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Scientific article
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Open access
English

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

Published inNetwork neuroscience, vol. 4, no. 1, p. 1-29
Publication date2020-01
Abstract

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take “baseline” intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

eng
Keywords
  • Connectome
  • EEG
  • Intrinsic
  • MEG
  • Multimodal
  • fMRI
Affiliation Not a UNIGE publication
Funding
  • National Institutes of Health - Cognitive Significance of Functional Connectome States [1R01MH116226-01A1]
Citation (ISO format)
SADAGHIANI, Sepideh, WIRSICH, Jonathan. Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches. In: Network neuroscience, 2020, vol. 4, n° 1, p. 1–29. doi: 10.1162/netn_a_00114
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Article (Published version)
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ISSN of the journal2472-1751
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