Verma Parul, Nagarajan Srikantan, Raj Ashish
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Netw Neurosci. 2023 Jan 1;7(1):48-72. doi: 10.1162/netn_a_00263. eCollection 2023.
We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.
我们探索了一种用于神经振荡的分层、线性化和解析谱图模型的稳定性和动态特性，该模型整合了大脑的结构布线。此前，我们已经表明，该模型无需区域变化参数就能准确捕捉从脑磁图记录中获得的α和β频段的频谱及空间模式。在此，我们表明，即使在介观水平未实施任何振荡，这种基于长程兴奋性连接的宏观模型也会呈现出α频段频率的动态振荡。我们表明，根据参数不同，该模型可以呈现出阻尼振荡、极限环或不稳定振荡的组合。我们确定了确保模型模拟振荡稳定性的模型参数界限。最后，我们估计了随时间变化的模型参数，以捕捉脑磁图活动中的时间波动。我们表明，由此可以采用一个具有一组简约的、具有生物物理可解释性的模型参数的动态谱图建模框架，来捕捉在各种脑状态和疾病的电生理数据中观察到的振荡波动。
