2024
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
Blair D, Miller R, Calhoun V. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. Entropy 2024, 26: 545. PMID: 39056908, PMCID: PMC11275472, DOI: 10.3390/e26070545.Peer-Reviewed Original ResearchSubjective trajectoriesBrain connectivity measuresPatient’s brain functionCognitive performancePsychiatric diseasesCourse of developmentBrain functionInformation theoryCortical hierarchyInformation processingConnectivity measuresSchizophreniaHealthy controlsDynamical systems theoryFunctional imagingTransit alterationsTransitionConnectivity statesPerspective of dynamical systemsStateTheoryEntropy generationDynamical systemsDynamicsNeuroimagingLearning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition
Belyaeva I, Gabrielson B, Wang Y, Wilson T, Calhoun V, Stephen J, Adali T. Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition. IEEE Transactions On Biomedical Engineering 2024, 71: 2189-2200. PMID: 38345949, PMCID: PMC11240882, DOI: 10.1109/tbme.2024.3364704.Peer-Reviewed Original ResearchSpatiotemporal brain dynamicsBrain dynamicsFunctional magnetic resonance imagingComplex spatiotemporal dynamicsStudy brain functionSpatial resolutionMillisecond scaleBrain functionTemporal resolutionBrain patternsHigh-level cognitive functionsBrain response patternsDynamicsSpatiotemporal dynamicsSensory processing pathwaysMagnetoencephalographyLow-performing subjectsResolution
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 informationDynamicsPhaseCapturing Spatial Dynamics Using Time-Resolved Referenced-Informed Network Estimation Techniques
Iraji A, Chen J, Faghiri A, Fu Z, Liu J, Bustillo J, Adali T, Dhamala M, Calhoun V. Capturing Spatial Dynamics Using Time-Resolved Referenced-Informed Network Estimation Techniques. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230735.Peer-Reviewed Original ResearchBrain dynamicsGlobal brain dynamicsTime-resolved mannerSignal-to-noise ratioPatterns of brain networksBrain networksResting-state fMRIFunctional network connectivityUnique informationLow signal-to-noise ratioIdentification of correspondencesNetwork estimation techniquesDynamicsShort time segmentsBetween-subjects