Population-level task-evoked functional connectivity via Fourier analysis.

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Kun Meng, Ani Eloyan
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引用次数: 0

Abstract

Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.

通过傅立叶分析实现群体级任务诱发功能连接。
功能磁共振成像(fMRI)是一种无创的体内成像技术,对测量大脑活动至关重要。功能连通性可用于研究大脑区域之间的关联,研究对象可在执行任务时或休息时进行研究。在本文中,我们提出了任务诱发的群体水平功能连通性(ptFC)的严格定义。重要的是,我们提出的ptFC可以在任务-MRI研究中进行解释。我们还提供了一种估算 ptFC 的算法。我们通过模拟展示了所提算法与现有功能连通性框架的性能比较。最后,我们在人类连接组计划的一项运动任务研究中应用了所提出的算法来估计ptFC。
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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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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