Dynamic causal modelling of phase-amplitude interactions
Dynamic causal modelling of phase-amplitude interactions
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DOI：
10.1016/j.neuroimage.2019.116452
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发表时间：
2020-03-01
期刊：
影响因子：
5.7
通讯作者：
Friston, Karl J.
中科院分区：
文献类型：
--
作者：
Fagerholm, Erik D.;Moran, Rosalyn J.;Friston, Karl J.
耦合相位振荡器模型用于描述神经成像中的各种现象。这些模型通常建立在振子动力学不超过其各自极限环的前提下，因此相互作用可以纯粹用相位差来描述。虽然在数学上很方便，但纯阶段模型的限制性会限制它们的解释力。因此，我们提出了一个包含相位和振幅的动态因果模型的概括。这允许单独量化相位和振幅对神经区域之间连接的贡献。我们表明，使用模型生成的数据和耦合摆的模拟，相位振幅模型可以比纯相位模型更有效地描述强耦合系统。我们将我们的发现与神经成像中常用的四个指标联系起来：Kuramoto顺序参数，相互关联，相位滞后指数和谱熵。我们发现，除了谱熵之外，相位振幅模型能够比纯相位模型更有效地捕获所有指标。然后，我们利用啮齿类动物的局部场电位记录和猕猴的功能磁共振成像证明，振荡器模型中的振幅在描述麻醉大脑状态下的神经动力学方面起着重要作用。
Models of coupled phase oscillators are used to describe a wide variety of phenomena in neuroimaging. These models typically rest on the premise that oscillator dynamics do not evolve beyond their respective limit cycles, and hence that interactions can be described purely in terms of phase differences. Whilst mathematically convenient, the restrictive nature of phase-only models can limit their explanatory power. We therefore propose a generalisation of dynamic causal modelling that incorporates both phase and amplitude. This allows for the separate quantifications of phase and amplitude contributions to the connectivity between neural regions. We show, using model-generated data and simulations of coupled pendula, that phase-amplitude models can describe strongly coupled systems more effectively than their phase-only counterparts. We relate our findings to four metrics commonly used in neuroimaging: the Kuramoto order parameter, cross-correlation, phase-lag index, and spectral entropy. We find that, with the exception of spectral entropy, the phase-amplitude model is able to capture all metrics more effectively than the phase-only model. We then demonstrate, using local field potential recordings in rodents and functional magnetic resonance imaging in macaque monkeys, that amplitudes in oscillator models play an important role in describing neural dynamics in anaesthetised brain states.