2024
Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis
Vu T, Laport F, Yang H, Calhoun V, Adal T. Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis. IEEE Transactions On Biomedical Engineering 2024, PP: 1-12. PMID: 39042541, DOI: 10.1109/tbme.2024.3432273.Peer-Reviewed Original ResearchIndependent vector analysisIndependent component analysisIVA approachesIndependent vector analysis algorithmMulti-subject functional magnetic resonance imagingHigher-order statistical informationMulti-subject dataSingle-subject mappingModel interferenceMultiple datasetsPrior informationNovel methodStatistical dependenceDatasetSeparation qualityStatistical informationComputational issuesVariable thresholdAlgorithmStatistical diversityModel matchingVector analysisQuality of separationComponent analysisInformationA Robust and Scalable Method with an Analytic Solution for Multi-Subject FMRI Data Analysis
Vu T, Yang H, Laport F, Gabrielson B, Calhoun V, Adalı T. A Robust and Scalable Method with an Analytic Solution for Multi-Subject FMRI Data Analysis. 2024, 00: 1831-1835. DOI: 10.1109/icassp48485.2024.10447397.Peer-Reviewed Original ResearchJoint blind source separationSource separationMulti-subject functional magnetic resonance imagingBlind source separationLatent sourcesSeparation of sourcesDemixing vectorsComputational complexityCompetitive performanceMultiple datasetsEstimation performanceDatasetSource templateMulti-subjectNumerical resultsEfficient methodRuntimeComponent analysisScalable methodPerformanceAlgorithmAnalytical solutionMethodOptimizationImplementationMaximum 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 performanceA Multi-dimensional Joint ICA Model with Gaussian Copula
Agcaoglu O, Silva R, Alacam D, Calhoun V. A Multi-dimensional Joint ICA Model with Gaussian Copula. Lecture Notes In Computer Science 2024, 14366: 152-163. DOI: 10.1007/978-3-031-51026-7_14.Peer-Reviewed Original ResearchIndependent component analysisBivariate distributionMarginal distributionsGaussian copulaLogistic distributionJoint ICAImage data miningSuper-Gaussian distributionImage datasetsFunctional magnetic resonance imaging datasetsInfomax principleAlzheimer's Disease Neuroimaging InitiativeProposed algorithmData miningIdentical marginalsMagnetic resonance imaging datasetICA modelMultimodal versionICA methodJoint independent component analysisCopulasDatasetMaximum likelihoodMixing matrixNeuroimaging dataIdentifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data
Lehmann I, Hasija T, Gabrielson B, Akhonda M, Calhoun V, Adali T. Identifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data. IEEE Access 2024, 12: 109443-109456. DOI: 10.1109/access.2024.3435526.Peer-Reviewed Original ResearchIndependent vector analysisTask datasetMultiple datasetsFeature extraction approachUser-defined thresholdsHigher-order statisticsMulti-task fMRI dataExtraction approachRelationship structureDatasetSimulation resultsHierarchical clusteringInterpretable componentsVector analysisFMRI-dataFMRI dataEffective wayMethodTaskDataActivated brain regionsHypothesis testingDistributional assumptionsInformation
2023
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*
Ellis C, Sattiraju A, Miller R, Calhoun V. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*. 2023, 00: 2466-2473. DOI: 10.1109/bibm58861.2023.10385424.Peer-Reviewed Original ResearchDeep learning methodsLearning methodsTransfer learningEEG datasetManually engineered featuresTransfer learning approachDeep learning modelsDeep learning performanceMachine learning methodsClassification datasetsLearned representationsElectroencephalography classifierDeep learningEEG classificationResting-state electroencephalographyDiagnosis of major depressive disorderRaw electroencephalographyLearning approachLearning modelsMajor depressive disorder diagnosisMajor depressive disorderLearning performanceClassifierDatasetEngineering featuresREGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSIS
Yang H, Gabrielson B, Calhoun V, Adali T. REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSIS. 2023, 00: 1443-1447. DOI: 10.1109/ieeeconf59524.2023.10476796.Peer-Reviewed Original ResearchConstrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data
Yang H, Ghayem F, Gabrielson B, Akhonda M, Calhoun V, Adali T. Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10095816.Peer-Reviewed Original ResearchIndependent vector analysisSynthetic dataConstrained independent component analysisEntropy bound minimizationComputational complexity limitationsDemixing matrixIndependent component analysisComputational costOrthogonality requirementData identificationAlgorithmFunctional networksNetworkComponent analysisDatasetFMRI dataComputerTaskEntropyOrthogonalitySubgroup identificationVector analysisBrain networksDensity modelCoupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion
Borsoi R, Lehmann I, Akhonda M, Calhoun V, Usevich K, Brie D, Adali T. Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096241.Peer-Reviewed Original ResearchCP tensor decompositionTensor factorization approachDataset-specific featuresTensor-based frameworkPost-processing stepExtract featuresFunctional magnetic resonance imagingHyperparameter selectionTensor decompositionData fusionMulti-taskingDiscover componentsMultiple datasetsTaskCoupling matrixFunctional magnetic resonance imaging dataHyperparametersDatasetFeaturesGroup differencesFactor approachDecompositionFusionIndependent Vector Analysis with Multivariate Gaussian Model: a Scalable Method by Multilinear Regression
Gabrielson B, Sun M, Akhonda M, Calhoun V, Adali T. Independent Vector Analysis with Multivariate Gaussian Model: a Scalable Method by Multilinear Regression. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096698.Peer-Reviewed Original ResearchJoint blind source separationIndependent vector analysisMultivariate Gaussian sourcesBlind source separationOverall estimation performanceGaussian sourceMultivariate Gaussian modelSource separationComputational costJoint decompositionEstimation performanceDatasetCost functionEstimated sourcesGaussian modelIntractable problemVector analysisMultilinear regressionEfficient methodScalable methodRegressorsFMRI dataMethodMultilinearEvaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis
Li X, Khosravinezhad D, Calhoun V, Silva R. Evaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230492.Peer-Reviewed Original ResearchIndependent vector analysisMultimodal neuroimaging datasetDeep latent variable modelBlind source separation methodMulti-network architectureIndependent Subspace AnalysisNeuroimaging datasetsSource separation methodPerformance trade-offsLatent spaceSubspace analysisTrade-offsPyTorch modulesLoss functionCross-platformMultiple datasetsLatent variable modelsDatasetCritical performance trade-offsOriginal frameworkVariable modelSimulation settingsModel performanceMultiple configurationsPlatformMultimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging Data
Li X, Adali T, Silva R, Calhoun V. Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging Data. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230605.Peer-Reviewed Original ResearchSubspace structureIndependent vector analysisSynthetic datasetsMultimodal neuroimaging datasetUnimodal analysisData modalitiesHidden relationshipsCanonical correlation analysisIncorrect onesNeuroimaging datasetsSubspaceLatent sourcesDatasetNeuroimaging modalitiesDataPhenotypic measurementsCorrelation analysisJoint Structural and Functional Connectivity Learning Based Independent Component Analysis
Fouladivanda M, Iraji A, Wu L, Calhoun V. Joint Structural and Functional Connectivity Learning Based Independent Component Analysis. 2023, 00: 1-5. DOI: 10.1109/mlsp55844.2023.10285932.Peer-Reviewed Original ResearchJoint learning procedureIndependent component analysisFunctional connectivity informationData-driven approachLearning procedureConnectivity informationICA approachMultiple modalitiesComplementary informationComponent analysisBrain network analysisIntrinsic connectivity networksJoint approachConnectivity networksInformationIncreasing developmentDatasetBrain's intrinsic connectivity networksNetworkNetwork analysisSensitive to group differences