ArticleMolecules (Basel, Switzerland)2019
Metabolic Brain Network Analysis of FDG-PET in Alzheimer's Disease Using Kernel-Based Persistent Features.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Individual-level metabolic connectivity captures cortical morphology and their coupling strengthens in the ageing brain.European journal of nuclear medicine and molecular imaging · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Individual-level metabolic connectivity captures cortical morphology and their coupling strengthens with age.bioRxiv : the preprint server for biology · 2026Article
- FDG-PET-based brain network analysis: a brief review of metabolic connectivity.EJNMMI reports · 2025Review
- Stable brain PET metabolic networks using a multiple sampling scheme.Network neuroscience (Cambridge, Mass.) · 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
- BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.IEEE transactions on medical imaging · 2023Article
- A Spatiotemporal Brain Network Analysis of Alzheimer's Disease Based on Persistent Homology.Frontiers in aging neuroscience · 2022Article
- White Matter Brain Network Research in Alzheimer's Disease Using Persistent Features.Molecules (Basel, Switzerland) · 2020Article
- Default Mode Network Analysis of APOE Genotype in Cognitively Unimpaired Subjects Based on Persistent Homology.Frontiers in aging neuroscience · 2020Article
- A Novel Individual Metabolic Brain Network for 18F-FDG PET Imaging.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors.
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Abstract
Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer's disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.
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