About this Research Topic
Epilepsy, which affects 50 million people worldwide, is among the most frequent chronic neurological conditions. Epilepsy is characterized by pathological electrical brain activity that manifests as a loss of control of body function. The majority of the epilepsies are of cryptogenic origin, making it impossible to identify risk factors to develop preventive interventions.
While neuronal hyperexcitability is an established key factor in epilepsy, the reasons underlying such hyperexcitable phenotype are yet not fully understood. Moreover, neurons are not the only actors in the epileptic brain. Recent evidence has indeed highlighted the relevant contribution of glial cells, inflammation, and the extracellular matrix to brain remodeling that leads to the establishment and progression of epileptic disorders. However, despite the plethora of studies pointing at the multifactorial origin of epilepsy, some fundamental questions still remain open: Why does a brain become epileptic? How does a seizure initiate? What self-limiting process underlies seizure termination?
Along with experimental work tackling the cellular, molecular, and network mechanisms underlying epilepsy, signal processing tools, computational models, machine learning techniques, and artificial intelligence algorithms are emerging as complementary yet fundamental means to understand the epileptic brain from a phenomenological perspective. These approaches ultimately aim at unveiling features and causal relationships hidden within the complex brain electrical patterns stemming from the polyhedral interactions between cells and molecules. The synergetic combination of mechanistic and phenomenological approaches to link electrical and molecular biomarkers may represent the new frontier in epilepsy research and in the clinical practice: it would help to shed more light on epileptogenesis, identifying risk predictors, and it would aid in the design of novel biomedical devices for personalized medicine, such as individually tailored deep-brain stimulation and drug delivery strategies.
This Research Topic addresses the phenomena underlying epileptogenesis, seizure generation, and termination from a multidisciplinary perspective. It embraces both biological studies and engineering tools, expanding beyond the canonical concept of neuronal hyperexcitability and excitation/inhibition imbalance, with the ultimate goal to further our understanding of the cellular, molecular, and network interactions in the epileptic brain, beyond neurons.
We aim to collect relevant contributions that could provide more insight into the dynamics of epileptogenesis, seizure generation, and termination, both from a mechanistic and a phenomenological perspective. The following article types are welcome:
• Original Research: in vitro, in vivo, clinical or theoretical studies (or a combination thereof) focusing on neurons and non-neuronal cells, brain networks interactions, neurogenesis and gliogenesis, inflammation, and mechanobiology
• Brief Research Report
• Methods: in vitro models addressing the contribution of multiple cell types and/or the extracellular matrix and/or inflammation in epileptogenesis and ictogenesis; novel signal processing, machine learning and artificial intelligence algorithms focusing electrical biomarkers of epileptogenesis, seizure detection and prediction, relevant to preventive and personalized medicine
• Technology and Code
Keywords: Epilepsy, Neuronal Cells, Non-Neuronal Cells, Extracellular Matrix, Signal Processing, Artificial Intelligence
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