2024
A 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, 1-31. 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 MRIAssociation 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 alterationsSchizophreniaDouble Functionally Independent Primitives Provide Disorder Specific Fingerprints of Mental Illnesses
Soleimani N, Pearlson G, Iraji A, Calhoun V. Double Functionally Independent Primitives Provide Disorder Specific Fingerprints of Mental Illnesses. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635116.Peer-Reviewed Original ResearchAutism spectrum disorderMental illnessBipolar disorderMental disordersManifestations of mental illnessAssociated with mental illnessFunctional network connectivityFunctional network connectivity patternsNetwork connectivity patternsDisorder-specificDepressive disorderNeural underpinningsSpectrum disorderPsychological disordersNeuroimaging techniquesConnectivity patternsDisordersSchizophreniaHealthy controlsIllnessBrainFunctional changesMDDAutismNetwork connectivityA 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 abnormalities
2023
Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study
Zhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biological Psychiatry 2023, 95: 828-838. PMID: 38151182, PMCID: PMC11006588, DOI: 10.1016/j.biopsych.2023.12.019.Peer-Reviewed Original ResearchFunctional network connectivityCognitive abilitiesABCD studyMental healthLongitudinal predictionBehavioral developmentSleep problemsBrain functional network connectivityHigher cognitive abilitiesDefault mode networkChildren's behavioral developmentUnique protective factorsBrain functional connectivityCognitive controlTriple interactionFamily conflictMode networkMediation analysisFunctional connectivityInfluence behaviorFunctional networksProtective factorsSchool environmentChildhood developmentEnvironmental exposuresA Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants
Yan W, Pearlson G, Fu Z, Li X, Iraji A, Chen J, Sui J, Volkow N, Calhoun V. A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants. Biological Psychiatry 2023, 95: 699-708. PMID: 37769983, PMCID: PMC10942727, DOI: 10.1016/j.biopsych.2023.09.017.Peer-Reviewed Original ResearchFunctional network connectivityHealthy control individualsPsychiatric disordersRisk scoreEarly psychosisPsychiatric riskControl individualsStudy participantsHigh-risk groupMajor depressive disorderHigh-risk patternsPsychiatric risk assessmentCognitive Development StudyUnaffected adolescentsAdolescent Brain Cognitive Development (ABCD) studyLarge adolescent populationDepressive disorderHigh riskPsychosis scoresBipolar disorderPotential biomarkersEarly screeningPsychiatric vulnerabilityAdolescent populationDisordersEvaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification
Rokham H, Falakshahi H, Fu Z, Pearlson G, Calhoun V. Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification. Human Brain Mapping 2023, 44: 3180-3195. PMID: 36919656, PMCID: PMC10171526, DOI: 10.1002/hbm.26273.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityFunctional network connectivityDSM-IVFMRI-based measuresResting-state fMRI dataBiomarker-based approachPsychosis disordersClinical courseBipolar-Schizophrenia NetworkClinical evaluationSymptomatic measuresHealthy controlsPsychotic illnessHealthy individualsNeurological observationsMental disordersReliability of diagnosisStatistical group differencesMental healthNeuroimaging techniquesStatistical ManualDiagnostic problemsGroup differencesIntermediate phenotypesDisorders
2022
Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder
DeRamus T, Wu L, Qi S, Iraji A, Silva R, Du Y, Pearlson G, Mayer A, Bustillo J, Stromberg S, Calhoun V. Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder. NeuroImage Clinical 2022, 35: 103056. PMID: 35709557, PMCID: PMC9207350, DOI: 10.1016/j.nicl.2022.103056.Peer-Reviewed Original ResearchConceptsResting-state functional network connectivityFunctional network connectivityGray matterFractional anisotropyMultimodal canonical correlation analysisSchizoaffective disorderBipolar disorderJoint independent component analysisDiagnostic categoriesFunctional brain featuresWhite matter fractional anisotropyBrain featuresPsychotic spectrum disordersClinical indicatorsMultiple diagnostic categoriesFunctional alterationsSubcortical structuresDisorders
2015
Classification of Schizophrenia and Bipolar Patients Using Static and Time-Varying Resting-State FMRI Brain Connectivity
Rashid B, Arbabshirani M, Damaraju E, Millar R, Cetin M, Pearlson G, Calhoun V. Classification of Schizophrenia and Bipolar Patients Using Static and Time-Varying Resting-State FMRI Brain Connectivity. 2015, 251-254. DOI: 10.1109/isbi.2015.7163861.Peer-Reviewed Original ResearchClassification of schizophreniaHigh-dimensional dataAutomatic differential diagnosisAutomatic classificationAccurate classifierDimensional dataChallenging taskNetwork connectivityDiscriminative analysisHigh accuracyPowerful informationClassificationTraining subjectsLarge amountPrevious workDynamic functional network connectivityConnectivityClassifierFunctional network connectivityFNC analysisTaskBrain connectivityRobustnessFrameworkAccuracy
2014
P859: Differences in resting-state functional magnetic resonance imaging functional network connectivity between patients with restless legs syndrome and controls
Cho Y, Moon H, Lee Y, Chang H, Ku J, Pearlson G, Lee H. P859: Differences in resting-state functional magnetic resonance imaging functional network connectivity between patients with restless legs syndrome and controls. Clinical Neurophysiology 2014, 125: s272. DOI: 10.1016/s1388-2457(14)50892-9.Peer-Reviewed Original Research
2007
A Maximal-Correlation Approach Using ICA for Testing Functional Network Connectivity Applied to Schizophrenia
Jafri M, Pearlson G, Calhoun V. A Maximal-Correlation Approach Using ICA for Testing Functional Network Connectivity Applied to Schizophrenia. 2007, 468-471. DOI: 10.1109/isbi.2007.356890.Peer-Reviewed Original Research