2024
4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia
Pusuluri K, Fu Z, Miller R, Pearlson G, Kochunov P, Van Erp T, Iraji A, Calhoun V. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia. Human Brain Mapping 2024, 45: e26773. PMID: 39045900, PMCID: PMC11267451, DOI: 10.1002/hbm.26773.Peer-Reviewed Original ResearchConceptsBrain networksFunctional magnetic resonance imagingAssociated with cognitive performanceDynamics of functional brain networksAssociated with cognitionFunctional brain networksVoxel-wise changesVolumetric couplingDynamical variablesCognitive performanceTypical controlsSchizophreniaCognitive impairmentNetwork pairsMagnetic resonance imagingPair of networksCognitionAtypical variabilityResonance imagingCouplingNetwork connectivityNetwork growthImpairmentBrainStatic networksA 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 ResearchCross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia
Zhao C, Jiang R, Bustillo J, Kochunov P, Turner J, Liang C, Fu Z, Zhang D, Qi S, Calhoun V. Cross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia. Human Brain Mapping 2024, 45: e26694. PMID: 38727014, PMCID: PMC11083889, DOI: 10.1002/hbm.26694.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingNegative symptomsFunctional connectivityCognitive impairmentPrediction of negative symptomsResting-state functional connectivityAssociated with reduced cognitive functionDebilitating mental illnessHealthy controlsPredicting functional connectivityEarly adulthood onsetPositive symptomsNeural underpinningsSchizophreniaCognitive functionSensorimotor networkPredicting symptomsMental illnessConnectivity patternsClinical interventionsMagnetic resonance imagingAdulthood onsetSymptomsImpairmentResonance imagingSubgroup 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 structureDistribution 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 testsA whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry
Jensen K, Calhoun V, Fu Z, Yang K, Faria A, Ishizuka K, Sawa A, Andrés-Camazón P, Coffman B, Seebold D, Turner J, Salisbury D, Iraji A. A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry. NeuroImage Clinical 2024, 41: 103584. PMID: 38422833, PMCID: PMC10944191, DOI: 10.1016/j.nicl.2024.103584.Peer-Reviewed Original ResearchConceptsFunctional network connectivityFirst-episodeEarly psychosisAberrant functional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingCorrelates of psychosisResting-state fMRI analysisWhole-brain approachPsychiatric disordersPsychiatric illnessSubcortical regionsCerebellar regionsFMRI analysisPsychosisControl participantsCognitive functionRs-fMRICerebellar connectivityMulti-site datasetFunctional circuitryMagnetic resonance imagingCircuitryResonance imagingProminent pattern
2023
Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics
Ellis C, Miller R, Calhoun V. Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics. Neuroimage Reports 2023, 3: 100186. DOI: 10.1016/j.ynirp.2023.100186.Peer-Reviewed Original ResearchEffect of schizophreniaDynamic functional network connectivityBrain network dynamicsNeuropsychiatric disordersBrain activityFunctional magnetic resonance imagingInteractions of brain regionsFunctional network connectivityNetwork dynamicsBrain regionsSchizophreniaClustering algorithmEffect of SZHealthy controlsLearning classificationBrainMagnetic resonance imagingDeep learning modelsDeep learning classificationDisordersNetwork interactionsMachine learning classificationResonance imagingClustersNovel measuresMultimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent Brain
Saha R, Saha D, Fu Z, Silva R, Calhoun V. Multimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent Brain. 2023, 00: 1-5. DOI: 10.1109/bhi58575.2023.10313489.Peer-Reviewed Original ResearchFunctional network connectivityAdolescent brainPotential gender-related differencesBilateral sensorimotor cortexStructural magnetic resonance imagingMagnetic resonance imagingBrain functional connectivityGender-related differencesSensorimotor cortexLongitudinal change patternsGrey matter dataResonance imagingJoint independent component analysisLongitudinal changesFunctional connectivityBrain developmentBrain functionEntire brainBrain connectivityBrain connectionsBrain analysisBrainSensorimotor domainModalitiesSMRI dataFunctional and Structural Longitudinal Change Patterns in Adolescent Brain
Saha R, Saha D, Fu Z, Silva R, Calhoun V. Functional and Structural Longitudinal Change Patterns in Adolescent Brain. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082649, DOI: 10.1109/embc40787.2023.10340079.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingStructural magnetic resonance imagingFunctional network connectivityWhole-brainGray matterBrain functional magnetic resonance imagingMagnetic resonance imagingAdolescent brainFunctional connectivityResonance imagingMultivariate patternsLongitudinal change patternsUnivariate changesAdolescentsLongitudinal changesBrainIncreasing ageFunctional changesComplementary techniquesNetwork connectivityA 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 imagingHyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia
Miller R, Vergara V, Calhoun V. Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083298, DOI: 10.1109/embc40787.2023.10341101.Peer-Reviewed Original ResearchTopological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia
Salman M, Iraji A, Lewis N, Calhoun V. Topological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230513.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingSchizophrenia patientsIntrinsic connectivity networksFMRI dataIndependent component analysisResting-state fMRI studiesAnalysis of fMRI dataSpatial independent component analysisHuman brain functionDynamic brain networksFMRI studyBrain networksBrain functionAberrant behaviorBrain disordersBrain statesSchizophreniaConnectivity networksMagnetic resonance imagingMulti-subject fMRI dataData-driven analysisResonance imagingDynamics of controlSpatial activityDisordersAn Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders
Du Y, Wu F, Niu J, Calhoun V. An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230805.Peer-Reviewed Original ResearchFashion-MNIST dataDeep clustering methodsFunctional magnetic resonance imagingMNIST dataAutism spectrum disorderClustering methodPsychiatric disordersSemi-supervised clusteringPsychiatric disorder symptomsUnlabeled samplesClustering performanceDeep clusteringLabeled samplesDeep learningClustering techniqueDisorder symptomsSpectrum disorderNeuroimaging dataUseful informationSchizophreniaTraditional methodsMagnetic resonance imagingDisordersResonance imagingHigh confidence level