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 analysisInformationSubgroup 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 structureA 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 solutionMethodOptimizationImplementation
2023
Reproducibility in Joint Blind Source Separation: Application to fMRI Analysis
Laport F, Vu T, Yang H, Calhoun V, Adali T. Reproducibility in Joint Blind Source Separation: Application to fMRI Analysis. 2023, 00: 1448-1452. DOI: 10.1109/ieeeconf59524.2023.10477028.Peer-Reviewed Original ResearchJoint blind source separationBlind source separationSource separationMulti-subject functional magnetic resonance imagingIndependent vector analysisRandom initializationLocal optimumNon-convexFlexible solutionCost functionModel complexityAccurate solutionsIterative methodFunctional magnetic resonance imaging analysisFMRI dataVector analysisSolutionNew Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning
Ghayem F, Yang H, Kantar F, Kim S, Calhoun V, Adali T. New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096473.Peer-Reviewed Original ResearchDictionary learningIndependent component analysisLearned atomsDiscovery of hidden informationNetwork connectivityMulti-subject functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional network connectivityDiscriminative featuresFeature vectorHidden informationEffective classificationSZ groupHealthy controlsResting-state fMRI dataExperimental resultsICA resultsDictionaryBrain functional network connectivityBrain networksMental disordersFMRI dataLearningRepresentationMental diseases