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使用扩散张量成像(DTI)和静息态功能磁共振成像(FMRI)数据对脑连接进行以纤维为中心的分析。

Fiber-centered analysis of brain connectivities using DTI and resting state FMRI data.

作者信息

Lv Jinglei, Guo Lei, Hu Xintao, Zhang Tuo, Li Kaiming, Zhang Degang, Yang Jianfei, Liu Tianming

机构信息

School of Automation, Northwestern Polytechnical University, Xi'an, China.

出版信息

Med Image Comput Comput Assist Interv. 2010;13(Pt 2):143-50. doi: 10.1007/978-3-642-15745-5_18.

Abstract

Recently, inference of functional connectivity between brain regions using resting state fMRI (rsfMRI) data has attracted significant interests in the neuroscience community. This paper proposes a novel fiber-centered approach to study the functional connectivity between brain regions using high spatial resolution diffusion tensor imaging (DTI) and rsfMRI data. We measure the functional coherence of a fiber as the time series' correlation of two gray matter voxels that this fiber connects. The functional connectivity strength between two brain regions is defined as the average functional coherence of fibers connecting them. Our results demonstrate that: 1) The functional coherence of fibers is correlated with the brain regions they connect; 2) The functional connectivity between brain regions is correlated with structural connectivity. And these two patterns are consistent across subjects. These results may provide new insights into the brain's structural and functional architecture.

摘要

最近,利用静息态功能磁共振成像(rsfMRI)数据推断脑区之间的功能连接性在神经科学界引起了广泛关注。本文提出了一种新颖的以纤维为中心的方法,利用高空间分辨率扩散张量成像(DTI)和rsfMRI数据来研究脑区之间的功能连接性。我们将纤维的功能相干性测量为该纤维所连接的两个灰质体素的时间序列相关性。两个脑区之间的功能连接强度定义为连接它们的纤维的平均功能相干性。我们的结果表明:1)纤维的功能相干性与其所连接的脑区相关;2)脑区之间的功能连接性与结构连接性相关。并且这两种模式在不同受试者之间是一致的。这些结果可能为大脑的结构和功能架构提供新的见解。

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