Topological learning for brain networks

DOI10.1214/22-AOAS1633MaRDI QIDQ2686041

Moo K. Chung, Tananun Songdechakraiwut

Publication date: 24 February 2023

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.00675

zbMATH Keywords

Wasserstein distancetopological data analysispersistent homologybirth-death decompositiontopological learningtwin brain imaging study

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• FLIRT
• MRtrix
• SECT

Cites Work

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• Fréchet means for distributions of persistence diagrams
• Stability of persistence diagrams
• Lipschitz functions have \(L_{p}\)-stable persistence
• Topological data analysis of single-trial electroencephalographic signals
• Signal classification with a point process distance on the space of persistence diagrams
• Fréchet means and Procrustes analysis in Wasserstein space
• LCN: a random graph mixture model for community detection in functional brain networks
• Principal component analysis of persistent homology rank functions with case studies of spatial point patterns, sphere packing and colloids
• Challenges in topological object data analysis
• Sliced and Radon Wasserstein barycenters of measures
• A dynamical systems approach to weighted graph matching
• Topology and data
• Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
• The Fréchet mean shape and the shape of the means
• Geometry Helps to Compare Persistence Diagrams
• Modeling and replicating statistical topology and evidence for CMB nonhomogeneity
• A Bayesian Framework for Persistent Homology
• Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis
• Persistence-Based Clustering in Riemannian Manifolds
• Barcodes: The persistent topology of data

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