Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control

The role of electroencephalography in epilepsy research-From seizures to interictal activity and comorbidities

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

Optogenetic stimulation of the dorsal striatum bidirectionally controls seizures

Despite a century of development of antiseizure medications, up to a third of people with epilepsy do not achieve seizure freedom with drug therapy. Deep brain stimulation is of growing use, but just as with pharmacotherapy, is not universally effective. Identifying new targets for deep brain stimulation-and in particular sites that are effective against a range of seizure types-may close this gap. Engagement of the basal ganglia experimental seizures was first observed almost 75 y ago. However, the role of the basal ganglia's input nucleus, the striatum, in seizure control is relatively understudied. To address this gap, we used an optogenetic approach to activate and inactivate neurons in the dorsal striatum of rats submitted to the gamma-butyrolactone (GBL) model of absence epilepsy, amygdala kindling model of temporal lobe epilepsy, and pilocarpine-induced Status Epilepticus (SE). Open-loop (continuous light delivery) optogenetic activation of dorsal striatal neurons robustly suppressed seizures in all models. By contrast, open-loop optogenetic silencing increased absence seizure expression and facilitated SE onset but had no effect on kindled seizures. In the GBL model, we also tested the effects of closed-loop modulation (light delivery in response to seizure detection). Closed-loop activation reduced duration of spike-wave discharges (SWDs), while closed-loop inhibition increased SWD duration. These results demonstrated previously unrecognized antiabsence effects associated with striatal neuromodulation. These findings demonstrate a robust, bidirectional role of the dorsal striatum in the control of multiple seizure types, suggesting that the striatum is a site that can exert broad-spectrum control of seizures.

Systematic Review of Experimental Deep Brain Stimulation in Rodent Models of Epilepsy

OBJECTIVES

Deep brain stimulation (DBS) is an established neuromodulatory technique for treating drug-resistant epilepsy. Despite its widespread use in carefully selected patients, the mechanisms underlying the antiseizure effects of DBS remain unclear. Herein, we provide a detailed overview of the current literature pertaining to experimental DBS in rodent models of epilepsy and identify relevant trends in this field.

MATERIALS AND METHODS

A systematic review was conducted using the PubMed MEDLINE database, following PRISMA guidelines. Data extraction focused on study characteristics, including stimulation protocol, seizure and behavioral outcomes, and reported mechanisms of action.

RESULTS

Of the 1788 resultant articles, 164 were included. The number of published articles has grown exponentially in recent decades. Most studies used chemically or electrically induced models of epilepsy. DBS targeting the anterior nucleus of the thalamus, hippocampal formation, or amygdala was most extensively studied. Effective stimulation parameters were identified, and novel stimulation designs were explored, such as closed-loop and unstructured stimulation approaches. Common mechanisms included synaptic modulation through the depression of excitatory neurotransmission and inhibitory release of GABA. At the network level, antiseizure effects were associated with the desynchronization of neural networks, characterized by decreased low-frequency oscillations.

CONCLUSIONS

Rodent models have significantly advanced the understanding of disease pathophysiology and the development of novel therapies. However, fundamental questions remain regarding DBS mechanisms, optimal targets, and parameters. Further research is necessary to improve DBS therapy and tailor treatment to individual patient circumstances.

The cerebellum in epilepsy

The cerebellum, a subcortical structure, is traditionally linked to sensorimotor integration and coordination, although its role in cognition and affective behavior, as well as epilepsy, is increasingly recognized. Cerebellar dysfunction in patients with epilepsy can result from genetic disorders, antiseizure medications, seizures, and seizure-related trauma. Impaired cerebellar function, regardless of cause, can cause ataxia (imbalance, impaired coordination, unsteady gait), tremor, gaze-evoked nystagmus, impaired slow gaze pursuit and saccade accuracy, as well as speech deficits (slurred, scanning, or staccato). We explore how cerebellar dysfunction can contribute to epilepsy, reviewing data on genetic, infectious, and neuroinflammatory disorders. Evidence of cerebellar dysfunction in epilepsy comes from animal studies as well as human neuropathology and structural magnetic resonance imaging (MRI), functional and diffusion tensor MRI, positron emission and single photon emission computerized tomography, and depth electrode electro-encephalography studies. Cerebellar lesions can infrequently cause epilepsy, with focal motor, autonomic, and focal to bilateral tonic-clonic seizures. Antiseizure medication-resistant epilepsy typically presents in infancy or before age 1 year with hemifacial clonic or tonic seizures ipsilateral to the cerebellar mass. Lesions are typically asymmetric benign or low-grade tumors in the floor of the fourth ventricle involving the cerebellar peduncles and extending to the cerebellar hemisphere. Electrical stimulation of the cerebellum has yielded conflicting results on efficacy, although methodological issues confound interpretation. Epilepsy-related comorbidities including cognitive and affective disorders, falls, and sudden unexpected death in epilepsy may also be impacted by cerebellar dysfunction. We discuss how cerebellar dysfunction may drive seizures and how genetic epilepsies, seizures and seizure therapies may drive cerebellar dysfunction, and how our understanding of epilepsy-related comorbidities through basic neuroscience, animals models and patient studies can advance our understanding and improve patient outcomes.

Thalamic stimulation induced changes in network connectivity and excitability in epilepsy

Background

The clinical effects of deep brain stimulation for neurological conditions manifest across multiple timescales, spanning seconds to months, and involve direct electrical modulation, neuroplasticity, and network reorganization. In epilepsy, the delayed effects of deep brain stimulation on seizures limit optimization. Single pulse electrical stimulation and the resulting pulse evoked potentials offer a measure network effective connectivity and excitability. This study leverages single pulse and high frequency thalamic stimulation during stereotactic electroencephalography to assess seizure network engagement, modulate network activity, and track changes in excitability and epileptiform abnormalities.

Methods

Ten individuals with drug resistant epilepsy undergoing clinical stereotactic electroencephalography were enrolled in this retrospective cohort study. Each underwent a trial of high frequency (145 Hz) thalamic stimulation. Pulse evoked potentials were acquired before and after high frequency stimulation. Baseline evoked potential root-mean-square amplitude assessed seizure network engagement, and modulation of amplitude (post high frequency stimulation versus baseline; Cohen's d effect size) assessed change in network excitability. Interictal epileptiform discharge rates were measured by an automated classifier at baseline and during high frequency stimulation. Statistical significance was determined using paired-sample t-tests (p<0.05 significance level). This study was approved by the Mayo Clinic Institutional Review Board, with informed consent obtained from all participants.

Results

Thalamic stimulation delivered for >1.5 hours significantly reduced pulse evoked potential amplitudes in connected areas compared to baseline, with the degree of modulation correlated with baseline connectivity strength. Shorter stimulation durations did not induce reliable changes. High frequency stimulation immediately suppressed interictal epileptiform discharge rates in seizure networks with strong baseline thalamocortical connectivity. Pulse evoked potentials delineated the anatomical distribution of network engagement, revealing distinct patterns across thalamic subfields.

Conclusion

Pulse evoked potentials and thalamic stimulation during stereotactic electroencephalography provide novel network biomarkers to evaluate target engagement and modulation of large-scale networks across acute and subacute timescales. This approach demonstrates potential for efficient, data-driven neuromodulation optimization, and a new paradigm for personalized deep brain stimulation in epilepsy.

The mechanisms of electrical neuromodulation

The central and peripheral nervous systems are specialized to conduct electrical currents that underlie behaviour. When this multidimensional electrical system is disrupted by degeneration, damage, or disuse, externally applied electrical currents may act to modulate neural structures and provide therapeutic benefit. The administration of electrical stimulation can exert precise and multi-faceted effects at cellular, circuit and systems levels to restore or enhance the functionality of the central nervous system by providing an access route to target specific cells, fibres of passage, neurotransmitter systems, and/or afferent/efferent communication to enable positive changes in behaviour. Here we examine the neural mechanisms that are thought to underlie the therapeutic effects seen with current neuromodulation technologies. To gain further insights into the mechanisms associated with electrical stimulation, we summarize recent findings from genetic dissection studies conducted in animal models. KEY POINTS: Electricity is everywhere around us and is essential for how our nerves communicate within our bodies. When nerves are damaged or not working properly, using exogenous electricity can help improve their function at distinct levels - inside individual cells, within neural circuits, and across entire systems. This method can be tailored to target specific types of cells, nerve fibres, neurotransmitters and communication pathways, offering significant therapeutic potential. This overview explains how exogenous electricity affects nerve function and its potential benefits, based on research in animal studies. Understanding these effects is important because electrical neuromodulation plays a key role in medical treatments for neurological conditions.

Limited cerebellar gradient extension in temporal lobe epilepsy with dystonic posturing

OBJECTIVE

Dystonic posturing (DP) is a common semiology in temporal lobe epilepsy (TLE). We aimed to explore cerebellar gradient alterations in functional connectivity in TLE patients with and without DP.

METHODS

Resting-state functional MRI data were obtained in 60 TLE patients and 32 matched healthy controls. Patients were further divided into two groups: TLE with DP (TLE + DP, 31 patients) and TLE without DP (TLP-DP, 29 patients). We explored functional gradient alterations in the cerebellum based on cerebellar-cerebral functional connectivity and combined with independent component analysis to evaluate cerebellar-cerebral functional integration and reveal the contribution of the motor components to the gradient.

RESULTS

There were no obvious differences in clinical features and postoperative seizure outcomes between TLE + DP and TLE-DP patients. Patients and controls all showed a clear unimodal-to-transmodal gradient transition in the cerebellum, while TLE patients demonstrated an extended principal gradient in functional connectivity compared to healthy controls, which was more limited in TLE + DP patients. Gradient alterations were more widespread in TLE-DP patients, involving bilateral cerebellum, while gradient alterations in TLE + DP patients were limited in the cerebellum ipsilateral to the seizure focus. In addition, more cerebellar motor components contributed to the gradient alterations in TLE + DP patients, mainly in ipsilateral cerebellum.

SIGNIFICANCE

Extended cerebellar principal gradients in functional connectivity revealed excessive functional segregation between unimodal and transmodal systems in TLE. The functional connectivity gradients were more limited in TLE + DP patients. Functional connectivity in TLE patients with dystonic posturing involved more contribution of cerebellar motor function to ipsilateral cerebellar gradient.

PLAIN LANGUAGE SUMMARY

Dystonic posturing contralateral to epileptic focus is a common symptom in temporal lobe epilepsy, and the cerebellum may be involved in its generation. In this study, we found cerebellar gradients alterations in functional connectivity in temporal lobe epilepsy patients with and without contralateral dystonic posturing. In particular, we found that TLE patients with dystonic posturing may have more limited cerebellar gradient in functional connectivity, involving more contribution of cerebellar motor function to ipsilateral cerebellar gradient. Our study suggests a close relationship between ipsilateral cerebellum and contralateral dystonic posturing.

Increased cerebellar vermis volume following repetitive transcranial magnetic stimulation in drug-resistant epilepsy: a voxel-based morphometry study

Introduction

Voxel-based morphometry (VBM) was applied to explore structural changes induced by repetitive transcranial magnetic stimulation (rTMS) and the relationship with clinical outcomes. Moreover, the relationship between each segmented regional gray matter (GM) volume was investigated to identify circuits involved in the rTMS treatment process in patients with drug-resistant epilepsy (DRE).

Methods

Nineteen patients with DRE were finally included in the analysis. A session of rTMS was applied for 5 consecutive days. Participants received either 1,000 or 3,000 pulses, at a frequency of 0.5 Hz and the intensity was set at 90% of the individual's resting motor threshold. VBM analysis was performed to explore regional GM volume changes 2 months after rTMS application. The regional volume change was correlated with seizure reduction rate. Relationships between changes in GM volume in each anatomically parcellated region were analyzed using a fully-automated segmentation pipeline.

Results

Compared to the baseline, seizure frequency was reduced, and quality of life was improved after rTMS treatment. Regional volume was increased in the cerebellar vermis 2 months after rTMS application. The increased cerebellar vermis volume correlated with the reduced seizure frequency. Regional volume changes in the cerebellar vermis were correlated with changes in the subcortical and cortical GM regions including the thalamus, caudate, and frontal cortex.

Discussion

These results indicate that rTMS treatment effectively reduced seizure frequency in patients with DRE. Increased volume in the cerebellar vermis and activations of the cerebello-thalamo-cortical circuit may be a crucial mechanism underlying the effectiveness of rTMS application in patients with DRE.

Targeted sonogenetic modulation of GABAergic interneurons in the hippocampal CA1 region in status epilepticus

Rationale: Sonogenetics is an advanced ultrasound-based neurostimulation approach for targeting neurons in specific brain regions. However, the role of sonogenetics in treating status epilepticus (SE) remains unclear. Here, we aimed to investigate the effects of ultrasound neurostimulation and MscL-G22S (a mechanosensitive ion channel that mediates Ca2+ influx)-mediated sonogenetics (MG-SOG) in a mouse model of kainic acid (KA)-induced SE. Methods: For MG-SOG, a Cre-dependent AAV expressing MscL-G22S was injected into parvalbumin (PV)-cre and somatostatin (SST)-cre mice to induce the expression of MscL-G22S-EGFP in PV interneurons (PV-INs) and SST interneurons (SST-INs), respectively; mice were stimulated with continuous pulses of ultrasound stimulation during the latency of generalized seizures (GSs), the latency to SE, in SE model mice. We performed calcium fiber photometry, patch-clamp recording, local field potential recording, and SE monitoring to investigate the role of MG-SOG in treating SE. Results: First, we observed obvious neuronal activation in the hippocampal CA1 region in SE model mice. Both excitatory neurons (ENs) and GABAergic interneurons (GABA-INs) in the CA1 region were activated in SE model mice; however, the inhibitory effect of GABA-INs on ENs seemed to be insufficient to reduce EN excitability despite the increased activation of GABA-INs in SE model mice. Thus, we speculated that MG-SOG-induced activation of GABA-INs, mainly SST-INs and PV-INs, in the CA1 region may protect against SE. We found that MG-SOG-mediated PV-IN activation in the CA1 region ameliorated SE and changed SE-related electrophysiological abnormalities in the CA1 region; however, MG-SOG-induced SST-IN activation in the CA1 region did not ameliorate SE. Conclusions: MG-SOG-mediated activation of PV-INs had a positive effect on relieving SE. Our work may promote the development of sonogenetic neurostimulation techniques for treating SE.

Optogenetic stimulation of dorsal striatum bidirectionally controls seizures

Engagement of the striatum (caudate/putamen) and other basal ganglia nuclei during seizures was first observed over 75 years ago. Basal ganglia output nuclei, and the substantia nigra pars reticulata, in particular, have well-established anti-seizure effects across a large array of experimental models. However, striatal control of seizures is understudied. To address this gap, we used optogenetic approaches to activate and inactivate neurons in the dorsal striatum of Sprague-Dawley rats submitted to the gamma-butyrolactone (GBL) model of absence epilepsy, amygdala kindling model of temporal lobe epilepsy, and pilocarpine-induced Status Epilepticus (SE). All tests were performed on a within-subject basis. Animals were tested in two different light frequencies (5 Hz and 100 Hz). Open-loop (continuous light delivery) optogenetic activation of the dorsal striatal neurons robustly suppressed seizures in all models. On the other hand, optogenetic silencing of the dorsal striatal neurons increased absence seizure expression and facilitated SE onset but had no effect on kindled limbic seizures. In the GBL model, we also verified if the closed- loop strategy (light delivery in response to seizure detection) would be enough to induce antiseizure effects. On-demand light delivery in ChR2-expressing animals reduced SWD duration, while the same approach in ArchT-expressing animals increased SWD duration. These results demonstrated previously unrecognized anti-absence effects associated with striatal continuous and on-demand neuromodulation. Together, these findings document a robust, bidirectional role of the dorsal striatum in the control of seizure generation and propagation in a variety of seizure models, including focal seizure onset and generalized seizures.

Levetiracetam Prevents Neurophysiological Changes and Preserves Cognitive Function in the Human Immunodeficiency Virus (HIV)-1 Transactivator of Transcription Transgenic Mouse Model of HIV-Associated Neurocognitive Disorder

Human immunodeficiency virus (HIV)-associated neurocognitive disorder (HAND) affects nearly half of the 39 million people living with HIV. HAND symptoms range from subclinical cognitive impairment to dementia; the mechanisms that underlie HAND remain unclear and there is no treatment. The HIV protein transactivator of transcription (TAT) is thought to contribute to HAND because it persists in the central nervous system and elicits neurotoxicity in animal models. Network hyperexcitability is associated with accelerated cognitive decline in neurodegenerative disorders. Here we show that the antiepileptic drug levetiracetam (LEV) attenuated aberrant excitatory synaptic transmission, protected synaptic plasticity, reduced seizure susceptibility, and preserved cognition in inducible TAT (iTAT) transgenic male mice. iTAT mice had an increased frequency of spontaneous excitatory postsynaptic currents in hippocampal slice recordings and impaired long-term potentiation, a form of synaptic plasticity that underlies learning and memory. Two-week administration of LEV by osmotic minipump prevented both impairments. Kainic acid administered to iTAT mice induced a higher maximum behavioral seizure score, longer seizure duration, and shorter latency to first seizure, consistent with a lower seizure threshold. LEV treatment prevented these in vivo signs of hyperexcitability. Lastly, in the Barnes maze, iTAT mice required more time to reach the goal, committed more errors, and received lower cognitive scores relative to iTAT mice treated with LEV. Thus, TAT expression drives functional deficits, suggesting a causative role in HAND. As LEV not only prevented aberrant synaptic activity in iTAT mice but also prevented cognitive dysfunction, it may provide a promising pharmacological approach to the treatment of HAND. SIGNIFICANCE STATEMENT: Approximately half of people living with human immunodeficiency virus (HIV) also suffer from HIV-associated neurocognitive disorder (HAND), for which there is no treatment. The HIV protein transactivator of transcription (TAT) causes toxicity that is thought to contribute to HAND. Here, the antiepileptic drug levetiracetam (LEV) prevented synaptic and cognitive impairments in a TAT-expressing mouse. LEV is widely used to treat seizures and is well-tolerated in humans, including those with HIV. This study supports further investigation of LEV-mediated neuroprotection in HAND.

Forgotten Tides: A Novel Strategy for Bayesian Optimization of Neurostimulation

From Dawn Till Dusk: Time-Adaptive Bayesian Optimization for Neurostimulation Fleming JE, Pont Sanchis I, Lemmens O, Denison-Smith A, West TO, Denison T, Cagnan H. PLoS Comput Biol. 2023;19(12):e1011674. doi:10.1371/journal.pcbi.1011674 . PMID: 38091368 Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson’s disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual “forgetting” and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

Objective. Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study.Approach.Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate and the mean arterial pressure for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control.Main results.Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems.Significance.We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Online Bayesian optimization of vagus nerve stimulation

Objective.In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects.Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating the effectiveness of our approach.Main results.The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target HRs and optimizing neural B-fiber activations despite high intersubject variability.Significance.An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

Closing the Loop for Precise Seizure Control

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Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory

The human brain, as a complex system, has long captivated multidisciplinary researchers aiming to decode its intricate structure and function. This intricate network has driven scientific pursuits to advance our understanding of cognition, behavior, and neurological disorders by delving into the complex mechanisms underlying brain function and dysfunction. Modelling brain dynamics using machine learning techniques deepens our comprehension of brain dynamics from a computational perspective. These computational models allow researchers to simulate and analyze neural interactions, facilitating the identification of dysfunctions in connectivity or activity patterns. Additionally, the trained dynamical system, serving as a surrogate model, optimizes neurostimulation strategies under the guidelines of control theory. In this chapter, we discuss the recent studies on modelling and controlling brain dynamics at the intersection of machine learning and control theory, providing a framework to understand and improve cognitive function, and treat neurological and psychiatric disorders.

Intravital photoacoustic brain stimulation with high-precision

Significance

Neural regulation at high precision vitally contributes to propelling fundamental understanding in the field of neuroscience and providing innovative clinical treatment options. Recently, photoacoustic brain stimulation has emerged as a cutting-edge method for precise neuromodulation and shows great potential for clinical application.

Aim

The goal of this perspective is to outline the advancements in photoacoustic brain stimulation in recent years. And, we also provide an outlook delineating several prospective paths through which this burgeoning approach may be substantively refined for augmented capability and wider implementations.

Approach

First, the mechanisms of photoacoustic generation as well as the potential mechanisms of photoacoustic brain stimulation are provided and discussed. Then, the state-of-the-art achievements corresponding to this technology are reviewed. Finally, future directions for photoacoustic technology in neuromodulation are provided.

Results

Intensive research endeavors have prompted substantial advancements in photoacoustic brain stimulation, illuminating the unique advantages of this modality for noninvasive and high-precision neuromodulation via a nongenetic way. It is envisaged that further technology optimization and randomized prospective clinical trials will enable a wide acceptance of photoacoustic brain stimulation in clinical practice.

Conclusions

The innovative practice of photoacoustic technology serves as a multifaceted neuromodulation approach, possessing noninvasive, high-accuracy, and nongenetic characteristics. It has a great potential that could considerably enhance not only the fundamental underpinnings of neuroscience research but also its practical implementations in a clinical setting.

The bidirectional relationship between the cerebellum and seizure networks: a double-edged sword

Epilepsy is highly prevalent and notoriously pharmacoresistant. New therapeutic interventions are urgently needed, both for preventing the seizures themselves as well as negative outcomes and comorbidities associated with chronic epilepsy. While the cerebellum is not traditionally associated with epilepsy or seizures, research over the past decade has outlined the cerebellum as a brain region that is uniquely suited for both therapeutic needs. This review discusses our current understanding of the cerebellum as a key node within seizure networks, capable of both attenuating seizures in several animal models, and conversely, prone to altered structure and function in chronic epilepsy. Critical next steps are to advance therapeutic modulation of the cerebellum more towards translation, and to provide a more comprehensive characterization of how the cerebellum is impacted by chronic epilepsy, in order to subvert negative outcomes.

Optogenetic stimulation of the superior colliculus suppresses genetic absence seizures

While anti-seizure medications are effective for many patients, nearly one-third of individuals have seizures that are refractory to pharmacotherapy. Prior studies using evoked preclinical seizure models have shown that pharmacological activation or excitatory optogenetic stimulation of the deep and intermediate layers of the superior colliculus (DLSC) display multi-potent anti-seizure effects. Here we monitored and modulated DLSC activity to suppress spontaneous seizures in the WAG/Rij genetic model of absence epilepsy. Female and male WAG/Rij adult rats were employed as study subjects. For electrophysiology studies, we recorded single unit activity from microwire arrays placed within the DLSC. For optogenetic experiments, animals were injected with virus coding for channelrhodopsin-2 or a control vector, and we compared the efficacy of continuous neuromodulation to that of closed-loop neuromodulation paradigms. For each, we compared three stimulation frequencies on a within-subject basis (5, 20, 100 Hz). For closed-loop stimulation, we detected seizures in real time based on the EEG power within the characteristic frequency band of spike-and-wave discharges (SWDs). We quantified the number and duration of each SWD during each 2 h-observation period. Following completion of the experiment, virus expression and fibre-optic placement was confirmed. We found that single-unit activity within the DLSC decreased seconds prior to SWD onset and increased during and after seizures. Nearly 40% of neurons displayed suppression of firing in response to the start of SWDs. Continuous optogenetic stimulation of the DLSC (at each of the three frequencies) resulted in a significant reduction of SWDs in males and was without effect in females. In contrast, closed-loop neuromodulation was effective in both females and males at all three frequencies. These data demonstrate that activity within the DLSC is suppressed prior to SWD onset, increases at SWD onset, and that excitatory optogenetic stimulation of the DLSC exerts anti-seizure effects against absence seizures. The striking difference between open- and closed-loop neuromodulation approaches underscores the importance of the stimulation paradigm in determining therapeutic effects.

The cerebellum contributes to generalized seizures by altering activity in the ventral posteromedial nucleus

Thalamo-cortical networks are central to seizures, yet it is unclear how these circuits initiate seizures. We test whether a facial region of the thalamus, the ventral posteromedial nucleus (VPM), is a source of generalized, convulsive motor seizures and if convergent VPM input drives the behavior. To address this question, we devise an in vivo optogenetic mouse model to elicit convulsive motor seizures by driving these inputs and perform single-unit recordings during awake, convulsive seizures to define the local activity of thalamic neurons before, during, and after seizure onset. We find dynamic activity with biphasic properties, raising the possibility that heterogenous activity promotes seizures. Virus tracing identifies cerebellar and cerebral cortical afferents as robust contributors to the seizures. Of these inputs, only microinfusion of lidocaine into the cerebellar nuclei blocks seizure initiation. Our data reveal the VPM as a source of generalized convulsive seizures, with cerebellar input providing critical signals.

Hidden behavioral fingerprints in epilepsy

Epilepsy is a major disorder affecting millions of people. Although modern electrophysiological and imaging approaches provide high-resolution access to the multi-scale brain circuit malfunctions in epilepsy, our understanding of how behavior changes with epilepsy has remained rudimentary. As a result, screening for new therapies for children and adults with devastating epilepsies still relies on the inherently subjective, semi-quantitative assessment of a handful of pre-selected behavioral signs of epilepsy in animal models. Here, we use machine learning-assisted 3D video analysis to reveal hidden behavioral phenotypes in mice with acquired and genetic epilepsies and track their alterations during post-insult epileptogenesis and in response to anti-epileptic drugs. These results show the persistent reconfiguration of behavioral fingerprints in epilepsy and indicate that they can be employed for rapid, automated anti-epileptic drug testing at scale.

Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys

Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve "prior" expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.

The little brain and the seahorse: Cerebellar-hippocampal interactions

There is a growing appreciation for the cerebellum beyond its role in motor function and accumulating evidence that the cerebellum and hippocampus interact across a range of brain states and behaviors. Acute and chronic manipulations, simultaneous recordings, and imaging studies together indicate coordinated coactivation and a bidirectional functional connectivity relevant for various physiological functions, including spatiotemporal processing. This bidirectional functional connectivity is likely supported by multiple circuit paths. It is also important in temporal lobe epilepsy: the cerebellum is impacted by seizures and epilepsy, and modulation of cerebellar circuitry can be an effective strategy to inhibit hippocampal seizures. This review highlights some of the recent key hippobellum literature.

LINCs Are Vulnerable to Epileptic Insult and Fail to Provide Seizure Control via On-Demand Activation

Temporal lobe epilepsy (TLE) is notoriously pharmacoresistant, and identifying novel therapeutic targets for controlling seizures is crucial. Long-range inhibitory neuronal nitric oxide synthase-expressing cells (LINCs), a population of hippocampal neurons, were recently identified as a unique source of widespread inhibition in CA1, able to elicit both GABAA-mediated and GABAB-mediated postsynaptic inhibition. We therefore hypothesized that LINCs could be an effective target for seizure control. LINCs were optogenetically activated for on-demand seizure intervention in the intrahippocampal kainate (KA) mouse model of chronic TLE. Unexpectedly, LINC activation at 1 month post-KA did not substantially reduce seizure duration in either male or female mice. We tested two different sets of stimulation parameters, both previously found to be effective with on-demand optogenetic approaches, but neither was successful. Quantification of LINCs following intervention revealed a substantial reduction of LINC numbers compared with saline-injected controls. We also observed a decreased number of LINCs when the site of initial insult (i.e., KA injection) was moved to the amygdala [basolateral amygdala (BLA)-KA], and correspondingly, no effect of light delivery on BLA-KA seizures. This indicates that LINCs may be a vulnerable population in TLE, regardless of the site of initial insult. To determine whether long-term circuitry changes could influence outcomes, we continued testing once a month for up to 6 months post-KA. However, at no time point did LINC activation provide meaningful seizure suppression. Altogether, our results suggest that LINCs are not a promising target for seizure inhibition in TLE.

Optogenetic activation of the superior colliculus attenuates spontaneous seizures in the pilocarpine model of temporal lobe epilepsy

OBJECTIVE

Decades of studies have indicated that activation of the deep and intermediate layers of the superior colliculus can suppress seizures in a wide range of experimental models of epilepsy. However, prior studies have not examined efficacy against spontaneous limbic seizures. The present study aimed to address this gap through chronic optogenetic activation of the superior colliculus in the pilocarpine model of temporal lobe epilepsy.

METHODS

Sprague Dawley rats underwent pilocarpine-induced status epilepticus and were maintained until the onset of spontaneous seizures. Virus coding for channelrhodopsin-2 was injected into the deep and intermediate layers of the superior colliculus, and animals were implanted with head-mounted light-emitting diodes at the same site. Rats were stimulated with either 5- or 100-Hz light delivery. Seizure number, seizure duration, 24-h seizure burden, and behavioral seizure severity were monitored.

RESULTS

Both 5- and 100-Hz optogenetic stimulation of the deep and intermediate layers of the superior colliculus reduced daily seizure number and total seizure burden in all animals in the active vector group. Stimulation did not affect either seizure duration or behavioral seizure severity. Stimulation was without effect in opsin-negative control animals.

SIGNIFICANCE

Activation of the deep and intermediate layers of the superior colliculus reduces both the number of seizures and total daily seizure burden in the pilocarpine model of temporal lobe epilepsy. These novel data demonstrating an effect against chronic experimental seizures complement a long history of studies documenting the antiseizure efficacy of superior colliculus activation in a range of acute seizure models.

Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal

In this paper, a reduced globus pallidus internal (GPI)-corticothalamic (GCT) model is developed, and a tri-phase delay stimulation (TPDS) with sequentially applying three pulses on the GPI representing the inputs from the striatal D ₁ neurons, subthalamic nucleus (STN), and globus pallidus external (GPE), respectively, is proposed. The GPI is evidenced to control absence seizures characterized by 2 Hz-4 Hz spike and wave discharge (SWD). Hence, based on the basal ganglia-thalamocortical (BGCT) model, we firstly explore the triple effects of D _l-GPI, GPE-GPI, and STN-GPI pathways on seizure patterns. Then, using the GCT model, we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked. The results show that the striatum D ₁, GPE, and STN can indeed jointly and significantly affect seizure patterns. In particular, the TPDS can effectively reproduce the seizure pattern if the D ₁-GPI, GPE-GPI, and STN-GPI pathways are cut off. In addition, the seizure abatement can be obtained by well tuning the TPDS stimulation parameters. This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia, which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.

Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor

Objective.Deep brain stimulation (DBS) programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician.Approach.Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient's response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented 'safe Bayesian optimization' to automatically discover tolerable exploration boundaries.Main results.We tested the system in 15 patients (nine with Parkinson's disease and six with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing15.1±0.7settings when maximum safe exploration boundaries were predefined, and17.7±4.9when the algorithm itself determined safe exploration boundaries.Significance.We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.