New review: electrophysiology for human connectomics

This new review article was commissioned by Neuroimage and co-authored with Dr. Sepideh Sadaghiani (University of Illinois, Urbana-Champaign, USA) and Dr. Matt Brookes (University of Nottingham, UK). It is available to everyone for free, in open access.

We had tremendous pleasure writing this piece all together. It was conceived as a go-to reference for both trainees and experienced researchers, who may be new to the field, or who want to capture the flexibility, diversity and versatility of current approaches to understand complex, networked brain activity with electrophysiology.

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.

Electrophysiological connectomics, from early inspiration by fMRI to testable, mechanistic principles of brain network signalling. A) Cross-correlation between BOLD resting-state and amplitude signal envelopes of typical electrophysiological frequency bands (multimodal non-human primate data collected simultaneously with fMRI and intracranial EEG). Note the negative cross-correlation of beta-band signals with BOLD, pointing at their possible distinctive role in brain networks, as discussed in Section 4; adapted from Schölvinck et al. (2010). B) Simplified illustration of the basic principles of hierarchical brain networks: exogenous (i.e., external stimulus) signals are registered by low-level, specialized neural circuits (in blue), which also receive endogenous signals from higher-order circuits (in pink). These latter are conceptualized as channelling predictive information about input signals. The input circuits compute a form of error signal between these “top-down” predictions and the actual input signal received. The resulting “bottom-up” error signals are relayed directly or indirectly (e.g., via (sub)cortical hub regions as dynamical relays) back to higher-order circuits, where the error signal is registered. This process induces the adaptation of behaviour and the updating of internal predictive models for immediate (reward) and subsequent (learning) behavioural benefits. C) A proposition for the possible biological implementation of these concepts. We illustrate local cross-frequency interactions between low (delta to alpha bands; red sine wave) and higher (gamma to high-gamma bands; blue bursts) frequency signals via e.g., cross-frequency phase-amplitude coupling in regional cell assemblies. Such assemblies are illustrated here as circuits of excitatory (E), fast inhibitory (FI) and slow inhibitory (SI) cells, which can generate such regimes of cross-frequency coupling and are distributed across the brain (Segneri et al., 2020). The illustration also shows beta-band signals as a top-down communication channel (pink). D) Power spectrum of the temporal fluctuations of regional phase-amplitude coupling in the human brain in the resting state. These fluctuations are slow, below 0.1 Hz, a dynamic range compatible with BOLD resting-state fluctuations in fMRI (MEG data from Florin and Baillet, 2015). E) Cross-correlation maps of phase-amplitude coupling fluctuations in the resting brain can be decomposed into spatial modes that are anatomically similar to the typical fMRI resting-state networks (n=12, 5-min resting-state MEG data and methods from Baillet (2017)).