Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Artificial intelligence in home-based serious illness care: a scoping review of applications supporting quality palliative care.

Annals of palliative medicine·2026
Same author

Compressing neonatal-perinatal medicine fellowship training: a critical appraisal of the American Board of Pediatrics proposed 2-year pathway.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

Evaluation of a Pediatric Surgical Risk Calculator for Postoperative Outcomes in Spinal Deformity.

Global spine journal·2026
Same author

Functional remodeling of the parasubthalamic nucleus drives alcohol drinking escalation in dependence.

bioRxiv : the preprint server for biology·2026
Same author

The dynamic adrenal response of children to cardiac surgery and cardiac catheterisation.

The Journal of clinical endocrinology and metabolism·2026
Same author

Discriminative Performance and Clinical utility of COPD Exacerbation Categories for Predicting Future Exacerbations.

American journal of respiratory and critical care medicine·2026

相关实验视频

Updated: Jan 9, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K

在模拟的脑电图数据中使用神经脆弱性的间接发性区域定位

Logan F Cook, Isabella Marinelli, Wessel Woldman

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    神经脆弱性在确定抗药性 (DRE) 手术中发作区域方面表现有前途. 这种计算式生物标志物有助于使用静止状态的内脑电图数据精确确定发性区域 (EZ).

    更多相关视频

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
    10:22

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

    Published on: December 6, 2016

    21.0K
    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
    09:57

    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

    Published on: September 20, 2024

    3.4K

    相关实验视频

    Last Updated: Jan 9, 2026

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
    09:32

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    Published on: December 18, 2016

    12.8K
    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
    10:22

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

    Published on: December 6, 2016

    21.0K
    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
    09:57

    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

    Published on: September 20, 2024

    3.4K

    科学领域:

    • 计算神经科学是一种计算神经科学.
    • 的研究研究.
    • 发现生物标志物的发现.

    背景情况:

    • 影响全球5000万,其中三分之一患有耐药性 (DRE).
    • 精确地定位发性区域 (EZ) 对于成功的手术至关重要.
    • 目前的EZ定位方法需要侵入性内EEG (iEEG) 监测和长时间住院治疗.

    研究的目的:

    • 评估神经脆弱性作为计算生物标志物,用于识别DRE中的发性节点.
    • 通过使用in-silico数据,评估神经脆弱性在定位发作区域的预测准确度.
    • 探索先进的计算方法,以改善手术规划.

    主要方法:

    • 使用了来自现象学网络模型的in-silico数据,并预先定义了EZs.
    • 评估神经脆弱性,基于动态网络的指标,基于静止状态iEEG数据.
    • 使用基于值的分类来识别基于脆弱性得分的发性节点.

    主要成果:

    • 神经脆弱性显示得分的双模分布.
    • 基于值的分类在两个数据集的模拟中,在45%和54%的模拟中准确地确定了发性节点.
    • 预测的变化表明需要调查影响准确性的因素.

    结论:

    • 神经脆弱性显示出作为DRE中EZ局部化的计算生物标志物的潜力.
    • 需要进一步的研究来优化网络模型,并通过临床iEEG数据和手术结果验证发现.
    • 这项工作有助于推进用于更精确的手术规划和改善患者结果的计算方法.