Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice

A novel computational method for rodent electrographic recording and analysis using off-the-shelf intracerebral depth electrodes

Electrographic recording of brain activity through either surface electrodes (electroencephalography, EEG) or implanted electrodes (electrocorticography, ECOG) are valuable research tools in neuroscience across many disciplines, including epilepsy, sleep science and more. Research techniques to perform recordings in rodents are wide-ranging and often require custom parts that may not be readily available. Moreover, the information required to connect individual components is often limited and can therefore be challenging to implement. The quantity of data obtained can also be large and therefore difficult to analyze manually, and existing software detection tools are often task specific and require extensive coding experience to use. In this methods paper, we provide step-by-step instructions using off-the-shelf parts for electrographic recording in mice using intracerebral depth electrodes. We also provide a novel software-based detection tool that requires limited prior coding knowledge to use and with detection parameters that can be easily customized. The method is summarized as follows:•The electrode unit is assembled and implanted;•Recordings are obtained and analyzed using the novel software tool;•This method was validated using recordings taken during status epilepticus and chronic epilepsy in the intrahippocampal kainate mouse model of temporal lobe epilepsy.

Enhancing glymphatic fluid transport by pan-adrenergic inhibition suppresses epileptogenesis in male mice

Epileptogenesis is the process whereby the previously normally functioning brain begins to generate spontaneous, unprovoked seizures. Status epilepticus (SE), which entails a massive release of neuronal glutamate and other neuroactive substances, is one of the best-known triggers of epileptogenesis. We here asked whether pharmacologically promoting glymphatic clearance during or after SE is beneficial and able to attenuate the subsequent epileptogenesis. We induced SE in adult male mice by intrahippocampal kainic acid (KA) infusion. Acute administration of a cocktail of adrenergic receptor antagonists (propranolol, prazosin, and atipamezole: PPA), enhanced glymphatic flow and effectively reduced the severity of spontaneous seizures in the chronic phase. The PPA treatment also reduced reactive gliosis and inhibited the loss of polarized expression of AQP4 water channels in the vascular endfeet of astrocytes. Administration of PPA after cessation of SE (30 hours post KA) also effectively suppressed epileptogenesis and improved outcome. Conversely, mice with constitutively low glymphatic transport due to genetic deletion of the aquaporin 4 (AQP4) water channel showed exacerbation of KA-induced epileptogenesis. We conclude that the pharmacological modulation of glymphatic fluid transport may represent a potential strategy to dampen epileptogenesis and the occurrence of spontaneous seizures following KA-induced SE.

Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons

The nucleus of Darkschewitsch (ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons (NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot shock. Optogenetic and chemogenetic manipulations revealed that NDGABA neurons mediate freezing behavior. Moreover, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray (lPAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part (GiV). Together, these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the lPAG to GiV, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.

Novel ML-Based Algorithm for Detecting Seizures from Single-Channel EEG

There is a need for seizure classification based on EEG signals that can be implemented with a portable device for in-home continuous minoring of epilepsy. In this study, we developed a novel machine learning algorithm for seizure detection suitable for wearable systems. Extreme gradient boosting (XGBoost) was implemented to classify seizures from single-channel EEG obtained from an open-source CHB-MIT database. The results of classifying 1-s EEG segments are shown to be sufficient to obtain the information needed for seizure detection and achieve a high seizure sensitivity of up to 89% with low computational cost. This algorithm can be impeded in single-channel EEG systems that use in- or around-the-ear electrodes for continuous seizure monitoring at home.

A simple, automated method of seizure detection in mouse models of temporal lobe epilepsy

The lack of preventive and disease modifying therapies for temporal lobe epilepsy (TLE) is a major unmet medical need. Search for such therapies utilize mouse models and require detection of seizures in electroencephalography (EEG) recordings. The labor-intensive nature of reviewing EEGs spanning many weeks underscores the need for a method of automated detection. Here we report a simple automated method of detecting seizures in long term EEG recordings from electrodes implanted in the hippocampus in animal models of TLE. We utilize a 2-pronged approach that relies on the increase in power within the gamma band range (20-50hz) during the seizure followed by suppression of activity following the seizure (post-ictal suppression [PIS]). We demonstrate the utility of this method for detecting seizures in hippocampal and amygdala EEG recordings from multiple models of TLE.

Transcranial static magnetic stimulation reduces seizures in a mouse model of Dravet syndrome

Dravet syndrome is a rare form of severe genetic epilepsy characterized by recurrent and long-lasting seizures. It appears around the first year of life, with a quick evolution toward an increase in the frequency of the seizures, accompanied by a delay in motor and cognitive development, and does not respond well to antiepileptic medication. Most patients carry a mutation in the gene SCN1A encoding the α subunit of the voltage-gated sodium channel Nav1.1, resulting in hyperexcitability of neural circuits and seizure onset. In this work, we applied transcranial static magnetic stimulation (tSMS), a non-invasive, safe, easy-to-use and affordable neuromodulatory tool that reduces neural excitability in a mouse model of Dravet syndrome. We demonstrate that tSMS dramatically reduced the number of crises. Furthermore, crises recorded in the presence of the tSMS were shorter and less intense than in the sham condition. Since tSMS has demonstrated its efficacy at reducing cortical excitability in humans without showing unwanted side effects, in an attempt to anticipate a possible use of tSMS for Dravet Syndrome patients, we performed a numerical simulation in which the magnetic field generated by the magnet was modeled to estimate the magnetic field intensity reached in the cerebral cortex, which could help to design stimulation strategies in these patients. Our results provide a proof of concept for nonpharmacological treatment of Dravet syndrome, which opens the door to the design of new protocols for treatment.

A machine learning approach to seizure detection in a rat model of post-traumatic epilepsy

Epilepsy is a common neurologic condition frequently investigated using rodent models, with seizures identified by electroencephalography (EEG). Given technological advances, large datasets of EEG are widespread and amenable to machine learning approaches for identification of seizures. While such approaches have been explored for human EEGs, machine learning approaches to identifying seizures in rodent EEG are limited. We utilized a predesigned deep convolutional neural network (DCNN), GoogLeNet, to classify images for seizure identification. Training images were generated through multiplexing spectral content (scalograms), kurtosis, and entropy for two-second EEG segments. Over 2200 h of EEG data were scored for the presence of seizures, with 95.6% of seizures identified by the DCNN and a false positive rate of 34.2% (1.52/h), as compared to visual scoring. Multiplexed images were superior to scalograms alone (scalogram-kurtosis-entropy 0.956 ± 0.010, scalogram 0.890 ± 0.028, t(7) = 3.54, p < 0.01) and a DCNN trained specifically for the individual animal was superior to using DCNNs across animals (intra-animal 0.960 ± 0.0094, inter-animal 0.811 ± 0.015, t(30) = 5.54, p < 0.01). For this dataset the DCNN approach is superior to a previously described algorithm utilizing longer local line lengths, calculated from wavelet-decomposition of EEG, to identify seizures. We demonstrate the novel use of a predesigned DCNN constructed to classify images, utilizing multiplexed images of EEG spectral content, kurtosis, and entropy, to rapidly and objectively identifies seizures in a large dataset of rat EEG with high sensitivity.

Lestaurtinib (CEP-701) reduces the duration of limbic status epilepticus in periadolescent rats

BACKGROUND

The timely abortion of status epilepticus (SE) is essential to avoid brain damage and long-term neurodevelopmental sequalae. However, available anti-seizure treatments fail to abort SE in 30% of children. Given the role of the tropomyosin-related kinase B (TrkB) receptor in hyperexcitability, we investigated if TrkB blockade with lestaurtinib (CEP-701) enhances the response of SE to a standard treatment protocol and reduces SE-related brain injury.

METHODS

SE was induced with intra-amygdalar kainic acid in postnatal day 45 rats under continuous electroencephalogram (EEG). Fifteen min post-SE onset, rats received intraperitoneal (i.p.) CEP-701 (KCEP group) or its vehicle (KV group). Controls received CEP-701 or its vehicle following intra-amygdalar saline. All groups received two i.p. doses of diazepam, followed by i.p. levetiracetam at 15 min intervals post-SE onset. Hippocampal TrkB dimer to monomer ratios were assessed by immunoblot 24 hr post-SE, along with neuronal densities and glial fibrillary acid protein (GFAP) levels.

RESULTS

SE duration was 50% shorter in the KCEP group compared to KV (p < 0.05). Compared to controls, SE induced a 1.5-fold increase in TrkB dimerization in KV rats (p < 0.05), but not in KCEP rats which were comparable to controls (p > 0.05). The KCEP group had lower GFAP levels than KV (p < 0.05), and both were higher than controls (p < 0.05). KCEP and KV rats had comparable hippocampal neuronal densities (p > 0.05), and both were lower than controls (p < 0.05).

CONCLUSIONS

Given its established human safety, CEP-701 is a promising adjuvant drug for the timely abortion of SE and the attenuation of SE-related brain injury.

Fast Detection and Quantification of Interictal Spikes and Seizures in a Rodent Model of Epilepsy Using an Automated Algorithm

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain's abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain's response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate asses sment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks

Background

Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance and reversion to uncontrolled drug-resistant epilepsy and are often associated with significant adverse effects. This has led to a trial-and-error system in which physicians spend months to years attempting to identify the optimal therapeutic approach.

Objective

To investigate the potential clinical utility from the context of optimal therapeutic prediction of characterizing cellular electrophysiology. It is well-established that genomic data alone can sometimes be predictive of effective therapeutic approach. Thus, to assess the predictive power of electrophysiological data, machine learning strategies are implemented to predict a subject’s genetically defined class in an in silico model using brief electrophysiological recordings obtained from simulated neuronal networks.

Methods

A dynamic network of isogenic neurons is modeled in silico for 1-s for 228 dynamically modeled patients falling into one of three categories: healthy, general sodium channel gain of function, or inhibitory sodium channel loss of function. Data from previous studies investigating the electrophysiological and cellular properties of neurons in vitro are used to define the parameters governing said models. Ninety-two electrophysiological features defining the nature and consistency of network connectivity, activity, waveform shape, and complexity are extracted for each patient network and t-tests are used for feature selection for the following machine learning algorithms: Neural Network, Support Vector Machine, Gaussian Naïve Bayes Classifier, Decision Tree, and Gradient Boosting Decision Tree. Finally, their performance in accurately predicting which genetic category the subjects fall under is assessed.

Results

Several machine learning algorithms excel in using electrophysiological data from isogenic neurons to accurately predict genetic class with a Gaussian Naïve Bayes Classifier predicting healthy, gain of function, and overall, with the best accuracy, area under the curve, and F1. The Gradient Boosting Decision Tree performs the best for loss of function models indicated by the same metrics.

Conclusions

It is possible for machine learning algorithms to use electrophysiological data to predict clinically valuable metrics such as optimal therapeutic approach, especially when combining several models.

An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation

BACKGROUND AND OBJECTIVE

Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both annotating and training new experts for this same task. In scenarios where epilepsy is considered, the need for automatic tools is more prominent, as both seizures and interictal events can occur on hours- or days-long recordings. Although other solutions have already been proposed, most of them are not integrated on clinical and basic science environments due to their complexity and required specialization. Here we present a pipeline that arises from coordinated efforts between life-science researchers, clinicians and data scientists to develop an interactive and iterative workflow to train machine-learning tools for the automatic detection of electroencephalographic events in a variety of scenarios.

METHODS

The approach consists on a series of subsequent steps covering data loading and configuration, event annotation, model training/re-training and event detection. With slight modifications, the combination of these blocks can cope with a variety of scenarios. To illustrate the flexibility and robustness of the approach, three datasets from clinical (patients of Dravet Syndrome) and basic research environments (mice model of the same disease) were evaluated. From them, and in response to researchers' daily needs, four real world examples of interictal event detection and seizure classification tasks were selected and processed.

RESULTS

Results show that the current approach was of great aid for event annotation and model development. It was capable of creating custom machine-learning solutions for each scenario with slight adjustments on the analysis protocol, easily accessible to users without programming skills. Final annotator similarity metrics reached values above 80% on all cases of use, reaching 92.3% on interictal event detection on human recordings.

CONCLUSIONS

The presented framework is easily adaptable to multiple real world scenarios and the interactive and ease-to-use approach makes it manageable to clinical and basic researches without programming skills. Nevertheless, it is conceived so data scientists can optimize it for specific scenarios, improving the knowledge transfer between these fields.

Inflammatory monocytes and microglia play independent roles in inflammatory ictogenesis

BACKGROUND

The pathogenic contribution of neuroinflammation to ictogenesis and epilepsy may provide a therapeutic target for reduction of seizure burden in patients that are currently underserved by traditional anti-seizure medications. The Theiler's murine encephalomyelitis virus (TMEV) model has provided important insights into the role of inflammation in ictogenesis, but questions remain regarding the relative contribution of microglia and inflammatory monocytes in this model.

METHODS

Female C57BL/6 mice were inoculated by intracranial injection of 2 × 105, 5 × 104, 1.25 × 104, or 3.125 × 103 plaque-forming units (PFU) of the Daniel's strain of TMEV at 4-6 weeks of age. Infiltration of inflammatory monocytes, microglial activation, and cytokine production were measured at 24 h post-infection (hpi). Viral load, hippocampal injury, cognitive performance, and seizure burden were assessed at several timepoints.

RESULTS

The intensity of inflammatory infiltration and the extent of hippocampal injury induced during TMEV encephalitis scaled with the amount of infectious virus in the initial inoculum. Cognitive performance was preserved in mice inoculated with 1.25 × 104 PFU TMEV relative to 2 × 105 PFU TMEV, but peak viral load at 72 hpi was equivalent between the inocula. CCL2 production in the brain was attenuated by 90% and TNFα and IL6 production was absent in mice inoculated with 1.25 × 104 PFU TMEV. Acute infiltration of inflammatory monocytes was attenuated by more than 80% in mice inoculated with 1.25 × 104 PFU TMEV relative to 2 × 105 PFU TMEV but microglial activation was equivalent between groups. Seizure burden was attenuated and the threshold to kainic acid-induced seizures was higher in mice inoculated with 1.25 × 104 PFU TMEV but low-level behavioral seizures persisted and the EEG exhibited reduced but detectable abnormalities.

CONCLUSIONS

The size of the inflammatory monocyte response induced by TMEV scales with the amount of infectious virus in the initial inoculum, despite the development of equivalent peak infectious viral load. In contrast, the microglial response does not scale with the inoculum, as microglial hyper-ramification and increased Iba-1 expression were evident in mice inoculated with either 1.25 × 104 or 2 × 105 PFU TMEV. Inoculation conditions that drive inflammatory monocyte infiltration resulted in robust behavioral seizures and EEG abnormalities, but the low inoculum condition, associated with only microglial activation, drove a more subtle seizure and EEG phenotype.

Hippocampal injury and learning deficits following non-convulsive status epilepticus in periadolescent rats

The effects of non-convulsive status epilepticus (NCSE) on the developing brain remain largely elusive. Here we investigated potential hippocampal injury and learning deficits following one or two episodes of NCSE in periadolescent rats. Non-convulsive status epilepticus was induced with subconvulsive doses of intrahippocampal kainic acid (KA) under continuous EEG monitoring in postnatal day 43 (P43) rats. The RKA group (repeated KA) received intrahippocampal KA at P43 and P44, the SKA group (single KA injection) received KA at P43 and an intrahippocampal saline injection at P44. Controls were sham-treated with saline. The modified two-way active avoidance (MAAV) test was conducted between P45 and P52 to assess learning of context-cued and tone-signaled electrical foot-shock avoidance. Histological analyses were performed at P52 to assess hippocampal neuronal densities, as well as potential reactive astrocytosis and synaptic dysfunction with GFAP (glial fibrillary acidic protein) and synaptophysin (Syp) staining, respectively. Kainic acid injections resulted in electroclinical seizures characterized by behavioral arrest, oromotor automatisms and salivation, without tonic-clonic activity. Compared to controls, both the SKA and RKA groups had lower rates of tone-signaled shock avoidance (p < 0.05). In contextual testing, SKA rats were comparable to controls (p > 0.05), but the RKA group had learning deficits (p < 0.05). Hippocampal neuronal densities were comparable in all groups. Compared to controls, both the SKA and RKA groups had higher hippocampal GFAP levels (p < 0.05). The RKA group also had lower hippocampal Syp levels compared to the SKA and control groups (p < 0.05), which were comparable (p > 0.05). We show that hippocampal NCSE in periadolescent rats results in a seizure burden-dependent hippocampal injury accompanied by cognitive deficits. Our data suggest that the diagnosis and treatment of NCSE should be prompt.

Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy

Objective.Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals.Approach.In this study, a machine learning-based seizure detection approach, 'Epi-AI', which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach.Main results.Epi-AI demonstrated a superior sensitivity, 91.4%-98.8%, and specificity, 93.1%-98.8%, in Models I-III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I-III ranged from 66.9%-91.3% sensitivity and 60.8%-97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I-III at 73.2%-80.1% and 75.8%-98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity.Significance.Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.

Long-term brain network reorganization predicts responsive neurostimulation outcomes for focal epilepsy

Responsive neurostimulation (RNS) devices, able to detect imminent seizures and to rapidly deliver electrical stimulation to the brain, are effective in reducing seizures in some patients with focal epilepsy. However, therapeutic response to RNS is often slow, is highly variable, and defies prognostication based on clinical factors. A prevailing view holds that RNS efficacy is primarily mediated by acute seizure termination; yet, stimulations greatly outnumber seizures and occur mostly in the interictal state, suggesting chronic modulation of brain networks that generate seizures. Here, using years-long intracranial neural recordings collected during RNS therapy, we found that patients with the greatest therapeutic benefit undergo progressive, frequency-dependent reorganization of interictal functional connectivity. The extent of this reorganization scales directly with seizure reduction and emerges within the first year of RNS treatment, enabling potential early prediction of therapeutic response. Our findings reveal a mechanism for RNS that involves network plasticity and may inform development of next-generation devices for epilepsy.

Searching for a paradigm shift in the research on the epilepsies and associated neuropsychiatric comorbidities. From ancient historical knowledge to the challenge of contemporary systems complexity and emergent functions

In this review, we will discuss in four scenarios our challenges to offer possible solutions for the puzzle associated with the epilepsies and neuropsychiatric comorbidities. We need to recognize that (1) since quite old times, human wisdom was linked to the plural (distinct global places/cultures) perception of the Universe we are in, with deep respect for earth and nature. Plural ancestral knowledge was added with the scientific methods; however, their joint efforts are the ideal scenario; (2) human behavior is not different than animal behavior, in essence the product of Darwinian natural selection; knowledge of animal and human behavior are complementary; (3) the expression of human behavior follows the same rules that complex systems with emergent properties, therefore, we can measure events in human, clinical, neurobiological situations with complexity systems' tools; (4) we can use the semiology of epilepsies and comorbidities, their neural substrates, and potential treatments (including experimental/computational modeling, neurosurgical interventions), as a source and collection of integrated big data to predict with them (e.g.: machine/deep learning) diagnosis/prognosis, individualized solutions (precision medicine), basic underlying mechanisms and molecular targets. Once the group of symptoms/signals (with a myriad of changing definitions and interpretations over time) and their specific sequences are determined, in epileptology research and clinical settings, the use of modern and contemporary techniques such as neuroanatomical maps, surface electroencephalogram and stereoelectroencephalography (SEEG) and imaging (MRI, BOLD, DTI, SPECT/PET), neuropsychological testing, among others, are auxiliary in the determination of the best electroclinical hypothesis, and help design a specific treatment, usually as the first attempt, with available pharmacological resources. On top of ancient knowledge, currently known and potentially new antiepileptic drugs, alternative treatments and mechanisms are usually produced as a consequence of the hard, multidisciplinary, and integrated studies of clinicians, surgeons, and basic scientists, all over the world. The existence of pharmacoresistant patients, calls for search of other solutions, being along the decades the surgeries the most common interventions, such as resective procedures (i.e., selective or standard lobectomy, lesionectomy), callosotomy, hemispherectomy and hemispherotomy, added by vagus nerve stimulation (VNS), deep brain stimulation (DBS), neuromodulation, and more recently focal minimal or noninvasive ablation. What is critical when we consider the pharmacoresistance aspect with the potential solution through surgery, is still the pursuit of localization-dependent regions (e.g.: epileptogenic zone (EZ)), in order to decide, no matter how sophisticated are the brain mapping tools (EEG and MRI), the size and location of the tissue to be removed. Mimicking the semiology and studying potential neural mechanisms and molecular targets - by means of experimental and computational modeling - are fundamental steps of the whole process. Concluding, with the conjunction of ancient knowledge, coupled to critical and creative contemporary, scientific (not dogmatic) clinical/surgical, and experimental/computational contributions, a better world and of improved quality of life can be offered to the people with epilepsy and neuropsychiatric comorbidities, who are still waiting (as well as the scientists) for a paradigm shift in epileptology, both in the Basic Science, Computational, Clinical, and Neurosurgical Arenas. This article is part of the Special Issue "NEWroscience 2018".

Potentiating Hemorrhage in a Periadolescent Rat Model of Closed-Head Traumatic Brain Injury Worsens Hyperexcitability but Not Behavioral Deficits

Post-traumatic epilepsy (PTE) and neurocognitive deficits are devastating sequelae of head injuries that are common in adolescents. Investigating desperately needed treatments is hindered by the difficulties in inducing PTE in rodents and the lack of established immature rat models of pediatric PTE. Hemorrhage is a significant risk factor for PTE, but compared to humans, rats are less prone to bleeding because of their rapid blood coagulation system. In this study, we promoted bleeding in the controlled cortical impact (CCI) closed-head injury model with a 20 min pre-impact 600 IU/kg intraperitoneal heparin injection in postnatal day 35 (P35) periadolescent rats, given the preponderance of such injuries in this age group. Temporo-parietal CCI was performed post-heparin (HTBI group) or post-saline (TBI group). Controls were subjected to sham procedures following heparin or saline administration. Continuous long-term EEG monitoring was performed for 3 months post-CCI. Sensorimotor testing, the Morris water maze, and a modified active avoidance test were conducted between P80 and P100. Glial fibrillary acidic protein (GFAP) levels and neuronal damage were also assessed. Compared to TBI rats, HTBI rats had persistently higher EEG spiking and increased hippocampal GFAP levels (p < 0.05). No sensorimotor deficits were detected in any group. Compared to controls, both HTBI and TBI groups had a long-term hippocampal neuronal loss (p < 0.05), as well as contextual and visuospatial learning deficits (p < 0.05). The hippocampal astrogliosis and EEG spiking detected in all rats subjected to our hemorrhage-promoting procedure suggest the emergence of hyperexcitable networks and pave the way to a periadolescent PTE rat model.

Accuracy of omni-planar and surface casting of epileptiform activity for intracranial seizure localization

OBJECTIVE

Intracranial electroencephalography (ICEEG) recordings are performed for seizure localization in medically refractory epilepsy. Signal quantifications such as frequency power can be projected as heatmaps on personalized three-dimensional (3D) reconstructed cortical surfaces to distill these complex recordings into intuitive cinematic visualizations. However, simultaneously reconciling deep recording locations and reliably tracking evolving ictal patterns remain significant challenges.

METHODS

We fused oblique magnetic resonance imaging (MRI) slices along depth probe trajectories with cortical surface reconstructions and projected dynamic heatmaps using a simple mathematical metric of epileptiform activity (line-length). This omni-planar and surface casting of epileptiform activity approach (OPSCEA) thus illustrated seizure onset and spread among both deep and superficial locations simultaneously with minimal need for signal processing supervision. We utilized the approach on 41 patients at our center implanted with grid, strip, and/or depth electrodes for localizing medically refractory seizures. Peri-ictal data were converted into OPSCEA videos with multiple 3D brain views illustrating all electrode locations. Five people of varying expertise in epilepsy (medical student through epilepsy attending level) attempted to localize the seizure-onset zones.

RESULTS

We retrospectively compared this approach with the original ICEEG study reports for validation. Accuracy ranged from 73.2% to 97.6% for complete or overlapping onset lobe(s), respectively, and ~56.1% to 95.1% for the specific focus (or foci). Higher answer certainty for a given case predicted better accuracy, and scorers had similar accuracy across different training levels.

SIGNIFICANCE

In an era of increasing stereo-EEG use, cinematic visualizations fusing omni-planar and surface functional projections appear to provide a useful adjunct for interpreting complex intracranial recordings and subsequent surgery planning.

Modelling epilepsy in the mouse: challenges and solutions

In most mouse models of disease, the outward manifestation of a disorder can be measured easily, can be assessed with a trivial test such as hind limb clasping, or can even be observed simply by comparing the gross morphological characteristics of mutant and wild-type littermates. But what if we are trying to model a disorder with a phenotype that appears only sporadically and briefly, like epileptic seizures? The purpose of this Review is to highlight the challenges of modelling epilepsy, in which the most obvious manifestation of the disorder, seizures, occurs only intermittently, possibly very rarely and often at times when the mice are not under direct observation. Over time, researchers have developed a number of ways in which to overcome these challenges, each with their own advantages and disadvantages. In this Review, we describe the genetics of epilepsy and the ways in which genetically altered mouse models have been used. We also discuss the use of induced models in which seizures are brought about by artificial stimulation to the brain of wild-type animals, and conclude with the ways these different approaches could be used to develop a wider range of anti-seizure medications that could benefit larger patient populations.

Summary: This Review discusses the challenges of modelling epilepsy in mice, a condition in which the outward manifestation of the disorder appears only sporadically, and reviews possible solutions encompassing both genetic and induced models.

Behavioral and EEGraphic Characterization of the Anticonvulsant Effects of the Predator Odor (TMT) in the Amygdala Rapid Kindling, a Model of Temporal Lobe Epilepsy

Background: Clinical and experimental evidence indicates that olfactory stimulation modulates limbic seizures, either blocking or inducing ictal activity.

Objective: We aim to evaluate the behavioral and electroencephalographic (EEGraphic) effects of dihydro-2,4,5-trimethylthiazoline (TMT) olfactory exposure on limbic seizures induced by amygdala rapid kindling (ARK).

Materials and Methods: Wistar male rats (280–300 g) underwent stereotaxic surgery for electrode implantation in piriform cortex (PC), hippocampal formation (HIP), and amygdaloid complex (AMYG). Part of the animals was exposed to a saturated chamber with water or TMT, while others had ARK and olfactory exposure prior to the 21st stimulus. Behavioral responses were measured by traditional seizure severity scales (Racine and Pinel and Rovner) and/or by sequential analysis/neuroethology. The electrographic activity of epileptogenic limbic networks was quantified by the occurrence of the first and second EEG afterdischarges, comparing the 1st and 21st stimulus. The spectral analysis [Fast Fourier Transform (FFT)] of the first afterdischarge was performed at the 21st stimulus.

Results: TMT olfactory exposure reduced the seizure severity in kindled rats, altering the displayed behavioral sequence. Moreover, TMT decreased the occurrence of first and second afterdischarges, at the 21st stimulus, and altered the spectral features.

Conclusions: Both behavioral and EEGraphic evaluations indicated that TMT, a potent molecule with strong biological relevance, in fact, “predator odor,” suppressed the epileptiform activity in limbic networks.

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.

Nest Carbon Dioxide Masks GABA-Dependent Seizure Susceptibility in the Naked Mole-Rat

African naked mole-rats were likely the first mammals to evolve eusociality, and thus required adaptations to conserve energy and tolerate the low oxygen (O₂) and high carbon dioxide (CO₂) of a densely populated fossorial nest. As hypercapnia is known to suppress neuronal activity, we studied whether naked mole-rats might demonstrate energy savings in GABAergic inhibition. Using whole-colony behavioral monitoring of captive naked mole-rats, we found a durable nest, characterized by high CO₂ levels, where all colony members spent the majority of their time. Analysis of the naked mole-rat genome revealed, uniquely among mammals, a histidine point variation in the neuronal potassium-chloride cotransporter 2 (KCC2). A histidine missense substitution mutation at this locus in the human ortholog of KCC2, found previously in patients with febrile seizures and epilepsy, has been demonstrated to diminish neuronal Cl^- extrusion capacity, and thus impairs GABAergic inhibition. Seizures were observed, without pharmacological intervention, in adult naked mole-rats exposed to a simulated hyperthermic surface environment, causing systemic hypocapnic alkalosis. Consistent with the diminished function of KCC2, adult naked mole-rats demonstrate a reduced efficacy of inhibition that manifests as triggering of seizures at room temperature by the GABA_A receptor (GABA_AR) positive allosteric modulator diazepam. These seizures are blocked in the presence of nest-like levels of CO₂ and likely to be mediated through GABA_AR activity, based on in vitro recordings. Thus, altered GABAergic inhibition adds to a growing list of adaptations in the naked mole-rat and provides a plausible proximate mechanism for nesting behavior, where a return to the colony nest restores GABA-mediated inhibition.

Interictal Epileptiform Discharges and the Quality of Human Intracranial Neurophysiology Data

Intracranial electroencephalography (IEEG) involves recording from electrodes placed directly onto the cortical surface or deep brain locations. It is performed on patients with medically refractory epilepsy, undergoing pre-surgical seizure localization. IEEG recordings, combined with advancements in computational capacity and analysis tools, have accelerated cognitive neuroscience. This Perspective describes a potential pitfall latent in many of these recordings by virtue of the subject population—namely interictal epileptiform discharges (IEDs), which can cause spurious results due to the contamination of normal neurophysiological signals by pathological waveforms related to epilepsy. We first discuss the nature of IED hazards, and why they deserve the attention of neurophysiology researchers. We then describe four general strategies used when handling IEDs (manual identification, automated identification, manual-automated hybrids, and ignoring by leaving them in the data), and discuss their pros, cons, and contextual factors. Finally, we describe current practices of human neurophysiology researchers worldwide based on a cross-sectional literature review and a voluntary survey. We put these results in the context of the listed strategies and make suggestions on improving awareness and clarity of reporting to enrich both data quality and communication in the field.

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy

BACKGROUND

Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms.

OBJECTIVE

To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition.

METHODS

We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions.

RESULTS

The receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges.

CONCLUSION

Unsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.

Behavioural phenotypes in the cuprizone model of central nervous system demyelination

The feeding of cuprizone (CPZ) to animals has been extensively used to model the processes of demyelination and remyelination, with many papers adopting a narrative linked to demyelinating conditions like multiple sclerosis (MS), the aetiology of which is unknown. However, no current animal model faithfully replicates the myriad of symptoms seen in the clinical condition of MS. CPZ ingestion causes mitochondrial and endoplasmic reticulum stress and subsequent apoptosis of oligodendrocytes leads to central nervous system demyelination and glial cell activation. Although there are a wide variety of behavioural tests available for characterizing the functional deficits in animal models of disease, including that of CPZ-induced deficits, they have focused on a narrow subset of outcomes such as motor performance, cognition, and anxiety. The literature has not been systematically reviewed in relation to these or other symptoms associated with clinical MS. This paper reviews these tests and makes recommendations as to which are the most important in order to better understand the role of this model in examining aspects of demyelinating diseases like MS.

Complex network based models of ECoG signals for detection of induced epileptic seizures in rats

The automatic detection of seizures bears a considerable significance in epileptic diagnosis as it can efficiently lead to a considerable reduction of the workload of the medical staff. The present study aims at automatic detecting epileptic seizures in epileptic rats. To this end, seizures were induced in rats implementing the pentylenetetrazole model, with the electrocorticogram (ECoG) signals during, before and after the seizure periods being recorded. For this purpose, five algorithms for transforming time series into complex networks based on visibility graph (VG) algorithm were used. In this study, VG based methods were used for the first time to analyze ECoG signals in rats. Afterward, Standard measures in network science (graph properties) were made to examine the topological structure of these networks produced on the basis of ECoG signals. Then these measures were given to a classifier as input features so that the ECoG signals could be classified into seizure periods and seizure-free periods. Artificial Neural Network, considered a popular classifier, was used in this work. The experimental results showed that the method managed to detect epileptic seizure in rats with a high accuracy of 92.13%. Our proposed method was also applied to the recorded EEG signals from Bonn database to show the efficiency of the proposed method for human seizure detection.

Levetiracetam Protects Against Cognitive Impairment of Subthreshold Convulsant Discharge Model Rats by Activating Protein Kinase C (PKC)-Growth-Associated Protein 43 (GAP-43)-Calmodulin-Dependent Protein Kinase (CaMK) Signal Transduction Pathway

Background

Subclinical epileptiform discharges (SEDs) are defined as epileptiform electroencephalographic (EEG) discharges without clinical signs of seizure in patients. The subthreshold convulsant discharge (SCD) is a frequently used model for SEDs. This study aimed to investigate the effect of levetiracetam (LEV), an anti-convulsant drug, on cognitive impairment of SCD model rats and to assess the associated mechanisms.

Material/Methods

A SCD rat model was established. Rats were divided into an SCD group, an SCD+ sodium valproate (VPA) group, and an SCD+ levetiracetam (LEV) group. The Morris water maze was used to evaluate the capacity of positioning navigation and space exploration. The field excitatory post-synaptic potentials (fEPSPs) were evaluated using a bipolar stimulation electrode. NCAM, GAP43, PS95, and CaMK II levels were detected using Western blot and RT-PCR, respectively. PKC activity was examined by a non-radioactive method.

Results

LEV shortens the latency of platform seeking in SCD rats in positioning navigation. fEPSP slopes were significantly lower in the SCD group, and LEV treatment significantly enhanced the fEPSP slopes compared to the SCD group (P<0.05). The NCAM and GAP-43 levels were increased and PSD-95 levels were increased in SCD rats (P<0.05), which were improved by LEV treatment. The PKC activity and CaMK II levels were decreased in SCD rats and LEV treatment significantly enhanced PKC activity and increased CaMK II levels.

Conclusions

Cognitive impairment in of SCD model rats may be caused by decreased PKC activity, low expression of CaMK II, and inhibition of LTP formation. LEV can improve cognitive function by activating the PKC-GAP-43-CaMK signal transduction pathway.

Enhanced setup for wired continuous long-term EEG monitoring in juvenile and adult rats: application for epilepsy and other disorders

BACKGROUND

The electroencephalogram (EEG) is a widely used laboratory technique in rodent models of epilepsy, traumatic brain injury (TBI), and other neurological diseases accompanied by seizures. Obtaining prolonged continuous EEG tracings over weeks to months is essential to adequately answer research questions related to the chronobiology of seizure emergence, and to the effect of potential novel treatment strategies. Current EEG recording methods include wired and the more recent but very costly wireless technologies. Wired continuous long-term EEG in rodents remains the mainstay approach but is often technically challenging due to the notorious frequent EEG cable disconnections from the rodent's head, and to poor signal-to-noise ratio especially when simultaneously monitoring multiple animals. Premature EEG cable disconnections and cable movement-related artifacts result from the animal's natural mobility, and subsequent tension on the EEG wires, as well as from potential vigorous and frequent seizures. These challenges are often accompanied by injuries to the scalp, and result in early terminations of costly experiments.

RESULTS

Here we describe an enhanced customized swivel-balance EEG-cage system that allows tension-free rat mobility. The cage setup markedly improves the safety and longevity of current existing wired continuous long-term EEG. Prevention of EEG cable detachments is further enhanced by a special attention to surgical electrode anchoring to the skull. In addition to mechanically preventing premature disconnections, the detailed stepwise approach to the electrical shielding, wiring and grounding required for artifact-free high signal-to-noise ratio recordings is also included. The successful application of our EEG cage system in various rat models of brain insults and epilepsy is described with illustrative high quality tracings of seizures and electrographic patterns obtained during continuous and simultaneous monitoring of multiple rats early and up to 3 months post-brain insult.

CONCLUSION

Our simple-to-implement key modifications to the EEG cage setup allow the safe acquisition of substantial high quality wired EEG data without resorting to the still costly wireless technologies.

A mouse model of seizures in anti-N-methyl-d-aspartate receptor encephalitis

OBJECTIVE

Seizures develop in 80% of patients with anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis, and these represent a major cause of morbidity and mortality. Anti-NMDAR antibodies have been linked to memory loss in encephalitis; however, their role in seizures has not been established. We determined whether anti-NMDAR antibodies from autoimmune encephalitis patients are pathogenic for seizures.

METHODS

We performed continuous intracerebroventricular infusion of cerebrospinal fluid (CSF) or purified immunoglobulin (IgG) from the CSF of patients with anti-NMDAR encephalitis or polyclonal rabbit anti-NMDAR IgG, in male C57BL/6 mice. Seizure status during a 2-week treatment was assessed with video-electroencephalography. We assessed memory, anxiety-related behavior, and motor function at the end of treatment and assessed the extent of neuronal damage and gliosis in the CA1 region of hippocampus. We also performed whole-cell patch recordings from the CA1 pyramidal neurons in hippocampal slices of mice with seizures.

RESULTS

Prolonged exposure to rabbit anti-NMDAR IgG, patient CSF, or human IgG purified from the CSF of patients with encephalitis induced seizures in 33 of 36 mice. The median number of seizures recorded in 2 weeks was 13, 39, and 35 per mouse in these groups, respectively. We observed only 18 brief nonconvulsive seizures in 11 of 29 control mice (median seizure count of 0) infused with vehicle (n = 4), normal CSF obtained from patients with noninflammatory central nervous system (CNS) conditions (n = 12), polyclonal rabbit IgG (n = 7), albumin (n = 3), and normal human IgG (n = 3). We did not observe memory deficits, anxiety-related behavior, or motor impairment measured at 2 weeks in animals treated with CSF from affected patients or rabbit IgG. Furthermore, there was no evidence of hippocampal cell loss or astrocyte proliferation in the same mice.

SIGNIFICANCE

Our findings indicate that autoantibodies can induce seizures in anti-NMDAR encephalitis and offer a model for testing novel therapies for refractory autoimmune seizures.

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

Automated quantification of EEG spikes and spike clusters as a new read out in Theiler's virus mouse model of encephalitis-induced epilepsy

Intracerebral infection of C57BL/6 mice with Theiler's murine encephalomyelitis virus (TMEV) replicates many features of viral encephalitis-induced epilepsy in humans, including neuroinflammation, early (insult-associated) and late (spontaneous) seizures, neurodegeneration in the hippocampus, and cognitive and behavioral alterations. Thus, this model may be ideally suited to study mechanisms involved in encephalitis-induced epilepsy as potential targets for epilepsy prevention. However, spontaneous recurrent seizures (SRS) occur too infrequently to be useful as a biomarker of epilepsy, e.g., for drug studies. This prompted us to evaluate whether epileptiform spikes or spike clusters in the cortical electroencephalogram (EEG) may be a useful surrogate of epilepsy in this model. For this purpose, we developed an algorithm that allows efficient and large-scale EEG analysis of early and late seizures, spikes, and spike clusters in the EEG. While 77% of the infected mice exhibited early seizures, late seizures were only observed in 33% of the animals. The clinical characteristics of early and late seizures did not differ except that late generalized convulsive (stage 5) seizures were significantly longer than early stage 5 seizures. Furthermore, the frequency of SRS was much lower than the frequency of early seizures. Continuous (24/7) video-EEG monitoring over several months following infection indicated that the latent period to onset of SRS was 61 (range 16-91) days. Spike and spike clusters were significantly more frequent in infected mice with late seizures than in infected mice without seizures or in mock-infected sham controls. Based on the results of this study, increases in EEG spikes and spike clusters in groups of infected mice may be used as a new readout for studies on antiepileptogenic or disease-modifying drug effects in this model, because the significant increase in average spike counts in mice with late seizures obviously indicates a proepileptogenic alteration.

Chemogenetic Recruitment of Specific Interneurons Suppresses Seizure Activity

Current anti-epileptic medications that boost synaptic inhibition are effective in reducing several types of epileptic seizure activity. Nevertheless, these drugs can generate significant side-effects and even paradoxical responses due to the broad nature of their action. Recently developed chemogenetic techniques provide the opportunity to pharmacologically recruit endogenous inhibitory mechanisms in a selective and circuit-specific manner. Here, we use chemogenetics to assess the potential of suppressing epileptiform activity by enhancing the synaptic output from three major interneuron populations in the rodent hippocampus: parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP) expressing interneurons. To target different neuronal populations, promoter-specific cre-recombinase mice were combined with viral-mediated delivery of chemogenetic constructs. Targeted electrophysiological recordings were then conducted in an in vitro model of chronic, drug-resistant epilepsy. In addition, behavioral video-scoring was performed in an in vivo model of acutely triggered seizure activity. Pre-synaptic and post-synaptic whole cell recordings in brain slices revealed that each of the three interneuron types increase their firing rate and synaptic output following chemogenetic activation. However, the interneuron populations exhibited different effects on epileptiform discharges. Recruiting VIP interneurons did not change the total duration of epileptiform discharges. In contrast, recruiting SST or PV interneurons produced robust suppression of epileptiform synchronization. PV interneurons exhibited the strongest effect per cell, eliciting at least a fivefold greater reduction in epileptiform activity than the other cell types. Consistent with this, we found that in vivo chemogenetic recruitment of PV interneurons suppressed convulsive behaviors by more than 80%. Our findings support the idea that selective chemogenetic enhancement of inhibitory synaptic pathways offers potential as an anti-seizure strategy.

Open-source system for millisecond-synchronized continuous video-EEG

The causes of epilepsy are incompletely understood, and rodent models enable valuable mechanistic investigations. Synchronized video-electroencephalography (video-EEG) data is critical for clinical assessment of seizure events and is similarly important in basic research on epilepsy, but commercial packages offer limited flexibility and are costly. We've developed and here make freely available OpenVEEG, fully open-source software for millisecond-synchronized video-EEG. With only hardware costs, the system price is approximately one-fifth that of a commercial system with similar capabilities. It is straightforward to use, readily extensible, and records robustly on the time scale of weeks.

Non-ketogenic combination of nutritional strategies provides robust protection against seizures

Epilepsy is a neurological condition that affects 1% of the world population. Conventional treatments of epilepsy use drugs targeting neuronal excitability, inhibitory or excitatory transmission. Yet, one third of patients presents an intractable form of epilepsy and fails to respond to pharmacological anti-epileptic strategies. The ketogenic diet is a well-established non-pharmacological treatment that has been proven to be effective in reducing seizure frequency in the pharmaco-resistant patients. This dietary solution is however extremely restrictive and can be associated with complications caused by the high [fat]:[carbohydrate + protein] ratio. Recent advances suggest that the traditional 4:1 ratio of the ketogenic diet is not a requisite for its therapeutic effect. We show here that combining nutritional strategies targeting specific amino-acids, carbohydrates and fatty acids with a low [fat]:[proteins + carbohydrates] ratio also reduces excitatory drive and protects against seizures to the same extent as the ketogenic diet. Similarly, the morphological and molecular correlates of temporal lobe seizures were reduced in animals fed with the combined diet. These results provide evidence that low-fat dietary strategies more palatable than the ketogenic diet could be useful in epilepsy.

Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy

BACKGROUND

Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to serve as an animal model of epilepsy in drug discovery. Thanks to their high fecundity and relatively low cost, they are amenable to high-throughput screening. However, the assessment of seizure occurrences in zebrafish larvae remains a bottleneck, as visual analysis is subjective and time-consuming.

NEW METHOD

For the first time, we present an automated algorithm to detect epileptic discharges in single-channel local field potential (LFP) recordings in zebrafish. First, candidate seizure segments are selected based on their energy and length. Afterwards, discriminative features are extracted from each segment. Using a labeled dataset, a support vector machine (SVM) classifier is trained to learn an optimal feature mapping. Finally, this SVM classifier is used to detect seizure segments in new signals.

RESULTS

We tested the proposed algorithm both in a chemically-induced seizure model and a genetic epilepsy model. In both cases, the algorithm delivered similar results to visual analysis and found a significant difference in number of seizures between the epileptic and control group.

COMPARISON WITH EXISTING METHODS

Direct comparison with multichannel techniques or methods developed for different animal models is not feasible. Nevertheless, a literature review shows that our algorithm outperforms state-of-the-art techniques in terms of accuracy, precision and specificity, while maintaining a reasonable sensitivity.

CONCLUSION

Our seizure detection system is a generic, time-saving and objective method to analyze zebrafish LPF, which can replace visual analysis and facilitate true high-throughput studies.

The Wistar Audiogenic Rat (WAR) strain and its contributions to epileptology and related comorbidities: History and perspectives

In the context of modeling epilepsy and neuropsychiatric comorbidities, we review the Wistar Audiogenic Rat (WAR), first introduced to the neuroscience international community more than 25years ago. The WAR strain is a genetically selected reflex model susceptible to audiogenic seizures (AS), acutely mimicking brainstem-dependent tonic-clonic seizures and chronically (by audiogenic kindling), temporal lobe epilepsy (TLE). Seminal neuroethological, electrophysiological, cellular, and molecular protocols support the WAR strain as a suitable and reliable animal model to study the complexity and emergent functions typical of epileptogenic networks. Furthermore, since epilepsy comorbidities have emerged as a hot topic in epilepsy research, we discuss the use of WARs in fields such as neuropsychiatry, memory and learning, neuroplasticity, neuroendocrinology, and cardio-respiratory autonomic regulation. Last, but not least, we propose that this strain be used in "omics" studies, as well as with the most advanced molecular and computational modeling techniques. Collectively, pioneering and recent findings reinforce the complexity associated with WAR alterations, consequent to the combination of their genetically-dependent background and seizure profile. To add to previous studies, we are currently developing more powerful behavioral, EEG, and molecular methods, combined with computational neuroscience/network modeling tools, to further increase the WAR strain's contributions to contemporary neuroscience in addition to increasing knowledge in a wide array of neuropsychiatric and other comorbidities, given shared neural networks. During the many years that the WAR strain has been studied, a constantly expanding network of multidisciplinary collaborators has generated a growing research and knowledge network. Our current and major wish is to make the WARs available internationally to share our knowledge and to facilitate the planning and execution of multi-institutional projects, eagerly needed to contribute to paradigm shifts in epileptology. This article is part of a Special Issue entitled "Genetic and Reflex Epilepsies, Audiogenic Seizures and Strains: From Experimental Models to the Clinic".

Development and Experimental Validation of a Dry Non-Invasive Multi-Channel Mouse Scalp EEG Sensor through Visual Evoked Potential Recordings

In this paper, we introduce a dry non-invasive multi-channel sensor for measuring brainwaves on the scalps of mice. The research on laboratory animals provide insights to various practical applications involving human beings and other animals such as working animals, pets, and livestock. An experimental framework targeting the laboratory animals has the potential to lead to successful translational research when it closely resembles the environment of real applications. To serve scalp electroencephalography (EEG) research environments for the laboratory mice, the dry non-invasive scalp EEG sensor with sixteen electrodes is proposed to measure brainwaves over the entire brain area without any surgical procedures. We validated the proposed sensor system with visual evoked potential (VEP) experiments elicited by flash stimulations. The VEP responses obtained from experiments are compared with the existing literature, and analyzed in temporal and spatial perspectives. We further interpret the experimental results using time-frequency distribution (TFD) and distance measurements. The developed sensor guarantees stable operations for in vivo experiments in a non-invasive manner without surgical procedures, therefore exhibiting a high potential to strengthen longitudinal experimental studies and reliable translational research exploiting non-invasive paradigms.

NKCC1 up-regulation contributes to early post-traumatic seizures and increased post-traumatic seizure susceptibility

Traumatic brain injury (TBI) is not only a leading cause for morbidity and mortality in young adults (Bruns and Hauser, Epilepsia 44(Suppl 10):210, 2003), but also a leading cause of seizures. Understanding the seizure-inducing mechanisms of TBI is of the utmost importance, because these seizures are often resistant to traditional first- and second-line anti-seizure treatments. The early post-traumatic seizures, in turn, are a contributing factor to ongoing neuropathology, and it is critically important to control these seizures. Many of the available anti-seizure drugs target gamma-aminobutyric acid (GABA_A) receptors. The inhibitory activity of GABA_A receptor activation depends on low intracellular Cl⁻, which is achieved by the opposing regulation of Na⁺–K⁺–Cl⁻ cotransporter 1 (NKCC1) and K⁺–Cl⁻–cotransporter 2 (KCC2). Up-regulation of NKCC1 in neurons has been shown to be involved in neonatal seizures and in ammonia toxicity-induced seizures. Here, we report that TBI-induced up-regulation of NKCC1 and increased intracellular Cl⁻ concentration. Genetic deletion of NKCC1 or pharmacological inhibition of NKCC1 with bumetanide suppresses TBI-induced seizures. TGFβ expression was also increased after TBI and competitive antagonism of TGFβ reduced NKKC1 expression, ameliorated reactive astrocytosis, and inhibited seizures. Thus, TGFβ might be an important pathway involved in NKCC1 up-regulation after TBI. Our findings identify neuronal up-regulation of NKCC1 and its mediation by TGFβ, as a potential and important mechanism in the early post-traumatic seizures, and demonstrate the therapeutic potential of blocking this pathway.

Neuroprotection mediated by inhibition of calpain during acute viral encephalitis

Neurologic complications associated with viral encephalitis, including seizures and cognitive impairment, are a global health issue, especially in children. We previously showed that hippocampal injury during acute picornavirus infection in mice is associated with calpain activation and is the result of neuronal death triggered by brain-infiltrating inflammatory monocytes. We therefore hypothesized that treatment with a calpain inhibitor would protect neurons from immune-mediated bystander injury. C57BL/6J mice infected with the Daniel's strain of Theiler's murine encephalomyelitis virus were treated with the FDA-approved drug ritonavir using a dosing regimen that resulted in plasma concentrations within the therapeutic range for calpain inhibition. Ritonavir treatment significantly reduced calpain activity in the hippocampus, protected hippocampal neurons from death, preserved cognitive performance, and suppressed seizure escalation, even when therapy was initiated 36 hours after disease onset. Calpain inhibition by ritonavir may be a powerful tool for preserving neurons and cognitive function and preventing neural circuit dysregulation in humans with neuroinflammatory disorders.

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

Epilepsy in young Tsc1(+/-) mice exhibits age-dependent expression that mimics that of human tuberous sclerosis complex

OBJECTIVE

To describe the epileptic phenotype of Tsc1(+/-) mice pups in comparison with age-related seizures in human tuberous sclerosis complex (TSC).

METHODS

Tsc1(+/-) and control mice underwent intracranial electroencephalography (EEG) recording at postnatal ages (P)8 to P33, with linear silicon probe implanted in the somatosensory cortex of one or both hemispheres for 8-24 h. Ictal events were classified visually by independent analyzers; distinct EEG patterns were related to age and analyzed to quantify field potential characteristics and signal dynamics between hemispheres. We collected retrospectively 20 infants with prenatally diagnosed TSC and EEG before seizure onset, and analyzed the electroclinical course of epilepsy, taking into account a first-line treatment by vigabatrin.

RESULTS

Spontaneous seizures were disclosed in 55% of Tsc1(+/-) mice at P9-18. Three ictal patterns were identified: from P9 to P12 "spike clusters" consisted of recurring large spikes without clinical correlate; "spasm-like" discharges dominated from P13 to P16 consisting of high amplitude large field potential superimposed with or followed by fast activity repeated every 2-10 s for at least 20 s, accompanied by rhythmic limb contractions; from P14 to P18 a "tonic-clonic like" pattern comprised rhythmic spikes of increasing amplitude with tonic-clonic movements. Early onset "spike clusters" were mainly unilateral, whereas "spasm-like" and "tonic-clonic like" patterns were bilateral. Interhemispheric propagation was significantly faster for "tonic-clonic like" than for "spasm-like" events. In infants diagnosed prenatally with TSC, clusters of sharp waves or spikes preceded the first seizure, and vigabatrin prevented the development of seizures. Patients treated after seizure onset developed spasms or focal seizures that were pharmacoresistant in 66.7% of cases.

SIGNIFICANCE

Tsc1(+/-) mice pups exhibit an age-dependent seizure pattern sequence mimicking early human TSC epilepsy features. Spike clusters before seizure onset in TSC should be considered as a first stage of epilepsy reinforcing the concept of preventive antiepileptic therapy.

Precaution for volume conduction in rodent cortical electroencephalography using high-density polyimide-based microelectrode arrays on the skull

In humans, significant progress has been made to link spatial changes in electroencephalographic (EEG) spectral density, connectivity strength, and phase-amplitude modulation to neurological, physiological, and psychological correlates. In contrast, standard rodent EEG techniques employ only few electrodes, which results in poor spatial resolution. Recently, a technique was developed to overcome this limitation in mice. This technique was based on a polyimide-based microelectrode (PBM) array applied on the mouse skull, maintaining a significant number of electrodes with consistent contact, electrode impedance, and mechanical stability. The present study built on this technique by extending it to rats. Therefore, a similar PBM array, but adapted to rats, was designed and fabricated. In addition, this array was connected to a wireless EEG headstage, allowing recording in untethered, freely moving rats. The advantage of a high-density array relies on the assumption that the signal recorded from the different electrodes is generated from distinct sources, i.e., not volume-conducted. Therefore, the utility and validity of the array were evaluated by determining the level of synchrony between channels due to true synchrony or volume conduction during basal vigilance states and following a subanesthetic dose of ketamine. Although the PBM array allowed recording with high signal quality, under both drug and drug-free conditions, high synchronization existed due to volume conduction between the electrodes even in the higher spectral frequency range. Discrimination existed only between frontally and centrally/distally grouped electrode pairs. Therefore, caution should be used in interpreting spatial data obtained from high-density PBM arrays in rodents.

Seizure phenotypes, periodicity, and sleep-wake pattern of seizures in Kcna-1 null mice

This study was undertaken to describe seizure phenotypes, natural progression, sleep-wake patterns, as well as periodicity of seizures in Kcna-1 null mutant mice. These mice were implanted with epidural electroencephalography (EEG) and electromyography (EMG) electrodes, and simultaneous video-EEG recordings were obtained while animals were individually housed under either diurnal (LD) condition or constant darkness (DD) over ten days of recording. The video-EEG data were analyzed to identify electrographic and behavioral phenotypes and natural progression and to examine the periodicity of seizures. Sleep-wake patterns were analyzed to understand the distribution and onset of seizures across the sleep-wake cycle. Four electrographically and behaviorally distinct seizure types were observed. Regardless of lighting condition that animals were housed in, Kcna-1 null mice initially expressed only a few of the most severe seizure types that progressively increased in frequency and decreased in seizure severity. In addition, a circadian periodicity was noted, with seizures peaking in the first 12h of the Zeitgeber time (ZT) cycle, regardless of lighting conditions. Interestingly, seizure onset differed between lighting conditions where more seizures arose out of sleep in LD conditions, whereas under DD conditions, the majority occurred out of the wakeful state. We suggest that this model be used to understand the circadian pattern of seizures as well as the pathophysiological implications of sleep and circadian disturbances in limbic epilepsies.

Optogenetic activation of superior colliculus neurons suppresses seizures originating in diverse brain networks

Because sites of seizure origin may be unknown or multifocal, identifying targets from which activation can suppress seizures originating in diverse networks is essential. We evaluated the ability of optogenetic activation of the deep/intermediate layers of the superior colliculus (DLSC) to fill this role. Optogenetic activation of DLSC suppressed behavioral and electrographic seizures in the pentylenetetrazole (forebrain+brainstem seizures) and Area Tempestas (forebrain/complex partial seizures) models; this effect was specific to activation of DLSC, and not neighboring structures. DLSC activation likewise attenuated seizures evoked by gamma butyrolactone (thalamocortical/absence seizures), or acoustic stimulation of genetically epilepsy prone rates (brainstem seizures). Anticonvulsant effects were seen with stimulation frequencies as low as 5 Hz. Unlike previous applications of optogenetics for the control of seizures, activation of DLSC exerted broad-spectrum anticonvulsant actions, attenuating seizures originating in diverse and distal brain networks. These data indicate that DLSC is a promising target for optogenetic control of epilepsy.

Toxoplasma gondii Infections Alter GABAergic Synapses and Signaling in the Central Nervous System

During infections with the protozoan parasite Toxoplasma gondii, gamma-aminobutyric acid (GABA) is utilized as a carbon source for parasite metabolism and also to facilitate parasite dissemination by stimulating dendritic-cell motility. The best-recognized function for GABA, however, is its role in the nervous system as an inhibitory neurotransmitter that regulates the flow and timing of excitatory neurotransmission. When this pathway is altered, seizures develop. Human toxoplasmosis patients suffer from seizures, suggesting that Toxoplasma interferes with GABA signaling in the brain. Here, we show that while excitatory glutamatergic presynaptic proteins appeared normal, infection with type II ME49 Toxoplasma tissue cysts led to global changes in the distribution of glutamic acid decarboxylase 67 (GAD67), a key enzyme that catalyzes GABA synthesis in the brain. Alterations in GAD67 staining were not due to decreased expression but rather to a change from GAD67 clustering at presynaptic termini to a more diffuse localization throughout the neuropil. Consistent with a loss of GAD67 from the synaptic terminals, Toxoplasma-infected mice develop spontaneous seizures and are more susceptible to drugs that induce seizures by antagonizing GABA receptors. Interestingly, GABAergic protein mislocalization and the response to seizure-inducing drugs were observed in mice infected with type II ME49 but not type III CEP strain parasites, indicating a role for a polymorphic parasite factor(s) in regulating GABAergic synapses. Taken together, these data support a model in which seizures and other neurological complications seen in Toxoplasma-infected individuals are due, at least in part, to changes in GABAergic signaling.

IMPORTANCE

Infections of the central nervous system can cause seizures. While inflammation in the brain has been proposed to initiate the onset of the seizures, relatively little is known about how inflammation impacts the structure and function of the neurons. Here we used a parasite called Toxoplasma gondii that infects the brain and showed that seizures arise due to a defect in signaling of GABA, which is the neurotransmitter primarily responsible for preventing the onset of seizures.

Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders

Distinctive features in sensory event-related potentials (ERPs) are endophenotypic biomarkers of psychiatric disorders, widely studied using electroencephalographic (EEG) methods in humans and model animals. Despite the popularity and unique significance of the mouse as a model species in basic research, existing EEG methods applicable to mice are far less powerful than those available for humans and large animals. We developed a new method for multi-channel epidural ERP characterization in behaving mice with high precision, reliability and convenience and report an application to time-domain ERP feature characterization of the Sp4 hypomorphic mouse model for schizophrenia. Compared to previous methods, our spatio-temporal ERP measurement robustly improved the resolving power of key signatures characteristic of the disease model. The high performance and low cost of this technique makes it suitable for high-throughput behavioral and pharmacological studies.