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癫痫的神经行为共病:迈向基于网络的精准分类学。

Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy.

机构信息

Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.

Department of Neurology, William S. Middleton Veterans Administration Hospital, Madison, WI, USA.

出版信息

Nat Rev Neurol. 2021 Dec;17(12):731-746. doi: 10.1038/s41582-021-00555-z. Epub 2021 Sep 22.

DOI:10.1038/s41582-021-00555-z
PMID:34552218
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8900353/
Abstract

Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.

摘要

认知和行为共病在儿童和成人癫痫中普遍存在,并造成了巨大的人力和经济负担。在过去的一个世纪里,理解这些共病的病因和病程的经典方法一直是通过癫痫的医学分类学的视角,包括其病因、病程、特征和综合征。尽管这种“病变模型”长期以来一直是该领域的组织范式,但来自神经影像学、神经病理学、神经心理学和网络科学等多种来源的大量挑战已经积累起来。在患者分层和表型方面的进展表明,癫痫的认知和行为共病需要一种新的分类法,这反映了它们临床表现的异质性,并提出了精准医学方法的可能性。正如我们在这篇综述中所讨论的,这些进展为修订病因学范式提供了信息,该范式纳入了复杂的神经生物学指标、基因组学、共病、多样性和逆境以及恢复力因素。我们描述了可改变的风险因素,这些因素可以指导癫痫患者认知和更广泛的神经行为共病的早期识别、治疗,最终预防,并提出了指导未来研究的路线图。

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The role of genetic polymorphisms in executive functioning performance in temporal lobe epilepsy.遗传多态性在颞叶癫痫执行功能表现中的作用。
Epilepsy Behav. 2021 Aug;121(Pt A):108088. doi: 10.1016/j.yebeh.2021.108088. Epub 2021 Jun 5.
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Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.采用患者报告结局和机器学习技术对癫痫患者的心理社会特征及其预测因素进行分析。
Epilepsia. 2020 Jun;61(6):1201-1210. doi: 10.1111/epi.16526. Epub 2020 May 20.
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Behavioral phenotypes of temporal lobe epilepsy.
颞叶癫痫中认知易损性的千变万化
Epilepsy Curr. 2025 Aug 13:15357597251362779. doi: 10.1177/15357597251362779.
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Decrease of Resolvin D1 and E1 in Patients with Chronic Epilepsy, Implicating Dysfunction of Neuroinflammation Resolution.慢性癫痫患者中消退素D1和E1的减少,提示神经炎症消退功能障碍。
J Inflamm Res. 2025 Jun 27;18:8541-8551. doi: 10.2147/JIR.S521679. eCollection 2025.
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