ArticleNetwork neuroscience (Cambridge, Mass.)2019
Exact topological inference of the resting-state brain networks in twins.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Node persistence from topological data analysis reveals changes in brain functional connectivity.Patterns (New York, N.Y.) · 2026Article
- Enhancing structural to functional brain network prediction using topological graph learning.Frontiers in neuroscience · 2026Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
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- Contrast subgraphs catch patterns of altered functional connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Enhanced perfusion following exposure to radiotherapy: A theoretical investigation.PLoS computational biology · 2024Article
- Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease.Frontiers in neuroscience · 2024Article
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Altered topological structure of the brain white matter in maltreated children through topological data analysis.Network neuroscience (Cambridge, Mass.) · 2024Article
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
- Article
- Hodge Laplacian of Brain Networks.IEEE transactions on medical imaging · 2023Article
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
- Article
- Quantification of vascular networks in photoacoustic mesoscopy.Photoacoustics · 2022Article
- Discussion of 'Event history and topological data analysis'.Biometrika · 2021Article
- Article
- Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques.Frontiers in neuroinformatics · 2021Article
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5 authors.
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Abstract
A cycle in a brain network is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. Whereas the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, it is unclear how to perform statistical inference on the number of cycles in the brain network. In this study, we present a new statistical inference framework for determining the significance of the number of cycles through the Kolmogorov-Smirnov (KS) distance, which was recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using the random network simulations with ground truths. By using a twin imaging study, which provides biological ground truth, the methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the resting-state functional connectivity in 217 twins obtained from the Human Connectome Project. The MATLAB codes as well as the connectivity matrices used in generating results are provided at http://www.stat.wisc.edu/∼mchung/TDA.
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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.