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基于刺激诱发电位的生物物理约束脑连接模型。

A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

机构信息

Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.

出版信息

J Neurosci Methods. 2024 May;405:110106. doi: 10.1016/j.jneumeth.2024.110106. Epub 2024 Mar 5.

Abstract

BACKGROUND

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

NEW METHOD

Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

RESULTS

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures.

COMPARISON WITH EXISTING METHOD

Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences.

CONCLUSIONS

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

摘要

背景

单脉冲电刺激 (SPES) 是一种已建立的技术,用于在接受颅内脑电图监测的治疗抵抗性癫痫患者中绘制功能有效连通性网络。虽然通过结构连通性的整合已经探索了刺激和记录部位之间的连通路径,但仍存在很大的差距,因此新的建模方法可能会增进我们对源自 SPES 研究的连通性的理解。

新方法

我们使用从接受立体脑电图 (sEEG) 评估的单个患者记录的颅内电生理学数据,采用自动检测方法来识别由 SPES 诱导的脉冲诱发电位 (PEP) 的早期响应成分 C1。C1 成分用于一种新颖的拓扑优化方法,对 3D 电导率进行建模,以推断来自刺激部位的神经通路。此外,比较了 PEP 特征与示踪度量,并根据解剖学特征分析了模型结果。

结果

所提出的优化模型以低误差解决了电导率路径。显示高误差的特定电极接触与解剖复杂性相关。C1 成分与其他 PEP 特征强烈相关,并与示踪测量显示出稳定的、微弱的相关性。

与现有方法的比较

用于估计神经信号通路的现有方法是基于成像的,因此依赖于解剖学推断。

结论

这些结果表明,用人类颅内 SPES 数据为拓扑优化方法提供信息是一种可行的方法,可生成将电通路与功能神经群联系起来的 3D 电导率图。PEP 估计的有效连通性与结构连通性相关,但又与之区分开来。在没有解剖学先验的情况下,模型化的电导率可解决连通性途径。

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本文引用的文献

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