Epilepsy: closing the loop for patients with epilepsy

Translational veterinary epilepsy: A win-win situation for human and veterinary neurology

Epilepsy is a challenging multifactorial disorder with a complex genetic background. Our current understanding of the pathophysiology and treatment of epilepsy has substantially increased due to animal model studies, including canine studies, but additional basic and clinical research is required. Drug-resistant epilepsy is an important problem in both dogs and humans, since seizure freedom is not achieved with the available antiseizure medications. The evaluation and exploration of pharmacological and particularly non-pharmacological therapeutic options need to remain a priority in epilepsy research. Combined efforts and sharing knowledge and expertise between human medical and veterinary neurologists are important for improving the treatment outcomes or even curing epilepsy in dogs. Such interactions could offer an exciting approach to translate the knowledge gained from people and rodents to dogs and vice versa. In this article, a panel of experts discusses the similarities and knowledge gaps in human and animal epileptology, with the aim of establishing a common framework and the basis for future translational epilepsy research.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy

OBJECTIVE

Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ).

METHODS

We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates.

RESULTS

We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep.

SIGNIFICANCE

The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

Neuromodulation Strategies to Reduce Inflammation and Improve Lung Complications in COVID-19 Patients

Since the outbreak of the COVID-19 pandemic, races across academia and industry have been initiated to identify and develop disease modifying or preventative therapeutic strategies has been initiated. The primary focus has been on pharmacological treatment of the immune and respiratory system and the development of a vaccine. The hyperinflammatory state (“cytokine storm”) observed in many cases of COVID-19 indicates a prognostically negative disease progression that may lead to respiratory distress, multiple organ failure, shock, and death. Many critically ill patients continue to be at risk for significant, long-lasting morbidity or mortality. The human immune and respiratory systems are heavily regulated by the central nervous system, and intervention in the signaling of these neural pathways may permit targeted therapeutic control of excessive inflammation and pulmonary bronchoconstriction. Several technologies, both invasive and non-invasive, are available and approved for clinical use, but have not been extensively studied in treatment of the cytokine storm in COVID-19 patients. This manuscript provides an overview of the role of the nervous system in inflammation and respiration, the current understanding of neuromodulatory techniques from preclinical and clinical studies and provides a rationale for testing non-invasive neuromodulation to modulate acute systemic inflammation and respiratory dysfunction caused by SARS-CoV-2 and potentially other pathogens. The authors of this manuscript have co-founded the International Consortium on Neuromodulation for COVID-19 to advocate for and support studies of these technologies in the current coronavirus pandemic.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Deep brain stimulation in the medial septum attenuates temporal lobe epilepsy via entrainment of hippocampal theta rhythm

Aims

Temporal lobe epilepsy (TLE), often associated with cognitive impairment, is one of the most common types of medically refractory epilepsy. Deep brain stimulation (DBS) shows considerable promise for the treatment of TLE. However, the optimal stimulation targets and parameters of DBS to control seizures and related cognitive impairment are still not fully illustrated.

Methods

In the present study, we evaluated the therapeutic potential of DBS in the medial septum (MS) on seizures and cognitive function in mouse acute and chronic epilepsy models.

Results

We found that DBS in the MS alleviated the severity of seizure activities in both kainic acid‐induced acute seizure model and hippocampal‐kindled epilepsy model. DBS showed antiseizure effects with a wide window of effective stimulation frequencies. The antiseizure effects of DBS were mediated by the hippocampal theta rhythm, as atropine, which reversed the DBS‐induced augmentation of the hippocampal theta oscillation, abolished the antiseizure effects of DBS. Further, in the kainic acid‐induced chronic TLE model, DBS in the MS not only reduced spontaneous seizures, but also improved behavioral performance in novel object recognition.

Conclusion

DBS in the MS is a promising approach to attenuate TLE probably through entrainment of the hippocampal theta rhythm, which may be therapeutically significant for refractory TLE treatment.

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed.

Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drug-refractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated “ON” and nonstimulated “OFF” periods and between responder (R) and nonresponder (NR) patients.

Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of ∼50% was documented. We analyzed the synchronization in EEG time series during “ON” and “OFF” periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients.

Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients.

Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity.

Spatial distribution of interictal spikes fluctuates over time and localizes seizure onset

The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network.

Novel therapies for epilepsy in the pipeline

Despite the availability of many antiepileptic drugs (AEDs) (old and newly developed) and, as recently suggested, their optimization in the treatment of patients with uncontrolled seizures, more than 30% of patients with epilepsy continue to experience seizures and have drug-resistant epilepsy; the management of these patients represents a real challenge for epileptologists and researchers. Resective surgery with the best rates of seizure control is not an option for all of them; therefore, research and discovery of new methods of treating resistant epilepsy are of extreme importance. In this article, we will discuss some innovative approaches, such as P-glycoprotein (P-gp) inhibitors, gene therapy, stem cell therapy, traditional and novel antiepileptic devices, precision medicine, as well as therapeutic advances in epileptic encephalopathy in children; these treatment modalities open up new horizons for the treatment of patients with drug-resistant epilepsy.

Closed Loop Deep Brain Stimulation for PTSD, Addiction, and Disorders of Affective Facial Interpretation: Review and Discussion of Potential Biomarkers and Stimulation Paradigms

The treatment of psychiatric diseases with Deep Brain Stimulation (DBS) is becoming more of a reality as studies proliferate the indications and targets for therapies. Opinions on the initial failures of DBS trials for some psychiatric diseases point to a certain lack of finesse in using an Open Loop DBS (OLDBS) system in these dynamic, cyclical pathologies. OLDBS delivers monomorphic input into dysfunctional brain circuits with modulation of that input via human interface at discrete time points with no interim modulation or adaptation to the changing circuit dynamics. Closed Loop DBS (CLDBS) promises dynamic, intrinsic circuit modulation based on individual physiologic biomarkers of dysfunction. Discussed here are several psychiatric diseases which may be amenable to CLDBS paradigms as the neurophysiologic dysfunction is stochastic and not static. Post-Traumatic Stress Disorder (PTSD) has several peripheral and central physiologic and neurologic changes preceding stereotyped hyper-activation behavioral responses. Biomarkers for CLDBS potentially include skin conductance changes indicating changes in the sympathetic nervous system, changes in serum and central neurotransmitter concentrations, and limbic circuit activation. Chemical dependency and addiction have been demonstrated to be improved with both ablation and DBS of the Nucleus Accumbens and as a serendipitous side effect of movement disorder treatment. Potential peripheral biomarkers are similar to those proposed for PTSD with possible use of environmental and geolocation based cues, peripheral signs of physiologic arousal, and individual changes in central circuit patterns. Non-substance addiction disorders have also been serendipitously treated in patients with OLDBS for movement disorders. As more is learned about these behavioral addictions, DBS targets and effectors will be identified. Finally, discussed is the use of facial recognition software to modulate activation of inappropriate responses for psychiatric diseases in which misinterpretation of social cues feature prominently. These include Autism Spectrum Disorder, PTSD, and Schizophrenia-all of which have a common feature of dysfunctional interpretation of facial affective clues. Technological advances and improvements in circuit-based, individual-specific, real-time adaptable modulation, forecast functional neurosurgery treatments for heretofore treatment-resistant behavioral diseases.

Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG

Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording. The algorithm was tested on a dataset of more than 918[Formula: see text]h of data coming from 17 patients containing 127 complex partial and generalized partial seizures. The algorithm was able to detect 81.89% of the seizures, with on average 1.97 false alarms per hour. These results show a strong drop in the number of false alarms of more than 50% compared to other heart rate-based patient-independent algorithms from the literature, at the expense of a slightly higher detection delay of 17.8s on average.

Mechanisms underlying different onset patterns of focal seizures

Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types-low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. We show that the two patterns are associated with different mechanisms at the spatial scale of a single ECoG electrode. The LAF onset is initiated by independent patches of localised activity, which slowly invade the surrounding tissue and coalesce over time. In contrast, the HAS onset is a global, systemic transition to a coexisting seizure state triggered by a local event. We find that such a global transition is enabled by an increase in the excitability of the "healthy" surrounding tissue, which by itself does not generate seizures, but can support seizure activity when incited. In our simulations, the difference in surrounding tissue excitability also offers a simple explanation of the clinically reported difference in surgical outcomes. Finally, we demonstrate in the model how changes in tissue excitability could be elucidated, in principle, using active stimulation. Taken together, our modelling results suggest that the excitability of the tissue surrounding the seizure core may play a determining role in the seizure onset pattern, as well as in the surgical outcome.

Innervation of the Human Cavum Conchae and Auditory Canal: Anatomical Basis for Transcutaneous Auricular Nerve Stimulation

The innocuous transcutaneous stimulation of nerves supplying the outer ear has been demonstrated to be as effective as the invasive direct stimulation of the vagus nerve for the treatment of some neurological and nonneurological disturbances. Thus, the precise knowledge of external ear innervation is of maximal interest for the design of transcutaneous auricular nerve stimulation devices. We analyzed eleven outer ears, and the innervation was assessed by Masson's trichrome staining, immunohistochemistry, or immunofluorescence (neurofilaments, S100 protein, and myelin-basic protein). In both the cavum conchae and the auditory canal, nerve profiles were identified between the cartilage and the skin and out of the cartilage. The density of nerves and of myelinated nerve fibers was higher out of the cartilage and in the auditory canal with respect to the cavum conchae. Moreover, the nerves were more numerous in the superior and posterior-inferior than in the anterior-inferior segments of the auditory canal. The present study established a precise nerve map of the human cavum conchae and the cartilaginous segment of the auditory canal demonstrating regional differences in the pattern of innervation of the human outer ear. These results may provide additional neuroanatomical basis for the accurate design of auricular transcutaneous nerve stimulation devices.

Electroresponsive Nanoparticles Improve Antiseizure Effect of Phenytoin in Generalized Tonic-Clonic Seizures

Previously, we developed electroresponsive hydrogel nanoparticles (ERHNPs) modified with angiopep-2 (ANG) to facilitate the delivery of the antiseizure drug phenytoin sodium (PHT). However, the electroresponsive characteristics were not verified directly in epileptic mice and the optimal preparation formula for electroresponsive ability is still unclear. Here, we further synthesized PHT-loaded ANG-ERHNPs (ANG-PHT-HNPs) and PHT-loaded nonelectroresponsive hydrogel nanoparticles (ANG-PHT-HNPs) by changing the content of sodium 4-vinylbenzene sulfonate in the preparation formulae. In vivo microdialysis analysis showed that ANG-PHT-ERHNPs not only have the characteristics of a higher distribution in the central nervous system, but also have electroresponsive ability, which resulted in a strong release of nonprotein-bound PHT during seizures. In both electrical- (maximal electrical shock) and chemical-induced (pentylenetetrazole and pilocarpine) seizure models, ANG-PHT-ERHNPs lowered the effective therapeutic doses of PHT and demonstrated the improved antiseizure effects compared with ANG-PHT-HNPs or PHT solution. These results demonstrate that ANG-ERHNPs are able to transport PHT into the brain efficiently and release them when epileptiform activity occurred, which is due to the content of sodium 4-vinylbenzene sulfonate in formula. This may change the therapeutic paradigm of existing drug treatment for epilepsy into a type of on-demand control for epilepsy in the future.

The adaptive deep brain stimulation challenge

Sub-optimal clinical outcomes of conventional deep brain stimulation (cDBS) in treating Parkinson's Disease (PD) have boosted the development of new solutions to improve DBS therapy. Adaptive DBS (aDBS), consisting of closed-loop, real-time changing of stimulation parameters according to the patient's clinical state, promises to achieve this goal and is attracting increasing interest in overcoming all of the challenges posed by its development and adoption. In the design, implementation, and application of aDBS, the choice of the control variable and of the control algorithm represents the core challenge. The proposed approaches, in fact, differ in the choice of the control variable and control policy, in the system design and its technological limits, in the patient's target symptom, and in the surgical procedure needed. Here, we review the current proposals for aDBS systems, focusing on the choice of the control variable and its advantages and drawbacks, thus providing a general overview of the possible pathways for the clinical translation of aDBS with its benefits, limitations and unsolved issues.

Low-frequency stimulation in anterior nucleus of thalamus alleviates kainate-induced chronic epilepsy and modulates the hippocampal EEG rhythm

High-frequency stimulation (HFS) of the anterior nucleus of thalamus (ANT) is a new and alternative option for the treatment of intractable epilepsy. However, the responder rate is relatively low. The present study was designed to determine the effect of low-frequency stimulation (LFS) in ANT on chronic spontaneous recurrent seizures and related pathological pattern in intra-hippocampal kainate mouse model. We found that LFS (1 Hz, 100 μs, 300 μA), but not HFS (100 Hz, 100 μs, 30 μA), in bilateral ANT significantly decreased the frequency of spontaneous recurrent seizures, either non-convulsive focal seizures or tonic-clonic generalized seizures. The anti-epileptic effect persisted for one week after LFS cessation, which manifested as a long-term inhibition of the frequency of seizures with short (20-60 s) and intermediate duration (60-120 s). Meanwhile, LFS decreased the frequency of high-frequency oscillations (HFOs) and interictal spikes, two indicators of seizure severity, whereas HFS increased the HFO frequency. Furthermore, LFS decreased the power of the delta band and increased the power of the gamma band of hippocampal background EEG. In addition, LFS, but not HFS, improved the performance of chronic epileptic mice in objection-location task, novel objection recognition and freezing test. These results provide the first evidence that LFS in ANT alleviates kainate-induced chronic epilepsy and cognitive impairment, which may be related to the modulation of the hippocampal EEG rhythm. This may be of great therapeutic significance for clinical treatment of epilepsy with deep brain stimulation.