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

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.

Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest

“…The presence and the importance of real zero-lag long-range FC are supported empirically and theoretically (Viriyopaseet al, 2012) (Finger et al, 2016). For more information and a comprehensive view of the topic of the relationships between M/EEG and fMRI signals, readers can refer to the recent review by Sadaghiani et al (Sadaghiani et al 2019).…”

Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

“…These observations speak to conceptual frameworks of oscillation-based FC incorporating a largely state-and frequency-invariant spatial organization beyond state-responsive and frequency-specific short-term dynamics of FC (Sadaghiani and Wirsich 2019). This is especially important because statistical tests of task-related FC differ in the adequacy with which their assumed null distribution captures task-independent FC already present in the pre-stimulus baseline (Mostame et al 2019).…”

“…For a century, task-independent ongoing oscillations have been the hallmark of EEG (Berger 1930). The spectral properties of such ongoing activity ("quantitative EEG") and their cross-areal connectivity during resting state have proven informative for understanding brain function and dysfunction (Kanda et al 2009;Sadaghiani et al 2019). The omnipresence and functional importance of electrophysiological oscillations and FC across all cognitive states therefore begs the question of whether they are governed by a state-invariant, i.e.…”

Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior

“…This adds an entire new dimension to the analysis of functional networks, namely the frequency these networks are operating at, which, in the mammalian brain can span ranges from ultraslow (<0.1Hz) to ultrafast (>100 Hz) frequency regimes (Buzsaki et al, 2013)). By examining the whole-brain networks defined by synchronous resting state in delineated frequency ranges we can shed light simultaneously on not only the relationships between oscillatory frequency and behaviour but also how different oscillatory networks are spatially organised (Sadaghiani and Wirsich, 2020).…”

“…However, what is not known is how meaningful these spectrally resolved networks are. Initial evidence for the multispectral nature of M/EEG connectivity is provided by findings from resting state activity that highlight the existence of spectrally resolved large-scale brain connectivity networks (Brookes et al, 2011a;Brookes et al, 2011b;Hillebrand et al, 2012;Hipp et al, 2012;Sadaghiani and Wirsich, 2020) . Recent efforts have identified large-scale (i.e.…”