Henderson James A, Aquino Kevin M, Robinson P A
School of Physics, The University of Sydney, ARC Center for Integrative Brain Function, The University of Sydney, Australia.
School of Physics, The University of Sydney, Turner Institute for Brain and Mental Health, Monash University, Australia.
Neuroimage Rep. 2022 Jun 17;2(3):100103. doi: 10.1016/j.ynirp.2022.100103. eCollection 2022 Sep.
Neural field theories successfully describe normal, awake mesoscale to macroscale brain activity using linearized partial differential equations that involve a Laplacian operator. These neural field theories therefore predict spatial brain activity patterns that are simple, smooth eigenfunctions of the Laplacian operator that satisfy the Helmholtz equation and extend across the entire cortex. However, complex, localized spatial patterns such as resting-state networks have been proposed from data analysis of brain activity measurements, primarily fMRI. Using Human Connectome Project data, the Helmholtz eigenmode prediction of neural field theory is shown to be consistent with eigenvectors of both fMRI and MEG covariance matrices. It is explained why these Helmholtz equation eigenmodes have been difficult to observe due to their similar spectra, effects of covariant input stimuli to the brain, and measurement noise. This implies that complex spatial patterns like resting-state networks may be the result of complex input to the brain but simple intrinsic brain dynamics, rather than simple stimuli and complex brain dynamics, as assumed in many existing models.
神经场理论使用涉及拉普拉斯算子的线性化偏微分方程成功地描述了正常清醒状态下中尺度到宏观尺度的大脑活动。因此，这些神经场理论预测的大脑空间活动模式是拉普拉斯算子的简单、平滑本征函数，满足亥姆霍兹方程并延伸至整个皮层。然而，从大脑活动测量（主要是功能磁共振成像）的数据分析中已经提出了诸如静息态网络等复杂的局部空间模式。利用人类连接体计划的数据，神经场理论的亥姆霍兹本征模式预测被证明与功能磁共振成像和脑磁图协方差矩阵的特征向量一致。文中解释了为何由于这些亥姆霍兹方程本征模式具有相似的频谱、大脑协变输入刺激的影响以及测量噪声，它们一直难以被观测到。这意味着像静息态网络这样的复杂空间模式可能是大脑复杂输入但简单内在大脑动力学的结果，而不是像许多现有模型所假设的那样是简单刺激和复杂大脑动力学的结果。
