ArticleEuropean journal of nuclear medicine and molecular imaging2020
Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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48 citing papers in PubMed.
- Personalized metabolic connectome analysis reveals glucose heterogeneity in ischemic cerebrovascular disease.European journal of nuclear medicine and molecular imaging · 2026Article
- Individualized metabolic brain network alterations in diffuse large B-cell lymphoma: anBMC medical imaging · 2026Article
- Network-based disease fingerprinting with neuroinflammation PET imaging.Journal of neuroinflammation · 2026Article
- Topological disruptions of metabolic brain networks in early-stage chronic kidney disease.BMC medical imaging · 2026Article
- Similar minds age alike: an MRI similarity approach for predicting age-related cognitive decline.npj aging · 2026Article
- Article
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- Modality-projection universal model for comprehensive full-body medical imaging segmentation.Nature communications · 2025Article
- Network-based disease fingerprinting with neuroinflammation PET imaging.Research square · 2025Article
- Multimodal metabolic and functional network signatures for diagnosis of parkinson's disease: a PET/MR study.Scientific reports · 2025Article
- Transcriptionally downregulated GABAergic genes associated with synaptic density network dysfunction in temporal lobe epilepsy.European journal of nuclear medicine and molecular imaging · 2025Article
- Individual-level metabolic connectivity from dynamic [European journal of nuclear medicine and molecular imaging · 2025Article
- FDG-PET-based brain network analysis: a brief review of metabolic connectivity.EJNMMI reports · 2025Review
- Comparison of Radiomics and conventional SUVr methods for Alzheimer's disease classification using AV45 PET imaging.Frontiers in neurology · 2025Article
- Structural MRI of brain similarity networks.Nature reviews. Neuroscience · 2025Review
- Molecular connectivity studies in neurotransmission: a scoping review.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Age-related changes of node degree in the multiple-demand network predict fluid intelligence.IBRO neuroscience reports · 2024Article
- Individual cerebellar metabolic connectome in patients with MTLE and NTLE associated with surgical prognosis.European journal of nuclear medicine and molecular imaging · 2024Article
- The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment.Translational psychiatry · 2024Article
- Individual metabolic brain network abnormalities associated with drug-resistant mTLE vary in surgical outcomes.Frontiers in neurology · 2024Article
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11 authors.
Funding
Abstract
purposePositron emission tomography (PET) with
methodsFDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual's metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell's concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.
resultsThe KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77-4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).
conclusionThe KLSE indicator identifies abnormal brain networks predicting an individual's risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.
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