2025
A telescopic independent component analysis on functional magnetic resonance imaging dataset
Mirzaeian S, Faghiri A, Calhoun V, Iraji A. A telescopic independent component analysis on functional magnetic resonance imaging dataset. Network Neuroscience 2025, 9: 61-76. PMCID: PMC11949590, DOI: 10.1162/netn_a_00421.Peer-Reviewed Original ResearchRight frontoparietal networkVisual networkIndependent component analysisBrain functionExtraction of informationFunctional magnetic resonance imaging datasetsImage datasetsFrontoparietal networkMagnetic resonance imaging datasetFMRI dataGroup differencesLeverage informationSmall networksDMNNetworkComponent analysisIncomplete viewAbstract Brain functionFunctional sourceAn Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis
Gao B, Yu A, Qiao C, Calhoun V, Stephen J, Wilson T, Wang Y. An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis. IEEE Transactions On Medical Imaging 2025, 44: 941-951. PMID: 39320999, DOI: 10.1109/tmi.2024.3467384.Peer-Reviewed Original ResearchSpatio-temporal informationDeep learning networkInter-node connectivitySpatio-temporal correlationMachine learning modelsNode representationsPoor explainabilityCoupling learningLearning frameworkDeep learningLearning networkLearning modelsExplainabilityTime series dataExperimental resultsCoupling associationFramework constructionLearningDynamic functional connectivityFrameworkBrain functional connectivity analysisBrain dynamic functional connectivityInformationConnectionNetwork
2024
Brain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 39708510, PMCID: PMC11877132, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchConceptsGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesArchitectureBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 37986729, PMCID: PMC10659448, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsMean square errorNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesExplainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development
Chen L, Qiao C, Ren K, Qu G, Calhoun V, Stephen J, Wilson T, Wang Y. Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development. NeuroImage 2024, 298: 120771. PMID: 39111376, PMCID: PMC11533345, DOI: 10.1016/j.neuroimage.2024.120771.Peer-Reviewed Original ResearchConceptsSpatio-temporal dependenciesSpatial neighborhoodGraph learning methodsBrain network analysisNode representationsEvolution mechanisms of complex networksAdjacency informationDynamic brain network analysisModel explainabilityLanguage processingGraph evolutionEvolution learningLearning methodsLocal informationMechanism of complex networksDynamic evolutionModel dynamic interactionsDynamic functional connectivityNetwork componentsNested subgraphsLearning moduleExperimental resultsNetworkNetwork transitionsBrain development studiesCopula linked parallel ICA jointly estimates linked structural and functional MRI brain networks
Agcaoglu O, Alacam D, Adalı T, Calhoun V, Silva R, Plis S, Bostami B. Copula linked parallel ICA jointly estimates linked structural and functional MRI brain networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40040121, DOI: 10.1109/embc53108.2024.10781658.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingStructural MRIAmplitude of low frequency fluctuationsBrain imaging methodsStructural MRI dataFunctional network connectivityLow frequency fluctuationsEstimated independent sourcesBrain networksRegional homogeneityFMRI networksTemporal informationMagnetic resonance imagingFrequency fluctuationsAlzheimer's studiesBrainResonance imagingFusion approachUnmixing matrixNetwork connectivityReal-dataSensorimotorNetworkCerebellumPath-based Differential Analysis in Near-centenarians and Centenarians Brain Network
Falakshahi H, Rokham H, Calhoun V. Path-based Differential Analysis in Near-centenarians and Centenarians Brain Network. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039654, DOI: 10.1109/embc53108.2024.10781732.Peer-Reviewed Original ResearchConceptsCognitive control domainsBrain networksCognitive functionPreservation of cognitive functionPromote cognitive healthInvestigate brain networksGaussian graphical modelsControl domainCognitive agingNeural mechanismsGraph theory techniquesGraphical modelsCognitive healthIntricate informationBrain graphsNear-centenariansGroup graphGraphNetworkGraph metricsGraph theoryAging StudyBrainTargeted interventionsYounger groupScepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks
Kazemivash B, Suresh P, Liu J, Ye D, Calhoun V. Scepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039527, DOI: 10.1109/embc53108.2024.10781876.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingDynamic brain networksDense predictionBrain networksDynamic patterns of neural activityPatterns of neural activityBrain dynamicsSpatiotemporal brain dynamicsConsistent with previous findingsWeakly supervised frameworkComputer visionWeak supervisionModel architectureNetwork issuesSupervised frameworkFMRI dataBrain parcellation methodBrain functionNeural activityNeuroscience researchComplexity of brain functionNeural interactionsDeep-stackingExperimental resultsNetworkA Deep Biclustering Framework for Brain Network Analysis
Rahaman A, Fu Z, Iraji A, Calhoun V. A Deep Biclustering Framework for Brain Network Analysis. 2024, 00: 5075-5085. DOI: 10.1109/cvprw63382.2024.00514.Peer-Reviewed Original ResearchDeep neural networksBrain networksState-of-the-artFunctional connectivityNeural networkFeature dimensionsBiclustering frameworkSuboptimal solutionBrain functional connectivityNeuroimaging datasetsBrain network analysisHuman brain dynamicsNetworkNeurobiological mechanismsBiclustering methodsNeural systemsAssigned probability distributionsProbability distributionBrain componentsBrain dynamicsCluster generalizationBiclusteringBrainFrameworkBN edgesVoxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning
Itkyal V, Abrol A, LaGrow T, Calhoun V. Voxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635831.Peer-Reviewed Original ResearchMulti-channel convolutional neural networkConvolutional neural networkMultimodal deep learningSpatial information extractionAmplitude of low-frequency fluctuationSaliency visualizationInformation extractionClassification performanceDeep learningAlzheimer's Disease Neuroimaging Initiative datasetFusion resultsNeural networkAUC scoreAD classificationTemporal dependenciesFMRI networksTraditional metricsTest accuracyInitiative datasetNetworkLow-frequency fluctuationsAnalysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition
Faghiri A, Iraji A, Adali T, Calhoun V. Analysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition. 2024, 00: 13346-13350. DOI: 10.1109/icassp48485.2024.10446864.Peer-Reviewed Original ResearchSubgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks
Yang H, Ortiz-Bouza M, Vu T, Laport F, Calhoun V, Aviyente S, Adali T. Subgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks. 2024, 00: 2141-2145. DOI: 10.1109/icassp48485.2024.10446076.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional networksResting-state fMRI dataMultiplex networksMulti-subject functional magnetic resonance imagingNature of psychiatric disordersFunctional connectivity networksDiagnostic heterogeneityPsychotic patientsIndividual functional networksPsychiatric disordersCommunity detectionGroup differencesFMRI dataData-driven methodMultiple networksConnectivity networksMagnetic resonance imagingIdentified subgroupsNetworkSubgroup identificationResonance imagingSubject correlationSubgroup structureInterpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Qu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson T, Stephen J, Calhoun V, Wang Y. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE Transactions On Medical Imaging 2024, 43: 1568-1578. PMID: 38109241, PMCID: PMC11090410, DOI: 10.1109/tmi.2023.3343365.Peer-Reviewed Original ResearchConceptsGraph transformation frameworkBrain imaging datasetsFunctional brain networksPhiladelphia Neurodevelopmental CohortConvolutional deep learningFeature embeddingPropagation weightsGraph embeddingHuman Connectome ProjectAttention mechanismImage datasetsDeep learningGraph transformationFunctional connectivityAnalyze functional brain networksTransformation frameworkDiffusion strategyBrain networksPositional encodingSpatial knowledgePrediction accuracyIndividual cognitive abilitiesEmbeddingNetworkGraphMaximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks
Yan W, Fu Z, Jiang R, Sui J, Calhoun V. Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks. IEEE Transactions On Biomedical Engineering 2024, 71: 1170-1178. PMID: 38060365, PMCID: PMC11005005, DOI: 10.1109/tbme.2023.3330087.Peer-Reviewed Original ResearchDownstream tasksPerformance of downstream tasksOriginal feature spaceState-of-the-artAdversarial generative networkGAN generatorAdversarial networkFeature spaceOriginal imageGeneration networksClassification performanceSmall-sample problemTask objectivesGenerative modelImproved performanceTaskHarmony frameworkAnatomical layoutNetworkHarmonious methodsMulti-site collaborationSimulated dataLayoutScanner effectsDatasetImproving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For Data Augmentation in Multivariate Time Series Analysis
Gao Y, Ellis C, Calhoun V, Miller R. Improving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For Data Augmentation in Multivariate Time Series Analysis. 2024, 00: 125-128. DOI: 10.1109/ssiai59505.2024.10508611.Peer-Reviewed Original ResearchLong short-term memoryDeep learning modelsData augmentationPerformance deep learning modelsLearning modelsMultivariate time series dataAge prediction taskShort-term memoryPrediction taskAugmented datasetDynamical forecastsComponent networksMultivariate time series analysisDatasetNeuroimaging datasetsRobust solutionTime series dataOriginal dataValidation frameworkTime series analysisSeries dataNetworkNeuroimaging fieldDataModel performanceMultiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis
Wang W, Xiao L, Qu G, Calhoun V, Wang Y, Sun X. Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis. Medical Image Analysis 2024, 94: 103144. PMID: 38518530, DOI: 10.1016/j.media.2024.103144.Peer-Reviewed Original ResearchConceptsGraph convolutional networkEmbedding learningConvolutional networkIrregular graph-structured dataFunctional connectivity network analysisGraph-structured dataSoft-max classifierHigh-order relationsFunctional connectivity networksHypergraph convolutional networkDiagnosis of brain diseasesClass consistMultiview informationSoft-maxEmbedding spaceModel brain networksHyperedge weightsDiagnosis decisionAutomated diagnosisMultiple verticesEmbeddingFixed weightNetworkConnectivity network analysisFunctional magnetic resonance imagingA 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 disordersExplainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Ellis C, Miller R, Calhoun V. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Frontiers In Psychiatry 2024, 15: 1165424. PMID: 38495909, PMCID: PMC10941842, DOI: 10.3389/fpsyt.2024.1165424.Peer-Reviewed Original ResearchHard clusteringNetwork dynamicsDynamic functional network connectivityFuzzy clustering frameworkExtract several featuresFuzzy clusteringC-meansExplainability approachesExplainability metricsData spaceClustering frameworkK-meansDynamic functional network connectivity stateNetwork connectivityModerate anticorrelationImage dataNetworkState dynamicsAnalysis frameworkConnectivity dynamicsFunctional network connectivityAnticorrelationCentroidFunctional magnetic resonance imaging dataFramework
2023
Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia
Geenjaar E, Lewis N, Fedorov A, Wu L, Ford J, Preda A, Plis S, Calhoun V. Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia. Human Brain Mapping 2023, 44: 5828-5845. PMID: 37753705, PMCID: PMC10619380, DOI: 10.1002/hbm.26479.Peer-Reviewed Original ResearchDenoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data
Zhang C, Lin Q, Niu Y, Li W, Gong X, Cong F, Wang Y, Calhoun V. Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data. Human Brain Mapping 2023, 44: 5712-5728. PMID: 37647216, PMCID: PMC10619417, DOI: 10.1002/hbm.26471.Peer-Reviewed Original ResearchConceptsComplex-valued dataComplex-valued fMRI dataBrain networksFMRI dataPhase informationHuman Connectome ProjectMapping frameworkMagnitude mapsExperimental fMRI dataConnectome ProjectPhase mapFMRI datasetsMagnitude dataDenoisingNetworkAmplitude thresholdComponent analysisPhase changePhaseSSP approachSpatial mappingFMRIUniversity of New MexicoThreshold
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