A BIOCOMPATIBLE GLASS-ENCAPSULATED TRIAXIAL FORCE SENSOR FOR IMPLANTABLE TACTILE SENSING APPLICATIONS.

This paper reports a microfabricated triaxial capacitive force sensor. The sensor is fully encapsulated with inert and biocompatible glass (fused silica) material. The sensor comprises two glass plates, on which four capacitors are located. The sensor is intended for subdermal implantation in fingertips and palms and providing tactile sensing capabilities for patients with paralyzed hands. Additional electronic components, such as passives and IC chips, can also be integrated with the sensor in a hermetic glass package to achieve an implantable tactile sensing system. Through attachment to a human palm, the sensor has been shown to respond appropriately to typical hand actions, such as squeezing or picking up a bottle.

Magnetic Resonance Imaging-Guided Frameless Stereotactic Injections of the Bilateral Cerebellar Dentate Nuclei in Nonhuman Primates: Technical Note.

BACKGROUND AND OBJECTIVES: Nonhuman primates (NHPs) are important preclinical models for evaluating therapeutics because of their anatomophysiological similarities to humans, and can be especially useful for testing new delivery targets. With the growing promise of cell and gene therapies for the treatment of neurological diseases, it is important to ensure the accurate and safe delivery of these agents to target structures in the brain. However, a standard guideline or method has not been developed for stereotactic targeting in NHPs. In this article, we describe the safe use of a magnetic resonance imaging-guided frameless stereotactic system to target bilateral cerebellar dentate nuclei for accurate, real-time delivery of viral vector in NHPs. METHODS: Seventeen rhesus macaques (Macaca mulatta) underwent stereotactic surgery under real-time MRI guidance using the ClearPoint® system. Bilateral cerebellar dentate nuclei were targeted through a single parietal entry point with a transtentorial approach. Fifty microliters of contrast-impregnated infusate was delivered to each dentate nucleus, and adjustments were made as necessary according to real-time MRI monitoring of delivery. Perioperative clinical outcomes and postoperative volumes of distribution were recorded. RESULTS: All macaques underwent bilateral surgery successfully. Superficial pin site infection occurred in 4/17 (23.5%) subjects, which resolved with antibiotics. Two episodes of transient neurological deficit (anisocoria and unilateral weakness) were recorded, which did not require additional postoperative treatment and resolved over time. Volume of distribution of infusate achieved satisfactory coverage of target dentate nuclei, and only 1 incidence (2.9%) of cerebrospinal fluid penetration was recorded. Mean volume of distribution was 161.22 ± 39.61 mm3 (left, 173.65 ± 48.29; right, 148.80 ± 23.98). CONCLUSION: MRI-guided frameless stereotactic injection of bilateral cerebellar dentate nuclei in NHPs is safe and feasible. The use of this technique enables real-time modification of the surgical plan to achieve adequate target coverage and can be readily translated to clinical use.

An implantable, wireless, battery-free system for tactile pressure sensing.

The sense of touch is critical to dexterous use of the hands and thus an essential component of efforts to restore hand function after amputation or paralysis. Prosthetic systems have addressed this goal with wearable tactile sensors. However, such wearable sensors are suboptimal for neuroprosthetic systems designed to reanimate a patient's own paralyzed hand. Here, we developed an implantable tactile sensing system intended for subdermal placement. The system is composed of a microfabricated capacitive pressure sensor, a custom integrated circuit supporting wireless powering and data transmission, and a laser-fused hermetic silica package. The miniature device was validated through simulations, benchtop assessment, and testing in a primate hand. The sensor implanted in the fingertip accurately measured applied skin forces with a resolution of 4.3 mN. The output from this novel sensor could be encoded in the brain with microstimulation to provide tactile feedback. More broadly, the materials, system design, and fabrication approach establish new foundational capabilities for various applications of implantable sensing systems.

Remote effects of temporal lobe epilepsy surgery: Long-term morphological changes after surgical resection.

OBJECTIVE: Epilepsy surgery is an effective treatment for drug-resistant patients. However, how different surgical approaches affect long-term brain structure remains poorly characterized. Here, we present a semiautomated method for quantifying structural changes after epilepsy surgery and compare the remote structural effects of two approaches, anterior temporal lobectomy (ATL), and selective amygdalohippocampectomy (SAH). METHODS: We studied 36 temporal lobe epilepsy patients who underwent resective surgery (ATL = 22, SAH = 14). All patients received same-scanner MR imaging preoperatively and postoperatively (mean 2 years). To analyze postoperative structural changes, we segmented the resection zone and modified the Advanced Normalization Tools (ANTs) longitudinal cortical pipeline to account for resections. We compared global and regional annualized cortical thinning between surgical treatments. RESULTS: Across procedures, there was significant cortical thinning in the ipsilateral insula, fusiform, pericalcarine, and several temporal lobe regions outside the resection zone as well as the contralateral hippocampus. Additionally, increased postoperative cortical thickness was seen in the supramarginal gyrus. Patients treated with ATL exhibited greater annualized cortical thinning compared with SAH cases (ATL: -0.08 ± 0.11 mm per year, SAH: -0.01 ± 0.02 mm per year, t = 2.99, P = 0.006). There were focal postoperative differences between the two treatment groups in the ipsilateral insula (P = 0.039, corrected). Annualized cortical thinning rates correlated with preoperative cortical thickness (r = 0.60, P < 0.001) and had weaker associations with age at surgery (r = -0.33, P = 0.051) and disease duration (r = -0.42, P = 0.058). SIGNIFICANCE: Our evidence suggests that selective procedures are associated with less cortical thinning and that earlier surgical intervention may reduce long-term impacts on brain structure.

An implantable wireless tactile sensing system.

The sense of touch is critical to dexterous use of the hands and thus an essential component to efforts to restore hand function after amputation or paralysis. Prosthetic systems have focused on wearable tactile sensors. But wearable sensors are suboptimal for neuroprosthetic systems designed to reanimate a patient's own paralyzed hand. Here, we developed an implantable tactile sensing system intended for subdermal placement. The system is composed of a microfabricated capacitive force sensor, a custom integrated circuit supporting wireless powering and data transmission, and a laser-fused hermetic silica package. The miniature device was validated through simulations, benchtop testing, and ex vivo testing in a primate hand. The sensor implanted in the fingertip accurately measured skin forces with a resolution of 4.3 mN. The output from this novel sensor could be encoded in the brain with microstimulation to provide tactile feedback. More broadly, the materials, system design, and fabrication approach establish new foundational capabilities for various applications of implantable sensing systems.

Normative intracranial EEG maps epileptogenic tissues in focal epilepsy.

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning.

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual's estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

Policies Restricting Overlapping Surgeries Negatively Impact Access to Care, Clinical Efficiency, and Hospital Revenue: A Forecasting Model for Surgical Scheduling.

OBJECTIVE: To model the financial impact of policies governing the scheduling of overlapping surgeries, and to identify optimal solutions that maximize operating efficiency that satisfy the fiduciary duty to patients. BACKGROUND: Hospitals depend on procedural revenue to maintain financial health as the recent pandemic has revealed. Proposed policies governing the scheduling of overlapping surgeries may dramatically impact hospital revenue. To date, the potential financial impact has not been modeled. METHODS: A linear forecasting model based on a logic matrix decision tree enabled an analysis of surgeon productivity annualized over a fiscal year. The model applies procedural and operational variables to policy constraints limiting surgical scheduling. Model outputs included case and financial metrics modeled over 1000-surgeon-year simulations. case metrics included annual case volume, case mix, operating room (OR) utilization, surgeon utilization, idle time, and staff overtime hours. Financial outputs included annual revenue, expenses, and contribution margin. RESULTS: The model was validated against surgical data. case and financial metrics decreased as a function of increasingly restrictive scheduling scenarios, with the greatest contribution margin loses ($1,650,000 per surgeon-year) realized with the introduction of policies mandating that a second patient could not enter the OR until the critical portion of the first surgery was completed. We identify an optimal scheduling scenario that maximizes surgeon efficiency, minimizes OR idle time and revenue loses, and satisfies ethical obligations to patients. CONCLUSIONS: Hospitals may expect significant financial loses with the introduction of policies restricting OR scheduling. We identify an optimal solution that maximizes efficiency while satisfying ethical duty to patients. This forecast is immediately relevant to any hospital system that depends upon procedural revenue.

MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation.

Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti3C2 MXene (a two-dimensional transition metal carbide nanomaterial) and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in applications ranging from mapping large-scale neuromuscular networks in humans to cortical neural recording and microstimulation in swine and rodent models. Last, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models.

Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.

The what and when of olfactory working memory in humans.

Encoding and retaining novel sequences of sensory stimuli in working memory is crucial for adaptive behavior. A fundamental challenge for the central nervous system is to maintain each sequence item in an active and discriminable state, while also preserving their temporal context. Nested neural oscillations have been postulated to disambiguate the "what" and "when" of sequences, but the mechanisms by which these multiple streams of information are coordinated in the human brain remain unclear. Drawing from foundational animal studies, we recorded local field potentials from the human piriform cortex and hippocampus during a working memory task in which subjects experienced sequences of three distinct odors. Our data revealed a unique organization of odor memories across multiple timescales of the theta rhythm. During encoding, odors elicited greater gamma at distinct theta phases in both regions, time stamping their positions in the sequence, whereby the robustness of this effect was predictive of temporal order memory. During maintenance, stimulus-driven patterns of theta-coupled gamma were spontaneously reinstated in piriform cortex, recapitulating the order of the initial sequence. Replay events were time compressed across contiguous theta cycles, coinciding with periods of enhanced piriform-hippocampal theta-phase synchrony, and their prevalence forecasted subsequent recall accuracy on a trial-by-trial basis. Our data provide a novel link between endogenous replay orchestrated by the theta rhythm and short-term retention of sequential memories in the human brain.

Design and Validation of a Multi-Point Injection Technology for MR-Guided Convection Enhanced Delivery in the Brain.

Convection enhanced delivery (CED) allows direct intracranial administration of neuro-therapeutics. Success of CED relies on specific targeting and broad volume distributions (VD). However, to prevent off-target delivery and tissue damage, CED is typically conducted with small cannulas and at low flow rates, which critically limit the maximum achievable VD. Furthermore, in applications such as gene therapy requiring injections of large fluid volumes into broad subcortical regions, low flow rates translate into long infusion times and multiple surgical trajectories. The cannula design is a major limiting factor in achieving broad VD, while minimizing infusion time and backflow. Here we present and validate a novel multi-point cannula specifically designed to optimize distribution and delivery time in MR-guided intracranial CED of gene-based therapeutics. First, we evaluated the compatibility of our cannula with MRI and common viral vectors for gene therapy. Then, we conducted CED tests in agarose brain phantoms and benchmarked the results against single-needle delivery. 3T MRI in brain phantoms revealed minimal susceptibility-induced artifacts, comparable to the device dimensions. Benchtop CED of adeno-associated virus demonstrated no viral loss or inactivation. CED in agarose brain phantoms at 3, 6, and 9 µL/min showed >3x increase in volume distribution and 60% time reduction compared to single-needle delivery. This study confirms the validity of a multi-point delivery approach for improving infusate distribution at clinically-compatible timescales and supports the feasibility of our novel cannula design for advancing safety and efficacy of MR-guided CED to the central nervous system.

Theta Synchrony Is Increased near Neural Populations That Are Active When Initiating Instructed Movement.

Theta oscillations (3-8 Hz) in the human brain have been linked to perception, cognitive control, and spatial memory, but their relation to the motor system is less clear. We tested the hypothesis that theta oscillations coordinate distributed behaviorally relevant neural representations during movement using intracranial electroencephalography (iEEG) recordings from nine patients (n = 490 electrodes) as they performed a simple instructed movement task. Using high frequency activity (HFA; 70-200 Hz) as a marker of local spiking activity, we identified electrodes that were positioned near neural populations that showed increased activity during instruction and movement. We found that theta synchrony was widespread throughout the brain but was increased near regions that showed movement-related increases in neural activity. These results support the view that theta oscillations represent a general property of brain activity that may also play a specific role in coordinating widespread neural activity when initiating voluntary movement.

Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes.

Implantable neuroelectronic interfaces have enabled breakthrough advances in the clinical diagnosis and treatment of neurological disorders, as well as in fundamental studies of brain function, behavior, and disease. Intracranial electroencephalography (EEG) mapping with stereo-EEG (sEEG) depth electrodes is routinely adopted for precise epilepsy diagnostics and surgical treatment, while deep brain stimulation has become the standard of care for managing movement disorders. Intracortical microelectrode arrays for high-fidelity recordings of neural spiking activity have led to impressive demonstrations of the power of brain-machine interfaces for motor and sensory functional recovery. Yet, despite the rapid pace of technology development, the issue of establishing a safe, long-term, stable, and functional interface between neuroelectronic devices and the host brain tissue still remains largely unresolved. A body of work spanning at least the last 15 years suggests that safe, chronic integration between invasive electrodes and the brain requires a close match between the mechanical properties of man-made components and the neural tissue. In other words, the next generation of invasive electrodes should be soft and compliant, without sacrificing biological and chemical stability. Soft neuroelectronic interfaces, however, pose a new and significant surgical challenge: bending and buckling during implantation that can preclude accurate and safe device placement. In this topical review, we describe the next generation of soft electrodes and the surgical implantation methods for safe and precise insertion into brain structures. We provide an overview of the most recent innovations in the field of insertion strategies for flexible neural electrodes such as dissolvable or biodegradable carriers, microactuators, biologically-inspired support structures, and electromagnetic drives. In our analysis, we also highlight approaches developed in different fields, such as robotic surgery, which could be potentially adapted and translated to the insertion of flexible neural probes.

A modular high-density µECoG system on macaque vlPFC for auditory cognitive decoding.

OBJECTIVE: A fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC). Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC's contribution to auditory perception has not been fully elucidated. Therefore, we developed a modular, chronic, high-resolution, multi-electrode array system with long-term viability in order to identify the information that could be decoded from µECoG vlPFC signals. APPROACH: We molded three separate µECoG arrays into one and implanted this system in a non-human primate. A custom 3D-printed titanium chamber was mounted on the left hemisphere. The molded 294-contact µECoG array was implanted subdurally over the vlPFC. µECoG activity was recorded while the monkey participated in a 'hearing-in-noise' task in which they reported hearing a 'target' vocalization from a background 'chorus' of vocalizations. We titrated task difficulty by varying the sound level of the target vocalization, relative to the chorus (target-to-chorus ratio, TCr). MAIN RESULTS: We decoded the TCr and the monkey's behavioral choices from the µECoG signal. We analyzed decoding accuracy as a function of number of electrodes, spatial resolution, and time from implantation. Over a one-year period, we found significant decoding with individual electrodes that increased significantly as we decoded simultaneously more electrodes. Further, we found that the decoding for behavioral choice was better than the decoding of TCr. Finally, because the decoding accuracy of individual electrodes varied on a day-by-day basis, electrode arrays with high channel counts ensure robust decoding in the long term. SIGNIFICANCE: Our results demonstrate the utility of high-resolution and high-channel-count, chronic µECoG recording. We developed a surface electrode array that can be scaled to cover larger cortical areas without increasing the chamber footprint.

Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

OBJECTIVE: Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. APPROACH: Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. MAIN RESULTS: We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention. SIGNIFICANCE: The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording.

Implantable microelectrode technologies have been widely used to elucidate neural dynamics at the microscale to gain a deeper understanding of the neural underpinnings of brain disease and injury. As electrodes are miniaturized to the scale of individual cells, a corresponding rise in the interface impedance limits the quality of recorded signals. Additionally, conventional electrode materials are stiff, resulting in a significant mechanical mismatch between the electrode and the surrounding brain tissue, which elicits an inflammatory response that eventually leads to a degradation of the device performance. To address these challenges, we have developed a process to fabricate flexible microelectrodes based on Ti3C2 MXene, a recently discovered nanomaterial that possesses remarkably high volumetric capacitance, electrical conductivity, surface functionality, and processability in aqueous dispersions. Flexible arrays of Ti3C2 MXene microelectrodes have remarkably low impedance due to the high conductivity and high specific surface area of the Ti3C2 MXene films, and they have proven to be exquisitely sensitive for recording neuronal activity. In this protocol, we describe a novel method for micropatterning Ti3C2 MXene into microelectrode arrays on flexible polymeric substrates and outline their use for in vivo micro-electrocorticography recording. This method can easily be extended to create MXene electrode arrays of arbitrary size or geometry for a range of other applications in bioelectronics and it can also be adapted for use with other conductive inks besides Ti3C2 MXene. This protocol enables simple and scalable fabrication of microelectrodes from solution-based conductive inks, and specifically allows harnessing the unique properties of hydrophilic Ti3C2 MXene to overcome many of the barriers that have long hindered the widespread adoption of carbon-based nanomaterials for high-fidelity neural microelectrodes.

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.

Model-based design for seizure control by stimulation.

OBJECTIVE: Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. APPROACH: Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. MAIN RESULTS: Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. SIGNIFICANCE: Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.