Cued Memory Retrieval Exhibits Reinstatement of High Gamma Power on a Faster Timescale in the Left Temporal Lobe and Prefrontal Cortex.

Converging evidence suggests that reinstatement of neural activity underlies our ability to successfully retrieve memories. However, the temporal dynamics of reinstatement in the human cortex remain poorly understood. One possibility is that neural activity during memory retrieval, like replay of spiking neurons in the hippocampus, occurs at a faster timescale than during encoding. We tested this hypothesis in 34 participants who performed a verbal episodic memory task while we recorded high gamma (62-100 Hz) activity from subdural electrodes implanted for seizure monitoring. We show that reinstatement of distributed patterns of high gamma activity occurs faster than during encoding. Using a time-warping algorithm, we quantify the timescale of the reinstatement and identify brain regions that show significant timescale differences between encoding and retrieval. Our data suggest that temporally compressed reinstatement of cortical activity is a feature of cued memory retrieval.SIGNIFICANCE STATEMENT We show that cued memory retrieval reinstates neural activity on a faster timescale than was present during encoding. Our data therefore provide a link between reinstatement of neural activity in the cortex and spontaneous replay of cortical and hippocampal spiking activity, which also exhibits temporal compression, and suggest that temporal compression may be a universal feature of memory retrieval.

Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex.

Phase-amplitude coupling (PAC) is hypothesized to coordinate neural activity, but its role in successful memory formation in the human cortex is unknown. Measures of PAC are difficult to interpret, however. Both increases and decreases in PAC have been linked to memory encoding, and PAC may arise due to different neural mechanisms. Here, we use a waveform analysis to examine PAC in the human cortex as participants with intracranial electrodes performed a paired associates memory task. We found that successful memory formation exhibited significant decreases in left temporal lobe and prefrontal cortical PAC, and these two regions exhibited changes in PAC within different frequency bands. Two underlying neural mechanisms, nested oscillations and sharp waveforms, were responsible for the changes in these regions. Our data therefore suggest that decreases in measured cortical PAC during episodic memory reflect two distinct underlying mechanisms that are anatomically segregated in the human brain.

Reinstatement of distributed cortical oscillations occurs with precise spatiotemporal dynamics during successful memory retrieval.

Reinstatement of neural activity is hypothesized to underlie our ability to mentally travel back in time to recover the context of a previous experience. We used intracranial recordings to directly examine the precise spatiotemporal extent of neural reinstatement as 32 participants with electrodes placed for seizure monitoring performed a paired-associates episodic verbal memory task. By cueing recall, we were able to compare reinstatement during correct and incorrect trials, and found that successful retrieval occurs with reinstatement of a gradually changing neural signal present during encoding. We examined reinstatement in individual frequency bands and individual electrodes and found that neural reinstatement was largely mediated by temporal lobe theta and high-gamma frequencies. Leveraging the high temporal precision afforded by intracranial recordings, our data demonstrate that high-gamma activity associated with reinstatement preceded theta activity during encoding, but during retrieval this difference in timing between frequency bands was absent. Our results build upon previous studies to provide direct evidence that successful retrieval involves the reinstatement of a temporal context, and that such reinstatement occurs with precise spatiotemporal dynamics.

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

Physiology of functional and effective networks in epilepsy.

Epilepsy is a network phenomenon characterized by atypical activity during seizure both at the level of single neurons and neural populations. The etiology of epilepsy is not completely understood but a common theme among proposed mechanisms is abnormal synchronization between neuronal populations. Recent advances in novel imaging and recording technologies have enabled the inference of comprehensive maps of both the anatomical and physiological inter-relationships between brain regions. Clinical protocols established for diagnosis and treatment of epilepsy utilize both advanced neuroimaging techniques and neurophysiological data. These growing clinical datasets can be further exploited to better understand the complex connectivity patterns in the epileptic brain. In this article, we review results and insights gained from the growing body of research focused on epilepsy from a network perspective. In particular, we put an emphasis on two different notions of network connectivity: functional and effective; and studies investigating these notions in epilepsy are highlighted. We also discuss limitations and opportunities in data collection and analyses that will further our understanding of epileptic networks and the mechanisms of seizures.