Gradient synchronization for multivariate functional data, with application to brain connectivity
多元函数型数据的梯度同步性及脑连接应用AI 解读 ↓
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多元函数型数据的梯度同步性及脑连接应用
- 研究问题
- 如何通过成对随机曲线的动态变化衡量关联，补充静态 Pearson 相关对脑区功能连接随时间变化刻画的不足？
- 方法与思路
- 作者依据成对光滑随机函数梯度的一致与不一致构造同步性指标，研究估计量的渐近正态性，并开展模拟和静息态 fMRI 应用。
- 材料陈述的贡献
- 摘要称，新指标能够反映函数相似性的动态特征；在所述模拟与脑影像应用中，作者报告不同疾病状态受试者的区分表现改善。
- 阅读关注点
- 应核对梯度指标定义、曲线平滑和求导方法、渐近正态性条件，以及疾病状态区分的比较基准、评价指标与验证设计。
- 阅读边界
- 仅据所给 Crossref 摘要；正则条件、改善幅度和验证细节未说明，不能据此认定临床有效性，也不能推断其他版本变化。
原文摘要
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.