2024
Gyrification across psychotic disorders: A bipolar-schizophrenia network of intermediate phenotypes study
Rychagov N, Del Re E, Zeng V, Oykhman E, Lizano P, McDowell J, Yassin W, Clementz B, Gershon E, Pearlson G, Sweeney J, Tamminga C, Keshavan M. Gyrification across psychotic disorders: A bipolar-schizophrenia network of intermediate phenotypes study. Schizophrenia Research 2024, 271: 169-178. PMID: 39032429, PMCID: PMC11384321, DOI: 10.1016/j.schres.2024.07.009.Peer-Reviewed Original ResearchBipolar-Schizophrenia NetworkPsychotic disordersDSM-IVIntermediate Phenotypes studyGyrification changesSchizophrenia compared to controlsBipolar I disorderRight cingulate cortexSchizoaffective disorder probandsBipolar disorder probandsDisorders compared to controlsAge-related differencesSchizoaffective disorderCingulate cortexVerbal memoryBipolar disorderAge-related changesFalse discovery rate correctionSchizophreniaCortical gyrificationHypogyriaFrontal lobeGyrificationDisordersHealthy controlsNeurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye L, Richard G, Fernandez-Cabello S, Parker N, Andreassen O, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin C, Tsai S, Rodrigue A, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León M, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul A, Uslu O, Burhanoglu B, Uyar Demir A, Rootes-Murdy K, Calhoun V, Sim K, Green M, Quidé Y, Chung Y, Kim W, Sponheim S, Demro C, Ramsay I, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park M, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen T, Rossell S, Hughes M, Woods W, Carruthers S, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen D, Preda A, Thomopoulos S, Jahanshad N, Cui L, Yao D, Thompson P, Turner J, van Erp T, Cheng W, Feng J. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nature Communications 2024, 15: 5996. PMID: 39013848, PMCID: PMC11252381, DOI: 10.1038/s41467-024-50267-3.Peer-Reviewed Original ResearchConceptsGray matter changesDisorder constructsEnlarged striatumPsychiatric conditionsMental disordersSubcortical regionsSchizophreniaBiological foundationsMatter changesBrain imagingStriatumDisordersBiological factorsIndividualsSubtypesHealthy subjectsCross-sectional brain imagingHippocampusTemporal trajectoriesInternational cohortSubgroup 2Subgroup 1SubgroupsDisentangling negative and positive symptoms in schizophrenia and autism spectrum disorder
Corbera S, Wexler B, Bell M, Pittman B, Pelphrey K, Pearlson G, Assaf M. Disentangling negative and positive symptoms in schizophrenia and autism spectrum disorder. Schizophrenia Research 2024, 271: 1-8. PMID: 39002525, PMCID: PMC11384336, DOI: 10.1016/j.schres.2024.07.002.Peer-Reviewed Original ResearchAutism spectrum disorderPositive and Negative Syndrome ScaleAutism Diagnostic Observation Schedule-GenericExperiential negative symptomsPositive symptomsNegative symptomsExploratory factor analysisSpectrum disorderNegative Syndrome ScaleTarget negative symptomsSyndrome ScaleDiscriminant function analysisSZ relativesAssociated with quality of lifeSchizophreniaSocial skillsSocial functioningDiagnostic classificationAutismFactor analysisAssociated with qualityPredictor of diagnosisSymptomsDisordersQuality of lifeEvidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis
Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr E, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher T, Keedy S, Bishop J, Ivleva E, McDowell J, Reilly J, Hill S, Pearlson G, Tamminga C, Keshavan M, Gershon E, Clementz B, Sweeney J, Hahn T, Dannlowski U, Lencer R. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis. Scientific Reports 2024, 14: 13859. PMID: 38879556, PMCID: PMC11180169, DOI: 10.1038/s41598-024-64487-6.Peer-Reviewed Original ResearchConceptsSmooth pursuit eye movementsPsychosis syndromePursuit eye movementsNon-psychotic bipolar disorderNon-psychotic affective disorderEye movementsSmooth pursuit dysfunctionMultivariate pattern analysisHealthy controlsPsychiatric sampleNeurobiological markersPsychosis probandsPsychotic syndromesAffective disordersPsychosis researchBipolar disorderPsychosis statusPsychosisSensorimotor functionSensorimotor measuresIndividual levelSensorimotor dysfunctionSensorimotorDisordersPattern analysisDouble 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 abnormalities75. Executive Working Memory Intervention Reduces Motor Activity in Adolescents With Attention-Deficit Hyperactivity Disorder
Beatty M, Sullivan A, Anderson J, Choi J, Pearlson G, Hawkins K, Bond D, Stevens M. 75. Executive Working Memory Intervention Reduces Motor Activity in Adolescents With Attention-Deficit Hyperactivity Disorder. Biological Psychiatry 2024, 95: s130. DOI: 10.1016/j.biopsych.2024.02.310.Peer-Reviewed Original Research449. Replication of Hallucination Severity Associating With Reduced Auditory-Language Cortex Connectivity in a Biological Subtype of Psychotic Disorders
Toscano I, Tamminga C, Ivleva E, Clementz B, McDowell J, Pearlson G, Keshavan M, Gershon E, Keedy S. 449. Replication of Hallucination Severity Associating With Reduced Auditory-Language Cortex Connectivity in a Biological Subtype of Psychotic Disorders. Biological Psychiatry 2024, 95: s283-s284. DOI: 10.1016/j.biopsych.2024.02.948.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 accuracy
2023
A 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 populationDisordersCharacterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization
Cetin-Karayumak S, Lyall A, Di Biase M, Seitz-Holland J, Zhang F, Kelly S, Elad D, Pearlson G, Tamminga C, Sweeney J, Clementz B, Schretlen D, Stegmayer K, Walther S, Lee J, Crow T, James A, Voineskos A, Buchanan R, Szeszko P, Malhotra A, Keshavan M, Shenton M, Rathi Y, Pasternak O, Kubicki M. Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization. Molecular Psychiatry 2023, 28: 2030-2038. PMID: 37095352, PMCID: PMC11146151, DOI: 10.1038/s41380-023-02068-1.Peer-Reviewed Original ResearchDuration of illnessIllness stageHealthy controlsWhole brain white matterDifferent illness stagesFree-water imagingSchizophrenia spectrum disordersYounger patientsBrain white matterExtracellular free waterProlonged illnessEarly psychosisDiffusion magnetic resonanceWhite matterDemographic dataIllnessSchizophreniaSmall effect sizesShort durationTime courseAgeEffect sizeSignificant global increaseInternational sitesDisordersIntrinsic neural timescales in autism spectrum disorder and schizophrenia. A replication and direct comparison study
Uscătescu L, Kronbichler M, Said-Yürekli S, Kronbichler L, Calhoun V, Corbera S, Bell M, Pelphrey K, Pearlson G, Assaf M. Intrinsic neural timescales in autism spectrum disorder and schizophrenia. A replication and direct comparison study. Schizophrenia 2023, 9: 18. PMID: 36997542, PMCID: PMC10063601, DOI: 10.1038/s41537-023-00344-1.Peer-Reviewed Original ResearchIntrinsic neural timescalesPatient groupBrain areasLeft lateral occipital gyrusRight post-central gyrusAutism spectrum disorderNeural timescalesPost-central gyrusLateral occipital gyrusDirect comparison studiesSpectrum disorderOccipital gyrusSymptom severitySchizophreniaGroup differencesGyrusDisordersPresent studyGroupSeverityEvaluation 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 structuresDisordersP523. Comparison of Lifetime Psychosis and Mood Symptoms in Schizophrenia, Schizoaffective Disorder, and Bipolar Disorder With Psychotic Features
Church H, Khera I, Kern B, Pearlson G, Clementz B, McDowell J, Keshavan M, Tamminga C, Ivleva E, Gershon E, Keedy S. P523. Comparison of Lifetime Psychosis and Mood Symptoms in Schizophrenia, Schizoaffective Disorder, and Bipolar Disorder With Psychotic Features. Biological Psychiatry 2022, 91: s300. DOI: 10.1016/j.biopsych.2022.02.760.Peer-Reviewed Original Research
2018
F254. Social Processing Subtyping and Functional Neural Architecture Across Autism Spectrum Disorder, Schizophrenia and Non-Clinical Sample
Assaf M, Corbera S, Rabany L, Brocke S, Pitman B, Johannesen J, Bell M, Calhoun V, Wexler B, Pearlson G. F254. Social Processing Subtyping and Functional Neural Architecture Across Autism Spectrum Disorder, Schizophrenia and Non-Clinical Sample. Biological Psychiatry 2018, 83: s337-s338. DOI: 10.1016/j.biopsych.2018.02.868.Peer-Reviewed Original Research
2015
Identifying Brain Dynamic Network States VIA GIG-ICA: Application to Schizophrenia, Bipolar and Schizoaffective Disorders
Du Y, Pearlson G, He H, Wu L, Chen J, Calhoun V. Identifying Brain Dynamic Network States VIA GIG-ICA: Application to Schizophrenia, Bipolar and Schizoaffective Disorders. 2015, 478-481. DOI: 10.1109/isbi.2015.7163915.Peer-Reviewed Original ResearchFunctional connectivity statesSchizoaffective disorderBipolar disorderSAD patientsDynamic functional networksFunctional networksConnectivity statesResting-state fMRI dataBP patientsHealthy controlsPatientsSZ patientsFunctional connectivitySimilar symptomsFMRI dataDisordersSchizophreniaMental diseasesSignificant differences
2012
D-Cycloserine for Treatment Nonresponders With Obsessive-Compulsive Disorder: A Case Report
Norberg M, Gilliam C, Villavicencio A, Pearlson G, Tolin D. D-Cycloserine for Treatment Nonresponders With Obsessive-Compulsive Disorder: A Case Report. Cognitive And Behavioral Practice 2012, 19: 338-345. DOI: 10.1016/j.cbpra.2011.05.002.Peer-Reviewed Original ResearchObsessive-compulsive disorderN-methyl-D-aspartate (NMDA) receptor partial agonist D-cycloserinePartial agonist D-cycloserineThird of patientsD-cycloserineCourse of ERPReuptake inhibitorsCase reportTreatment nonrespondersEffective treatmentPatientsPrior treatmentResponse preventionGreater reductionTreatmentDisordersPresent studyAdequate responseNonrespondersPrevention
2010
Functional imaging of schizophrenia
Pearlson G. Functional imaging of schizophrenia. 2010, 30-47. DOI: 10.1017/cbo9780511782091.003.Peer-Reviewed Original Research
2002
Macroanatomical Findings in Post-Mortem Brain Tissue
Pearlson G. Macroanatomical Findings in Post-Mortem Brain Tissue. Neurobiological Foundation Of Aberrant Behaviors 2002, 4: 277-289. DOI: 10.1007/978-1-4757-3631-1_16.Peer-Reviewed Original Research