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.focal epilepsy | seizure localization | network analysis | eigenvector centrality | ECoG signals E pilepsy affects over 60 million people worldwide, and approximately 40% of patients have drug-resistant epilepsy (DRE) with recurrent seizures that are not controlled by available medications (1-3). It is now routine to consider drugresistant partial epilepsy patients, who represent the largest cohort of patients with uncontrolled seizures, for possible resective seizure surgery (4). Successful seizure surgery is predicated upon the ability to localize the seizure onset zone. Although some patients (e.g., those with mesial temporal sclerosis or lesional epilepsy) can proceed to surgery following scalp recordings of seizures delineating a seizure onset zone (5), a significant number of patients have seizures that are challenging to localize with scalp ictal (i.e., seizure) recordings. In this case, ictal recordings using intracranial electrodes (e.g., subdural strips, grids, or depth electrode arrays) are necessary. The purpose of these intracranial recording arrays is to provide information about seizure onset and propagation, representing spatiotemporal changes in cerebral function.Using intracranial electrocorticographic (ECoG) recordings taken over several days to capture ictal events, clinicians visually inspect the ECoG recordings at the onset of the seizures an...

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.R einstatement of neural activity is hypothesized to underlie our ability to recover the internal representation of a previous experience, a process described as mental time travel (1-4). These internal representations, which may reflect the external environment or internal mental states, form the context in which an episodic memory is embedded. Central to the hypothesis of mental time travel is that context representations in the brain gradually change over time, and that successful retrieval of an episodic memory involves mentally jumping back in time to reexperience a particular context (5-8). Consistent with this paradigm, when an episode is successfully retrieved from memory, the memory for neighboring episodes that occurred close in time is enhanced, an effect known as contiguity (9). However, despite substantial behavioral data supporting this hypothesis, a number of important yet unanswered questions remain regarding its underlying neural mechanisms.Empiric support for neural reinstatement has largely emerged from functional MRI (fMRI) studies that have used multivoxel pattern analysis (MVPA) (10-12). MVPA relies on classifying neural activity during retrieval to dissociate broad manipulations such as category or task that are present during encoding (13-16). MVPA, however, is unable to directly examine whether successful retrieval reinstates the neural representations of individual items. Representational similarity analysis supports neural reinstatement of individual stimuli (17-19), but this alternative fMRI approach does not examine to what extent retrieval reinstates a changing neural representation of context. In addition, the limited temporal resolution of fMRI studies makes them unable to identify the precise spatiotemporal dynamics o...

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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.

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.

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.

Epilepsy is a neurological condition with a prevalence of 1%, and 14-34% have medically refractory epilepsy (MRE). Seizures in focal MRE are generated by a single epileptogenic zone (or focus), thus there is potentially a curative procedure - surgical resection. This procedure depends significantly on correct identification of the focus, which is often uncertain in clinical practice. In this study, we analyzed intracranial stereotaxic EEG (sEEG) data recorded in two human patients with drug-resistant epilepsy prior to undergoing resection surgery. We view the sEEG data as samples from the brain network and hypothesize that seizure foci can be identified based on their network connectivity during seizure. Specifically, we computed a time sequence of connectivity matrices from EEG recordings that represent network structure over time. For each patient, connectivity between electrodes was measured using the coherence in a given frequency band. Matrix structure was analyzed using singular value decomposition and the leading singular vector was used to estimate each electrode's time dependent centrality (importance to the network's connectivity). Our preliminary study suggests that seizure foci may be the most weakly connected regions in the brain during the beginning of a seizure and the most strongly connected regions towards the end of a seizure. Additionally, in one of the patients analyzed, the network connectivity under anesthesia highlights seizure foci. Ultimately, network centrality computed from sEEG activity may be used to develop an automated, reliable, and computationally efficient algorithm for identifying seizure foci.

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.2 Keywords iEEG; nested oscillations; sharp waveforms