2025
Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis
Premi E, Cantoni V, Benussi A, Iraji A, Calhoun V, Corbo D, Gasparotti R, Tinazzi M, Borroni B, Magoni M. Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis. NeuroImage Clinical 2025, 45: 103731. PMID: 39764901, PMCID: PMC11762193, DOI: 10.1016/j.nicl.2025.103731.Peer-Reviewed Original ResearchDynamic functional network connectivitySomatomotor networkSalience networkFunctional network connectivityGABAergic neurotransmissionResting-state functional MRI scansResting-state fMRI dataFunctional MRI scansDynamic brain statesBrain network dynamicsStatic functional connectivityDynamic brain networksBrain networksGlutamatergic transmissionNeurophysiological correlatesFunctional connectivityTranscranial magnetic stimulation protocolFMRI dataGABAergic inhibitionMagnetic stimulation protocolBrain statesNeurotransmissionHealthy controlsDMNNetwork connectivity
2024
Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia
Zhang Y, Gao S, Liang C, Bustillo J, Kochunov P, Turner J, Calhoun V, Wu L, Fu Z, Jiang R, Zhang D, Jiang J, Wu F, Peng T, Xu X, Qi S. Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia. NeuroImage Clinical 2024, 45: 103726. PMID: 39700898, PMCID: PMC11721508, DOI: 10.1016/j.nicl.2024.103726.Peer-Reviewed Original ResearchNon-treatment-resistant schizophreniaTreatment-resistant schizophreniaFunctional connectivityDiagnosis of SZHealthy controlsFrontal-parietalResting-state functional connectivityAutomated anatomical labelingDysfunctional brain connectivityBrain functional connectivityAffiliated Brain Hospital of Nanjing Medical UniversityFrontal limbBrain connectivitySchizophreniaMedication dosageTreatment resistanceNeural pathwaysNanjing Medical UniversityDisease progressionMedical UniversityClinical practiceSpecific biomarkersDiagnosisAnatomical labelingA 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, 8: 1212-1242. PMID: 39735500, PMCID: PMC11674407, 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 MRINetworks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Kinsey S, Kazimierczak K, Camazón P, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp T, Dhamala M, Calhoun V, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. Nature Mental Health 2024, 2: 1464-1475. PMID: 39650801, PMCID: PMC11621020, DOI: 10.1038/s44220-024-00341-y.Peer-Reviewed Original ResearchSelf-referential cognitionFunctional magnetic resonance imaging connectivityFunctional brain connectivityCingulo-opercularDefault-modeSchizophrenia diagnosisExecutive regionsFMRI connectivityFunctional connectivityConnectivity analysisSchizophreniaSensitive to differencesBrain connectivityFunctional connectivity structureWidespread alterationsImaging connectivityIndependent component analysisBrain phenomenaNetwork integrationHypoconnectivityPsychosisCognitionCore regionNonlinear networksCase-control datasetAdolescent brain maturation associated with environmental factors: a multivariate analysis
Ray B, Jensen D, Suresh P, Thapaliya B, Sapkota R, Farahdel B, Fu Z, Chen J, Calhoun V, Liu J. Adolescent brain maturation associated with environmental factors: a multivariate analysis. Frontiers In Neuroimaging 2024, 3: 1390409. PMID: 39629197, PMCID: PMC11613425, DOI: 10.3389/fnimg.2024.1390409.Peer-Reviewed Original ResearchLeft medial orbitofrontal cortexReduced gray matter densityMedial orbitofrontal cortexMiddle temporal gyrusGray matter densityIncreased functional connectivityIncrease cognitive performanceSusceptible to environmental influencesBilateral occipital regionsDelayed brain maturationIncreased cortical thicknessOrbitofrontal cortexAdolescent brainHuman adolescentsTemporal gyrusBrain characteristicsCognitive performanceBrain regionsBrain age estimationBrain metricsFunctional connectivityBrain agingBrain maturationBrain variationBrain modalitiesExploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots
Li Q, Calhoun V, Pham T, Iraji A. Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots. Chaos An Interdisciplinary Journal Of Nonlinear Science 2024, 34: 103123. PMID: 39393183, DOI: 10.1063/5.0203926.Peer-Reviewed Original ResearchConceptsFuzzy recurrence plotsPhase portraitsComplex brain networksConnectivity descriptorsLow-dimensional dynamicsField of statistical physicsNonlinear dynamicsNeural mass modelMass modelRecurrence plotsStatistical physicsNeural time seriesFunctional connectivityLimit cycle attractorNonlinear phenomenaHidden informationComplex networksLatent informationPhase trajectoriesHigh-dimensionalDynamical theoryBrain functional connectivityBrain connectivityBrain networksNeural dynamicsA Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Zhang A, Zhang G, Cai B, Wilson T, Stephen J, Calhoun V, Wang Y. A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. Network Neuroscience 2024, 8: 791-807. PMID: 39355441, PMCID: PMC11349030, DOI: 10.1162/netn_a_00384.Peer-Reviewed Original ResearchPhiladelphia Neurodevelopmental CohortEmotional circuitryFunctional connectivityBrain's emotional circuitryEmotion identification skillBrain network organizationIndividuals aged 8Emotional processingEmotion perceptionBrain circuitsNeurodevelopmental CohortFMRI dataCognitive developmentIdentification skillsEmotional changesAged 8Adolescent stageAdolescentsNetwork organizationGroup-specific patternsIntermodular connectionsEmotionsCircuit developmentAccurate performanceBrainNeurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework
DeRosa J, Friedman N, Calhoun V, Banich M. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. NeuroImage 2024, 299: 120827. PMID: 39245397, PMCID: PMC11779700, DOI: 10.1016/j.neuroimage.2024.120827.Peer-Reviewed Original ResearchConceptsResting-state functional connectivityAdolescent Brain Cognitive DevelopmentIndividual’s resting-state functional connectivityAdolescent Brain Cognitive Development StudyFunctional brain organizationMental health profilesMental health characteristicsRsFC dataBrain organizationFunctional connectivityDevelopmental trajectoriesChildren aged 9Emotional functioningCognitive developmentLate childhoodAged 9SubtypesAdolescentsHealth characteristicsHealth profileChildhoodCommon and unique brain aging patterns between females and males quantified by large‐scale deep learning
Du Y, Yuan Z, Sui J, Calhoun V. Common and unique brain aging patterns between females and males quantified by large‐scale deep learning. Human Brain Mapping 2024, 45: e70005. PMID: 39225381, PMCID: PMC11369911, DOI: 10.1002/hbm.70005.Peer-Reviewed Original ResearchConceptsBrain functional changesFunctional connectivityCognitive controlBrain agingBrain functionPatterns of brain agingResting-state brain functional connectivityBrain functional interactionsBrain functional connectivityHuman brain functionBrain aging patternsGender commonalitiesAge-related changesDeep learningHealthy participantsNormal agingNegative connectionFunctional changesBrainPositive connectionDeep learning modelsFunctional domainsAge effectsFunctional interactionsCross-validation schemeCognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population
Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population. Molecular Psychiatry 2024, 30: 402-413. PMID: 39085394, PMCID: PMC11746149, DOI: 10.1038/s41380-024-02683-6.Peer-Reviewed Original ResearchDynamic functional connectivity statesDynamic functional connectivityAdolescent Brain Cognitive DevelopmentCognitive performanceDynamic functional connectivity patternsSensory networksAnalysis of dynamic functional connectivityFunctional connectivity statesDefault-modeNeurological underpinningsAttention problemsPsychiatric relevanceFunctional connectivitySensorimotor networkMediation analysisCognitive developmentChild's brainBrain statesMental healthMental problemsBrain dynamicsSliding-window approachMental behaviorBrainCerebellumDynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence
Treves I, Marusak H, Decker A, Kucyi A, Hubbard N, Bauer C, Leonard J, Grotzinger H, Giebler M, Torres Y, Imhof A, Romeo R, Calhoun V, Gabrieli J. Dynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence. Biological Psychiatry Global Open Science 2024, 4: 100367. PMID: 39286525, PMCID: PMC11402920, DOI: 10.1016/j.bpsgos.2024.100367.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingTrait mindfulnessFunctional connectivity analysisDynamic functional connectivity analysisBrain statesConnectivity analysisSelf-reported trait mindfulnessResting-state fMRI scansHigher trait mindfulnessPresent-moment experienceFunctional connectivity correlatesDynamic brain statesStatic functional connectivityState-of-mindTest-retest reliabilityAdolescent anxietyFMRI scanningNeural basisPsychiatric disordersDepressive symptomsNeural mechanismsLower anxietyFunctional connectivityEarly adolescenceConnectivity correlationsCross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers
Hassanzadeh R, Abrol A, Hassanzadeh H, Calhoun V. Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039975, DOI: 10.1109/embc53108.2024.10781737.Peer-Reviewed Original ResearchConceptsFunctional network connectivityGenerative adversarial networkStructural similarity index measureT1-weighted structural magnetic resonance imaging dataAdversarial networkStructural magnetic resonance imaging dataIncreased functional connectivityMagnetic resonance imaging dataSimilarity index measureCross-modal transformerCross-modal translationPatterns of atrophyAlzheimer's diseaseFunctional connectivityReduced connectivityMotor-visualTemporal regionsWeak supervisionAlzheimer's disease biomarkersControl networkCycle-GANCross-modalAlzheimer patientsContext of Alzheimer's diseaseGeneration approachBeyond Artifacts: Rethinking Motion-Related Signals in Resting-State fMRI Analysis
Kumar S, Kinsey S, Jensen K, Bajracharya P, Calhoun V, Iraji A. Beyond Artifacts: Rethinking Motion-Related Signals in Resting-State fMRI Analysis. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40040138, DOI: 10.1109/embc53108.2024.10782518.Peer-Reviewed Original ResearchConceptsFunctional network connectivityBOLD time seriesImpact of head motionHead motion dataLarge-scale brain networksIntrinsic brain functional connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional brain connectivityResting-state fMRI analysisRsfMRI dataBOLD fMRIHead motionBrain functional connectivityHealthy controlsBOLD signalBrain connectivityBrain networksMotion dataFMRI analysisFunctional connectivityClinical populationsMotion-related signalsClinical implicationsBOLDMultiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses
Behzadfar N, Iraji A, Calhoun V. Multiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-5. PMID: 40040173, DOI: 10.1109/embc53108.2024.10782601.Peer-Reviewed Original ResearchConceptsIndependent component analysisTask-related componentsDynamics of functional connectivitySubband informationGroup independent component analysisMultisubject fMRI dataFunctional network connectivityFMRI dataNetwork connectivityBandpass filterSampling rateSubbandBack-reconstructionApplication of bandpass filtersSpatially independent mapsFrequency rangeFunctional connectivityFMRI analysisA Deep Biclustering Framework for Brain Network Analysis
Rahaman A, Fu Z, Iraji A, Calhoun V. A Deep Biclustering Framework for Brain Network Analysis. 2024, 00: 5075-5085. DOI: 10.1109/cvprw63382.2024.00514.Peer-Reviewed Original ResearchDeep neural networksBrain networksState-of-the-artFunctional connectivityNeural networkFeature dimensionsBiclustering frameworkSuboptimal solutionBrain functional connectivityNeuroimaging datasetsBrain network analysisHuman brain dynamicsNetworkNeurobiological mechanismsBiclustering methodsNeural systemsAssigned probability distributionsProbability distributionBrain componentsBrain dynamicsCluster generalizationBiclusteringBrainFrameworkBN edgesThe 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 dynamicsExplainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment
Patel B, Orlichenko A, Patel A, Qu G, Wilson T, Stephen J, Calhoun V, Wang Y. Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment. Applied Sciences 2024, 14: 4144. DOI: 10.3390/app14104144.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingBlood oxygen level-dependentGraph isomorphism networkGraph neural networksBrain networksFunctional magnetic resonance imaging paradigmFunctional magnetic resonance imaging blood oxygen level-dependentSex differencesClassification accuracyExploration of sex differencesInterpreting sex differencesOxygen level-dependentState-of-the-art algorithmsAdolescent neurodevelopmentState-of-the-artNeuropsychiatric conditionsFunctional connectivityTask-related dataDeep learning modelsLevel-dependentMouth movementsFMRI datasetsFunctional networksGraph structureAdolescentsCross‐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 risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study
Fazio G, Olivo D, Wolf N, Hirjak D, Schmitgen M, Werler F, Witteman M, Kubera K, Calhoun V, Reith W, Wolf R, Sambataro F. The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study. Addiction Biology 2024, 29: e13395. PMID: 38709211, PMCID: PMC11072977, DOI: 10.1111/adb.13395.Peer-Reviewed Original ResearchConceptsRisk of cannabis use disorderCannabis use disorderDynamic functional connectivityFunctional connectivityUse disorderTreatment of cannabis use disorderAt-risk individualsResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingCannabis-related problemsDefault-mode networkPatterns of FCCognitive-controlCUDIT-RBrain mechanismsSubcortical functionBrain networksSelf-screening questionnaireBrain connectivityBrain functionSensory-motorNeurostimulation treatmentsMagnetic resonance imagingBrainCluster statesInterpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Qu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson T, Stephen J, Calhoun V, Wang Y. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE Transactions On Medical Imaging 2024, 43: 1568-1578. PMID: 38109241, PMCID: PMC11090410, DOI: 10.1109/tmi.2023.3343365.Peer-Reviewed Original ResearchConceptsGraph transformation frameworkBrain imaging datasetsFunctional brain networksPhiladelphia Neurodevelopmental CohortConvolutional deep learningFeature embeddingPropagation weightsGraph embeddingHuman Connectome ProjectAttention mechanismImage datasetsDeep learningGraph transformationFunctional connectivityAnalyze functional brain networksTransformation frameworkDiffusion strategyBrain networksPositional encodingSpatial knowledgePrediction accuracyIndividual cognitive abilitiesEmbeddingNetworkGraph
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply