Intrinsic/extrinsic duality of large-scale neural functional integration in the human brain

Abstract

Human brain activity is not merely responsive to environmental context but includes intrinsic dynamics, as suggested by the discovery of functionally meaningful neural networks at rest, i.e., even without explicit engagement of the corresponding function. Yet, the neurophysiological coupling mechanisms distinguishing intrinsic (i.e., task-invariant) from extrinsic (i.e., task-dependent) brain networks remain indeterminate. Here, we investigated functional brain integration using magnetoencephalography throughout rest and various tasks recruiting di↵erent functional systems and modulating perceptual/cognitive loads. We demonstrated that two distinct modes of neural communication continually operate in parallel: extrinsic coupling supported by phase synchronization and intrinsic integration encoded in amplitude correlation. Intrinsic integration also contributes to phase synchronization, especially over short (second-long) timescales, through modulatory e↵ects of amplitude correlation. Our study establishes the foundations of a novel conceptual framework for human brain function that fundamentally relies on electrophysiological features of functional integration. This framework blurs the boundary between resting-state and task-related neuroimaging.

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

“…These neuromagnetometers have identical sensor layout (i.e., 102 magnetometers and 102 pairs of orthogonal planar gradiometers) and only differ in sensor dynamic range and background magnetic environment, neither of which substantially affect data quality after preprocessing. In particular, previous research mixing resting-state recordings from these two systems did not disclose any significant difference (Coquelet et al, 2020b, 2020a; Naeije et al, 2020; Sjøgård et al, 2020a, 2020b). Therefore, we did not take the MEG system type into account in later analyses.…”

“…They also wax and wane spontaneously at rest (i.e., in the absence of any explicit task performance). The resulting fluctuations in their amplitude are key to intrinsic functional connectivity (Siegel et al, 2012; Sjøgård et al, 2020a). When measured with electroencephalography (EEG) or magnetoencephalography (MEG), this oscillatory dynamics leads to signal power time courses whose correlation structure identifies functional brain networks (Brookes et al, 2011; Coquelet et al, 2020a; Hipp et al, 2012; Liu et al, 2017; Siems et al, 2016; Wens et al, 2014).…”

Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis

“…Still, there is a growing literature on the relationship between phase‐ and amplitude‐based coupling measures. They have been shown to be moderately to strongly correlated (Colclough et al, 2016; Siems & Siegel, 2020; Sjøgård et al, 2020) while still providing complementary, nonredundant information (Siems & Siegel, 2020). Furthermore, both of them have been shown to be related to fMRI rsFC in some ways (e.g., Tewarie et al, 2014, 2016).…”

“…Furthermore, both of them have been shown to be related to fMRI rsFC in some ways (e.g., Tewarie et al, 2014, 2016). There is also evidence that, as rsFC based on power envelope correlation, phase coupling also displays some intrinsic (i.e., task independent) properties, although to a lesser degree than envelope correlation (Sjøgård et al, 2020). However, MEG power envelope correlation is closely related to rs‐fMRI functional connectivity, which is why we have split out discussion between the fMRI literature and the (phase‐based) MEG literature.…”

Structural basis of envelope and phase intrinsic coupling modes of the cerebral cortex

“…Additionally, we solely focused on the awake resting-state periods in order to make concrete comparisons with the existing literature mostly based on that brain state. Little is known about the eventual modulation of the structure-function relationships across diverse contexts, including, for example, sleep stages or when animals interact with the environment, deserving further investigations [52,61]. Regarding computational modelling, we made use of standard models together with their default settings (except for the coupling strength and the average delay).…”