Co-activation patterns in resting-state fMRI signals

Abstract

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns.

Keywords: Co-activation brain patterns; Dynamic brain connectivity; Resting-state fMRI.

Copyright © 2018 Elsevier Inc. All rights reserved.

Figures

Figure 1

Three major methods that focus on co-activation events in resting-state fMRI. (A) Two task-unrelated activation events detected by the parameter free mapping (PFM) and their spatial patterns. The activation and deactivation events are detected from fMRI signals of a representative subject as spikes in activation time series (ACT) derived using the PFM (black and red traces in the top panel). In addition to those evoked by visually cued tapping (VCT) and self-paced tapping (SPT) tasks, there were also two task-unrelated activation events (RSA and RSB) were detected in the positive ACT. The spatial activation patterns of the RSA and RSB are derived using the PFM and general linear modal (GLM) and shown in the bottom panel. All panels in (A) are adapted from (Gaudes et al., 2011). (B) Point process analysis (PPA) identifies supra-threshold events in fMRI signals. The point process events (red dots in the top panel) were defined as time points where the normalized fMRI signals cross a threshold of 1 (red dashed line in the first row) from below. These events coincide well with the peaks of de-convolved fMRI signals derived using either the hemodynamic response function (HRF) or the rBeta function (the second row of the top panel). Conditional rate maps of these events (the right three columns of the bottom panel), which indicate the probability of seeing such events at different brain regions conditional on seeing one at a given seed, show very similar network patterns as those derived by probabilistic ICA (the left column of the bottom row). All panels in (B) are adapted from (Tagliazucchi et al., 2012a). (C) Co-activation patterns (CAPs) and dynamic resting-state fMRI connectivity. Thirteen examples of single fMRI volumes show clear instantaneous patterns of brain co-activations that even include thalamic nuclei and hippocampus (the third row). They are corresponding to black solid circles shown in the normalized fMRI signal from the posterior cingulate cortex (PCC) region (the second row with a unit of standard deviation (S.D.)). Seed-based correlation maps (the top row) within four short time windows (16.1 seconds, 7 fMRI volumes) are largely determined by instantaneous brain co-activation patterns of included time points. For example, the presence of ventral posterolateral nucleus (VPL) in one of the sensorimotor maps is attributed to the co-activation at the time point 3, and the presence of the amygdala (AMY) and hippocampus (HP) in one of the PCC maps can be explained by the instantaneous pattern at time point 11. CAPs were derived by grouping all time points into subgroups using clustering and then taking the means (centroids) of these subgroups (the bottom row).

Figure 2

Co-activation patterns can temporally decompose stationary correlations and reveal fine-scale thalamocortical synchronization. (A) Negative correlations between the default mode network (DMN) and task-positive networks that was found previously with seed-based correlation analysis (Fox et al., 2005) may actually reflect anti-phase co-(de)activations between the DMN and different sets of task-positive network at different time points, as shown by a set of CAPs derived using the whole-brain CAP approach (Liu et al., 2013). (B) Sensorimotor co-activation is accompanied by specific deactivations in subcortical regions, including the anterior and medial dorsal nuclei (AN/MDN) of the thalamus (big arrows), a shell-shape structure around the thalamus that we tentatively regard as the thalamic reticular nucleus (TRN) (the top row), and the substantia nigra (SN) sitting right above the pons (the bottom row). These subcortical structures are either much less clear or completely absent in outcomes of the seed-based correlation analysis (the second row) and spatial ICA (the third row).

Figure 3

Global rsfMRI co-activation pattern (CAP) and sequential spectral transition (SST) electrophysiological events observed in three independent datasets. (A) The global CAP, derived from the Human Connectome Project (HCP) data by averaging the spatial pattern of time points showing the highest 16.6% global signals, shows widespread but sensory-dominant co-activations (top). Interestingly, this nearly whole-brain co-activation is associated with opposite de-activations in subcortical arousal-promoting regions, including the Nucleus Basalis (NB) at the basal forebrain (bottom), adapted from (Liu et al., 2018). (B) The global mean spectrogram of 128-channel electrocorticography (ECoG) recordings from monkeys during light sleep shows a characteristic SST event that occurs repeatedly (black arrows in the bottom panel). The averaged time-frequency pattern of SST (top right) indicates sequential power changes in three distinct frequency bands, among which the gamma-band (42–87 Hz) power increase at the SST is widespread but show highest amplitude at sensory regions (top left). (C) Local field potential (LFP) recorded by a single electrode in a macaque’s frontal cortex shows recurring SST events (top), each of which is followed by a peak in the global fMRI signal (red lines and circles, bottom) measured concurrently.