2024
A 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
Towards 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