Figure 3 - available from: Scientific Reports
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Electrode localization for RNS System patient (R1). Location of selected electrodes (Left Hipp 1-2) indicated by yellow arrows (A: Coronal, B: Axial, C: Sagittal; Coronal and Axial Views show only LH2).
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Direct recordings from the human brain have historically involved epilepsy patients undergoing invasive electroencephalography (iEEG) for surgery. However, these measurements are temporally limited and affected by clinical variables. The RNS System (NeuroPace, Inc.) is a chronic, closed-loop electrographic seizure detection and stimulation system....
Citations
... In particular, gamma oscillations (30-80 Hz) are a physiological measure of brain function that is thought to support cognitive processes including working memory and other tasks of cognition [44][45][46]. Indeed, the power of hippocampal CA1 gamma oscillations is reported to predict the memory of spatial memory judgments and associative memory performance [47,48], while encoding impairment with age is associated with dysfunctional gamma oscillatory activity [49]. These findings suggest that gamma oscillation is closely linked to brain activity related to the memory process. ...
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.
... In particular, gamma oscillations (30-80 Hz) are a physiological measure of brain function that is thought to support cognitive processes including working memory and other tasks of cognition [44][45][46]. Indeed, the power of hippocampal CA1 gamma oscillations is reported to predict the memory of spatial memory judgments and associative memory performance [47,48], while encoding impairment with age is associated with dysfunctional gamma oscillatory activity [49]. These findings suggest that gamma oscillation is closely linked to brain activity related to the memory process. ...
... By comparing overnight sleep and sleep deprivation, we could also investigate how protracted wakefulness affects the neural correlates of learning. Specifically, EEG recordings were acquired during paired-associates learning to test the hypothesis that sleep deprivation disrupts theta (4-8 Hz) and gamma (> 40 Hz) synchronisation, which support item binding in episodic memory (Henin et al. 2019;Köster et al. 2018;Osipova et al. 2006;Summerfield and Mangels 2005). Furthermore, in an exploratory analysis, we investigated the effect of sleep deprivation on 12-20 Hz beta desynchronization; an established marker of successful learning (Griffiths et al. 2016;Hanslmayr et al. 2014;Hanslmayr et al. 2009;Hanslmayr et al. 2012;Hanslmayr et al. 2011). ...
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 behavioural 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 behavioural performance was predicted by the overnight consolidation of episodic associations formed 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 sleep-associated consolidation.
... We compared overnight sleep and sleep deprivation so that we could also investigate how protracted sleep loss affects the neurocognitive mechanisms of learning. Specifically, EEG recordings were acquired during paired-associates learning to test the hypothesis that sleep deprivation would disrupt theta (4-8 Hz) and gamma (>40 Hz) synchronisation, which support item binding in episodic memory (Köster et al., 2018;Summerfield & Mangels, 2005;Henin et al., 2019). Furthermore, in exploratory analyses, we investigated the effect of sleep deprivation on 12-20 Hz beta desynchronization; an established marker of successful learning that reflects depth of information processing (Hanslmayr et al., 2009;. ...
Sleep supports memory consolidation as well as next-day learning. The 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, crossover design, we assessed behavioural and electrophysiological indices of episodic encoding after a night of sleep or total sleep deprivation in healthy adult humans (aged 18-25 years), and investigated whether the behavioural measures were predicted by the overnight consolidation of episodic associations formed 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 EEG marker of successful encoding - was observed after sleep, sleep deprivation disrupted beta desynchrony during successful learning. Taken together, our findings suggest that effective learning mechanisms are critically dependent on sleep, but not necessarily sleep-associated consolidation.
... Another FDA-approved neurostimulation device for epilepsy, the RNS Ò System, operates with a closed-loop design [22] and is one of only two commercial devices that stores a limited form of chronic intracranial electroencephalography (cEEG) [23]. RNS cEEG has been used to address challenges in clinical epilepsy-seizure lateralization [24] and localization [25], spell characterization [26], evaluating anti-seizure medications (ASMs) [27], and seizure forecasting [28]-and it has also proven to be a powerful tool for basic neuroscience research on cortical language representation [29], spatial memory [30], and other cognitive functions [31,32]. RNS cEEG has helped characterize neural desynchronization related to vagus nerve stimulation [33] but, to our knowledge, it has not been used to quantify neurophysiological effects of an ANT DBS device implanted in the same individual. ...
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.
... A few neuroscientists have begun to capitalize on the opportunity to use chronically implanted neural devices, such as the RNS System (NeuroPace) (Aghajan et al., 2017;Meisenhelter et al., 2019;Henin et al., 2019;Rao et al., 2017). However, these studies did not provide methods for real-time viewing and control or the ability to deliver intracranial electrical stimulation (iES) and perform precise synchronization of intracranial electroencephalography (iEEG) with externally acquired data during free movement. ...
... The Mo-DBRS platform includes three key improvements compared to previous work (Aghajan et al., 2017;Meisenhelter et al., 2019;Henin et al., 2019;Rao et al., 2017): (1) improved accuracy of synchronization, (2) mobility, and (3) integration with multiple wearables. More important, the Mark-based Three key features are compared, including capability for use during ambulatory behaviors (mobility), integration with wearable technologies (wearables), synchronization method (sync method), relative latency (mean) of iEEG synchronization with the experimental task paradigm (task-iEEG sync relative latency [mean]), and relative latency (SD) of iEEG synchronization with the experimental task paradigm (task-iEEG sync relative latency [SD]). ...
... These Research Tools are listed ll NeuroResource below, some of which come with the commercially available RNS System and others that can be provided by NeuroPace upon request or built by the user using the circuit and software code details provided here. Previous studies (Aghajan et al., 2017;Meisenhelter et al., 2019;Henin et al., 2019;Rao et al., 2017) have used variations of the Programmer, Programmer Tool, Wand, Wand Tool, and Electromagnet. ...
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.
... A few neuroscientists have begun to capitalize on the opportunity to use chronically implanted neural devices, such as the RNS System (NeuroPace) (Aghajan et al., 2017;Meisenhelter et al., 2019;Henin et al., 2019;Rao et al., 2017). However, these studies did not provide methods for real-time viewing and control or the ability to deliver intracranial electrical stimulation (iES) and perform precise synchronization of intracranial electroencephalography (iEEG) with externally acquired data during free movement. ...
... The Mo-DBRS platform includes three key improvements compared to previous work (Aghajan et al., 2017;Meisenhelter et al., 2019;Henin et al., 2019;Rao et al., 2017): (1) improved accuracy of synchronization, (2) mobility, and (3) integration with multiple wearables. More important, the Mark-based Three key features are compared, including capability for use during ambulatory behaviors (mobility), integration with wearable technologies (wearables), synchronization method (sync method), relative latency (mean) of iEEG synchronization with the experimental task paradigm (task-iEEG sync relative latency [mean]), and relative latency (SD) of iEEG synchronization with the experimental task paradigm (task-iEEG sync relative latency [SD]). ...
... These Research Tools are listed ll NeuroResource below, some of which come with the commercially available RNS System and others that can be provided by NeuroPace upon request or built by the user using the circuit and software code details provided here. Previous studies (Aghajan et al., 2017;Meisenhelter et al., 2019;Henin et al., 2019;Rao et al., 2017) have used variations of the Programmer, Programmer Tool, Wand, Wand Tool, and Electromagnet. ...
Current implantable devices that allow for recording and stimulation of brain activity in humans are not inherently designed for research and thus lack programmable control and integration with wearable sensors. We developed a platform that enables wireless and programmable intracranial electroencephalographic recording and deep brain stimulation integrated with wearable technologies. This methodology, when used in freely moving humans with implanted neural devices, can provide an ecologically valid environment conducive to elucidating the neural mechanisms underlying naturalistic behaviors and developing viable therapies for neurologic and psychiatric disorders.
... The technique allows investigators to probe neuronal activity with the same temporal resolution within which related cognitive functions are postulated to occur. Gamma oscillations have been investigated in a free recall memory paradigm, and shown to predict successful memory formation during encoding and to distinguish true from false memories at recall (Henin et al., 2019;Sederberg, Kahana, Howard, Donner, & Madsen, 2003;Sederberg et al., 2007). However, no study to date has used high gamma activity to investigate the spatiotemporal dynamics of the verbal fluency task, which combines a free recall format with long-term memory and word production processes, in real time. ...
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.
... Despite offering better coverage of the brain surface than the intracranial recordings do, they have lower spatial resolution, and cannot accurately measure high-frequency activity 27 or target deep brain structures. Also, those studies have mainly focused on finding active electrodes for task-related classification problems and have not addressed the physiological relevance of the identified electrodes, leading to manual or semiautomatic identification of active electrodes in iEEG 3,4,9,28 . Studies aimed at addressing the physiological relevance of identified electrodes with automatic electrode selection have typically used a combination of different statistical methods based on iEEG power or event-related potentials 6-8 . ...
... The frequency ranges discussed above are denoted as follows: low-theta θ 1 = [2, 5] Hz, high-theta θ h = [6,9] Hz, alpha α = [10,15] Hz, beta β = [16,25] Definitions of the metrics used to identify active electrodes. Induced power. ...
... The majority of the prior work on identification of active electrodes, also called channel selection, was limited to scalp EEG 25 . Active electrode identification has been less documented for intracranial EEG (iEEG) and has typically been done with manual or semiautomatic selection of a subset of channels with meaningful signals, as exemplified in several previous studies 3,4,9,28 . Manual identification of active electrodes poses several challenges. ...
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.
Traditional approaches to recording deep brain activity in humans require participants to remain immobile, limiting the ecological validity and breadth of cognitive neuroscience questions that can be asked. Individuals with neurostimulator devices that are chronically implanted for clinical purposes present a rare opportunity to record intracranial electroencephalography (iEEG) from the human brain while participants are mobile and interacting with their environment in a natural way. Research-related benefits of such chronic neurostimulator devices include resistance to motion artifacts, access to deep brain structures, measurement of neural activity with high temporal resolution, as well as the possibility to perform closed-loop neuromodulation through stimulation that can be associated with specific behavioral or neurophysiological features. Furthermore, recent technical developments have streamlined the integration of numerous wearables with wireless iEEG recordings, including virtual and augmented reality headsets, which substantially broadens the scope of possible cognitive neuroscience experiments that can be implemented. Here, we provide an overview of the methodological and technical aspects of mobile iEEG recordings in human research participants and discuss associated promises and challenges. With this overview, we aim to inspire innovative future applications of mobile iEEG to advance our understanding of rich human behaviors in health and disease.
