2025
The Impact of Cognitive Impairment on Cardiovascular Disease
Jamil Y, Krishnaswami A, Orkaby A, Stimmel M, Brown Iv C, Mecca A, Forman D, Rich M, Nanna M, Damluji A. The Impact of Cognitive Impairment on Cardiovascular Disease. Journal Of The American College Of Cardiology 2025, 85: 2472-2491. PMID: 40562512, DOI: 10.1016/j.jacc.2025.04.057.Peer-Reviewed Original ResearchConceptsImpact of cognitive impairmentCognitive impairmentCardiovascular diseaseHealth care utilizationOlder adult populationEvidence-based management strategiesMedical adverse eventsHealth literacyCare utilizationOlder adultsMitigate cognitive impairmentUnder-prescribedReduced participationU.S. populationTreatment of patientsAdult populationGuideline-directedExcess morbidityCardiac patientsRisk factorsOlder patientsAdverse eventsHealthMechanism of cognitive impairmentInterventional managementCerebrospinal fluid and brain positron emission tomography measures of synaptic vesicle glycoprotein 2A: Biomarkers of synaptic density in Alzheimer's disease
Mecca A, Ashton N, Chen M, O'Dell R, Toyonaga T, Zhao W, Young J, Salardini E, Bates K, Ra J, Goodcase S, Silva‐Rudberg J, Nabulsi N, Brinkmalm A, Kvartsberg H, Schöll M, Nilsson J, Arnsten A, Huang Y, Hansson O, Zetterberg H, Carson R, Blennow K, van Dyck C. Cerebrospinal fluid and brain positron emission tomography measures of synaptic vesicle glycoprotein 2A: Biomarkers of synaptic density in Alzheimer's disease. Alzheimer's & Dementia 2025, 21: e70344. PMID: 40491249, PMCID: PMC12149441, DOI: 10.1002/alz.70344.Peer-Reviewed Original ResearchConceptsSynaptic vesicle glycoprotein 2APositron emission tomographyAlzheimer's diseaseSynaptic densityEnzyme-linked immunosorbent assayC]UCB-J positron emission tomographyPositron emission tomography measurementsEmission tomographyAxonal proteinsCN participantsImmunosorbent assaySymptomatic Alzheimer's diseaseAD groupProteinAssayParticipantsSV2AAlzheimerCerebrospinal fluidBrainInvestigate associationsCerebrospinal fluid assaysSV2A positron emission tomographyDetection of emergency department patients at risk of dementia through artificial intelligence
Cohen I, Taylor R, Xue H, Faustino I, Festa N, Brandt C, Gao E, Han L, Khasnavis S, Lai J, Mecca A, Sapre A, Young J, Zanchelli M, Hwang U. Detection of emergency department patients at risk of dementia through artificial intelligence. Alzheimer's & Dementia 2025, 21: e70334. PMID: 40457744, PMCID: PMC12130574, DOI: 10.1002/alz.70334.Peer-Reviewed Original ResearchConceptsElectronic health record dataHealth record dataEmergency departmentDetect dementiaDementia detectionYale New Haven HealthRecord dataRisk of dementiaEmergency department patientsBalance detection accuracyDementia algorithmsImprove patient outcomesCare coordinationCare transitionsDementia diagnosisReal-time applicationsClinical decision-makingClinician supportED usePatient safetyProbable dementiaMachine learning algorithmsED workflowED visitsED encountersSex differences in the confounders influencing the relationships linking socioeconomic factors and cognitive performance with family history of Alzheimer's disease and related dementias
He J, Cabrera‐Mendoza B, Friligkou E, Mecca A, van Dyck C, Pathak G, Polimanti R. Sex differences in the confounders influencing the relationships linking socioeconomic factors and cognitive performance with family history of Alzheimer's disease and related dementias. Alzheimer's & Dementia 2025, 21: e70215. PMID: 40421744, PMCID: PMC12107445, DOI: 10.1002/alz.70215.Peer-Reviewed Original ResearchConceptsSocioeconomic factorsFamily historySocioeconomic statusFamily history of Alzheimer's diseaseAssociation of socioeconomic factorsHistory of Alzheimer's diseaseUs Research ProgramCognitive performanceHigher socioeconomic statusSex-specific associationsAlzheimer's disease assessmentSex differencesUK BiobankADRDAlzheimer's diseaseGenetically informed analysesDementiaSample sizeSexGenetic analysisAlzheimerLimited informationStatusConfoundingBiobank0434 Comparison of Machine Learning Models for fMRI-Based Sleep Staging
Cho G, Hahm D, Mecca A, Miner B. 0434 Comparison of Machine Learning Models for fMRI-Based Sleep Staging. Sleep 2025, 48: a190-a190. DOI: 10.1093/sleep/zsaf090.0434.Peer-Reviewed Original Research0074 The Relationship of Sleep Disorder History with Gray Matter BOLD-CSF Coupling Among Persons at Risk for and with Alzheimer’s Disease
Cho G, Liu X, Mecca A, Miner B. 0074 The Relationship of Sleep Disorder History with Gray Matter BOLD-CSF Coupling Among Persons at Risk for and with Alzheimer’s Disease. Sleep 2025, 48: a34-a34. DOI: 10.1093/sleep/zsaf090.0074.Peer-Reviewed Original ResearchAssessment of the relationship between synaptic density and metabotropic glutamate receptors in early Alzheimer’s disease: a multi-tracer PET study
Salardini E, O’Dell R, Tchorz E, Nabulsi N, Huang Y, Carson R, van Dyck C, Mecca A. Assessment of the relationship between synaptic density and metabotropic glutamate receptors in early Alzheimer’s disease: a multi-tracer PET study. Alzheimer's Research & Therapy 2025, 17: 98. PMID: 40329311, PMCID: PMC12054321, DOI: 10.1186/s13195-025-01739-1.Peer-Reviewed Original ResearchConceptsDistribution volume ratioMedial temporal lobePositron emission tomographyTemporal lobeSynaptic densityMetabotropic glutamate receptor subtype 5Multi-tracer PET studiesAlzheimer's diseaseCerebellum reference regionAmyloid-positive participantsSynaptic lossSynaptic vesicle glycoprotein 2AMetabotropic glutamate receptorsPositron emission tomography scanNeocortical regionsSubtype 5MGluR5Entorhinal cortexAD groupReference regionGlutamate receptorsExploratory analysisWidespread reductionsLongitudinal studyReceptor bindingAssessing Synaptic Density in Behavioral Variant Frontotemporal Dementia: A Head-to-Head Comparison of 18F-SynVesT-1 and 18F-FDG PET (P9-3.019)
Cayir S, Yang Y, Ibrahim W, Naganawa M, Gallezot J, Toyonaga T, Nabulsi N, huang H, Carson R, Vandyck C, Mecca A, Fesharaki-Zadeh A, Matuskey D. Assessing Synaptic Density in Behavioral Variant Frontotemporal Dementia: A Head-to-Head Comparison of 18F-SynVesT-1 and 18F-FDG PET (P9-3.019). Neurology 2025, 104 DOI: 10.1212/wnl.0000000000212121.Peer-Reviewed Original ResearchGenerating synthetic brain PET images of synaptic density based on MR T1 images using deep learning
Zheng X, Worhunsky P, Liu Q, Guo X, Chen X, Sun H, Zhang J, Toyonaga T, Mecca A, O’Dell R, van Dyck C, Angarita G, Cosgrove K, D’Souza D, Matuskey D, Esterlis I, Carson R, Radhakrishnan R, Liu C. Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning. EJNMMI Physics 2025, 12: 30. PMID: 40163154, PMCID: PMC11958861, DOI: 10.1186/s40658-025-00744-5.Peer-Reviewed Original ResearchCannabis use disorderStructural similarity indexPET imagingImages of higher qualityMR-T1 imagesMean square errorUse disorderEncoder-decoderDeep learningCross-validation processData-driven approachDiagnostic categoriesLow-dose scansPredicted imageTemporal regionsBrain disordersGround truthT1-weighted MRISynaptic densityHuman brainSimilarity indexDisordersSevere neurological disordersTranslation accuracyNoise reductionLower slow wave sleep and rapid eye movement sleep are associated with brain atrophy of AD-vulnerable regions.
Cho G, Mecca A, Buxton O, Liu X, Miner B. Lower slow wave sleep and rapid eye movement sleep are associated with brain atrophy of AD-vulnerable regions. Journal Of Clinical Sleep Medicine 2025 PMID: 40110600, DOI: 10.5664/jcsm.11630.Peer-Reviewed Original ResearchRapid eye movementSlow wave sleepSleep architectureArousal indexAssociated with smaller volumesCerebral microbleedsLobar cerebral microbleedsProportion of rapid eye movementBaseline sleep architectureRapid eye movement sleepProportion of slow wave sleepAssociated with cerebral microbleedsEye movement sleepSleep architecture variablesModifiable risk factorsAtrophy of hippocampusSleep deficiencyMedian ageParietal regionsMovement sleepMRI outcomesAssociated with brain atrophyAnatomical featuresRisk factorsCuneus regionsCerebrospinal Fluid Biomarkers and Cognition in Alzheimer Disease and Frontotemporal Dementia in a Memory Clinic Setting
Cayir S, Sadabad F, Mecca A, Matuskey D, Fesharaki-Zadeh A. Cerebrospinal Fluid Biomarkers and Cognition in Alzheimer Disease and Frontotemporal Dementia in a Memory Clinic Setting. Alzheimer Disease & Associated Disorders 2025, 39: 22-27. PMID: 40397510, DOI: 10.1097/wad.0000000000000656.Peer-Reviewed Original ResearchMoCA scoresMemory clinic settingHospital memory clinicCognitive performanceMontreal Cognitive AssessmentFrontotemporal dementiaAlzheimer's diseaseCognitive test scoresP-tauMemory clinicElectronic records of patientsRetrospective cohort studyT-tauCohort studyCognitive AssessmentElectronic recordsMoCACerebrospinal fluidRecords of patientsIndex scoreTau-related pathologyClinical settingDementiaConcentrations of T-tauScores
2024
Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease
de Bruin H, Groot C, ADNI, Barthel H, Bischof G, Boellaard R, Brendel M, Cash D, Coath W, Day G, Dickerson B, Doering E, Drzezga A, van Dyck C, van Eimeren T, van der Flier W, Fredericks C, Fryer T, van de Giessen E, Gordon B, Graff‐Radford J, Hobbs D, Höglinger G, Hönig M, Irwin D, Jones P, Josephs K, Katsumi Y, Lee E, Levin J, Malpetti M, McGinnis S, Mecca A, Nasrallah I, O'Brien J, O'Dell R, Palleis C, Perneczky R, Phillips J, Pijnenburg Y, Putcha D, Rahmouni N, Rosa‐Neto P, Rowe J, Rullmann M, Sabri O, Saur D, Schildan A, Schott J, Schroeter M, Servaes S, Sintini I, Stevenson J, Therriault J, Touroutoglou A, Trainer A, Visser D, Weston P, Whitwell J, Wolk D, Franzmeier N, Ossenkoppele R. Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease. Alzheimer's & Dementia 2024, 20: e085869. PMCID: PMC11714601, DOI: 10.1002/alz.085869.Peer-Reviewed Original ResearchAlzheimer's diseaseTau spreadingProgression of Alzheimer's diseaseTau-PETFunctional proximityPosterior patterningAD variantsTauPersonalized medicineTau-PET standardized uptake value ratiosVariantsWidespread patternAtypical ADDominant patternAtypical Alzheimer's diseaseRegionNeurodegenerationPositive probabilityPatternsConnectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease
de Bruin H, Groot C, ADNI, Barthel H, Bischof G, Boellaard R, Brendel M, Cash D, Coath W, Day G, Dickerson B, Doering E, Drzezga A, van Dyck C, van Eimeren T, van der Flier W, Fredericks C, Fryer T, van de Giessen E, Gordon B, Graff‐Radford J, Hobbs D, Höglinger G, Hönig M, Irwin D, Jones P, Josephs K, Katsumi Y, Lee E, Levin J, Malpetti M, McGinnis S, Mecca A, Nasrallah I, O'Brien J, O'Dell R, Palleis C, Perneczky R, Phillips J, Pijnenburg Y, Putcha D, Rahmouni N, Rosa‐Neto P, Rowe J, Rullmann M, Sabri O, Saur D, Schildan A, Schott J, Schroeter M, Servaes S, Sintini I, Stevenson J, Therriault J, Touroutoglou A, Trainer A, Visser D, Weston P, Whitwell J, Wolk D, Franzmeier N, Ossenkoppele R. Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease. Alzheimer's & Dementia 2024, 20: e093663. PMCID: PMC11713789, DOI: 10.1002/alz.093663.Peer-Reviewed Original ResearchAlzheimer's diseaseTau spreadingProgression of Alzheimer's diseaseTau-PETFunctional proximityPosterior patterningAD variantsTauPersonalized medicineTau-PET standardized uptake value ratiosVariantsWidespread patternAtypical ADDominant patternAtypical Alzheimer's diseaseRegionNeurodegenerationPatternsLower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions
Cho G, Mecca A, Buxton O, Liu X, Miner B. Lower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions. Alzheimer's & Dementia 2024, 20: e093827. PMCID: PMC11713325, DOI: 10.1002/alz.093827.Peer-Reviewed Original ResearchInferior parietal regionsSlow wave sleepAD-vulnerable regionsAssociated with smaller volumesParietal regionsPresence of cerebral microbleedsSleep architectureAlzheimer's diseaseCerebral microbleedsIncreased risk of Alzheimer's diseaseProportion of timeRisk of Alzheimer's diseaseBaseline cognitive functionLobar cerebral microbleedsModifiable risk factorsAssociated with lower volumesAssociated with brain atrophyBaseline sleep architectureSleep architecture variablesSleep deficiencyAssociated with ADFalse discovery rateAtherosclerosis RiskAD pathogenesisEye-movement sleepLower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions
Cho G, Mecca A, Buxton O, Liu X, Miner B. Lower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions. Alzheimer's & Dementia 2024, 20: e089369. PMCID: PMC11716384, DOI: 10.1002/alz.089369.Peer-Reviewed Original ResearchInferior parietal regionsSlow wave sleepAD-vulnerable regionsAssociated with smaller volumesParietal regionsPresence of cerebral microbleedsSleep architectureAlzheimer's diseaseCerebral microbleedsIncreased risk of Alzheimer's diseaseProportion of timeRisk of Alzheimer's diseaseBaseline cognitive functionLobar cerebral microbleedsModifiable risk factorsAssociated with lower volumesAssociated with brain atrophyBaseline sleep architectureSleep architecture variablesSleep deficiencyAssociated with ADFalse discovery rateAtherosclerosis RiskAD pathogenesisEye-movement sleepSelf‐reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort
Miller A, Sharp E, Wang S, Zhao Y, Mecca A, van Dyck C, O'Dell R, Initiative F. Self‐reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort. Alzheimer's & Dementia 2024, 20: 7847-7858. PMID: 39324520, PMCID: PMC11567835, DOI: 10.1002/alz.14252.Peer-Reviewed Original ResearchSelf-reported hearing lossFunctional Activities QuestionnaireHearing lossMild cognitive impairmentModifiable risk factorsMild cognitive impairment participantsFunctional declineImpairment diagnosisModified Preclinical Alzheimer Cognitive CompositeRate of functional declineRate of cognitive declinePreclinical Alzheimer Cognitive CompositeRisk factorsCognitive impairmentSignificant longitudinal associationsActivity QuestionnaireLongitudinal associationsAlzheimer's Disease Neuroimaging InitiativeLongitudinal relationshipCognitive compositeCN participantsIncreased riskCognitive declineParticipantsDiagnostic conversionValidation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET
Young J, O’Dell R, Naganawa M, Toyonaga T, Chen M, Nabulsi N, Huang Y, Cooper E, Miller A, Lam J, Bates K, Ruan A, Nelsen K, Salardini E, Carson R, van Dyck C, Mecca A. Validation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET. Journal Of Nuclear Medicine 2024, 65: jnumed.124.267419. PMID: 39299782, PMCID: PMC11533916, DOI: 10.2967/jnumed.124.267419.Peer-Reviewed Original ResearchDistribution volume ratioSUV ratioSynaptic densityEffect sizeAlzheimer's diseaseLongitudinal study of Alzheimer's diseaseMethods:</b> ParticipantsLongitudinal studyMeasure synaptic densityAD participantsStudy of Alzheimer's diseaseNormal cognitionReference regionOlder adultsMulticenterDensity alterationsDiffusion Imaging of Gray Matter Microstructure in Alzheimer’s Disease
Silva-Rudberg J, Mecca A. Diffusion Imaging of Gray Matter Microstructure in Alzheimer’s Disease. Journal Of Alzheimer’s Disease 2024, 101: 437-439. PMID: 39213077, DOI: 10.3233/jad-240673.Peer-Reviewed Original ResearchCT1812 biomarker signature from a meta‐analysis of CSF proteomic findings from two Phase 2 clinical trials in Alzheimer's disease
Lizama B, Williams C, North H, Pandey K, Duong D, Di V, Mecca A, Blennow K, Zetterberg H, Levey A, Grundman M, van Dyck C, Caggiano A, Seyfried N, Hamby M. CT1812 biomarker signature from a meta‐analysis of CSF proteomic findings from two Phase 2 clinical trials in Alzheimer's disease. Alzheimer's & Dementia 2024, 20: 6860-6880. PMID: 39166791, PMCID: PMC11485314, DOI: 10.1002/alz.14152.Peer-Reviewed Original ResearchVolumetric magnetic resonance imagingMagnetic resonance imagingPharmacodynamic biomarkersMeta-analysisClinical developmentCerebrospinal fluidPhase 2 clinical trialResonance imagingAlzheimer's diseaseMechanism of actionClinical trialsTandem mass tag-mass spectrometryClinical cohortMild to moderate ADCandidate biomarkersCT1812CohortBiomarker signaturesBiomarkersProteomic findingsUnbiased analysisNetwork analysisAmyloid-betaSynaptic biologyBiological impactRelationship between neuroimaging and cognition in frontotemporal dementia: An FDG‐PET and structural MRI study
Cayir S, Volpi T, Toyonaga T, Gallezot J, Yang Y, Sadabad F, Mulnix T, Mecca A, Fesharaki‐Zadeh A, Matuskey D. Relationship between neuroimaging and cognition in frontotemporal dementia: An FDG‐PET and structural MRI study. Journal Of Neuroimaging 2024, 34: 627-634. PMID: 38676301, PMCID: PMC11511789, DOI: 10.1111/jon.13206.Peer-Reviewed Original ResearchMoCA scoresFDG-PETAssociation of cognitionStandardized uptake value ratioMontreal Cognitive AssessmentSignificant positive associationFrontotemporal dementiaPrimary outcome measurePosterior cingulate cortexDecline of cognitive functionYears of ageGM volumeFrontal cortexOutcome measuresCognitive dysfunctionGray matterCognitive AssessmentMoCAAssociated with cognitive dysfunctionFluorodeoxyglucose (FDG)-PETPositive associationMagnetic resonance imagingPartial volume correctionCognitive functionDementia
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