2024
A core tensor sparsity enhancement method for solving Tucker-2 model of multi-subject fMRI data
Han Y, Lin Q, Kuang L, Zhao B, Gong X, Cong F, Wang Y, Calhoun V. A core tensor sparsity enhancement method for solving Tucker-2 model of multi-subject fMRI data. Biomedical Signal Processing And Control 2024, 95: 106471. DOI: 10.1016/j.bspc.2024.106471.Peer-Reviewed Original ResearchTucker-2 modelMulti-subject fMRI dataFactor matricesCore tensorHalf-quadratic splittingTensor structure informationLow-rank constraintTensor sparsitySparsity constraintQuadratic splittingTask-related fMRI dataImprovement of accuracyEnhancement methodOrthogonality constraintsFMRI dataProcrustes solutionSimulated fMRI dataTucker-3 modelSparsityTemporal evidenceResting-state fMRI dataIdentity matrixDecomposition modelIntrinsic relationshipStructural informationA Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Zhang A, Zhang G, Cai B, Wilson T, Stephen J, Calhoun V, Wang Y. A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. Network Neuroscience 2024, 1-17. DOI: 10.1162/netn_a_00384.Peer-Reviewed Original ResearchPhiladelphia Neurodevelopmental CohortEmotional circuitryFunctional connectivityBrain's emotional circuitryEmotion identification skillBrain network organizationIndividuals aged 8Emotional processingEmotion perceptionBrain circuitsNeurodevelopmental CohortFMRI dataCognitive developmentIdentification skillsEmotional changesAged 8Adolescent stageAdolescentsNetwork organizationGroup-specific patternsIntermodular connectionsEmotionsCircuit developmentAccurate performanceBrainA new transfer entropy method for measuring directed connectivity from complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Calhoun V. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Frontiers In Neuroscience 2024, 18: 1423014. PMID: 39050665, PMCID: PMC11266018, DOI: 10.3389/fnins.2024.1423014.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFMRI dataBrain regionsAnatomical Automatic LabelingTransfer entropyFunctional magnetic resonance imaging dataConnectivity of brain regionsFrontal-parietal regionsConsistent with previous findingsSignificant group differencesRight frontal-parietal regionPartial transfer entropyPredicting mental disordersMental disordersParietal regionsGroup differencesMagnitude effectExperimental fMRI dataDirectional connectivityComplex-valued fMRI dataSchizophreniaMagnetic resonance imagingComplex-valued approachEntropyMagnitude dataEstimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Li H, Calhoun V. Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data. Journal Of Neuroscience Methods 2024, 409: 110207. PMID: 38944128, DOI: 10.1016/j.jneumeth.2024.110207.Peer-Reviewed Original ResearchConceptsComplex-valued fMRI dataMutual informationJoint entropyNetwork connectivityComplex-valued signalsFunctional network connectivityMagnitude-phase dependenceDensity estimation methodMI estimationHistogram-basedKernel density estimation methodFMRI dataEstimation accuracyProbability density functionJoint probability density functionSimulated signalsChain rulePhase dependenceEstimation methodHigh-orderDensity functionControl networkInaccurate estimationNonlinear dependenceDependenceSubgroup 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 structureSMART (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, 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 analysisMore reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method
Xing Y, van Erp T, Pearlson G, Kochunov P, Calhoun V, Du Y. More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method. IScience 2024, 27: 109319. PMID: 38482500, PMCID: PMC10933544, DOI: 10.1016/j.isci.2024.109319.Peer-Reviewed Original ResearchDiagnosis of mental disordersMental disordersDiagnostic labelsIntegration of neuroimagingSchizophrenia patientsNeuroimaging measuresNeuroimaging perspectiveFMRI dataStable abnormalitiesNeuroimagingDisordersHealthy controlsIndependent subjectsSchizophreniaFMRIDimensional predictionsSubjectsAccurate diagnosisClassification accuracyIdentifying 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
Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
Li W, Lin Q, Zhao B, Kuang L, Zhang C, Han Y, Calhoun V. Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia. Journal Of Neuroscience Methods 2023, 403: 110049. PMID: 38151187, DOI: 10.1016/j.jneumeth.2023.110049.Peer-Reviewed Original ResearchConceptsSchizophrenia patientsFMRI dataFunctional network connectivityHealthy controlsDynamic functional network connectivityPsychotic diagnosesMental disordersSchizophreniaComplex-valued fMRI dataPotential imaging biomarkersDetect functional alterationsFMRIState transitionsNetwork connectivityPhase informationFunctional alterationsComplex valuesBrain informationMutual informationDynamicsPhaseCfMRIPrep: A MATLAB toolbox for integrated preprocessing of complex-valued fMRI data
Li S, Ma M, Lin Q, Calhoun V. CfMRIPrep: A MATLAB toolbox for integrated preprocessing of complex-valued fMRI data. 2023, 00: 167-171. DOI: 10.1109/icist59754.2023.10367157.Peer-Reviewed Original ResearchComplex-valued fMRI dataPhase fMRI dataMATLAB toolboxFMRIB Software LibraryNew MATLAB toolboxMotion correctionSoftware libraryManual approachFMRI dataExperimental resultsHead motionPhase unwrappingSpatial normalizationToolboxDifferent subjectsPhase dataMagnitude dataPreprocessingDataProcessingUnwrappingLibraryExtraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to Schizophrenia
Han Y, Lin Q, Kuang L, Hao Y, Li W, Gong X, Calhoun V. Extraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to Schizophrenia. Communications In Computer And Information Science 2023, 1963: 518-527. DOI: 10.1007/978-981-99-8138-0_41.Peer-Reviewed Original ResearchAmplitude of low frequency fluctuationsFMRI dataLow frequency fluctuationsSchizophrenia groupHealthy controlsMulti-subject fMRI dataInferior parietal lobuleProperties of brain functionParietal lobuleFMRI-dataMental disordersSchizophreniaBrain functionActivity differencesFrequency fluctuationsTwo-sample t-testSliding-window techniqueTucker decompositionReproducibility 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 analysisSolutionREGRESSION-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 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 MexicoThresholdA Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data
Saha D, Bohsali A, Saha R, Hajjar I, Calhoun V. A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083351, DOI: 10.1109/embc40787.2023.10340631.Peer-Reviewed Original ResearchConceptsWhole-brain functional connectomePositron emission tomographyResting fMRIResting fMRI dataBrain positron emission tomographyBrain functional connectomePositron emission tomography dataResting networksMagnetic resonance imagingConnectomeFunctional connectomeBrain networksConnectome patternsFMRIFMRI dataBrain functionSubject expressionPiB-PET scansBrainEmission tomographySpatial mappingSpatial networksClinical Relevance-This studyPET scansResonance imagingConstrained 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 diseasesIndependent 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 dataMethodMultilinearHigher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy
Li Q, Yu S, Madsen K, Calhoun V, Iraji A. Higher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193346.Peer-Reviewed Original ResearchS-entropyHigher-order interactionsResting-state fMRI dataEstimate statistical dependenciesProbability distribution functionFMRI dataInformation processingHuman brainHigher-orderInformation interactionMutual informationMultivariate mutual informationTotal correlationOrderMultivariate time seriesInteractionHigher-order informationDependenceThe Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional Interaction
Iraji A, Kazimierczak K, Chen J, Motlaghian S, Specht K, Adali T, Calhoun V. The Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional Interaction. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230347.Peer-Reviewed Original Research