ArticleIEEE transactions on medical imaging2020
Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation.Human brain mapping · 2025Article
- Multimodality model investigating the impact of brain atlases, connectivity measures, and dimensionality reduction techniques on Attention Deficit Hyperactivity Disorder diagnosis using resting state functional connectivity.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence.Human brain mapping · 2023Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.Human brain mapping · 2022Article
- Learning Common Harmonic Waves on Stiefel Manifold - A New Mathematical Approach for Brain Network Analyses.IEEE transactions on medical imaging · 2021Article
- Influence of different acoustic cues in L1 lexical tone on the perception of L2 lexical stress using principal component analysis: an ERP study.Experimental brain research · 2020Article
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3 authors.
Funding
Abstract
Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques.
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Registered trials
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