Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA
Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA
复制标题
DOI：
10.1016/j.mri.2009.12.016
复制
发表时间：
2010-10-01
影响因子：
2.5
通讯作者：
Logothetis, Nikos K.
中科院分区：
文献类型：
--
作者：
Murayama, Yusuke;Biessmann, Felix;Logothetis, Nikos K.
基于所谓的血氧水平依赖（BOLD）对比度的功能磁共振成像（fMRI）是一种强大的工具，用于研究大脑功能，不仅在局部，而且在大规模上。大多数研究假设神经活动和BOLD活动之间存在简单的关系，尽管阐明神经活动的“何时”和“什么”成分如何与fMRI数据的“何处”相关是很重要的。在这里，我们进行了同步记录的神经和BOLD信号波动的初级视觉（V1）皮层麻醉猴。我们探讨了神经血管的关系，在自发活动期间，使用时间内核典型相关分析（tkCCA）。tkCCA是一种多变量方法，可以考虑单变量分析无法考虑的信号中的任何特征。该方法检测体素空间（fMRI数据）和频率时间空间（神经数据），最大化的神经血管相关性，而没有任何假设的血流动力学响应函数（HRF）中的过滤器。我们的研究结果表明，一个积极的神经血管耦合的滞后4-5秒和更大的贡献，从当地的场电位（LFFs）的γ范围比低频LFPs或尖峰活动。该方法还检测到一个更高的相关性周围的记录网站在并发的空间地图，即使该模式覆盖了大部分的枕部V1。这些结果与以前的研究是一致的，并代表颅内电生理和高分辨率功能磁共振成像的第一个多变量分析。(C)2010年爱思唯尔公司All rights reserved.
Functional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the large scale. Most studies assume a simple relationship between neural and BOLD activity, in spite of the fact that it is important to elucidate how the "when" and "what" components of neural activity are correlated to the "where" of fMRI data. Here we conducted simultaneous recordings of neural and BOLD signal fluctuations in primary visual (V1) cortex of anesthetized monkeys. We explored the neurovascular relationship during periods of spontaneous activity by using temporal kernel canonical correlation analysis (tkCCA). tkCCA is a multivariate method that can take into account any features in the signals that univariate analysis cannot. The method detects filters in voxel space (for fMRI data) and in frequency time space (for neural data) that maximize the neurovascular correlation without any assumption of a hemodynamic response function (HRF). Our results showed a positive neurovascular coupling with a lag of 4-5 s and a larger contribution from local field potentials (LFFs) in the gamma range than from low-frequency LFPs or spiking activity. The method also detected a higher correlation around the recording site in the concurrent spatial map, even though the pattern covered most of the occipital part of V1. These results are consistent with those of previous studies and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI. (C) 2010 Elsevier Inc. All rights reserved.