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Neuroscienze

利用 SPM 软件分析颅内脑电图数据的神经活动和连接性

Published: October 30, 2018
doi:
10.3791/58187
, , , , , , , ,
1Kokoro Research Center,Kyoto University, 2Brain Activity Imaging Center,Advanced Telecommunications Research Institute International, 3Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine,Kyoto University, 4National Epilepsy Center, 5Shizuoka Institute of Epilepsy and Neurological Disorders, 6Department of System Neuroscience,Sapporo Medical University, 7Faculty of Human Health Science, Graduate School of Medicine,Kyoto University

Summary

我们提出两种分析协议, 可用于分析颅内脑电图数据使用统计参数映射 (SPM) 软件: 时间-频率统计参数映射分析的神经活动, 和动态因果关系对内部和区域间连通性的诱导响应建模。

Abstract

在高空间和时间分辨率下测量与认知功能相关的神经活动和连通性是认知神经科学的一个重要目标。颅内脑电图 (EEG) 可以直接记录电神经活动, 并具有实现这一目标的独特潜力。传统上, 应用平均分析法分析颅内脑电图数据;然而, 有几种新的技术可以用来描述神经活动和区域间的连接。在这里, 我们引入了两个分析协议, 我们最近使用统计参数映射 (SPM) 软件分析颅内脑电图数据: 时间-频率 SPM 分析的神经活动和动态因果模型的诱发反应内部和区域间的连接。我们报告我们的颅内脑电图数据的分析在面部观察作为代表性的结果。结果表明, 下枕回 (内部监督组) 在早期阶段 (110 ms) 表现为对面部的伽玛波段活动, 内部监督组和杏仁核均使用各种类型的振荡表现出快速的内、区域间连通性。这些分析协议有可能识别具有高空间和时态轮廓的认知功能的神经机制。

Introduction

在高空间和时间分辨率下测量与认知功能相关的神经活动和连接性是认知神经科学的主要目标之一。然而, 完成这个目标并不容易。一种常用的记录神经活动的方法是功能磁共振成像 (MRI)。虽然功能磁共振成像提供了一些优势, 如在毫米水平和非侵入性记录的高空间分辨率, 功能磁共振成像的一个明显的缺点是它的低时间分辨率。此外, 功能磁共振成像测量血氧水平依赖性信号, 这只是间接反映电神经活动。受欢迎的电生理方法, 包括脑电图 (eeg) 和脑磁图 (MEG), 在毫秒级有很高的时间分辨率。然而, 他们有相对较低的空间分辨率, 因为他们记录在头皮的电/磁信号, 必须解决困难的反问题, 以描绘大脑活动。

颅内脑电图可直接记录高时间 (毫秒) 和空间 (厘米) 分辨率1的电神经活动。这一措施可以为了解神经活动和连接提供有价值的机会, 尽管它有明确的局限性 (例如, 可测量区域仅限于临床标准)。一些颅内脑电图研究应用传统的平均分析来描述神经活动。虽然平均分析可以灵敏地检测锁定和低频波段激活, 但它无法检测到非锁相和/或高频 (例如伽玛波段) 激活。此外, 在颅内脑电图记录的文献中还没有深入分析功能性神经耦合。最近开发了几种新技术来描述功能磁共振成像和脑电图/MEG 记录中的神经活动和区域间连接, 可用于分析颅内脑电图数据。

在这里, 我们介绍了最近使用统计参数映射 (SPM) 软件分析颅内脑电图数据的分析协议。首先, 为了揭示何时, 以及在哪个频率, 大脑区域可以被激活, 我们执行时频 SPM 分析2。该分析利用连续小波变换同时分解时间和频域, 用随机场理论对时频映射中的族误差 (少数) 率进行了适当的校正。其次, 为了揭示大脑区域的沟通方式, 我们应用了诱发反应的动态因果模型 (DCM)4。DCM 支持对有效连通性的调查 (例如,大脑区域之间的因果和方向影响5)。尽管 dcm 最初被建议为分析功能性 MRI 数据5的工具, 但已将诱导反应的 dcm 扩展到电生理信号的时变功率谱分析4。此分析允许描述内部和区域间的神经连接。一些神经生理学研究表明, 局部区域内计算和远距离区域间通信主要使用伽玛和θ波段振荡, 它们的相互作用 (例如截留) 可以由θ-伽玛十字频率耦合3、6、7、8反射。本报告侧重于数据分析协议;如欲了解颅内脑电图的背景信息9、10和记录协议11 , 请参阅文献。

Protocol

我们的研究得到了当地机构伦理委员会的批准。 1. 基本信息 注: 分析协议可应用于各种类型的数据, 不受特定参与者、电极、参考方法或电极位置的限制。在本例中, 我们测试了六名患有药理顽固性病灶癫痫的患者。我们测试了在感兴趣区域没有癫痫灶的患者。 在靶电极的认知实验中记录颅内脑电图数据。 植入深度电极采用立体定向法<sup cl…

Representative Results

使用本文提供的协议, 我们分析了颅内脑电图数据, 以响应面18,19。我们在被动查看面部、马赛克和房屋的直立和倒置方向上记录了六名患者的数据。直立面与直立马赛克 、直立面与直立房屋的对比揭示了脸部效应 (即相对于其他物体的面部特定的大脑活动)。直立面与倒置面的对比揭示了人脸反转效应 …

Discussion

本文介绍的基于 SPM 软件的颅内脑电图数据分析规程与功能磁共振成像相比具有多种优点。首先, 这些协议可以在高时间分辨率下描述神经激活。因此, 研究结果表明, 神经激活的认知相关性是否在处理的早期或后期实现。在我们的示例中, 在视觉处理的早期阶段 (即110 ms) 中标识了面效果。此外, 与不同的心理功能相关的神经活动的时态剖面的比较提供了有趣的含义。特别是, 我们的例子揭示?…

Divulgazioni

The authors have nothing to disclose.

Acknowledgements

这项研究得到了倍乐生之公司, 日本促进科学协会 (jsp) 资助计划为下一代世界领先的研究人员 (LZ008), 促进神经发育紊乱研究组织的资金支持,和 jsp KAKENHI (15K04185; 18K03174)。

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Tags

Intracranial EEGNeural ActivityNeural ConnectivitySPM SoftwareTime-frequency AnalysisWavelet DecompositionMorlet WaveletsData TransformationBaseline CorrectionData SmoothingGeneral Linear ModelStatistical InferenceRandom Field TheoryDynamic Causal ModelingCognitive Neuroscience

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Sato, W., Kochiyama, T., Uono, S., Usui, N., Kondo, A., Matsuda, K., Usui, K., Toichi, M., Inoue, Y. Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software. J. Vis. Exp. (140), e58187, doi:10.3791/58187 (2018).
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