Looking for complexity in quantitative semiology of frontal and temporal lobe seizures using neuroethology and graph theory

Symptom network analysis of prefrontal seizures

OBJECTIVE

Prefrontal seizures pose significant challenges in accurately identifying the complex interactions between clinical manifestations and brain electrophysiological activities. This proof-of-concept study aims to propose a new approach to rigorously support electroclinical reasoning in the field of epilepsy.

METHODS

We analyzed stereoelectroencephalographic data from 42 patients with drug-resistant focal epilepsy, whose seizures involved prefrontal cortex at seizure onset. Semiological and brain activities features were scored by expert observers. We performed a symptom network analysis of semiological feature and a hybrid network analysis, coupling semiological features with network analysis of ictal brain activities. Centrality measures were used to identify the most influential features in the networks.

RESULTS

Our analysis identified impairment of consciousness as the most central feature in the semiological network. In the hybrid network, the anterior cingulate area (here incorporating Brodmann area [BA]-32 and/or rostral part of BA-24) emerged as the most central brain activity feature.

SIGNIFICANCE

By integrating semiological features with brain electrophysiological activities into hybrid networks, symptom network analysis offers an effective quantitative tool for examining the relationships between seizure semiology and brain activity correlates in prefrontal seizures. This study provides an opportunity to advance a novel approach to rigorously investigate the intricacies of electroclinical correlations, sustaining the development of dynamic models, on different series of focal epilepsies, larger cohorts, and semiological features automatically extracted by artificial intelligence, that better reflect the temporal and spatial complexities of seizure propagation in patients with complex seizures.

Deep learning approaches for seizure video analysis: A review

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.

Effect of Vagus Nerve Stimulation on the GASH/Sal Audiogenic-Seizure-Prone Hamster

Vagus nerve stimulation (VNS) is an adjuvant neuromodulation therapy for the treatment of refractory epilepsy. However, the mechanisms behind its effectiveness are not fully understood. Our aim was to develop a VNS protocol for the Genetic Audiogenic Seizure Hamster from Salamanca (GASH/Sal) in order to evaluate the mechanisms of action of the therapy. The rodents were subject to VNS for 14 days using clinical stimulation parameters by implanting a clinically available neurostimulation device or our own prototype for laboratory animals. The neuroethological assessment of seizures and general behavior were performed before surgery, and after 7, 10, and 14 days of VNS. Moreover, potential side effects were examined. Finally, the expression of 23 inflammatory markers in plasma and the left-brain hemisphere was evaluated. VNS significantly reduced seizure severity in GASH/Sal without side effects. No differences were observed between the neurostimulation devices. GASH/Sal treated with VNS showed statistically significant reduced levels of interleukin IL-1β, monocyte chemoattractant protein MCP-1, matrix metalloproteinases (MMP-2, MMP-3), and tumor necrosis factor TNF-α in the brain. The described experimental design allows for the study of VNS effects and mechanisms of action using an implantable device. This was achieved in a model of convulsive seizures in which VNS is effective and shows an anti-inflammatory effect.

A complex systems view on the current hypotheses of epilepsy pharmacoresistance

Drug-resistant epilepsy remains to this day as a highly prevalent condition affecting around one-third of patients with epilepsy, despite all the research and the development of several new antiseizure medications (ASMs) over the last decades. Epilepsies are multifactorial complex diseases, commonly associated with psychiatric, neurological, and somatic comorbidities. Thus, to solve the puzzling problem of pharmacoresistance, the diagnosis and modeling of epilepsy and comorbidities need to change toward a complex system approach. In this review, we have summarized the sequence of events for the definition of epilepsies and comorbidities, the search for mechanisms, and the major hypotheses of pharmacoresistance, drawing attention to some of the many converging aspects between the proposed mechanisms, their supporting evidence, and comorbidities-related alterations. The use of systems biology applied to epileptology may lead to the discovery of new targets and the development of new ASMs, as may advance our understanding of the epilepsies and their comorbidities, providing much deeper insight on multidrug pharmacoresistance.

Sequential Semiology of Seizures and Brain Perfusion Patterns in Patients with Drug-Resistant Focal Epilepsies: A Perspective from Neural Networks

Ictal semiology and brain single-photon emission computed tomography have been performed in approaching the epileptogenic zone in drug-resistant focal epilepsies. The authors aim to describe the brain structures involved in the ictal and interictal epileptogenic network from sequential semiology and brain perfusion quantitative patterns analysis. A sequential representation of seizures was performed (n = 15). A two-level analysis (individual and global) was carried out for the analysis of brain perfusion quantification and estimating network structures from the perfusion indexes. Most of the subjects started with focal seizures without impaired consciousness, followed by staring, automatisms, language impairments and evolution to a bilateral tonic-clonic seizure (temporal lobe and posterior quadrant epilepsy). Frontal lobe epilepsy seizures continued with upper limb clonus and evolution to bilateral tonic-clonic. The perfusion index of the epileptogenic zone ranged between 0.439-1.362 (mesial and lateral structures), 0.826-1.266 in dorsolateral frontal structures and 0.678-1.507 in the occipital gyrus. The interictal epileptogenic network proposed involved the brainstem and other subcortical structures. For the ictal state, it included the rectus gyrus, putamen and cuneus. The proposed methodology provides information about the brain structures in the neural networks in patients with drug-resistant focal epilepsies.

Alterations in Cortical-Subcortical Metabolism in Temporal Lobe Epilepsy With Impaired Awareness Seizures

Objective

The features of cerebral metabolism associated with loss of consciousness in patients with temporal lobe epilepsy (TLE) have not been fully elucidated. We aim to investigate the alterations in cortical-subcortical metabolism in temporal lobe epilepsy with impaired awareness seizures (IAS).

Methods

Regional cerebral metabolism was measured using fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET) in patients with TLE-IAS and healthy controls. All patients had a comprehensive evaluation to confirm their seizure origin and lateralization. Videos of all seizures were viewed and rated by at least two epileptologists to identify the state of consciousness when a seizure occurred. By synthesizing the seizure history, semeiology, and video EEG of all patients, as long as the patients had one seizure with impaired awareness, she/he will be included. 76 patients with TLE-IAS and 60 age-matched healthy controls were enrolled in this study. Regional cerebral metabolic patterns were analyzed for TLE-IAS and healthy control groups using statistical parametric mapping. Besides, we compared the MRI-negative patients and MRI-positive patients with healthy controls, respectively.

Results

There were no significant differences in the age and sex of TLE-IAS patients and healthy control. TLE-IAS patients showed extensive bilateral hypermetabolism in the frontoparietal regions, cingulate gyrus, corpus callosum, occipital lobes, basal ganglia, thalamus, brainstem, and cerebellum. The region of metabolic change was more extensive in right TLE-IAS than that of the left, including extensive hypometabolism in the ipsilateral temporal, frontal, parietal, and insular lobes. And contralateral temporal lobe, bilateral frontoparietal regions, occipital lobes, the anterior and posterior regions of the cingulate gyrus, bilateral thalamus, bilateral basal ganglia, brainstem, and bilateral cerebellum showed hypermetabolism. The TLE patients with impaired awareness seizure showed hypermetabolism in the cortical-subcortical network including the arousal system. Additionally, 48 MRI-positive and 28 MRI-negative TLE-IAS patients were included in our study. TLE-IAS patients with MRI-negative and MRI-positive were both showed hypermetabolism in the cingulate gyrus. Hypometabolism in the bilateral temporal lobe was showed in the TLE-IAS with MRI-positive.

Conclusion

These findings suggested that the repetitive consciousness impairing ictal events may have an accumulative effect on brain metabolism, resulting in abnormal interictal cortical-subcortical metabolic disturbance in TLE patients with impaired awareness seizure. Understanding these metabolic mechanisms may guide future clinical treatments to prevent seizure-related awareness deficits and improve quality of life in people with TLE.

Digital Semiology: A Prototype for Standardized, Computer-Based Semiologic Encoding of Seizures

Video-EEG monitoring (VEM) is imperative in seizure classification and presurgical assessment of epilepsy patients. Analysis of VEM is currently performed in most institutions using a freeform report, a time-consuming process resulting in a non-standardized report, limiting the use of this essential diagnostic tool. Herein we present a pilot feasibility study of our experience with “Digital Semiology” (DS), a novel seizure encoding software. It allows semiautomated annotation of the videos of suspected events from a predetermined, hierarchal set of options, with highly detailed semiologic descriptions, somatic localization, and timing. In addition, the software's semiologic extrapolation functions identify characteristics of focal seizures and PNES, sequences compatible with a Jacksonian march, and risk factors for SUDEP. Sixty episodes from a mixed adult and pediatric cohort from one level 4 epilepsy center VEM archives were analyzed using DS and the reports were compared with the standard freeform ones, written by the same epileptologists. The behavioral characteristics appearing in the DS and freeform reports overlapped by 78–80%. Encoding of one episode using DS required an average of 18 min 13 s (standard deviation: 14 min and 16 s). The focality function identified 19 out of 43 focal episodes, with a sensitivity of 45.45% (CI 30.39–61.15%) and specificity of 87.50% (CI 61.65–98.45%). The PNES function identified 6 of 12 PNES episodes, with a sensitivity of 50% (95% CI 21.09–78.91%) and specificity of 97.2 (95% CI 88.93–99.95%). Eleven events of GTCS triggered the SUDEP risk alert. Overall, these results show that video recordings of suspected seizures can be encoded using the DS software in a precise manner, offering the added benefit of semiologic alerts. The present study represents an important step toward the formation of an annotated video archive, to be used for machine learning purposes. This will further the goal of automated VEM analysis, ultimately contributing to wider utilization of VEM and therefore to the reduction of the treatment gap in epilepsy.

Brain injuries can set up an epileptogenic neuronal network

Development of epilepsy or epileptogenesis promotes recurrent seizures. As of today, there are no effective prophylactic therapies to prevent the onset of epilepsy. Contributing to this deficiency of preventive therapy is the lack of clarity in fundamental neurobiological mechanisms underlying epileptogenesis and lack of reliable biomarkers to identify patients at risk for developing epilepsy. This limits the development of prophylactic therapies in epilepsy. Here, neural network dysfunctions reflected by oscillopathies and microepileptiform activities, including neuronal hyperexcitability and hypersynchrony, drawn from both clinical and experimental epilepsy models, have been reviewed. This review suggests that epileptogenesis reflects a progressive and dynamic dysfunction of specific neuronal networks which recruit further interconnected groups of neurons, with this resultant pathological network mediating seizure occurrence, recurrence, and progression. In the future, combining spatial and temporal resolution of neuronal non-invasive recordings from patients at risk of developing epilepsy, together with analytics and computational tools, may contribute to determining whether the brain is undergoing epileptogenesis in asymptomatic patients following brain injury.

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".

Chronic cannabidiol (CBD) administration induces anticonvulsant and antiepileptogenic effects in a genetic model of epilepsy

Cannabidiol (CBD) is a marijuana compound implicated in epilepsy treatment in animal models and pharmacoresistant patients. However, little is known about chronic CBD administration's effects in chronic models of seizures, especially regarding its potential antiepileptogenic effects. In the present study, we combined a genetic model of epilepsy (the Wistar Audiogenic Rat strain - WARs), a chronic protocol of seizures (the audiogenic kindling - AuK), quantitative and sequential behavioral analysis (neuroethology), and microscopy imaging to analyze the effects of chronic CBD administration in a genetic model of epilepsy. The acute audiogenic seizure is characterized by tonic-clonic seizures and intense brainstem activity. However, during the AuK WARs can develop limbic seizures associated with the recruitment of forebrain and limbic structures. Here, chronic CBD administration, twice a day, attenuated brainstem, tonic-clonic seizures, prevented limbic recruitment, and suppressed limbic (kindled) seizures, suggesting CBD antiepileptogenic effects. Additionally, CBD prevented chronic neuronal hyperactivity, suppressing FosB immunostaining in the brainstem (inferior colliculus and periaqueductal gray matter) and forebrain (basolateral amygdala nucleus and piriform cortex), structures associated with tonic-clonic and limbic seizures, respectively. Chronic seizures increased cannabinoid receptors type 1 (CB1R) immunostaining in the hippocampus and the BLA, while CBD administration prevented changes in CB1R expression induced by the AuK. The neuroethological analysis provided details about CBD's protective effects against brainstem and limbic seizures associated with FosB expression. Our results strongly suggest chronic CBD anticonvulsant and antiepileptogenic effects associated with reduced chronic neuronal activity and modulation of CB1R expression. We also support the chronic use of CBD for epilepsies treatments.

The anticonvulsant effects of cannabidiol in experimental models of epileptic seizures: From behavior and mechanisms to clinical insights

Epilepsy is a neurological disorder characterized by the presence of seizures and neuropsychiatric comorbidities. Despite the number of antiepileptic drugs, one-third of patients did not have their seizures under control, leading to pharmacoresistance epilepsy. Cannabis sativa has been used since ancient times in Medicine for the treatment of many diseases, including convulsive seizures. In this context, Cannabidiol (CBD), a non-psychoactive phytocannabinoid present in Cannabis, has been a promising compound for treating epilepsies due to its anticonvulsant properties in animal models and humans, especially in pharmacoresistant patients. In this review, we summarize evidence of the CBD anticonvulsant activities present in a great diversity of animal models. Special attention was given to behavioral CBD effects and its translation to human epilepsies. CBD anticonvulsant effects are associated with a great variety of mechanisms of action such as endocannabinoid and calcium signaling. CBD has shown effectiveness in the clinical scenario for epilepsies, but its effects on epilepsy-related comorbidities are scarce even in basic research. More detailed and complex behavioral evaluation about CBD effects on seizures and epilepsy-related comorbidities are required.

Pharmacological and neuroethological study of the acute and chronic effects of lamotrigine in the genetic audiogenic seizure hamster (GASH:Sal)

The present study aimed to investigate the behavioral and anticonvulsant effects of lamotrigine (LTG) on the genetic audiogenic seizure hamster (GASH:Sal), an animal model of audiogenic seizure that is in the validation process. To evaluate the efficiency of acute and chronic treatments with LTG, GASH:Sals were treated with LTG either acutely via intraperitoneal injection (5-20mg/kg) or chronically via oral administration (20-25mg/kg/day). Their behavior was assessed via neuroethological analysis, and the anticonvulsant effect of LTG was evaluated based on the appearance and the severity of seizures. The results showed that acute administration of LTG exerts an anticonvulsant effect at the lowest dose tested (5mg/kg) and that chronic oral LTG treatment exerts an anticonvulsant effect at a dose of 20-25mg/kg/day. Furthermore, LTG treatment induced a low rate of secondary adverse effects. This article is part of a Special Issue entitled "Genetic and Reflex Epilepsies, Audiogenic Seizures and Strains: From Experimental Models to the Clinic".

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".

Ictal Coprolalia: A Case Report and Review of Ictal Speech as a Localizing Feature in Epilepsy

BACKGROUND

Recognizing ictal semiology is an essential component to localization of seizure onset, especially in intractable epilepsy where surgical therapies may be beneficial. Ictal speech can be a common component of seizure semiology, but the various forms of ictal speech may have different lateralizing and localizing value. Coprolalia is a very rare form of ictal speech.

METHODS

We present a 15 year old with medically intractable seizures characterized by agitation and coprolalia.

RESULTS

The patient underwent surgical evaluation including video EEG, MRI, and functional neuroimaging. These studies indicated onset within the dominant frontal lobe which was further localized using stereo-electroencephalography prior to focal cortical resection.

CONCLUSIONS

Ictal coprolalia is a rare presentation of ictal speech. We review the various forms of ictal speech and their value in localizing seizure onset.