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Challenges and future directions of SUDEP models.

JiaXuan GuWeiHui ShaoLu LiuYuLing WangYue YangZhuoYue ZhangYaXuan WuQing XuLeYuan GuYuanLi ZhangYue ShenHaiTing ZhaoChang ZengHonghai Zhang
Published in: Lab animal (2024)
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.
Keyphrases
  • artificial intelligence
  • public health
  • endothelial cells
  • deep learning
  • risk assessment
  • minimally invasive
  • big data
  • healthcare
  • risk factors
  • climate change
  • subarachnoid hemorrhage
  • temporal lobe epilepsy