2024
A 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 dataA survey of brain functional network extraction methods using fMRI data
Du Y, Fang S, He X, Calhoun V. A survey of brain functional network extraction methods using fMRI data. Trends In Neurosciences 2024, 47: 608-621. PMID: 38906797, DOI: 10.1016/j.tins.2024.05.011.Peer-Reviewed Original ResearchThe dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity
Wiafe S, Faghiri A, Fu Z, Miller R, Preda A, Calhoun V. The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity. Imaging Neuroscience 2024, 2: 1-23. DOI: 10.1162/imag_a_00187.Peer-Reviewed Original ResearchDynamic time warpingDynamics of brain networksBrain networksBrain network interactionsFunctional magnetic resonance imagingFunctional connectivity measuresComplexity of brain functionDiverse timescalesTime warpingBrain dynamicsVisual cortexFunctional magnetic resonance imaging dataTimescalesFunctional connectivityBrain connectivityCoupled stretchingCouplingDynamic time warping methodBrain regionsTransient couplingConnectivity measuresFunctional connectivity metricsNeuroimaging researchCluster centroidsIntricate dynamicsSMART (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 analysisStriatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges
Jiang S, Pei H, Chen J, Li H, Liu Z, Wang Y, Gong J, Wang S, Li Q, Duan M, Calhoun V, Yao D, Luo C. Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges. International Journal Of Neural Systems 2024, 34: 2450017. PMID: 38372049, DOI: 10.1142/s0129065724500175.Peer-Reviewed Original ResearchConceptsSalience networkSensorimotor cortexFunctional magnetic resonance imaging dataModerating effectInterictal epileptic dischargesIdiopathic generalized epilepsyMagnetic resonance imaging dataInteraction of regionsDecreased connectivityStriatumDMNThalamocortical circuitsCortical interactionsSimultaneous electroencephalogramCortical targetsEpileptic dischargesCerebellumThalamusHierarchical connectionEpileptic networkNeuromodulation techniquesIndirect moderating effectStateCryptogenic etiologyExplainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Ellis C, Miller R, Calhoun V. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Frontiers In Psychiatry 2024, 15: 1165424. PMID: 38495909, PMCID: PMC10941842, DOI: 10.3389/fpsyt.2024.1165424.Peer-Reviewed Original ResearchHard clusteringNetwork dynamicsDynamic functional network connectivityFuzzy clustering frameworkExtract several featuresFuzzy clusteringC-meansExplainability approachesExplainability metricsData spaceClustering frameworkK-meansDynamic functional network connectivity stateNetwork connectivityModerate anticorrelationImage dataNetworkState dynamicsAnalysis frameworkConnectivity dynamicsFunctional network connectivityAnticorrelationCentroidFunctional magnetic resonance imaging dataFramework
2023
A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjectsCoupled 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 approachDecompositionFusion