Hippocampal gamma predicts associative memory performance as measured by acute and chronic intracranial EEG

Utility of Chronic Intracranial Electroencephalography in Responsive Neurostimulation Therapy

Responsive neurostimulation (RNS) therapy is an effective treatment for reducing seizures in some patients with focal epilepsy. Utilizing a chronically implanted device, RNS involves monitoring brain activity signals for user-defined patterns of seizure activity and delivering electrical stimulation in response. Devices store chronic data including counts of detected activity patterns and brief recordings of intracranial electroencephalography signals. Data platforms for reviewing stored chronic data retrospectively may be used to evaluate therapy performance and to fine-tune detection and stimulation settings. New frontiers in RNS research can leverage raw chronic data to reverse engineer neurostimulation mechanisms and improve therapy effectiveness.

Dysfunction of NRG1/ErbB4 Signaling in the Hippocampus Might Mediate Long-term Memory Decline After Systemic Inflammation

Accumulating evidence has suggested that a great proportion of sepsis survivors suffer from long-term cognitive impairments after hospital discharge, leading to decreased life quality and substantial caregiving burdens for family members. However, the underlying mechanism remains unclear. In the present study, we established a mouse model of systemic inflammation by repeated lipopolysaccharide (LPS) injections. A combination of behavioral tests, biochemical, and in vivo electrophysiology techniques were conducted to test whether abnormal NRG1/ErbB4 signaling, parvalbumin (PV) interneurons, and hippocampal neural oscillations were involved in memory decline after repeated LPS injections. Here, we showed that LPS induced long-term memory decline, which was accompanied by dysfunction of NRG1/ErbB4 signaling and PV interneurons, and decreased theta and gamma oscillations. Notably, NRG1 treatment reversed LPS-induced decreases in p-ErbB4 and PV expressions, abnormalities in theta and gamma oscillations, and long-term memory decline. Together, our study demonstrated that dysfunction of NRG1/ErbB4 signaling in the hippocampus might mediate long-term memory decline in a mouse model of systemic inflammation induced by repeated LPS injections. Thus, targeting NRG1/ErbB4 signaling in the hippocampus may be promising for the prevention and treatment of this long-term memory decline.

Sleep loss disrupts the neural signature of successful learning

Sleep supports memory consolidation as well as next-day learning. The influential "Active Systems" account of offline consolidation suggests that sleep-associated memory processing paves the way for new learning, but empirical evidence in support of this idea is scarce. Using a within-subjects (n = 30), crossover design, we assessed behavioral and electrophysiological indices of episodic encoding after a night of sleep or total sleep deprivation in healthy adults (aged 18-25 years) and investigated whether behavioral performance was predicted by the overnight consolidation of episodic associations from the previous day. Sleep supported memory consolidation and next-day learning as compared to sleep deprivation. However, the magnitude of this sleep-associated consolidation benefit did not significantly predict the ability to form novel memories after sleep. Interestingly, sleep deprivation prompted a qualitative change in the neural signature of encoding: Whereas 12-20 Hz beta desynchronization-an established marker of successful encoding-was observed after sleep, sleep deprivation disrupted beta desynchrony during successful learning. Taken together, these findings suggest that effective learning depends on sleep but not necessarily on sleep-associated consolidation.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Brain Stimulation Using Responsive Neurostimulation Improves Verbal Memory: A Crossover Case-Control Study

BACKGROUND

The effects of brain stimulation on memory formation in humans have shown conflicting results in previous studies. We hypothesized that direct cortical stimulation using an implanted responsive neurostimulation (RNS) system will improve memory.

OBJECTIVE

To evaluate whether direct cortical stimulation using RNS improves memory as measured with recall scores of a list-learning task.

METHODS

During outpatient visits, a list-learning task (Hopkins Verbal Learning Test-Revised) was administered to 17 patients with RNS implants. Patients were read a list of 12 semantically related words and asked to recall the list after 3 different learning trials. True or sham stimulations were performed for every third word presented for immediate recall. Most patients had frontotemporal network stimulation-one patient each had insular and parietal stimulations. After a 20-min delay, they were asked to recall the list again, first freely and then through a "yes/no" recognition paradigm. A crossover design was used in which half the patients had true stimulation during the initial visit and half had sham stimulation-followed by crossover to the other group at the next visit.

RESULTS

The Hopkins Verbal Learning Test-Revised delayed recall raw score was higher for the stimulation condition compared with the nonstimulation condition (paired t -test, P = .04, effect size d = 0.627).

CONCLUSION

Verbal memory improves by direct cortical stimulation during a list-learning task. The RNS system can be effectively used in memory research using direct cortical stimulation. This study has implications in the development of neurostimulation devices for cognitive enhancement in conditions such as epilepsy, dementia, and traumatic brain injury.

Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects

INTRODUCTION

Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons.

AREAS COVERED

This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development.

EXPERT OPINION

The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.

Effects of anterior thalamic nuclei stimulation on hippocampal activity: Chronic recording in a patient with drug-resistant focal epilepsy

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Devices for RNS and thalamic DBS were implanted in a single person with epilepsy.

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RNS electrocorticography enabled characterization of acute and chronic DBS effects.

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DBS caused acute, phasic, frequency-dependent responses in hippocampus and cortex.

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DBS modulated functional connectivity and suppressed epileptiform activity over time.

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Chronic electrocorticography elucidates progressive effects of thalamic stimulation.

Implanted neurostimulation devices are gaining traction as palliative treatment options for certain forms of drug-resistant epilepsy, but clinical utility of these devices is hindered by incomplete mechanistic understanding of their therapeutic effects. Approved devices for anterior thalamic nuclei deep brain stimulation (ANT DBS) are thought to work at a network level, but limited sensing capability precludes characterization of neurophysiological effects outside the thalamus. Here, we describe a patient with drug-resistant temporal lobe epilepsy who was implanted with a responsive neurostimulation device (RNS System), involving hippocampal and ipsilateral temporal neocortical leads, and subsequently received ANT DBS. Over 1.5 years, RNS System electrocorticography enabled multiscale characterization of neurophysiological effects of thalamic stimulation. In brain regions sampled by the RNS System, ANT DBS produced acute, phasic, frequency-dependent responses, including suppression of hippocampal low frequency local field potentials. ANT DBS modulated functional connectivity between hippocampus and neocortex. Finally, ANT DBS progressively suppressed hippocampal epileptiform activity in relation to the extent of hippocampal theta suppression, which informs stimulation parameter selection for ANT DBS. Taken together, this unique clinical scenario, involving hippocampal recordings of unprecedented chronicity alongside ANT DBS, sheds light on the therapeutic mechanism of thalamic stimulation and highlights capabilities needed in next-generation devices.

Wireless Programmable Recording and Stimulation of Deep Brain Activity in Freely Moving Humans

Uncovering the neural mechanisms underlying human natural ambulatory behavior is a major challenge for neuroscience. Current commercially available implantable devices that allow for recording and stimulation of deep brain activity in humans can provide invaluable intrinsic brain signals but are not inherently designed for research and thus lack flexible control and integration with wearable sensors. We developed a mobile deep brain recording and stimulation (Mo-DBRS) platform that enables wireless and programmable intracranial electroencephalographic recording and electrical stimulation integrated and synchronized with virtual reality/augmented reality (VR/AR) and wearables capable of external measurements (e.g., motion capture, heart rate, skin conductance, respiration, eye tracking, and scalp EEG). When used in freely moving humans with implanted neural devices, this platform is adaptable to ecologically valid environments conducive to elucidating the neural mechanisms underlying naturalistic behaviors and to the development of viable therapies for neurologic and psychiatric disorders.

Electrocorticography reveals spatiotemporal neuronal activation patterns of verbal fluency in patients with epilepsy

Verbal fluency is commonly used to evaluate cognitive dysfunction in a variety of neuropsychiatric diseases, yet the neurobiology underlying performance of this task is incompletely understood. Electrocorticography (ECoG) provides a unique opportunity to investigate temporal activation patterns during cognitive tasks with high spatial and temporal precision. We used ECoG to study high gamma activity (HGA) patterns in patients undergoing presurgical evaluation for intractable epilepsy as they completed an overt, free-recall verbal fluency task. We examined regions demonstrating changes in HGA during specific timeframes relative to speech onset. Early pre-speech high gamma activity was present in left frontal regions during letter fluency and in bifrontal regions during category fluency. During timeframes typically associated with word planning, a distributed network was engaged including left inferior frontal, orbitofrontal and posterior temporal regions. Peri-Rolandic activation was observed during speech onset, and there was post-speech activation in the bilateral posterior superior temporal regions. Based on these observations in the context of prior studies, we propose a model of neocortical activity patterns underlying verbal fluency.

Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.