Electrophysiological foundations of the human default-mode network revealed by intracranial-EEG recordings during resting-state and cognition

Abstract

Investigations using noninvasive functional magnetic resonance imaging (fMRI) have provided significant insights into the unique functional organization and profound importance of the human default mode network (DMN), yet these methods are limited in their ability to resolve network dynamics across multiple timescales. Electrophysiological techniques are critical to address these challenges, yet few studies have explored the neurophysiological underpinnings of the DMN. Here we investigate the electrophysiological organization of the DMN in a common large-scale network framework consistent with prior fMRI studies. We used intracranial EEG (iEEG) recordings, and evaluated intra- and cross-network interactions during resting-state and its modulation during a cognitive task involving episodic memory formation. Our analysis revealed significantly greater intra-DMN phase iEEG synchronization in the slow-wave (< 4 Hz), while DMN interactions with other brain networks was higher in the beta (12-30 Hz) and gamma (30-80 Hz) bands. Crucially, slow-wave intra-DMN synchronization was observed in the task-free resting-state and during both verbal memory encoding and recall. Compared to resting-state, slow-wave intra-DMN phase synchronization was significantly higher during both memory encoding and recall. Slow-wave intra-DMN phase synchronization increased during successful memory retrieval, highlighting its behavioral relevance. Finally, analysis of nonlinear dynamic causal interactions revealed that the DMN is a causal outflow network during both memory encoding and recall. Our findings identify frequency specific neurophysiological signatures of the DMN which allow it to maintain stability and flexibility, intrinsically and during task-based cognition, provide novel insights into the electrophysiological foundations of the human DMN, and elucidate network mechanisms by which it supports cognition.

Fig. 1.

(a) iEEG recording sites for the 7 fMRI-derived brain networks investigated in this study. The Yeo cortical atlas was used to map the default mode (DMN), dorsal attention (DAN), ventral attention (VAN), frontoparietal (FPN), visual (VISN), somatomotor (SMN), and limbic (LIMN) networks. In addition to cortical areas, the DMN also included hippocampal regions determined using the Brainnetome atlas (Figs. S1, S2). (b) Cognitive task structure. Participants performed multiple trials of a “free recall” experiment, where they were first presented with a list of words and later asked to recall as many as possible from the original list (see Methods for details).

Fig. 2.

Intra-DMN phase synchronization during (a) Resting-state, (b) Memory encoding, and (c) Memory recall. In all three conditions, intra-DMN connectivity was characterized by synchronization in the slow-wave frequency band (< 4 Hz) while cross-network interaction of the DMN was dominated by higher frequencies. Intra-DMN denotes phase locking values (PLVs) between DMN electrodes and DMN-Other denotes PLV between DMN electrodes and electrodes in the 6 other brain networks. Data from each trial was analyzed separately and PLVs were averaged across trials for each condition (see Methods for more details). Error bars denote standard error of the mean (SEM) across all pairs of electrodes. *** p < 0.001, * p < 0.05 (FDR-corrected q < 0.05, two-way ANOVA).

Fig. 3.

Intra-DMN phase synchronization during memory encoding and memory recall, compared to resting-state. Intra-DMN phase synchronization, assessed using phase locking values (PLVs), was higher during both memory encoding and recall compared to resting-state, in the slow-wave frequency band. Intra-DMN phase synchronization was also higher during memory recall compared to memory encoding. The duration of memory encoding and recall, and resting-state epochs were matched to preclude trial-length effects (see Methods for more details). Error bars denote standard error of the mean (SEM) across all pairs of electrodes. *** p < 0.001, ** p < 0.01 (FDR-corrected q < 0.05, two-way ANOVA).

Fig. 4.

Intra-DMN phase synchronization during (a) successful vs. unsuccessful memory encoding and (b) successful vs. unsuccessful memory recall. Intra-DMN phase synchronization was higher during successful encoding/recall compared to unsuccessful encoding/recall in the slow-wave frequency band. Error bars denote standard error of the mean (SEM) across all pairs of electrodes. ** p < 0.01, * p < 0.05 (FDR-corrected q<0.05, two-way ANOVA).

Fig. 5.

Causal network influences, measured using phase transfer entropy (PTE), during (a) Memory encoding and (b) Memory recall. The DMN showed significantly higher net causal outflow to the 6 other networks, than the reverse. DMN→Other denotes PTE from DMN electrodes to electrodes in the 6 other brain networks; Other→DMN denotes PTE from electrodes in the 6 other networks to the DMN. Error bars denote standard error of the mean (SEM) across all pairs of electrodes. *** p < 0.001 (FDR-corrected q < 0.05, two-way ANOVA).

Fig. 6.

Causal network influences of the DMN on other networks during resting-state, memory encoding, and memory recall. Causal outflow from the DMN was higher during both memory encoding and memory recall compared to resting-state. Causal outflow from the DMN was also higher during memory encoding compared to memory recall. The duration of memory encoding and recall, and resting-state epochs were matched to preclude trial-length effects (see Methods for more details). Error bars denote standard error of the mean (SEM) across all pairs of electrodes. *** p < 0.001 (FDR-corrected q<0.05, two-way ANOVA).

Fig. 7.

Visualization of the main results reported in this study. (a) Intra-DMN connectivity was dominated by synchronization in the slow-wave frequency band (< 4 Hz) while cross-network interaction of the DMN was dominated by beta and gamma frequencies. Phase locking value (PLV) was used determine frequency-specific instantaneous phase synchronization that capture linear as well as nonlinear intermittent and nonstationary dynamics observed in iEEG data. (b) Causal network influences between the DMN and the 6 other networks was characterized by significantly higher net causal outflow from the DMN to the 6 other networks, than the reverse. Phase transfer entropy (PTE), which provides a robust and powerful measure for characterizing nonlinear information flow between brain regions based on phase coupling, was used to estimate causal network interactions. (See Results and Methods for details).