Spectral graph theory of brain oscillations--Revisited and improved.
Spectral graph theory of brain oscillations--Revisited and improved.
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DOI：
10.1016/j.neuroimage.2022.118919
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发表时间：
2022-04-01
期刊：
影响因子：
5.7
通讯作者：
Raj, Ashish
中科院分区：
文献类型：
--
作者：
Verma, Parul;Nagarajan, Srikantan;Raj, Ashish
功能活动和大脑结构连接之间关系的数学建模主要是使用非线性和生物物理详细的数学模型，具有区域变化的参数。虽然这种方法为我们提供了丰富的多稳态动态，可以由大脑显示，但它的计算要求很高。此外，尽管神经元动力学在微观水平上是非线性和混沌的，但尚不清楚是否需要这样详细的非线性模型来捕捉涌现的中观（区域种群集合）和宏观（整个大脑）行为，这些行为在很大程度上是确定性的，并且在个体之间可重复。事实上，最近基于谱图理论的建模工作表明，没有区域变化参数和多稳定动力学的分析模型可以准确地捕获经验脑磁图频谱和α和β频段的空间模式。在这项工作中，我们展示了一种改进的分层、线性化和基于分析谱图理论的模型，该模型可以捕获从静息健康受试者的脑磁图记录中获得的频谱。我们根据经典神经质量模型重新制定了谱图理论模型，从而提供了更多生物可解释的参数，特别是在局部尺度上。通过对比模拟的频谱与脑磁图记录的频谱相关性，我们证明了该模型优于原始模型。该模型在预测经验α和β频段的空间格局方面也表现良好。
Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.
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