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 source
2024
A spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 8: 1212-1242. PMID: 39735500, PMCID: PMC11674407, DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRIConstrained 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, 71: 3531-3542. PMID: 39042541, PMCID: PMC11754528, 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 analysisInformationNetworks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Kinsey S, Kazimierczak K, Camazón P, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp T, Dhamala M, Calhoun V, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. Nature Mental Health 2024, 2: 1464-1475. PMID: 39650801, PMCID: PMC11621020, DOI: 10.1038/s44220-024-00341-y.Peer-Reviewed Original ResearchSelf-referential cognitionFunctional magnetic resonance imaging connectivityFunctional brain connectivityCingulo-opercularDefault-modeSchizophrenia diagnosisExecutive regionsFMRI connectivityFunctional connectivityConnectivity analysisSchizophreniaSensitive to differencesBrain connectivityFunctional connectivity structureWidespread alterationsImaging connectivityIndependent component analysisBrain phenomenaNetwork integrationHypoconnectivityPsychosisCognitionCore regionNonlinear networksCase-control datasetData augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model
Yang Y, Ma S, Cao S, Jia S, Bi Y, Calhoun V. Data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 13252: 1325214-1325214-7. DOI: 10.1117/12.3044654.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional network connectivity matricesIndependent component analysisVision Transformer (ViTAdvanced artificial intelligence techniquesTraditional U-NetArtificial intelligence techniquesFunctional magnetic resonance imaging dataGroup independent component analysisNetwork connectivity matrixDenoising functionData augmentationImage generationIntelligence techniquesU-NetSmall datasetsDiagnosed schizophreniaSchizophrenia diagnosisGeneration taskNeuroimaging dataSchizophreniaComputational burdenConnectivity matrixMagnetic resonance imagingRelevant informationNeuroMark PET: Replicable positron emission tomography ICA templates for florbetapir and florbetaben radioligands
Eierud C, Fu Z, Petropoulos H, Bohsali A, Iraji A, Ganz M, Pernet C, Calhoun V. NeuroMark PET: Replicable positron emission tomography ICA templates for florbetapir and florbetaben radioligands. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039357, DOI: 10.1109/embc53108.2024.10782228.Peer-Reviewed Original ResearchIdentifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age
Bajracharya P, Faghiri A, Fu Z, Calhoun V, Shultz S, Iraji A. Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039283, DOI: 10.1109/embc53108.2024.10782404.Peer-Reviewed Original ResearchConceptsIntrinsic connectivity networksStatic functional network connectivitySubject-specific intrinsic connectivity networksResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional brain organizationResting-state fMRIFunctional network connectivityConnectivity networksCognitive domainsCognitive processesBrain organizationSub-corticalRsfMRI dataIndependent component analysisMagnetic resonance imagingNeuromarkersDistinct patternsMotor controlNeurodevelopmental disabilitiesResonance imagingEarly identificationSensory perceptionAssociated with ageFMRIParallel 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 imagingMultiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses
Behzadfar N, Iraji A, Calhoun V. Multiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-5. PMID: 40040173, DOI: 10.1109/embc53108.2024.10782601.Peer-Reviewed Original ResearchConceptsIndependent component analysisTask-related componentsDynamics of functional connectivitySubband informationGroup independent component analysisMultisubject fMRI dataFunctional network connectivityFMRI dataNetwork connectivityBandpass filterSampling rateSubbandBack-reconstructionApplication of bandpass filtersSpatially independent mapsFrequency rangeFunctional connectivityFMRI analysisRevealing 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 patternsNeurobiologyDistribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls
Maksymchuk N, Miller R, Calhoun V. Distribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls. 2024, 00: 37-40. DOI: 10.1109/ssiai59505.2024.10508663.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingGroup independent component analysisSchizophrenia patientsCognitive controlResting-state functional magnetic resonance imagingIntrinsic connectivity networksHealthy controlsGender-matched healthy controlsSZ patientsNeuropsychiatric disordersBrain areasBrain networksSchizophreniaDisrupted integrityBrain domainsConnection strengthIndependent component analysisConnectivity networksMagnetic resonance imagingSomatomotorDistribution of connection strengthsResonance imagingCross-sectional dataPatientsDiagnostic testsSMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks
He X, Calhoun V, Du Y. SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks. Neuroscience Bulletin 2024, 40: 905-920. PMID: 38491231, PMCID: PMC11637147, DOI: 10.1007/s12264-024-01184-4.Peer-Reviewed Original ResearchConceptsIndependent component analysisFunctional magnetic resonance imagingClustering independent componentsFunctional networksIndependent component analysis methodMulti-subject fMRI dataIndependent componentsBrain functional networksFMRI dataSubject-specific functional networksFunctional magnetic resonance imaging dataOptimal model orderSmartComponent analysisIntra-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 investigationsA 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 data
2023
F71. NETWORK OF CO-METHYLATION ASSOCIATED WITH GREY MATTER MATURATION IN HUMAN ADOLESCENCE
Jensen D, Chen J, Turner J, Stephen J, Wang Y, Wilson T, Calhoun V, Liu J. F71. NETWORK OF CO-METHYLATION ASSOCIATED WITH GREY MATTER MATURATION IN HUMAN ADOLESCENCE. European Neuropsychopharmacology 2023, 75: s258-s259. DOI: 10.1016/j.euroneuro.2023.08.455.Peer-Reviewed Original ResearchStructural MRIBrain maturationNeuronal systemsCo-methylation network analysisPeriod of brain maturationAdolescent brain developmentAdolescent brain maturationPhases of neurodevelopmentIndependent component analysisGray matterHuman brain structureGM maturationDNAm changesCo-methylation modulesPrefrontal cortexExecutive functionFrontal poleGM volumeTime pointsSubjects aged 9Brain structuresCpG sitesSynaptic pruningBrain developmentDNA methylationPsychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls
Allen C, Maurer J, Gullapalli A, Edwards B, Aharoni E, Harenski C, Anderson N, Harenski K, Calhoun V, Kiehl K. Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls. Frontiers In Neuroimaging 2023, 2: 1216494. PMID: 37554634, PMCID: PMC10406221, DOI: 10.3389/fnimg.2023.1216494.Peer-Reviewed Original ResearchAmplitude of low-frequency fluctuationPsychopathic traitsResting-state networksLow-frequency fluctuationsAssociation of psychopathic traitsCorrelates of psychopathic traitsIncarcerated adolescent girlsResting-state fMRI scansResting-state functional connectivityElevated psychopathic traitsCognitive control networkGroup independent component analysisMode network componentsInter-network connectivityIntra-network connectivityHigh-risk adolescent girlsIndependent component analysisResting-state measuresFunctional network propertiesAdolescent girlsFMRI scanningParalimbic regionsMode networkIncarcerated boysFunctional connectivityDecentralized Parallel Independent Component Analysis for Multimodal, Multisite Data
Panichvatana C, Chen J, Baker B, Thapaliya B, Calhoun V, Liu J. Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083130, DOI: 10.1109/embc40787.2023.10340070.Peer-Reviewed Original ResearchConceptsParallel independent component analysisParallel ICAStudy of mental healthIndependent component analysisGenetic dataOmics dataGenetic componentBrain abnormalitiesResting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank
Sendi M, Zendehrouh E, Miller R, Salat D, Calhoun V. Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065832.Peer-Reviewed Original ResearchFunctional network connectivityDynamic FNCDynamic functional network connectivityCognitive scoresFluid intelligenceCognitive declineAge-related cognitive declineGroup independent component analysisResting-state functional MRIBrain functional changesResting-state fMRIBrain functional network connectivityReaction timeParticipants of UK BiobankRT taskFunctional MRIRs-fMRIPairing taskCognitionIndependent component analysisUK BiobankBrainNetwork connectivityHealthy adultsData-driven componentsConstrained 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 modelNew 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
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