Connectomics of human electrophysiology

Abstract

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.

Figure 1 --

a schematic overview showing the process by which connectivity is calculated in electrophysiological data.

Figure 2 --. The presence of an intrinsic whole-brain connectivity organization in electrophysiological data.

A) Sensory and motor ICNs as observed with seed-based connectivity in source-space MEG amplitude coupling. The spectral plot (right) indicates a strong contribution from alpha and beta band oscillations to these intrinsic networks (adapted from Hipp et al., 2012). B) Temporal ICA of band-specific oscillation amplitudes in MEG yields numerous ICNs (four are shown as examples), including sensory/motor as well as higher-order networks. Alpha and especially beta bands captured ICN organization well. A direct comparison between the MEG-derived (bottom row) and the fMRI-derived (top-row) independent component maps demonstrates high spatial similarity (adapted from Brookes et al., 2011). C) Connection-wise connectivity strength is spatially associated between fMRI and intracranial electrophysiology (ECoG amplitude coupling, pooled over patients). The strength of this correlation is around ~0.35 for all frequency bands (adapted from Betzel et al., 2019). D) A similar spatial association of connection-wise connectivity strength is observed between fMRI and concurrently recorded scalp EEG (phase coupling). The left scatterplot shows an example for the beta band, where each data point is from one connection (region pair) of the connectome averaged across subjects (adapted from Wirsich et al., 2017). This relationship is reproducible at similar effect size across various MRI field strengths (1.5–7T) and EEG densities (64–256 channels) (adapted from Wirsich et al., 2021).

Figure 3:. Millisecond dynamics of functional networks.

A/B) (Adapted from Baker et al. 2014) A) The spatial signatures of 8 brain states, extracted using a Hidden Markov Model (HMM) applied to resting state MEG data. Each state is determined by a specific topography. These state maps are similar to typical intrinsic connectivity networks (ICNs) commonly observed with fMRI. B) The timescales associated with HMM states are shown in Panel A. Notice that on each state visit, the networks were stable only for short periods of time (100–300 ms) implying that ICNs may fluctuate on a rapid timescale. C) (Adapted from Higgins et al., 2020) “Replay” is the process by which the brain spontaneously revisits recently acquired information in order to e.g., consolidate memories. These replay events are associated with altered probability of occurrence of specific HMM states. The graph on the left shows the change in probability of occurrence of HMM states during a ‘replay’ event (events at time t = 0). The maps on the right show the four brain networks more likely to be expressed during replay, which prominently included the default mode and parietal alpha networks – both known to be associated with inwardly directed attention.

Figure 4 --. 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)).