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相关概念视频

Arteries of the Lower Limbs01:24

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194
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
194

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Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
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补充结构和功能异常以局部化发性组织.

Jonathan J Horsley1, Rhys H Thomas2, Fahmida A Chowdhury3

  • 1CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.

EBioMedicine
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

将扩散权重MRI (dMRI) 和内EEG (iEEG) 数据结合起来,可以改善手术患者的发性区域的定位. 这种方法通过识别和切除最大异常来提高发作自由率.

关键词:
扩散权重的MRI测试结果是一种病.内脑电图 (EEG) 是一种脑内脑电图.机器学习是机器学习.多模式分析多模式分析手术预测手术预测

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科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 发病学 (Epileptology) 是一个专业的学科.

背景情况:

  • 药物耐药的焦点需要精确地定位发作的发作区域进行手术干预.
  • 内EEG (iEEG) 和扩散权重MRI (dMRI) 分别用于绘制活动和白质道的地图.
  • 确定发性区域 (EZ) 对于成功的手术至关重要.

研究的目的:

  • 调查dMRI的结构连接与功能性iEEG异常的结合是否可以改善EZ局部化.
  • 为了确定切除两种模式的最大异常是否同时提高了外科手术的结果.
  • 探索dMRI衍生连接在指导iEEG电极放置中的实用性.

主要方法:

  • 在iEEG植入后接受手术的43名患者的回顾性分析.
  • 与规范数据相比,对间接EEG功能和dMRI结构连接异常的量化.
  • 探索最大异常切除和发作自由度 (ILAE 1或2在一年内) 之间的关系.

主要成果:

  • 58%的患者在一年内实现了无.
  • 切除最大连接和iEEG异常与自由率增加15倍有关 (p=0.008).
  • 使用这两种方法的决策树对84%的患者进行了正确的分类,对手术结果进行了正确分类.

结论:

  • 无论是dMRI衍生的结构连接性还是iEEG异常,都能有效地定位发性组织.
  • 整合这两种方法为的手术前评估提供了补充信息.
  • 这种综合方法有可能改善药物耐药焦点患者的手术结果.