Faculty Bibliography

Dynamic interactions between remote but functionally specialized brain regions enable complex information processing. This intercortical communication is disrupted in the neural networks of patients with focal epilepsy, and epileptic activity can exert widespread effects within the brain. Using large-scale human intracranial electroencephalography recordings, we show that interictal epileptiform discharges (IEDs) are significantly coupled with spindles in discrete, individualized brain regions outside of the epileptic network. We found that a substantial proportion of these localized spindles travel across the cortical surface. Brain regions that participate in this IED-driven oscillatory coupling express spindles that have a broader spatial extent and higher tendency to propagate than spindles occurring in uncoupled regions. These altered spatiotemporal oscillatory properties identify areas that are shaped by epileptic activity independent of IED or seizure detection. Our findings suggest that IED-spindle coupling may be an important mechanism of interictal global network dysfunction that could be targeted to prevent disruption of normal neural activity.

We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings. We used a custom system to continuously record high definition video precisely time-aligned to clinical iEEG data. From these video recordings, movement periods were annotated via semi-automatic tracking based on dense optical flow. We found that neural signal features (8--32 Hz and 76--100 Hz power) previously identified from task-based experiments are also modulated before and during a variety of movement behaviors. These movement behaviors are coarsely labeled by time period and movement side (e.g. `Idle' and `Move', `Right' and `Left'); movements within a label can include a wide variety of uninstructed behaviors. A rigorous nested cross-validation framework was used to classify both movement onset and lateralization with statistical significance for all subjects. We demonstrate an evaluation framework to study neural activity related to natural movements not evoked by a task, annotated over hours of video. This work further establishes the feasibility to study neural correlates of unstructured behavior through continuous recording in the epilepsy monitoring unit. The insights gained from such studies may advance our understanding of how the brain naturally controls movement, which may inform the development of more robust and generalizable brain-computer interfaces.

The principles underlying functional asymmetries in cortex remain debated. For example, it is accepted that speech is processed bilaterally in auditory cortex, but a left hemisphere dominance emerges when the input is interpreted linguistically. The mechanisms, however, are contested, such as what sound features or processing principles underlie laterality. Recent findings across species (humans, canines and bats) provide converging evidence that spectrotemporal sound features drive asymmetrical responses. Typically, accounts invoke models wherein the hemispheres differ in time-frequency resolution or integration window size. We develop a framework that builds on and unifies prevailing models, using spectrotemporal modulation space. Using signal processing techniques motivated by neural responses, we test this approach, employing behavioural and neurophysiological measures. We show how psychophysical judgements align with spectrotemporal modulations and then characterize the neural sensitivities to temporal and spectral modulations. We demonstrate differential contributions from both hemispheres, with a left lateralization for temporal modulations and a weaker right lateralization for spectral modulations. We argue that representations in the modulation domain provide a more mechanistic basis to account for lateralization in auditory cortex.

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.

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.

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.

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."

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.