2024
Associations of Plasma Tau with Amyloid and Tau PET: Results from the Community-Based Framingham Heart Study
Ramos-Cejudo J, Scott M, Tanner J, Pase M, McGrath E, Ghosh S, Osorio R, Thibault E, Fakhri G, Johnson K, Beiser A, Seshadri S. Associations of Plasma Tau with Amyloid and Tau PET: Results from the Community-Based Framingham Heart Study. Journal Of Alzheimer's Disease 2024, 100: 487-494. PMID: 38875034, DOI: 10.3233/jad-231320.Peer-Reviewed Original ResearchConceptsCommunity-based Framingham Heart StudyFramingham Heart StudyHeart StudyCross-sectional associationsPlasma total tauMiddle-aged participantsRisk of ADCognitively healthy individualsCommunity-basedCompound B (PiB)-PETMiddle-agedPositive associationPlasma tauParticipantsTau-PETAssociationTotal tau levelsAD brain pathologyTotal-tauHealthy individualsAPOEe4Tau levelsBurdenRhinal cortexPrecuneus regions
2023
Measurement of Cerebral Perfusion Indices from the Early Phase of [18F]MK6240 Dynamic Tau PET Imaging
Guehl N, Dhaynaut M, Hanseeuw B, Moon S, Lois C, Thibault E, Fu J, Price J, Johnson K, El Fakhri G, Normandin M. Measurement of Cerebral Perfusion Indices from the Early Phase of [18F]MK6240 Dynamic Tau PET Imaging. Journal Of Nuclear Medicine 2023, 64: 968-975. PMID: 36997330, PMCID: PMC10241011, DOI: 10.2967/jnumed.122.265072.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseAniline CompoundsBrainCerebrovascular CirculationCognitive DysfunctionHumansPositron-Emission TomographyConceptsTime-activity curvesCerebral perfusionMetabolite-corrected arterial input functionBrain time-activity curvesEarly phaseRegional time-activity curvesIndices of cerebral perfusionDynamic [<sup>18</sup>FBlood-brain barrierPlasma to brain tissueStatistically significant differenceArterial blood samplesForty-nine subjectsCNArterial input functionPathophysiological mechanismsPerfusion indicatorsPET imagingBlood samplesSignificant differenceSurrogate indexNoninvasive estimationAnatomical informationCompound BForty-nine
2017
Hierarchical Organization of Tau and Amyloid Deposits in the Cerebral Cortex
Sepulcre J, Grothe M, Sabuncu M, Chhatwal J, Schultz A, Hanseeuw B, Fakhri G, Sperling R, Johnson K. Hierarchical Organization of Tau and Amyloid Deposits in the Cerebral Cortex. JAMA Neurology 2017, 74: 813-820. PMID: 28558094, PMCID: PMC5710537, DOI: 10.1001/jamaneurol.2017.0263.Peer-Reviewed Original ResearchConceptsHeteromodal areasTemporal lobeHarvard Aging Brain StudyHeteromodal association regionsAging Brain StudyCognitively normal participantsElderly brainsPositron emission tomographicPrimary somatomotor cortexDistribution volume ratioAlzheimer's diseaseTau depositionStandardized uptake value ratioVisual regionsNormal participantsAb depositionSomatomotor cortexTemporal areaPittsburgh compound BCarbon 11-labeled Pittsburgh Compound BBrain studiesAssociation regionsHuman brainPathological hallmarks of Alzheimer's diseaseCortical distributionTau and amyloid β proteins distinctively associate to functional network changes in the aging brain
Sepulcre J, Sabuncu M, Li Q, Fakhri G, Sperling R, Johnson K. Tau and amyloid β proteins distinctively associate to functional network changes in the aging brain. Alzheimer's & Dementia 2017, 13: 1261-1269. PMID: 28366797, PMCID: PMC5623176, DOI: 10.1016/j.jalz.2017.02.011.Peer-Reviewed Original ResearchConceptsAging brainFunctional connectivityAlzheimer's disease-related pathologyCognitively normal individualsPositron emission tomography scanHyperconnected regionsFunctional network changesMisfolded tauDisease-related pathologyBrain areasEmission tomography scanAmyloid-bFunctional reorganizationB proteinHuman brainNeuronal circuitsTauBrainNeuronal functionNegative associationAmyloidCortical patternsNetwork changesElderly subjectsPositive association
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
Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification
Hu C, Cheng L, Sepulcre J, El Fakhri G, Lu Y, Li Q. Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification. Lecture Notes In Computer Science 2013, 23: 1-12. PMID: 24683953, DOI: 10.1007/978-3-642-38868-2_1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseAniline CompoundsBenzothiazolesBrainBrain MappingConnectomeHumansImage EnhancementImage Interpretation, Computer-AssistedNerve NetNeural PathwaysPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityThiazolesTissue DistributionConceptsBrain network classificationNetwork classification problemWeighted energy detectorPrinciple component analysisSub-manifold structureTraditional principle component analysisSubspace detectionTraining dataEnergy detectorGraph structureProblem of Alzheimer's diseaseGraph LaplacianNetwork classificationNoise varianceLevel of smoothnessWeighted graphSignal detectionIntrinsic structureSignal modelGraphSubspaceIsing modelNoiseSignal variationsComponent analysis