Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra.

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-09-01 Epub Date: 2017-09-02 DOI:10.1007/978-3-319-67159-8_15

David S Lee, Amber Leaver, Katherine L Narr, Roger P Woods, Shantanu H Joshi

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引用次数: 2

Abstract

We present a shape matching approach for functional magnetic resonance imaging (fMRI) time course and spectral alignment. We use ideas from differential geometry and functional data analysis to define a functional representation for fMRI signals. The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their power spectral densities. Experimental results show significant increases in pairwise node to node correlations and coherences following alignment. We apply this method for finding group differences in connectivity between patients with major depression and healthy controls.

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通过fMRI时间过程和光谱的形状分析测量大脑连通性。

我们提出了一种用于功能磁共振成像(fMRI)时间过程和光谱对准的形状匹配方法。我们使用微分几何和函数数据分析的思想来定义fMRI信号的函数表示。然后，fMRI函数的空间配备了一个再参数化不变黎曼度量，使fMRI时间过程的振幅和相位以及它们的功率谱密度都能够弹性对齐。实验结果表明，对齐后两两节点间的相关性和相干性显著增加。我们应用这种方法来发现重度抑郁症患者和健康对照者之间连通性的组间差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)

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