Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP

Sex-specific differences in mortality and neurocardiac interactions in the Kv1.1 knockout mouse model of sudden unexpected death in epilepsy (SUDEP)

Sudden unexpected death in epilepsy (SUDEP) is a devastating complication of epilepsy with possible sex-specific risk factors, although the exact relationship between sex and SUDEP remains unclear. To investigate this, we studied Kcna1 knockout (Kcna1-/-) mice, which lack voltage-gated Kv1.1 channel subunits and are widely used as a SUDEP model that mirrors key features in humans. To assess sex differences, we first performed survival analysis, EEG-ECG recordings, seizure threshold testing and retrospective analysis of previous intracardiac pacing data. We then applied a novel modelling approach across organs (organomics) to uncover potential sex-specific differences in brain-heart communication. Our findings revealed female Kcna1-/- mice have significantly longer lifespans than males, suggesting lower SUDEP rates. Although no sex differences were found in seizure frequency, duration, burden, susceptibility or interictal heart rate variability, females showed a higher incidence of bradycardia during spontaneous seizures than males, as well as resistance to inducible ventricular tachyarrhythmias in response to programmed electrical stimulation. Two captured SUDEP events, one per sex, displayed similar patterns of ictal bradycardia in both sexes, progressing to postictal cardiorespiratory failure. Going beyond traditional seizure and cardiac metrics, organomics analysis revealed that seizures affect brain-heart communication differently between sexes. Females exhibited more effective resetting of brain-heart interactions postictally than males. This finding may contribute to the lower SUDEP risk in females and underscores the complex interplay between sex, cardiac function and brain-heart communication in determining SUDEP susceptibility. Furthermore, seizure-resetting measures could represent a promising class of biomarkers for SUDEP risk stratification. KEY POINTS: Female Kcna1-/- mice live longer than males, suggesting lower sudden unexpected death in epilepsy (SUDEP) rates. There are no sex differences in seizure metrics or interictal heart rate variability. Females show more bradycardia during seizures and are resistant to inducible ventricular tachyarrhythmias. Seizures affect brain-heart communication differently between the sexes. Seizures in females reset brain-heart interactions more effectively postictally, potentially lowering SUDEP risk.

Challenges and future directions of SUDEP models

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death among patients with epilepsy, causing a global public health burden. The underlying mechanisms of SUDEP remain elusive, and effective prevention or treatment strategies require further investigation. A major challenge in current SUDEP research is the lack of an ideal model that maximally mimics the human condition. Animal models are important for revealing the potential pathogenesis of SUDEP and preventing its occurrence; however, they have potential limitations due to species differences that prevent them from precisely replicating the intricate physiological and pathological processes of human disease. This Review provides a comprehensive overview of several available SUDEP animal models, highlighting their pros and cons. More importantly, we further propose the establishment of an ideal model based on brain-computer interfaces and artificial intelligence, hoping to offer new insights into potential advancements in SUDEP research. In doing so, we hope to provide valuable information for SUDEP researchers, offer new insights into the pathogenesis of SUDEP and open new avenues for the development of strategies to prevent SUDEP.

Novel Genetic Variants Expand the Functional, Molecular, and Pathological Diversity of KCNA1 Channelopathy

The KCNA1 gene encodes Kv1.1 voltage-gated potassium channel α subunits, which are crucial for maintaining healthy neuronal firing and preventing hyperexcitability. Mutations in the KCNA1 gene can cause several neurological diseases and symptoms, such as episodic ataxia type 1 (EA1) and epilepsy, which may occur alone or in combination, making it challenging to establish simple genotype-phenotype correlations. Previous analyses of human KCNA1 variants have shown that epilepsy-linked mutations tend to cluster in regions critical for the channel's pore, whereas EA1-associated mutations are evenly distributed across the length of the protein. In this review, we examine 17 recently discovered pathogenic or likely pathogenic KCNA1 variants to gain new insights into the molecular genetic basis of KCNA1 channelopathy. We provide the first systematic breakdown of disease rates for KCNA1 variants in different protein domains, uncovering potential location biases that influence genotype-phenotype correlations. Our examination of the new mutations strengthens the proposed link between the pore region and epilepsy and reveals new connections between epilepsy-related variants, genetic modifiers, and respiratory dysfunction. Additionally, the new variants include the first two gain-of-function mutations ever discovered for KCNA1, the first frameshift mutation, and the first mutations located in the cytoplasmic N-terminal domain, broadening the functional and molecular scope of KCNA1 channelopathy. Moreover, the recently identified variants highlight emerging links between KCNA1 and musculoskeletal abnormalities and nystagmus, conditions not typically associated with KCNA1. These findings improve our understanding of KCNA1 channelopathy and promise to enhance personalized diagnosis and treatment for individuals with KCNA1-linked disorders.

Autonomic dysfunction in epilepsy mouse models with implications for SUDEP research

Epilepsy has a high prevalence and can severely impair quality of life and increase the risk of premature death. Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in drug-resistant epilepsy and most often results from respiratory and cardiac impairments due to brainstem dysfunction. Epileptic activity can spread widely, influencing neuronal activity in regions outside the epileptic network. The brainstem controls cardiorespiratory activity and arousal and reciprocally connects to cortical, diencephalic, and spinal cord areas. Epileptic activity can propagate trans-synaptically or via spreading depression (SD) to alter brainstem functions and cause cardiorespiratory dysfunction. The mechanisms by which seizures propagate to or otherwise impair brainstem function and trigger the cascading effects that cause SUDEP are poorly understood. We review insights from mouse models combined with new techniques to understand the pathophysiology of epilepsy and SUDEP. These techniques include in vivo, ex vivo, invasive and non-invasive methods in anesthetized and awake mice. Optogenetics combined with electrophysiological and optical manipulation and recording methods offer unique opportunities to study neuronal mechanisms under normal conditions, during and after non-fatal seizures, and in SUDEP. These combined approaches can advance our understanding of brainstem pathophysiology associated with seizures and SUDEP and may suggest strategies to prevent SUDEP.

Toward automated prediction of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.

The performance evaluation of the state-of-the-art EEG-based seizure prediction models

The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.

Linear and Nonlinear Directed Connectivity Analysis of the Cardio-Respiratory System in Type 1 Diabetes

In this study, we explored the possibility of developing non-invasive biomarkers for patients with type 1 diabetes (T1D) by quantifying the directional couplings between the cardiac, vascular, and respiratory systems, treating them as interconnected nodes in a network configuration. Towards this goal, we employed a linear directional connectivity measure, the directed transfer function (DTF), estimated by a linear multivariate autoregressive modelling of ECG, respiratory and skin perfusion signals, and a nonlinear method, the dynamical Bayesian inference (DBI) analysis of bivariate phase interactions. The physiological data were recorded concurrently for a relatively short time period (5 min) from 10 healthy control subjects and 10 T1D patients. We found that, in both control and T1D subjects, breathing had greater influence on the heart and perfusion with respect to the opposite coupling direction and that, by both employed methods of analysis, the causal influence of breathing on the heart was significantly decreased (p < 0.05) in T1D patients compared to the control group. These preliminary results, although obtained from a limited number of subjects, provide a strong indication for the usefulness of a network-based multi-modal analysis for the development of biomarkers of T1D-related complications from short-duration data, as well as their potential in the exploration of the pathophysiological mechanisms that underlie this devastating and very widespread disease.

The brain-heart interaction in epilepsy: implications for diagnosis, therapy, and SUDEP prevention

The influence of the central nervous system and autonomic system on cardiac activity is being intensively studied, as it contributes to the high rate of cardiologic comorbidities observed in people with epilepsy. Indeed, neuroanatomic connections between the brain and the heart provide links that allow cardiac arrhythmias to occur in response to brain activation, have been shown to produce arrhythmia both experimentally and clinically. Moreover, seizures may induce a variety of transient cardiac effects, which include changes in heart rate, heart rate variability, arrhythmias, asystole, and other ECG abnormalities, and can trigger the development of Takotsubo syndrome. People with epilepsy are at a higher risk of death than the general population, and sudden unexpected death in epilepsy (SUDEP) is the most important direct epilepsy-related cause of death. Although the cause of SUDEP is still unknown, cardiac abnormalities during and between seizures could play a significant role in its pathogenesis, as highlighted by studies on animal models of SUDEP and registration of SUDEP events. Recently, genetic mutations in genes co-expressed in the heart and brain, which may result in epilepsy and cardiac comorbidity/increased risk for SUDEP, have been described. Recognition and a better understanding of brain-heart interactions, together with new advances in sequencing techniques, may provide new insights into future novel therapies and help in the prevention of cardiac dysfunction and sudden death in epileptic individuals.

Identifying Refractory Epilepsy Without Structural Abnormalities by Fusing the Common Spatial Patterns of Functional and Effective EEG Networks

Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.