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利用 fMRI 数据的多变量非线性相干性来描述视觉系统中的有向功能途径。

Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data.

机构信息

Department of Neurology, Hadassah Hebrew University Medical Center, Jerusalem, Israel.

Edmond and Lily Safra Center for Brain Sciences (ELSC), the Hebrew University of Jerusalem, Jerusalem, Israel.

出版信息

Sci Rep. 2018 Nov 5;8(1):16362. doi: 10.1038/s41598-018-34672-5.

DOI:10.1038/s41598-018-34672-5
PMID:30397245
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6218499/
Abstract

A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hierarchy and functional connectivity. Patterns of signal progression were defined at frequency windows by appealing to a hierarchy based upon phase differences, and their significance was assessed by permutation testing. Assuming consistent phase relationship between neuronal and fMRI signals and unidirectional coupling, we were able to characterize directed pathways in the visual system. The ventral and dorsal systems were found to have different functional organizations. The dorsal system, particularly of the left hemisphere, had numerous feedforward pathways connecting the striate and extrastriate cortices with non-visual regions. The ventral system had fewer pathways primarily of two types: (1) feedback pathways initiated in the fusiform gyrus that were either confined to the striate and the extrastriate cortices or connected to the temporal cortex, (2) feedforward pathways initiated in V2, excluded the striate cortex, and connected to non-visual regions. The multivariate measure demonstrated higher specificity than bivariate (pairwise) measure. The analysis can be applied to other neuroimaging and electrophysiological data.

摘要

使用 40 名健康受试者的静息态 fMRI 数据,采用一种多维的有向功能连接测量方法,来识别人类视觉系统中的信号传递有向通路。该方法利用相互作用的 BOLD 信号的 4 节点网络来获得它们的时间层次结构和功能连接。通过基于相位差的层次结构,在频窗内定义信号传递模式,并通过置换检验评估其显著性。假设神经元和 fMRI 信号之间存在一致的相位关系和单向耦合,我们能够描述视觉系统中的有向通路。腹侧和背侧系统具有不同的功能组织。背侧系统,特别是左半球,具有许多前馈通路,将纹状和外纹状皮质与非视觉区域连接起来。腹侧系统的通路较少,主要有两种类型:(1)在梭状回起始的反馈通路,这些通路仅限于纹状和外纹状皮质,或与颞叶皮质相连;(2)在 V2 起始的前馈通路,排除了纹状皮质,与非视觉区域相连。多维测量方法比双变量(成对)测量方法具有更高的特异性。该分析可应用于其他神经影像学和电生理学数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/10c9dd5a1ca1/41598_2018_34672_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/4fc962aeed57/41598_2018_34672_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/99427a440d99/41598_2018_34672_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/bc1c30cd261e/41598_2018_34672_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/10c9dd5a1ca1/41598_2018_34672_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/4fc962aeed57/41598_2018_34672_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/99427a440d99/41598_2018_34672_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/bc1c30cd261e/41598_2018_34672_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89f6/6218499/10c9dd5a1ca1/41598_2018_34672_Fig4_HTML.jpg

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