ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2024
Gradient synchronization for multivariate functional data, with application to brain connectivity.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Graph theory methods for analyzing functional connectivity in multiple spike trains: application to data recorded from the visual cortex of a cat.Cognitive neurodynamics · 2025Article
- Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.Biometrical journal. Biometrische Zeitschrift · 2020Article
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6 authors.
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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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