Faculty Bibliography

Electrical stimulation in the anterior nucleus of the thalamus (ANT) has previously been found to be efficacious for reducing seizure frequency in patients with epilepsy. Bilateral deep brain stimulation (DBS) of the ANT is an open-loop system that can be used in the management of treatment-resistant epilepsy. In contrast, the responsive neurostimulation (RNS) system is a closed-loop device that delivers treatment in response to prespecified electrocorticographic triggers. The efficacy and safety of RNS targeting the ANT is unknown. We describe 3 patients with treatment-resistant multifocal epilepsy who were implanted with an RNS system, which included unilateral stimulation of the ANT. After >33 months of follow-up, there were no adverse effects on mood, memory or behavior. Two patients had ≥50% reduction in disabling seizures and one patient had a 50% reduction compared to pretreatment baseline. Although reduction in seizure frequency has been modest to date, these findings support responsive neurostimulation of the ANT as feasible, safe, and well-tolerated. Further studies are needed to determine optimal stimulation parameters.

OBJECTIVE:Current brain-computer interface (BCI) studies demonstrate the potential to decode neural signals obtained from structured and trial-based tasks to drive actuators with high performance within the context of these tasks. Ideally, to maximize utility, such systems will be applied to a wide range of behavioral settings or contexts. Thus, we explore the potential to augment such systems with the ability to decode abstract behavioral contextual states from neural activity. APPROACH/METHODS:To demonstrate the feasibility of such context decoding, we used electrocorticography (ECoG) and stereo-electroencephalography (sEEG) data recorded from the cortical surface and deeper brain structures, respectively, continuously across multiple days from three subjects. During this time, the subjects were engaged in a range of naturalistic behaviors in a hospital environment. Behavioral contexts were labeled manually from video and audio recordings; four states were considered: engaging in dialogue, rest, using electronics, and watching television. We decode these behaviors using a factor analysis and support vector machine (SVM) approach. MAIN RESULTS/RESULTS:We demonstrate that these general behaviors can be decoded with high accuracies of 73% for a four-class classifier for one subject and 71% and 62% for a three-class classifier for two subjects. SIGNIFICANCE/CONCLUSIONS:To our knowledge, this is the first demonstration of the potential to disambiguate abstract naturalistic behavioral contexts from neural activity recorded throughout the day from implanted electrodes. This work motivates further study of context decoding for BCI applications using continuously recorded naturalistic activity in the clinical setting.

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. When adapted by investigators for research, it facilitates cognitive testing in a controlled ambulatory setting, with measurements collected over months to years. We utilized an associative learning paradigm in 5 patients with traditional iEEG and 3 patients with chronic iEEG, and found increased hippocampal gamma (60-100 Hz) sustained at 1.3-1.5 seconds during encoding in successful versus failed trials in surgical patients, with similar results in our RNS System patients (1.4-1.6 seconds). Our findings replicate other studies demonstrating that sustained hippocampal gamma supports encoding. Importantly, we have validated the RNS System to make sensitive measurements of hippocampal dynamics during cognitive tasks in a chronic ambulatory research setting.

BACKGROUND:Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD/METHODS:We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS:The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS/UNASSIGNED:Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales simultaneously. CONCLUSIONS:This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

For drug-resistant epilepsy, nonpharmacologic treatments should be considered early rather than late. Of the nondrug treatments, only resective surgery can be curative. Neurostimulation is palliative, i.e., not expected to achieve a seizure-free outcome. While resective surgery is the goal, other options are necessary because the majority of patients with drug-resistant epilepsy are not surgical candidates, and others have seizures that fail to improve with surgery or have only partial improvement but not seizure freedom. Neurostimulation modalities include vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS), each with its own advantages, disadvantages, and side effects. In most scenarios, determined by noninvasive evaluation, especially EEG and MRI, several strategies are reasonable. For focal epilepsies, the choices are between resective surgery, with or without intracranial EEG, and all three modalities of neurostimulation. In situations where resective surgery is likely to result in seizure freedom, such as mesiotemporal lobe epilepsy or lesional focal epilepsy, resection (standard, laser, or radiofrequency) is preferred. For difficult cases like extratemporal nonlesional epilepsies, neurostimulation offers a less invasive option than resective surgery. For generalized and multifocal epilepsies, VNS is an option, RNS is not, and DBS has only limited evidence. "This article is part of the Supplement issue Neurostimulation for Epilepsy."

The running-down phenomenon refers to 2 analogous but distinct entities that may be seen after epilepsy surgery. The first is clinical, and denotes a progressive diminution in seizures after epilepsy surgery in which the epileptogenic zone could not be completely removed (Modern Problems of Psychopharmacology 1970;4:306, Brain 1996:989). The second is electrographic, and refers to a progressive deactivation of a secondary seizure focus after removal of the primary epileptogenic zone. This progressive decrease in epileptiform activity may represent a reversal of secondary epileptogenesis, where a primary epileptogenic zone is postulated to activate epileptiform discharges at a second site and may become independent.³ The electrographic running-down phenomenon has been reported in only limited numbers of patients, using serial postoperative routine scalp electroencephalography (EEG) (Arch Neurol 1985;42:318). We present what is, to our knowledge, the most detailed demonstration of the electrographic running-down phenomenon in humans, made possible by chronic electrocorticography (ECoG). Our patient's left temporal seizure focus overlapped with language areas, limiting the resection to a portion of the epileptogenic zone, followed by implantation of a direct brain-responsive neurostimulator (RNS System, NeuroPace Inc.) to treat residual epileptogenic tissue. Despite the limited extent of the resection, the patient remains seizure-free more than 2 years after surgery, with the RNS System recording ECoG without delivering stimulation. We reviewed the chronic recordings with automated spike detection and inspection of electrographic episodes marked by the neurostimulator. These recordings demonstrate progressive diminution in spiking and rhythmic discharges, consistent with an electrographic running-down phenomenon.

Reliable posture labels in hospital environments can augment research studies on neural correlates to natural behaviors and clinical applications that monitor patient activity. However, many existing pose estimation frameworks are not calibrated for these unpredictable settings. In this paper, we propose a semi-automated approach for improving upper-body pose estimation in noisy clinical environments, whereby we adapt and build around an existing joint tracking framework to improve its robustness to environmental uncertainties. The proposed framework uses subject-specific convolutional neural network models trained on a subset of a patient's RGB video recording chosen to maximize the feature variance of each joint. Furthermore, by compensating for scene lighting changes and by refining the predicted joint trajectories through a Kalman filter with fitted noise parameters, the extended system yields more consistent and accurate posture annotations when compared with the two state-of-the-art generalized pose tracking algorithms for three hospital patients recorded in two research clinics.

Our visual environment constantly changes, yet we experience the world as a stable, unified whole. How is this stability achieved? It has been proposed that the brain preserves an implicit perceptual memory in sensory cortices [1] which stabilizes perception towards previously experienced states [2,3]. The role of higher-order areas, especially prefrontal cortex (PFC), in perceptual memory is less explored. Because PFC exhibits long neural time constants, invariance properties, and large receptive fields which may stabilize perception against time-varying inputs, it seems particularly suited to implement perceptual memory [4]. Support for this idea comes from a neuroimaging study reporting that dorsomedial PFC (dmPFC) correlates with perceptual memory [5]. But dmPFC also participates in decision making [6], so its contribution to perceptual memory could arise on a post-perceptual, decisional level [7]. To determine which role, if any, PFC plays in perceptual memory, we obtained direct intracranial recordings in six epilepsy patients while they performed sequential orientation judgements on ambiguous stimuli known to elicit perceptual memory [8]. We found that dmPFC activity in the high gamma frequency band (HGB, 70-150 Hz) correlates with perceptual memory. This effect is anatomically specific to dmPFC and functionally specific for memories of preceding percepts. Further, dmPFC appears to play a causal role, as a patient with a lesion in this area showed impaired perceptual memory. Thus, dmPFC integrates current sensory information with prior percepts, stabilizing visual experience against the perpetual variability of our surroundings.

Employing predictions based on environmental regularities is fundamental for adaptive behaviour. While it is widely accepted that predictions across different stimulus attributes (e.g., time and content) facilitate sensory processing, it is unknown whether predictions across these attributes rely on the same neural mechanism. Here, to elucidate the neural mechanisms of predictions, we combine invasive electrophysiological recordings (human electrocorticography in 4 females and 2 males) with computational modelling while manipulating predictions about content ('what') and time ('when'). We found that 'when' predictions increased evoked activity over motor and prefrontal regions both at early (∼180 ms) and late (430-450 ms) latencies. 'What' predictability, however, increased evoked activity only over prefrontal areas late in time (420-460 ms). Beyond these dissociable influences, we found that 'what' and 'when' predictability interactively modulated the amplitude of early (165 ms) evoked responses in the superior temporal gyrus. We modelled the observed neural responses using biophysically realistic neural mass models, to better understand whether 'what' and 'when' predictions tap into similar or different neurophysiological mechanisms. Our modelling results suggest that 'what' and 'when' predictability rely on complementary neural processes: 'what' predictions increased short-term plasticity in auditory areas, while 'when' predictability increased synaptic gain in motor areas. Thus, content and temporal predictions engage complementary neural mechanisms in different regions, suggesting domain-specific prediction signalling along the cortical hierarchy. Encoding predictions through different mechanisms may endow the brain with the flexibility to efficiently signal different sources of predictions, weight them by their reliability, and allow for their encoding without mutual interference.SIGNIFICANCE STATEMENTPredictions of different stimulus features facilitate sensory processing. However, it is unclear whether predictions of different attributes rely on similar or different neural mechanisms. By combining invasive electrophysiological recordings of cortical activity with experimental manipulations of participants' predictions about content and time of acoustic events, we found that the two types of predictions had dissociable influences on cortical activity, both in terms of the regions involved and the timing of the observed effects. Further, our biophysical modelling analysis suggests that predictability of content and time rely on complementary neural processes: short-term plasticity in auditory areas and synaptic gain in motor areas, respectively. This suggests that predictions of different features are encoded with complementary neural mechanisms in different brain regions.