ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2018
A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Transfer of motor learning is associated with patterns of activity in the default mode network.PLoS biology · 2025Article
- Visual Statistical Learning Alters Low-Dimensional Cortical Architecture.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Distinct patterns of connectivity with the motor cortex reflect different components of sensorimotor learning.PLoS biology · 2024Article
- Article
- Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence.Human brain mapping · 2023Article
- Distinct patterns of cortical manifold expansion and contraction underlie human sensorimotor adaptation.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Neural excursions from manifold structure explain patterns of learning during human sensorimotor adaptation.eLife · 2022Article
- Taking the 4D Nature of fMRI Data Into Account Promises Significant Gains in Data Completion.IEEE access : practical innovations, open solutions · 2021Article
- Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices.IEEE transactions on medical imaging · 2020Article
- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis.Information processing in medical imaging : proceedings of the ... conference · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Even though the number of longitudinal resting-state-fMRI studies is increasing, accurately characterizing the changes in functional connectivity across visits is a largely unexplored topic. To improve characterization, we design a Riemannian framework that represents the functional connectivity pattern of a subject at a visit as a point on a Riemannian manifold. Geodesic regression across the 'sample' points of a subject on that manifold then defines the longitudinal trajectory of their connectivity pattern. To identify group differences specific to regions of interest (ROI), we map the resulting trajectories of all subjects to a common tangent space via the Lie group action. We account for the uncertainty in choosing the common tangent space by proposing a test procedure based on the theory of latent
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.