Cross-regional phase amplitude coupling supports the encoding of episodic memories

Abstract

Phase amplitude coupling (PAC) between theta and gamma oscillations represents a key neurophysiological mechanism that promotes the temporal organization of oscillatory activity. For this reason, PAC has been implicated in item/context integration for episodic processes, including coordinating activity across multiple cortical regions. While data in humans has focused principally on PAC within a single brain region, data in rodents has revealed evidence that the phase of the hippocampal theta oscillation modulates gamma oscillations in the cortex (and vice versa). This pattern, termed cross-regional PAC (xPAC), has not previously been observed in human subjects engaged in mnemonic processing. We use a unique dataset with intracranial electrodes inserted simultaneously into the hippocampus and seven cortical regions across 40 human subjects to (1) test for the presence of significant cross-regional PAC (xPAC), (2) to establish that the magnitude of xPAC predicts memory encoding success, (3) to describe specific frequencies within the broad 2-9 Hz theta range that govern hippocampal-cortical interactions in xPAC, and (4) compare anterior versus posterior hippocampal xPAC patterns. We find that strong functional xPAC occurs principally between the hippocampus and other mesial temporal structures, namely entorhinal and parahippocampal cortices, and that xPAC is overall stronger for posterior hippocampal connections. We also show that our results are not confounded by alternative factors such as inter-regional phase synchrony, local PAC occurring within cortical regions, or artifactual theta oscillatory waveforms.

Keywords: episodic memory; functional connectivity; hippocampus; memory encoding; phase amplitude coupling.

FIGURE 1

Example trace of an xPAC computing procedure. (a) Instantaneous phase and amplitude from hippocampal and parahippocampal cortex (PHC) recordings. The unfiltered trace from the PHC is shown in row A1. iEEG data obtained during memory encoding were bandpass filtered in the slow gamma range (30–70 Hz, PHC, row A2) and slow theta (2–5 Hz, hippocampus, row A3) ranges. The amplitude of PHC gamma oscillations (row A2) and the phase of hippocampal slow theta (row A3) were obtained via Hilbert transform. The real and imaginary components of the signal were used in the MI calculation (row A4). (b) Analytical signals: complex-valued analytic signal in complex plane where red dot is the mean. Raw coupling magnitude M_raw is 9.8638 and preferred phase φ_pf is 268°. Then, 250 surrogate (shuffled) samples of the analytical signal were computed to normalize the coupling magnitude by z-scoring the M_raw with the mean and s.d. of surrogate magnitudes. The green dot denotes the MI, whose magnitude M_norm is 0.8217

FIGURE 2

(a) iEEG electrode maps: numbers of electrodes in each region from 40 subjects, with a minimum of 60 electrodes and a maximum of 201 electrodes contributing to 3,767 hippocampal-cortical electrode pairs overall. (b) Significant functional (SuE vs. UnsuE) xPAC connections, identified by mixed effects models. For all significant connections, xPAC also had significantly greater magnitude than expected by chance (MI Z >1.96). Left and right hemispheric connections are shown on each side of circle connectivity plots. AH, anterior hippocampus; ERC, entorhinal cortex; LMT, lateral middle temporal gyrus; LP, lateral parietal cortex; LPF, lateral prefrontal cortex; PC, posterior cingulate cortex; PH, posterior hippocampus; PHC, parahippocampal cortex; BTL, basal temporal cortex (fusiform and inferior temporal gyrus). (c) MI functional effects for the ERC/PHC versus other regions. The observed MI functional effect (red line) was obtained by comparing the distributions of MI differences (MI during SuE–MI during UnsuE) for ERC/PHC and other five regions, and the null distribution H₀ was obtained by 1,000 random shuffles. (d) Mean t-stats describing the functional xPAC effects for slow and fast theta bands in anterior versus posterior hippocampus in two hemispheres via ANOVA. * and ** denote p <.05 and p <.01, respectively

FIGURE 3

(a) Example trace of coronal plane T2 MR slices immediately to the front and back of the gyrus intralimbicus for ERC (upper, in red) and PHC (lower, in blue), respectively. (b) Functional effects of xPAC in the theta-gamma spectrum, shown as the t-statistic at each frequency–frequency pixel from a mixed effects model. Red indicates greater magnitude during SuE, and blue indicates greater magnitude during UnsuE. Areas outlined in white indicate the significant xPAC patterns after FDR correction across theta/gamma spectrum (q = 0.05). (c) Functional effects of xPAC at each theta frequency determined by mixed effects modeling. Results reflect aggregate values across regions of interest. Functional effects are represented by t-statistics extracted from the MEM, where positive values indicate greater xPAC magnitudes during SuE than UnsuE, and vice versa. (d) Distribution of preferred phases of xPAC during successful encoding. Preferred phases were averaged (angular mean) across trials for each significant electrode pair. Color-mapping denotes the hippocampal theta frequencies, and ** indicates p <.01 via Watson–Williams test

FIGURE 4

(a) Schematic illustrations for the control analysis of xPAC, phase synchrony (PLV), cortical local PAC, and oscillatory power (gamma for cortices and theta for hippocampus). Functional effect (SuE vs. UnsuE) of each connectivity/functional measure was computed by an independent mixed effects model. (b) Predicting models of xPAC magnitude during successful encoding. R² for each predictor indicates the independent model of the predictor using its magnitude during SuE, whereas R² for PAC: PLV denotes the combined effects (magnitude) of phase synchrony (PLV) and cortical local PAC in predicting xPAC. The variance of xPAC magnitude explained by these predictors (including the PLV:PAC interaction model) were all less than 5% (red line). (c) Predicting models of xPAC functional effects. R² for each predictor indicates the independent model of the predictor using its functional effects, whereas R² for baseline denotes the combined effects (functional effects) of PLV, PAC, cortical gamma power (gPower), and hippocampal theta power (tPower) in predicting xPAC. The variance of xPAC functional effects explained by these predictors (including two interaction models) were all less than 1% (red line)