功能磁共振成像
动态功能连接
相似性(几何)
多元统计
计算机科学
功能连接
功能数据分析
同步(交流)
模式识别(心理学)
人工智能
神经影像学
功能集成
静息状态功能磁共振成像
神经功能成像
数据挖掘
数学
机器学习
神经科学
心理学
图像(数学)
计算机网络
数学分析
频道(广播)
积分方程
作者
Yaqing Chen,Shun-Chieh Lin,Yang Zhou,Owen Carmichael,Hans‐Georg Müller,Jane-Ling Wang
标识
DOI:10.1093/jrsssb/qkad140
摘要
Abstract Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer’s Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.
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