ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Topological Learning and Its Application to Multimodal Brain Network Integration.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.Scientific reports · 2025Article
- H-VIP: quantifying regional topological contributions of the brain network to cognition.Frontiers in radiology · 2025Article
- Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.ArXiv · 2024Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Hodge Laplacian of Brain Networks.IEEE transactions on medical imaging · 2023Article
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
- Article
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Authors and funding
3 authors.
Funding
Abstract
A long-standing challenge in multimodal brain network analyses is to integrate topologically different brain networks obtained from diffusion and functional MRI in a coherent statistical framework. Existing multimodal frameworks will inevitably destroy the topological difference of the networks. In this paper, we propose a novel topological learning framework that integrates networks of different topology through persistent homology. Such challenging task is made possible through the introduction of a new topological loss that bypasses intrinsic computational bottlenecks and thus enables us to perform various topological computations and optimizations with ease. We validate the topological loss in extensive statistical simulations with ground truth to assess its effectiveness of discriminating networks. Among many possible applications, we demonstrate the versatility of topological loss in the twin imaging study where we determine the extend to which brain networks are genetically heritable.
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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.