Suppr超能文献

局灶性癫痫的发作间期抑制假说:网络层面的支持证据。

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence.

机构信息

Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.

Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA.

出版信息

Brain. 2023 Jul 3;146(7):2828-2845. doi: 10.1093/brain/awad016.

Abstract

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

摘要

为什么局灶性癫痫患者不会持续发作?先前的神经元信号研究表明,γ-氨基丁酸平衡是癫痫发作产生和终止的关键,但大脑中的高级分布式网络是否参与抑制癫痫发作?最近的颅内脑电图证据表明,发作起始区的内向连接增加,这可能与发作间期抑制癫痫活动有关。因此,我们假设在发作间期,发作起始区被大脑网络的其余部分主动抑制。要充分验证这一假设,需要跨多个神经科学领域进行合作。我们专注于在 81 名接受术前评估的耐药性局灶性癫痫患者的脑电图网络层面上部分测试这一假设。我们使用颅内脑电图静息状态和神经刺激记录来评估发作起始、早期传播和未受累区域的网络连接。然后,我们使用弥散成像获取白质连接的估计值,以评估结构-功能连接对连接发现的影响。最后,我们生成了一个静息状态分类模型,以帮助临床医生在不需要多次发作记录的情况下检测发作起始和传播区。我们的发现表明,使用静息状态(单向方差分析,P 值=3.13×10-13)和神经刺激分析(单向方差分析,P 值=2.5×10-3)评估诱发反应,发作起始和早期传播区显示出明显增加的内向连接和减少的外向连接。当控制区域之间的距离时,在大脑连接的 80 毫米范围内,内向和外向连接之间的差异保持稳定(双向重复测量方差分析,组效应 P 值为 2.6×10-12)。结构-功能连接分析显示,与推测的健康组织相比,发作起始区周围区域表现出异常增强的连接(超连接)(双向重复测量方差分析,交互效应 P 值为 9.76×10-21)。利用这些观察结果,我们的支持向量分类模型在分类早期传播和发作起始区时达到了 92.0±2.2%的最大保留测试集准确率。这些结果表明,发作起始区被一个广泛的大脑网络主动隔离和抑制。此外,这种电生理观察到的功能抑制与发作起始区任何观察到的结构连接改变不成比例。这些发现对仅使用短暂的脑电图记录来识别发作起始区以降低患者发病率和增强耐药性癫痫的术前评估具有重要意义。进一步测试发作间期抑制假说可以为潜在的新的切除、消融和神经调节方法提供见解,以提高耐药性局灶性癫痫患者的手术成功率。

相似文献

3
It's All About the Networks.一切都与网络有关。
Epilepsy Curr. 2019 May-Jun;19(3):165-167. doi: 10.1177/1535759719843301. Epub 2019 Apr 29.

引用本文的文献

2
Machine learning detection of epileptic seizure onset zone from iEEG.基于颅内脑电图的机器学习癫痫发作起始区检测
Biomed Eng Lett. 2025 May 27;15(4):677-692. doi: 10.1007/s13534-025-00480-w. eCollection 2025 Jul.
10
Can brain network analyses guide epilepsy surgery?脑网络分析能否指导癫痫手术?
Curr Opin Neurol. 2025 Apr 1;38(2):105-110. doi: 10.1097/WCO.0000000000001346. Epub 2025 Jan 31.

本文引用的文献

6
A framework For brain atlases: Lessons from seizure dynamics.大脑图谱框架:癫痫动力学的启示。
Neuroimage. 2022 Jul 1;254:118986. doi: 10.1016/j.neuroimage.2022.118986. Epub 2022 Mar 23.
8
Epilepsy and brain network hubs.癫痫与脑网络枢纽
Epilepsia. 2022 Mar;63(3):537-550. doi: 10.1111/epi.17171. Epub 2022 Jan 28.
10

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验