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 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 dataAssociation between the oral microbiome and brain resting state connectivity in schizophrenia
Lin D, Fu Z, Liu J, Perrone-Bizzozero N, Hutchison K, Bustillo J, Du Y, Pearlson G, Calhoun V. Association between the oral microbiome and brain resting state connectivity in schizophrenia. Schizophrenia Research 2024, 270: 392-402. PMID: 38986386, DOI: 10.1016/j.schres.2024.06.045.Peer-Reviewed Original ResearchOral microbiomeMicrobial speciesArea under curveResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingMicrobial 16S rRNA sequencingBrain circuit dysfunctionHealthy controlsBrain functional connectivity alterationsFunctional connectivity alterationsFunctional neuroimaging techniquesHypothalamic-pituitary-adrenal axisBrain functional connectivityFunctional network connectivityBrain functional activityBrain functional network connectivityHealthy control subjectsNeurotransmitter signaling pathwaysBeta diversityMicrobiome communitiesOral microbiome dysbiosisRRNA sequencingCircuit dysfunctionConnectivity alterationsSchizophreniaA 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 systemsDynamicsNeuroimagingA spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 1-76. DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRIA confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity
Hassanzadeh R, Abrol A, Pearlson G, Turner J, Calhoun V. A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity. PLOS ONE 2024, 19: e0293053. PMID: 38768123, PMCID: PMC11104643, DOI: 10.1371/journal.pone.0293053.Peer-Reviewed Original ResearchConceptsResting-state functional network connectivityFunctional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingAlzheimer's diseaseClassification of schizophreniaNetwork pairsPatients to healthy controlsSchizophrenia patientsNeurobiological mechanismsSZ patientsSubcortical networksCerebellum networkSchizophreniaRs-fMRIDisorder developmentMotor networkCompare patient groupsSubcortical domainSZ disorderHealthy controlsMagnetic resonance imagingDisordersNetwork connectivityFunctional abnormalitiesCross‐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 imagingCortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC
Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns M, Loughland C, Carr V, Catts S, Jablensky A, Green M, Henskens F, Kiltschewskij D, Michie P, Mowry B, Pantelis C, Rasser P, Reay W, Schall U, Scott R, Watkeys O, Roberts G, Mitchell P, Fullerton J, Overs B, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev P, Brodaty H, Wen W, Jiang J, Fani N, Ely T, Lorio A, Stevens J, Ressler K, Jovanovic T, van Rooij S, Federmann L, Jockwitz C, Teumer A, Forstner A, Caspers S, Cichon S, Plis S, Sarwate A, Calhoun V. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. Patterns 2024, 5: 100987. PMID: 39081570, PMCID: PMC11284501, DOI: 10.1016/j.patter.2024.100987.Peer-Reviewed Original ResearchPsychiatric disordersStructural neuroimaging studiesPattern of gray matterAutism spectrum disorderGray matterDepressive disorderMood disordersNeuroimaging studiesNeuroanatomical basisSubcortical regionsGM alterationsSpectrum disorderVBM analysisMental illnessGM patternsDisordersCollaborative InformaticsSchizophreniaMoodNeuroimaging Suite ToolkitAutismNeuroimagingVulnerabilityLarge-scale dataDeficitsDifferences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset
Sharkey R, Bacon C, Peterson Z, Rootes-Murdy K, Salvador R, Pomarol-Clotet E, Karuk A, Homan P, Ji E, Omlor W, Homan S, Georgiadis F, Kaiser S, Kirschner M, Ehrlich S, Dannlowski U, Grotegerd D, Goltermann J, Meinert S, Kircher T, Stein F, Brosch K, Krug A, Nenadic I, Sim K, Spalletta G, Banaj N, Sponheim S, Demro C, Ramsay I, King M, Quidé Y, Green M, Nguyen D, Preda A, Calhoun V, Turner J, van Erp T, Nickl-Jockschat T. Differences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset. Molecular Psychiatry 2024, 1-11. PMID: 38671214, DOI: 10.1038/s41380-024-02563-z.Peer-Reviewed Original ResearchNegative FTDBrain structural changesPositive FTDSymptom dimensionsBrain regionsENIGMA Schizophrenia Working GroupNegative formal thought disorderNeural correlates of schizophreniaRegional brain volume lossFormal thought disorderSchizophrenia Working GroupCorrelates of schizophreniaLateral temporal cortexBrain volume lossThought disorderNeurobiological underpinningsNeural correlatesNeuroimaging studiesTemporal cortexSchizophreniaCortical regionsCortical thicknessCellular underpinningsFTDMulti-site cohortThe overlap across psychotic disorders: A functional network connectivity analysis
Dini H, Bruni L, Ramsøy T, Calhoun V, Sendi M. The overlap across psychotic disorders: A functional network connectivity analysis. International Journal Of Psychophysiology 2024, 201: 112354. PMID: 38670348, PMCID: PMC11163820, DOI: 10.1016/j.ijpsycho.2024.112354.Peer-Reviewed Original ResearchConceptsFunctional network connectivitySchizoaffective disorderPsychotic disordersHealthy controlsBipolar-Schizophrenia NetworkFunctional network connectivity analysisStatic functional network connectivityResting-state fMRINetwork connectivity analysisPatterns of activityPsychiatric disordersDisorder groupSchizophreniaConnectivity analysisHC groupBipolarConnectivity patternsDisordersPatient groupSymptom scoresGroup of patientsPANSSSchizoaffectiveFMRINetwork connectivityMIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in lateral ventricles and corpus callosum volume
Blokland G, Maleki N, Jovicich J, Mesholam-Gately R, DeLisi L, Turner J, Shenton M, Voineskos A, Kahn R, Roffman J, Holt D, Ehrlich S, Kikinis Z, Dazzan P, Murray R, Lee J, Sim K, Lam M, de Zwarte S, Walton E, Kelly S, Picchioni M, Bramon E, Makris N, David A, Mondelli V, Reinders A, Oykhman E, Morris D, Gill M, Corvin A, Cahn W, Ho N, Liu J, Gollub R, Manoach D, Calhoun V, Sponheim S, Buka S, Cherkerzian S, Thermenos H, Dickie E, Ciufolini S, Marques T, Crossley N, Purcell S, Smoller J, van Haren N, Toulopoulou T, Donohoe G, Goldstein J, Keshavan M, Petryshen T, del Re E. MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in lateral ventricles and corpus callosum volume. International Journal Of Clinical And Health Psychology 2024, 24: 100458. PMID: 38623146, PMCID: PMC11017057, DOI: 10.1016/j.ijchp.2024.100458.Peer-Reviewed Original ResearchIncreased LV volumesLV ratioLateral ventricleLV volumesCorpus callosumEphrin pathwaysSZ probandsHallmarks of schizophreniaEnlarged lateral ventriclesSNP-based heritability estimatesGlobal Assessment of FunctioningBrain MRI scansGlobal functioningCorpus callosum volumeAssessment of FunctioningClinical dataCC abnormalitiesMicroRNA MIR137MRI scansCC volumeGlobal assessmentMid-anteriorInverse correlationSchizophreniaMIR137Distribution 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 testsMarkov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia
Miller R, Vergara V, Calhoun V. Markov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia. 2024, 00: 13-16. DOI: 10.1109/ssiai59505.2024.10508684.Peer-Reviewed Original ResearchMore 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 accuracyConnectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study
Georgiadis F, Larivière S, Glahn D, Hong L, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens F, Green M, Cairns M, Michie P, Rasser P, Catts S, Tooney P, Scott R, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite T, Karuk A, Pomarol- Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay I, Sponheim S, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein D, Howells F, Temmingh H, Diaz Zuluaga A, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk S, Thompson P, van Erp T, Turner J, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Molecular Psychiatry 2024, 1-13. PMID: 38336840, DOI: 10.1038/s41380-024-02442-7.Peer-Reviewed Original ResearchConnectivity profilesCortical alterationsCourse of schizophreniaBrain morphological alterationsAssociation of schizophreniaBrain network architectureAnatomical MRI scansTransdiagnostic comparisonsHuman Connectome ProjectDepressive disorderAffective disordersPathophysiological continuityPatient-specific symptomsSchizophreniaFrontal regionsDisease-related alterationsENIGMA studyCortical thinningNormative dataConnectome architectureIndividual symptomsConnectome ProjectDisease stageAlteration patternsHealthy controls
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 informationDynamicsPhasePairing 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 measuresExtraction 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 decompositionRevisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia
Harikumar A, Solovyeva K, Misiura M, Iraji A, Plis S, Pearlson G, Turner J, Calhoun V. Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia. Current Neurology And Neuroscience Reports 2023, 23: 937-946. PMID: 37999830, PMCID: PMC11126894, DOI: 10.1007/s11910-023-01325-8.Peer-Reviewed Original ResearchConceptsNetwork dysconnectivityFunctional dysconnectivityExecutive functioningState fMRI studyAttentional deficitsFMRI studyHypothesized modelSalience networkBrain networksConnectivity findingsBehavioral symptomsNeurodevelopmental modelSymptom severityDysconnectivityHypothesized mechanismsSchizophreniaDeficitsVital modelsSummaryThis paperMotor symptomsFunctioningSymptomsFindingsPurpose of ReviewOverThoughtChromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia
Geenjaar E, Lewis N, Fedorov A, Wu L, Ford J, Preda A, Plis S, Calhoun V. Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia. Human Brain Mapping 2023, 44: 5828-5845. PMID: 37753705, PMCID: PMC10619380, DOI: 10.1002/hbm.26479.Peer-Reviewed Original Research