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Erratum to 'An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks' [NeuroImage 60/2 (2012) 1117-1126]

机译:勘误表“创建基于组的代表性全脑连接网络的指数随机图建模方法” [NeuroImage 60/2(2012)1117-1126]

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摘要

Strong periodic stimuli such as bright flashing lights evoke nonlinear responses in the brain and interact nonlinearly with ongoing cortical activity, but the underlying mechanisms for these phenomena are poorly understood at present. The dominant features of these experimentally observed dynamics are reproduced by the dynamics of a quantitative neural field model subject to periodic drive. Model power spectra over a range of drive frequencies show agreement with multiple features of experimental measurements, exhibiting nonlinear effects including entrainment over a range of frequencies around the natural alpha frequency f α, subharmonic entrainment near 2f α, and harmonic generation. Further analysis of the driven dynamics as a function of the drive parameters reveals rich nonlinear dynamics that is predicted to be observable in future experiments at high drive amplitude, including period doubling, bistable phase-locking, hysteresis, wave mixing, and chaos indicated by positive Lyapunov exponents. Moreover, photosensitive seizures are predicted for physiologically realistic model parameters yielding bistability between healthy and seizure dynamics. These results demonstrate the applicability of neural field models to the new regime of periodically driven nonlinear dynamics, enabling interpretation of experimental data in terms of specific generating mechanisms and providing new tests of the theory.
机译:强烈的周期性刺激(例如明亮的闪光灯)会引起大脑中的非线性反应,并与正在进行的皮层活动发生非线性相互作用,但是目前对于这些现象的潜在机制了解甚少。这些实验观察到的动力学的主要特征是通过周期性驱动的定量神经场模型的动力学再现的。在一系列驱动频率上的模型功率谱显示出与实验测量的多个特征一致,表现出非线性效应,包括在自然α频率fα附近的一定频率范围内的夹带,在2fα附近的次谐波夹带以及谐波的产生。对驱动动力学作为驱动参数的函数的进一步分析揭示了丰富的非线性动力学,预计在未来的实验中,在高驱动幅度下,可以观察到丰富的非线性动力学,包括周期倍增,双稳态锁相,磁滞,波动混合以及由正向表示的混沌李雅普诺夫指数。此外,可以预测光敏性癫痫发作的生理现实模型参数,从而在健康和癫痫发作动力学之间产生双稳态。这些结果证明了神经场模型适用于周期性驱动的非线性动力学的新机制,从而能够根据特定的生成机制解释实验数据,并提供理论的新检验。

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