Scholarlink

<p>Epilepsy is a prevalent neurological disorder marked by abnormal synchronized neuronal firing, which can often lead to long-term cognitive and physical impairments. In this work, we introduce a reliable biomarker for seizure prediction. Through simulations of a conductance-based neuronal network model that reproduces spontaneous seizure-like events, we identify that slow potassium channels play an important role in seizure generation. Our key finding is the consistent presence of a prolonged period of neuronal silence that precedes the seizure onset, establishing it as a physiologically relevant biomarker for seizure prediction. Notably, this silence is also identified in human electrophysiological data, confirming its physiological and clinical relevance. Based on this biomarker, we develop a targeted suppression strategy that, in our simulations, significantly shortens long seizure duration by up to 93%. Our results establish the network silence as a predictive and clinically translatable biomarker for seizure dynamics, opening new avenues for improved forecasting and personalized neuromodulation therapies in epilepsy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Neuronal silence as a predictive biomarker and target for epileptic seizures suppression

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

查看全文
  • Diogo L. M. Souza,
  • Lucas E. Bentivoglio,
  • Enrique C. Gabrick,
  • Paulo R. Protachevicz,
  • Iberê L. Caldas,
  • Kelly C. Iarosz,
  • Salvador Dura-Bernal,
  • Antonio M. Batista,
  • Fernando S. Borges

摘要

Epilepsy is a prevalent neurological disorder marked by abnormal synchronized neuronal firing, which can often lead to long-term cognitive and physical impairments. In this work, we introduce a reliable biomarker for seizure prediction. Through simulations of a conductance-based neuronal network model that reproduces spontaneous seizure-like events, we identify that slow potassium channels play an important role in seizure generation. Our key finding is the consistent presence of a prolonged period of neuronal silence that precedes the seizure onset, establishing it as a physiologically relevant biomarker for seizure prediction. Notably, this silence is also identified in human electrophysiological data, confirming its physiological and clinical relevance. Based on this biomarker, we develop a targeted suppression strategy that, in our simulations, significantly shortens long seizure duration by up to 93%. Our results establish the network silence as a predictive and clinically translatable biomarker for seizure dynamics, opening new avenues for improved forecasting and personalized neuromodulation therapies in epilepsy.