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. PMID: 40161992, 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
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, 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 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 solutionMethodOptimizationImplementationSMART (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 analysisSynaptic density patterns in early Alzheimer’s disease assessed by independent component analysis
Fang X, Raval N, O’Dell R, Naganawa M, Mecca A, Chen M, van Dyck C, Carson R. Synaptic density patterns in early Alzheimer’s disease assessed by independent component analysis. Brain Communications 2024, 6: fcae107. PMID: 38601916, PMCID: PMC11004947, DOI: 10.1093/braincomms/fcae107.Peer-Reviewed Original ResearchMedial temporal brain regionsAlzheimer's diseaseTemporal brain regionsCognitive deficitsBrain regionsCognitive impairmentPostmortem studiesBinds to SV2ASynaptic densityReduction of synaptic densityIndependent component analysisSynaptic lossAlzheimerDeficitsImpairmentBrainNeocortexComponent analysisPrimary pathologySV2A
2023
Phantom oscillations in principal component analysis
Shinn M. Phantom oscillations in principal component analysis. Proceedings Of The National Academy Of Sciences Of The United States Of America 2023, 120: e2311420120. PMID: 37988465, PMCID: PMC10691246, DOI: 10.1073/pnas.2311420120.Peer-Reviewed Original ResearchHigh-dimensional spaceHigh-dimensional data analysisDimensionality reduction methodsTraditional cross-validationLow-dimensional patternsProperties of dataPrincipal component analysisNeuroscience dataImpact data analysisComponent analysisMathematical proofsCross-validationOscillationsPhantomData analysisDenoising 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 MexicoThresholdConstrained 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 modelsfkit: a web-based toolkit for secure and federated genomic analysis.
Mendelsohn S, Froelicher D, Loginov D, Bernick D, Berger B, Cho H. sfkit: a web-based toolkit for secure and federated genomic analysis. Nucleic Acids Research 2023, 51: w535-w541. PMID: 37246709, PMCID: PMC10320181, DOI: 10.1093/nar/gkad464.Peer-Reviewed Original ResearchConceptsCommand line interfaceGroup of collaboratorsCryptographic techniquesPrivacy concernsCollaborative workflowsUse casesWeb-based toolkitWeb serverComputational environmentCollaborative toolsMultiple partiesEssential taskDatasetServerPrivacyGenomic data collectionPrincipal component analysisToolkitData collectionWorkflowToolTaskComponent analysisRecent workComplexityMulti-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study
Meng X, Iraji A, Fu Z, Kochunov P, Belger A, Ford J, McEwen S, Mathalon D, Mueller B, Pearlson G, Potkin S, Preda A, Turner J, van Erp T, Sui J, Calhoun V. Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study. NeuroImage Clinical 2023, 38: 103434. PMID: 37209635, PMCID: PMC10209454, DOI: 10.1016/j.nicl.2023.103434.Peer-Reviewed Original ResearchConceptsIndependent component analysisData-driven approachData miningF1 scoreClassification modelReference algorithmNetwork connectivityMagnetic resonance imaging dataNetworkImaging dataPredictive resultsPatient dataFunctional magnetic resonance imaging (fMRI) dataData acquisition timeConnectivity networksFrameworkConnectivityPromising approachNew subjectMiningAnalytic approachAlgorithmDatasetAcquisition timeComponent analysisAny-Way Independent Component Analysis with Reference
Duan K, Silva R, Liu J, Agcaoglu O, Calhoun V. Any-Way Independent Component Analysis with Reference. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230369.Peer-Reviewed Original ResearchCross-modal correlationIndependent component analysisMultiset canonical correlation analysisOptimal global solutionMultimodal fusionNoisy conditionsOrthogonality requirementCanonical correlation analysisIndependence of sourcesJoint independent component analysisSimulation resultsComponent analysisImproved accuracyComponent matricesGlobal solutionICAMultisetsMultimodal patternsOrthogonalityJoint 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
2019
Sparse principal component analysis with missing observations
Park S, Zhao H. Sparse principal component analysis with missing observations. The Annals Of Applied Statistics 2019, 13: 1016-1042. DOI: 10.1214/18-aoas1220.Peer-Reviewed Original ResearchHigh-dimensional settingsPrincipal subspaceStep estimation procedureRate of convergenceSparse principal component analysisDimensional settingSimulated examplesMissing observationsStatistical methodsEstimation procedureSparse PCA methodsSingle-cell dataSubspacePCA methodSingle-cell RNA-sequencing dataNumber of featuresCompetitive performancePrincipal component analysisConvergenceSample sizeEstimationWide rangeComponent analysis
2018
Capturing Shared and Individual Information in fMRI Data
Turek J, Ellis C, Skalaban L, Turk-Browne N, Willke T. Capturing Shared and Individual Information in fMRI Data. 2018, 00: 826-830. DOI: 10.1109/icassp.2018.8462175.Peer-Reviewed Original Research
2017
Automatic 1D Convolutional Neural Network-Based Detection of Artifacts in MEG Acquired Without Electrooculography or Electrocardiography
Garg P, Davenport E, Murugesan G, Wagner B, Whitlow C, Maldjian J, Montillo A. Automatic 1D Convolutional Neural Network-Based Detection of Artifacts in MEG Acquired Without Electrooculography or Electrocardiography. International Workshop On Pattern Recognition In NeuroImaging 2017, 2017: 1-4. PMID: 31656826, PMCID: PMC6814172, DOI: 10.1109/prni.2017.7981506.Peer-Reviewed Original ResearchEye blinkIndependent component analysisNeural network-based detectionNetwork-based detectionConvolutional neural networkAccurate classifierNeural networkModel trainingUser confidenceTest setArtifact suppressionElectrooculographyMultivariate decomposition approachDecomposition approachMuscle movementEKG electrodesFunctional neuroimaging toolsNon-neuronal sourcesArtifactsCardiac activityMEG dataClassifierComponent analysisFacial twitchingUsers
2016
Characterisation of the Immunophenotype of Dogs with Primary Immune-Mediated Haemolytic Anaemia
Swann J, Woods K, Wu Y, Glanemann B, Garden O. Characterisation of the Immunophenotype of Dogs with Primary Immune-Mediated Haemolytic Anaemia. PLOS ONE 2016, 11: e0168296. PMID: 27942026, PMCID: PMC5152924, DOI: 10.1371/journal.pone.0168296.Peer-Reviewed Original ResearchConceptsImmune-mediated haemolytic anaemiaPrimary immune-mediated haemolytic anaemiaHealthy control dogsControl dogsDisease of dogsEDTA anti-coagulated blood samplesMeasure serum concentrations of cytokinesHealthy dogsConcentrations of serum cytokinesAffected animalsInvestigate cytokine gene expressionPrincipal component analysisLymphocyte subsetsDogsAbsolute numbers of TregsExpression of cytokine genesInflammatory diseasesAutoimmune diseasesComponent analysisFrequency of regulatory T cellsHaemolytic anaemiaCytokine genesPeripheral blood mononuclear cell expressionSerum concentrations of cytokinesFrequency of Tregs
2013
Differential-Private Data Publishing Through Component Analysis.
Jiang X, Ji Z, Wang S, Mohammed N, Cheng S, Ohno-Machado L. Differential-Private Data Publishing Through Component Analysis. 2013, 6: 19-34. PMID: 24409205, PMCID: PMC3883117.Peer-Reviewed Original ResearchData publishingPrivate data publishingLinear discriminant analysisPrivacy budgetData disseminationExponential mechanismArt methodsClassification purposesBetter utilityDissemination toolsPrivacyPrincipal component analysisPPDPImproved utilityComponent analysisPublishingDiscriminant analysisPerformance differencesReasonable compromiseMatched Signal Detection on Graphs: Theory and Application to Brain Network Classification
Hu C, Cheng L, Sepulcre J, El Fakhri G, Lu Y, Li Q. Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification. Lecture Notes In Computer Science 2013, 23: 1-12. PMID: 24683953, DOI: 10.1007/978-3-642-38868-2_1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseAniline CompoundsBenzothiazolesBrainBrain MappingConnectomeHumansImage EnhancementImage Interpretation, Computer-AssistedNerve NetNeural PathwaysPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityThiazolesTissue DistributionConceptsBrain network classificationNetwork classification problemWeighted energy detectorPrinciple component analysisSub-manifold structureTraditional principle component analysisSubspace detectionTraining dataEnergy detectorGraph structureProblem of Alzheimer's diseaseGraph LaplacianNetwork classificationNoise varianceLevel of smoothnessWeighted graphSignal detectionIntrinsic structureSignal modelGraphSubspaceIsing modelNoiseSignal variationsComponent analysis
2012
Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia
Sui J, He H, Liu J, Yu Q, Adali T, Pearlson G, Calhoun V. Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2012, 2012: 2692-2695. PMID: 23366480, DOI: 10.1109/embc.2012.6346519.Peer-Reviewed Original ResearchConceptsMulti-set canonical correlation analysisData fusionMulti-modal fusionDisparate data setsMultiple data typesJoint independent component analysisData typesFusion modelJoint informationData setsIndependent component analysisHigher decomposition accuracyEffective mannerCanonical correlation analysisDecomposition accuracyLimited viewEffective approachPromising approachBiomedical imagingFusionComponent analysisAccuracyIllness biomarkersInformationSet
2009
Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
Singer A, Erban R, Kevrekidis IG, Coifman RR. Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. Proceedings Of The National Academy Of Sciences Of The United States Of America 2009, 106: 16090-16095. PMID: 19706457, PMCID: PMC2752552, DOI: 10.1073/pnas.0905547106.Peer-Reviewed Original ResearchConceptsStochastic dynamical systemsModel reduction approachHigh dimensional dynamic dataDynamical systemsNonlinear independent component analysisLocal principal component analysisSlow variablesMarkov matrixGood observablesDiffusion mapsNetwork simulationAnisotropic diffusionReduction approachData analysis techniqueAnalysis techniquesEigenvectorsDynamic dataObservablesIndependent component analysisComponent analysisSimulationsMatrix
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