2024
Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-drivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoderDiffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39530051, PMCID: PMC11554386, DOI: 10.1109/isbi56570.2024.10635805.Peer-Reviewed Original ResearchPosterior distributions of kinetic parametersDenoising diffusion probabilistic modelHyperphosphorylated tauP-tauDiffusion probabilistic modelAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersPosterior distributionInference efficiencyComputational needsEstimate kinetic parametersProbabilistic modelComputation timeHead-to-head comparison of [18F]-Flortaucipir, [18F]-MK-6240 and [18F]-PI-2620 postmortem binding across the spectrum of neurodegenerative diseases
Aguero C, Dhaynaut M, Amaral A, Moon S, Neelamegam R, Scapellato M, Carazo-Casas C, Kumar S, El Fakhri G, Johnson K, Frosch M, Normandin M, Gómez-Isla T. Head-to-head comparison of [18F]-Flortaucipir, [18F]-MK-6240 and [18F]-PI-2620 postmortem binding across the spectrum of neurodegenerative diseases. Acta Neuropathologica 2024, 147: 25. PMID: 38280071, PMCID: PMC10822013, DOI: 10.1007/s00401-023-02672-z.Peer-Reviewed Original ResearchConceptsNon-AD tauopathiesTau aggregationTau PET tracersDNA-binding proteinsBinds to neurofibrillary tanglesSecond-generation tau tracersTransactive response DNA-binding proteinSpectrum of neurodegenerative diseasesNeurofibrillary tanglesTau lesionsMelanin-containing cellsTDP-43Binding signalTauopathiesBinding targetsCerebral amyloid angiopathyOff-target bindingB-amyloidBinding patternsNeurodegenerative diseasesTau tracersTauBinding to areasBinding profilesBinding
2019
Autoradiography validation of novel tau PET tracer [F-18]-MK-6240 on human postmortem brain tissue
Aguero C, Dhaynaut M, Normandin M, Amaral A, Guehl N, Neelamegam R, Marquie M, Johnson K, El Fakhri G, Frosch M, Gomez-Isla T. Autoradiography validation of novel tau PET tracer [F-18]-MK-6240 on human postmortem brain tissue. Acta Neuropathologica Communications 2019, 7: 37. PMID: 30857558, PMCID: PMC6410510, DOI: 10.1186/s40478-019-0686-6.Peer-Reviewed Original ResearchConceptsIn vivo detection of neurofibrillary tanglesNeurofibrillary tanglesDetection of neurofibrillary tanglesAlzheimer's diseaseTDP-43Binds to neurofibrillary tanglesFrontotemporal lobar degeneration-tauOff-target bindingDNA-binding protein 43Binding patternsNon-Alzheimer tauopathiesHuman postmortem brain tissueTau aggregationPostmortem brain tissueBinding signalBinding targetsCerebral amyloid angiopathyIn vivo detectionB-amyloidNeurodegenerative diseasesHuman brain tissueTauTau positron emission tomographyBindingMK-6240
2015
A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
Hu C, Cheng L, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease. PLOS ONE 2015, 10: e0128136. PMID: 26024224, PMCID: PMC4449104, DOI: 10.1371/journal.pone.0128136.Peer-Reviewed Original ResearchConceptsNetwork featuresAlzheimer's diseaseConsistent with known pathologyUnknown graphConnection weightsReconstruction networkCortical hubsDegree statisticsData modelSmooth signalsFeatures of brain pathologyOptimization frameworkAmyloid-bPartial correlation estimationImage dataNetworkGraphGlobal connectivity measuresPositron emission tomographyConnectivity measuresNeurodegenerative diseasesConnectivity patternsSample correlationClinical ADSimulated data
2013
A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE
Hu C, Cheng L, Sepulcre J, Fakhri G, Lu Y, Li Q. A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE. 2013, 616-619. DOI: 10.1109/isbi.2013.6556550.Peer-Reviewed Original Research