2024
Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, PMCID: PMC11491165, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchMeSH KeywordsAdultBiomarkersBrainFemaleHumansMagnetic Resonance ImagingMaleNeuroimagingSchizophreniaConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder predictionJoint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders
Ji Y, Silva R, Adali T, Wen X, Zhu Q, Jiang R, Zhang D, Qi S, Calhoun V. Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders. NeuroImage Clinical 2024, 43: 103663. PMID: 39226701, DOI: 10.1016/j.nicl.2024.103663.Peer-Reviewed Original Research4D 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 ResearchMeSH KeywordsAdultBrainCognitive DysfunctionConnectomeFemaleHumansMagnetic Resonance ImagingMaleNerve NetSchizophreniaYoung AdultConceptsBrain 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 networksEstimation 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 dependenceDependenceA 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 imagingThe 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 connectivityA 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 ResearchMeSH KeywordsBrainBrain MappingCerebellumHumansMagnetic Resonance ImagingPsychotic DisordersSchizophreniaConceptsFunctional 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
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 informationDynamicsPhaseRevisiting 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 ResearchMeSH KeywordsBrainBrain MappingHumansMagnetic Resonance ImagingNeural PathwaysNeurotransmitter AgentsSchizophreniaConceptsNetwork 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 ResearchMeSH KeywordsBrainDiffusion Magnetic Resonance ImagingHumansMagnetic Resonance ImagingNeuroimagingSchizophreniaAuditory oddball hypoactivation in schizophrenia
Nakahara S, Male A, Turner J, Calhoun V, Lim K, Mueller B, Bustillo J, O'Leary D, Voyvodic J, Belger A, Preda A, Mathalon D, Ford J, Guffanti G, Macciardi F, Potkin S, Van Erp T. Auditory oddball hypoactivation in schizophrenia. Psychiatry Research Neuroimaging 2023, 335: 111710. PMID: 37690161, DOI: 10.1016/j.pscychresns.2023.111710.Peer-Reviewed Original ResearchConceptsSchizophrenia polygenic risk scoresFunctional magnetic resonance imagingPolygenic risk scoresCognitive performanceOddball targetsAssociated with cognitive performanceRight supramarginal cortexCognitive domain performanceBrain imaging studiesAuditory oddball taskPrecuneus activationSchizophrenia individualsFrontal poleSchizophreniaOddball taskBrain activityRight thalamusHippocampal cortexRegional activityHypoactivationSample 2Sample 1PrecuneusHealthy volunteersCross-sectional data setsA 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 noiseSubjectsDecentralized Parallel Independent Component Analysis for Multimodal, Multisite Data
Panichvatana C, Chen J, Baker B, Thapaliya B, Calhoun V, Liu J. Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083130, DOI: 10.1109/embc40787.2023.10340070.Peer-Reviewed Original ResearchHyperlocal 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 ResearchA Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis*
Ellis C, Miller R, Calhoun V. A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis*. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083353, DOI: 10.1109/embc40787.2023.10340173.Peer-Reviewed Original ResearchA Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis*
Ellis C, Miller R, Calhoun V. A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis*. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083554, DOI: 10.1109/embc40787.2023.10340375.Peer-Reviewed Original ResearchNeuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *
Ellis C, Miller R, Calhoun V. Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083012, DOI: 10.1109/embc40787.2023.10340837.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityResting-state functional magnetic resonanceFunctional magnetic resonanceNeuropsychiatric disordersFunctional network connectivityCharacterization of schizophreniaCognitive controlDeep learning classifierContext of schizophreniaAuditory networkBrain activityBrain networksVisual networkSubcortical networksCerebellar networkLarge-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium
Schijven D, Postema M, Fukunaga M, Matsumoto J, Miura K, de Zwarte S, van Haren N, Cahn W, Pol H, Kahn R, Ayesa-Arriola R, de la Foz V, Tordesillas-Gutierrez D, Vázquez-Bourgon J, Crespo-Facorro B, Alnæs D, Dahl A, Westlye L, Agartz I, Andreassen O, Jönsson E, Kochunov P, Bruggemann J, Catts S, Michie P, Mowry B, Quidé Y, Rasser P, Schall U, Scott R, Carr V, Green M, Henskens F, Loughland C, Pantelis C, Weickert C, Weickert T, de Haan L, Brosch K, Pfarr J, Ringwald K, Stein F, Jansen A, Kircher T, Nenadić I, Krämer B, Gruber O, Satterthwaite T, Bustillo J, Mathalon D, Preda A, Calhoun V, Ford J, Potkin S, Chen J, Tan Y, Wang Z, Xiang H, Fan F, Bernardoni F, Ehrlich S, Fuentes-Claramonte P, Garcia-Leon M, Guerrero-Pedraza A, Salvador R, Sarró S, Pomarol-Clotet E, Ciullo V, Piras F, Vecchio D, Banaj N, Spalletta G, Michielse S, van Amelsvoort T, Dickie E, Voineskos A, Sim K, Ciufolini S, Dazzan P, Murray R, Kim W, Chung Y, Andreou C, Schmidt A, Borgwardt S, McIntosh A, Whalley H, Lawrie S, du Plessis S, Luckhoff H, Scheffler F, Emsley R, Grotegerd D, Lencer R, Dannlowski U, Edmond J, Rootes-Murdy K, Stephen J, Mayer A, Antonucci L, Fazio L, Pergola G, Bertolino A, Díaz-Caneja C, Janssen J, Lois N, Arango C, Tomyshev A, Lebedeva I, Cervenka S, Sellgren C, Georgiadis F, Kirschner M, Kaiser S, Hajek T, Skoch A, Spaniel F, Kim M, Bin Kwak Y, Oh S, Kwon J, James A, Bakker G, Knöchel C, Stäblein M, Oertel V, Uhlmann A, Howells F, Stein D, Temmingh H, Diaz-Zuluaga A, Pineda-Zapata J, López-Jaramillo C, Homan S, Ji E, Surbeck W, Homan P, Fisher S, Franke B, Glahn D, Gur R, Hashimoto R, Jahanshad N, Luders E, Medland S, Thompson P, Turner J, van Erp T, Francks C. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium. Proceedings Of The National Academy Of Sciences Of The United States Of America 2023, 120: e2213880120. PMID: 36976765, PMCID: PMC10083554, DOI: 10.1073/pnas.2213880120.Peer-Reviewed Original Research