络方面的作用。
