2025
Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum
Dolci G, Ellis C, Cruciani F, Brusini L, Abrol A, Galazzo I, Menegaz G, Calhoun V, . Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum. Network Neuroscience 2025, 9: 259-279. PMCID: PMC11949592, DOI: 10.1162/netn_a_00423.Peer-Reviewed Original ResearchNeuropathological hallmarks of Alzheimer's diseaseHallmarks of Alzheimer's diseaseHyperphosphorylated tau proteinAmyloid-bTau proteinNeurofibrillary tanglesNeuropathological hallmarksAmyloid accumulationAlzheimer's diseaseAb accumulationDepositional signatureIdentification of individualsAmyloid statusAccumulationAmyloidShed lightTanglesAlzheimer's disease continuumProtein
2024
A multimodal Neuroimaging-Based risk score for mild cognitive impairment
Zendehrouh E, Sendi M, Abrol A, Batta I, Hassanzadeh R, Calhoun V. A multimodal Neuroimaging-Based risk score for mild cognitive impairment. NeuroImage Clinical 2024, 45: 103719. PMID: 39637673, PMCID: PMC11664180, DOI: 10.1016/j.nicl.2024.103719.Peer-Reviewed Original ResearchMild cognitive impairment riskMild cognitive impairmentMild cognitive impairment groupRisk of mild cognitive impairmentRisk scoreUK Biobank participantsFunctional network connectivityCognitive impairmentPrecursor to ADSignificant cognitive declineBiobank participantsUK BiobankMild cognitive impairment individualsGenetic risk factorsAlzheimer's diseaseFunctional MRIHigh-risk groupStructural MRIAD riskRisk factorsCognitive declineFeatures of CNGray matterDifferentiate CNParticipantsCross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers
Hassanzadeh R, Abrol A, Hassanzadeh H, Calhoun V. Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039975, DOI: 10.1109/embc53108.2024.10781737.Peer-Reviewed Original ResearchConceptsFunctional network connectivityGenerative adversarial networkStructural similarity index measureT1-weighted structural magnetic resonance imaging dataAdversarial networkStructural magnetic resonance imaging dataIncreased functional connectivityMagnetic resonance imaging dataSimilarity index measureCross-modal transformerCross-modal translationPatterns of atrophyAlzheimer's diseaseFunctional connectivityReduced connectivityMotor-visualTemporal regionsWeak supervisionAlzheimer's disease biomarkersControl networkCycle-GANCross-modalAlzheimer patientsContext of Alzheimer's diseaseGeneration approachA deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer’s in asymptomatic individuals
Wei Y, Abrol A, Lah J, Qiu D, Calhoun V. A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer’s in asymptomatic individuals. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039841, DOI: 10.1109/embc53108.2024.10781740.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityFunctional magnetic resonance imagingSpatio-temporal attention modelNetwork connectivityMild cognitive impairmentDeep learning advancesFunctional network connectivityMachine learning methodsSelf-attentionAttention modelAt-risk subjectsLearning methodsLearning advancesAlzheimer's diseaseNetwork dependenceParallel Multilink Joint ICA for Multimodal Fusion of Gray Matter and Multiple Resting fMRI Networks
Khalilullah K, Agcaoglu O, Duda M, Calhoun V. Parallel Multilink Joint ICA for Multimodal Fusion of Gray Matter and Multiple Resting fMRI Networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039683, DOI: 10.1109/embc53108.2024.10782528.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingJoint independent component analysisAssociated with Alzheimer's diseaseFalse discovery rateMultimodal fusion approachGray matterAssess group differencesHealthy controlsMultimodal fusionIndependent component analysisFusion approachSensorimotor domainBrain regionsSMRI dataGroup differencesParacentral lobuleBrain functionAD pathologyConnectivity patternsDiscovery rateJoint ICAJoint relationshipAlzheimer's diseaseActivity patternsMagnetic resonance imagingRevealing Alzheimer's Disease Dementia Patterns in [18F]Florbetapir PET with Independent Component Analysis
Khasayeva N, Eierud C, Jensen K, Premi E, Borroni B, Calhoun V, Iraji A. Revealing Alzheimer's Disease Dementia Patterns in [18F]Florbetapir PET with Independent Component Analysis. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039485, DOI: 10.1109/embc53108.2024.10782873.Peer-Reviewed Original ResearchConceptsPositron emission tomographyFrontal componentIndependent component analysisAlzheimer's Disease Neuroimaging InitiativeInteraction effects of diagnosisPositron emission tomography brain imagingEffect of diagnosisSignificant group effectAlzheimer's diseasePotential of independent component analysisAD dementia groupsSignificant interaction effectEvaluate group differencesGroup differencesDementia groupGeneralized linear modelGroup effectBrain imagingSalienceIC weightsEmission tomographyAD dementiaDementiaDementia patternsNeurobiologyDiffusion MRI Allows Capturing the Amyloid-β and τ Proteins Status in Alzheimer’s Disease Continuum
Dolci G, Cruciani F, Brusini L, Pini L, Galazzo I, Calhoun V, Menegaz G. Diffusion MRI Allows Capturing the Amyloid-β and τ Proteins Status in Alzheimer’s Disease Continuum. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635105.Peer-Reviewed Original ResearchA confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity
Hassanzadeh R, Abrol A, Pearlson G, Turner J, Calhoun V. A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity. PLOS ONE 2024, 19: e0293053. PMID: 38768123, PMCID: PMC11104643, DOI: 10.1371/journal.pone.0293053.Peer-Reviewed Original ResearchConceptsResting-state functional network connectivityFunctional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingAlzheimer's diseaseClassification of schizophreniaNetwork pairsPatients to healthy controlsSchizophrenia patientsNeurobiological mechanismsSZ patientsSubcortical networksCerebellum networkSchizophreniaRs-fMRIDisorder developmentMotor networkCompare patient groupsSubcortical domainSZ disorderHealthy controlsMagnetic resonance imagingDisordersNetwork connectivityFunctional abnormalitiesMultimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Batta I, Abrol A, Calhoun V, Initiative A. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. Journal Of Neuroscience Methods 2024, 406: 110109. PMID: 38494061, PMCID: PMC11100582, DOI: 10.1016/j.jneumeth.2024.110109.Peer-Reviewed Original ResearchConceptsBrain subspacesStandard machine learning algorithmsHigh-dimensional neuroimaging dataTrain machine learning modelsUnsupervised decompositionMachine learning algorithmsMachine learning modelsFunctional sub-systemsSubspace analysisSubspace componentsLearning algorithmsSupervised approachBiological traitsLearning modelsSub-systemsAlzheimer's diseaseSubspaceComputational frameworkActive subspaceCross-validation procedureNeuroimaging dataAD-related brain regionsAutomated identificationPredictive performanceOrientation subspacesA Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
Batta I, Abrol A, Calhoun V. A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480204.Peer-Reviewed Original ResearchLearning frameworkBrain subsystemsSubspace learning frameworkBrain networksHigh-dimensional neuroimaging dataConvolutional neural networkLow-dimensional subspaceSupervised learning approachDeep learning frameworkStructural brain featuresPredictive performanceUnsupervised approachNeural networkAutomated frameworkDimensional subspaceAlzheimer's diseaseLearning approachBrain changesFeature importanceTraining procedureNeuroimaging dataBrain featuresSalient networkNetworkBrain disordersIntra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment
Kolla S, Falakshahi H, Abrol A, Fu Z, Calhoun V. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors 2024, 24: 814. PMID: 38339531, PMCID: PMC10857295, DOI: 10.3390/s24030814.Peer-Reviewed Original ResearchConceptsGraph metricsFunctional network connectivityIndependent component analysisResting state fMRI dataData-driven methodologyNetwork connectivityNovel metricFunctional nodesNode sizeNodesLocal graph metricsMetricsNode dimensionsGraphAlzheimer's diseaseMild cognitive impairmentNetwork neuroscienceNeuroimaging researchNeuroimaging investigationsCorrelates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health
Burzynska A, Anderson C, Arciniegas D, Calhoun V, Choi I, Mendez Colmenares A, Kramer A, Li K, Lee J, Lee P, Thomas M. Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health. Cerebral Circulation - Cognition And Behavior 2024, 6: 100203. PMID: 38292016, PMCID: PMC10827486, DOI: 10.1016/j.cccb.2024.100203.Peer-Reviewed Original ResearchNeurologically healthy adultsHealthy adultsLow metabolic riskAdult spanNeural targetsSex differencesIntracranial volumeModifiable health risk factorsMetabolic riskMyelin contentHealth risk factorsAlzheimer's diseaseWhite matterNeurite orientation dispersionPeripheral metabolismMRI approachModifiable factorsAdiposity scoreAdultsMetabolic healthRisk factorsMetabolic syndromeVulnerabilityNeurologyWM
2023
A brain‐wide risk score of Alzheimer’s disease based on multimodal neuroimaging predicts cognition in large N>37000 population data
Zendehrouh E, Sendi M, Calhoun V. A brain‐wide risk score of Alzheimer’s disease based on multimodal neuroimaging predicts cognition in large N>37000 population data. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.077591.Peer-Reviewed Original ResearchMild cognitive impairmentAD riskGenetic risk of ADCognitive scoresRisk of ADFunctional network connectivityUK BiobankAlzheimer's diseaseGenetic riskRisk scoreBackground Alzheimer’s diseaseFI scoreFluid intelligenceCognitive impairmentPopulation dataEvaluate individualsGray matterScoresRiskReaction timeBiotypesTowards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data
Zendehrouh E, Sendi M, Calhoun V. Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083709, DOI: 10.1109/embc40787.2023.10340414.Peer-Reviewed Original ResearchDeep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data
Dolci G, Rahaman M, Galazzo I, Cruciani F, Abrol A, Chen J, Fu Z, Duan K, Menegaz G, Calhoun V. Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193683.Peer-Reviewed Original ResearchInterpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment
Gao Y, Lewis N, Calhoun V, Miller R. Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Computers In Biology And Medicine 2023, 161: 107005. PMID: 37211004, PMCID: PMC10365638, DOI: 10.1016/j.compbiomed.2023.107005.Peer-Reviewed Original ResearchConceptsMild cognitive impairmentDynamic functional network connectivityCognitive impairmentResting-state functional magnetic resonance imagingDementia interventionsFunctional magnetic resonance imagingAlzheimer's diseaseCognitive healthPatient deteriorationFunctional network connectivityCognitive abilitiesShort-term memoryRs-fMRIBrain connectivityResolved measurementsDynamic brain connectivityMagnetic resonance imaging
2022
Safety and biomarker effects of candesartan in non-hypertensive adults with prodromal Alzheimer’s disease
Hajjar I, Okafor M, Wan L, Yang Z, Nye J, Bohsali A, Shaw L, Levey A, Lah J, Calhoun V, Moore R, Goldstein F. Safety and biomarker effects of candesartan in non-hypertensive adults with prodromal Alzheimer’s disease. Brain Communications 2022, 4: fcac270. PMID: 36440097, PMCID: PMC9683395, DOI: 10.1093/braincomms/fcac270.Peer-Reviewed Original ResearchProdromal Alzheimer's diseaseMild cognitive impairmentCognitive effectsCognitive functionCognitive impairmentNon-hypertensive individualsAlzheimer's diseasePositive cognitive effectsAssociated with improved executive functionComposite cognitive scoreGlobal cognitive functionFunctional network connectivityImproving global cognitive functionBrain amyloid accumulationExecutive functionExploratory outcomesCognitive measuresSubcortical networksHippocampal volumeNon-hypertensive adultsFalse discovery rate correctionParahippocampal regionIntention-to-treat approachPlacebo-controlled trialNon-hypertensive participants
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