Department of Psychology, University of Illinois, Urbana-Champaign, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, IL, United States.
Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG72RD, United Kingdom.
Neuroimage. 2022 Feb 15;247:118788. doi: 10.1016/j.neuroimage.2021.118788. Epub 2021 Dec 12.
We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.
我们介绍了电生理学测量在探索人类连接组的性质和功能方面所面临的挑战和优势的科学概述和概念立场。我们讨论了该领域如何受到功能磁共振成像(fMRI)发现和方法的启发,并受到少数重要的多模态实证研究的影响,这些研究表明,在 fMRI 中常见的典型网络实际上是植根于电生理过程。这篇综述也是一个机会,可以对当前可用于从电生理学中得出静态和动态连接组的现有数据模态和分析方法进行简短的、最新的批判性调查。我们回顾了挑战当前电生理学连接组研究意义和影响的障碍。然后,我们鼓励该领域克服对电生理学信号明显令人不安的复杂性的担忧,从而充分利用电生理学信号。我们的立场是,电生理学连接组学有望为可测试的机制模型提供信息,这些模型整合了来自可观察的振荡和非周期性信号成分及其多节奏相互作用的分层脑网络中的信息。