4.7 Article

Connectomics of human electrophysiology

期刊

NEUROIMAGE
卷 247, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2021.118788

关键词

-

资金

  1. NIH [R01 MH116226, R01 EB026299]
  2. Engineering and Physical Sciences Research Council (EPSRC) [EP/V047264/1, EP/T001046/1]
  3. Natural Science and Engineering Research Council of Canada [436355-13]
  4. CIHR Canada Research Chair in Neural Dynamics of Brain Systems
  5. Brain Canada Foundation
  6. Health Canada
  7. Canada First Research Excellence Fund
  8. EPSRC [EP/T001046/1] Funding Source: UKRI

向作者/读者索取更多资源

The paper presents a scientific overview and conceptual positions on the challenges and benefits of electrophysiological measurements in understanding the human connectome. It emphasizes the importance of electrophysiological signals, current data modalities, and analytical methods. Furthermore, it encourages the field to embrace the complexity of electrophysiological signals and develop testable mechanistic models for information integration in hierarchical brain networks.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据