Spontaneous Neural Dynamics and Multi-scale Network Organization.
Spontaneous Neural Dynamics and Multi-scale Network Organization.
复制标题
DOI：
10.3389/fnsys.2016.00007
复制
发表时间：
2016
影响因子：
3
通讯作者：
Saalmann YB
中科院分区：
文献类型：
--
作者：
Foster BL;He BJ;Honey CJ;Jerbi K;Maier A;Saalmann YB
自发神经活动历来被视为与任务无关的噪音，应该通过实验设计加以控制，并通过数据分析加以消除。然而，电生理学和功能磁共振成像研究的自发活动模式，这大大增加了在过去的十年中，已经揭示了这些内在的模式和功能性脑回路的结构网络架构之间的密切对应关系。特别是，通过分析自发血流动力学的大规模协变，研究人员能够可靠地识别人脑中的功能网络。随后的工作试图通过电生理测量来识别相应的神经特征，因为这将阐明自发血流动力学的神经起源，并揭示这些过程在较慢和较快的时间尺度上的时间动力学。在这里，我们调查常见的方法来量化自发性神经活动，审查他们的经验成功，以及他们的对应关系的神经影像学的结果。我们强调侵入性的电生理测量，这是服从振幅和相位为基础的分析，并可以报告连接的变化与高时空精度。在总结了人类大脑的主要发现之后，我们调查了显示类似多尺度特性的动物模型的工作。我们强调，在许多时空尺度上，自发神经活动的协方差结构反映了神经网络的结构特性，并动态跟踪其功能库。
Spontaneous neural activity has historically been viewed as task-irrelevant noise that should be controlled for via experimental design, and removed through data analysis. However, electrophysiology and functional MRI studies of spontaneous activity patterns, which have greatly increased in number over the past decade, have revealed a close correspondence between these intrinsic patterns and the structural network architecture of functional brain circuits. In particular, by analyzing the large-scale covariation of spontaneous hemodynamics, researchers are able to reliably identify functional networks in the human brain. Subsequent work has sought to identify the corresponding neural signatures via electrophysiological measurements, as this would elucidate the neural origin of spontaneous hemodynamics and would reveal the temporal dynamics of these processes across slower and faster timescales. Here we survey common approaches to quantifying spontaneous neural activity, reviewing their empirical success, and their correspondence with the findings of neuroimaging. We emphasize invasive electrophysiological measurements, which are amenable to amplitude- and phase-based analyses, and which can report variations in connectivity with high spatiotemporal precision. After summarizing key findings from the human brain, we survey work in animal models that display similar multi-scale properties. We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire.