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 controlsAssociation 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 alterationsSchizophreniaEvidence 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 abnormalitiesCerebral Blood Flow Changes and Neuroticism in Late-Life Depression
Anderson T, Steffens D, Wang L, Pearlson G. Cerebral Blood Flow Changes and Neuroticism in Late-Life Depression. American Journal Of Geriatric Psychiatry 2024, 32: s49-s50. DOI: 10.1016/j.jagp.2024.01.121.Peer-Reviewed Original ResearchMontgomery Asberg Depression Rating ScaleLate-life depressionAsberg Depression Rating ScaleLeft putamenFrontal poleLow neuroticismGroup differencesDecreased CBFLLD subjectsDepression severityDepression groupLLD groupCerebral blood flowNever-depressed control subjectsNever-depressed healthy controlsNEO Personality InventoryNever-depressed groupDepression Rating ScaleNeuroticism personality traitFrontal lobe structuresGroup effectBrain morphological changesT1-weighted MPRAGE imagesHealthy controlsSignificant group effectMore 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
Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering
Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner J, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun V. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophrenia Research 2023, 264: 130-139. PMID: 38128344, DOI: 10.1016/j.schres.2023.12.013.Peer-Reviewed Original ResearchPsychosis subtypesSchizoaffective disorderBipolar disorderClinical phenotypeFirst-degree relativesTemporal-occipital cortexAmygdala-hippocampusClinical symptomsNeuroimaging featuresBipolar-Schizophrenia NetworkBrain alterationsHealthy controlsIntermediate Phenotypes (B-SNIP) consortiumOccipital cortexDecreased connectivitySubtypesStructural covarianceFractional amplitudeSubtype IILow-frequency fluctuationsNeurobiological heterogeneityGreater predispositionPsychosis spectrumGroup differencesDiagnostic classificationSupervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP)
Koen J, Lewis L, Rugg M, Clementz B, Keshavan M, Pearlson G, Sweeney J, Tamminga C, Ivleva E. Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP). Scientific Reports 2023, 13: 12980. PMID: 37563219, PMCID: PMC10415369, DOI: 10.1038/s41598-023-38101-0.Peer-Reviewed Original ResearchConceptsPsychosis biotypesPsychosis casesBrain-based biomarkersLogistic regression modelsT1-weighted imagesBipolar-Schizophrenia NetworkHealthy controlsDisease neurobiologyPsychotic disordersClinical diagnosisStructural MRIBrain structuresGrey matter density mapsDSM diagnosesEvidence of specificityAbove-chance classification accuracyPeripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis
Lizano P, Kiely C, Mijalkov M, Meda S, Keedy S, Hoang D, Zeng V, Lutz O, Pereira J, Ivleva E, Volpe G, Xu Y, Lee A, Rubin L, Hill S, Clementz B, Tamminga C, Pearlson G, Sweeney J, Gershon E, Keshavan M, Bishop J. Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis. Brain Behavior And Immunity 2023, 114: 3-15. PMID: 37506949, PMCID: PMC10592140, DOI: 10.1016/j.bbi.2023.07.014.Peer-Reviewed Original ResearchConceptsResting-state networksHealthy controlsInter-network connectivityWorse verbal fluencyAnterior default mode networkC-reactive proteinResting-state functional networksDefault mode network connectivityRight frontoparietal networkMode network connectivityWorse cognitive performanceResting-state fMRIDefault mode networkFunctional network topologyInflammatory signatureSystemic inflammationInflammatory subgroupIL-6Neuroanatomical alterationsPsychosis probandsCo-activation patternsPsychosis spectrum disordersNetwork dysfunctionMultiple comparison correctionClinical implicationsCharacterization 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 sitesDisordersEvaluation 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
2021
Anterior-posterior axis of hippocampal subfields across psychoses: A B-SNIP study
del Re E, Zeng V, Alliey-Rodriguez N, Lizano P, Bolo N, Lutz O, Pearlson G, Sweeney J, Clementz B, Gershon E, Tamminga C, Keshavan M. Anterior-posterior axis of hippocampal subfields across psychoses: A B-SNIP study. Biomarkers In Neuropsychiatry 2021, 5: 100037. DOI: 10.1016/j.bionps.2021.100037.Peer-Reviewed Original ResearchVolumetric abnormalitiesPsychosis probandsBipolar type 1Granule cell layerT MRI scansB-SNIP studyB-SNIPDentate gyrusBipolar-Schizophrenia NetworkHealthy controlsClinical dataHippocampal subfieldsDSM categoriesMRI scansSchizoaffective disorderHippocampusType 1Schizophrenia NetworkAbnormalitiesUnaffected relativesAnterior-posterior axisSchizophreniaPsychosisProbandsConclusions Differences
2020
O10.6. ANTERIOR VERSUS POSTERIOR HIPPOCAMPUS WITHIN PSYCHOSIS: A BSNIP STUDY
del Re E, Zeng V, Lutz O, Pearlson G, Sweeney J, Clementz B, Gershon E, Keedy S, Ivleva E, Tamminga C, Keshavan M. O10.6. ANTERIOR VERSUS POSTERIOR HIPPOCAMPUS WITHIN PSYCHOSIS: A BSNIP STUDY. Schizophrenia Bulletin 2020, 46: s26-s27. PMCID: PMC7233936, DOI: 10.1093/schbul/sbaa028.060.Peer-Reviewed Original ResearchDiagnostic groupsSum of anteriorAnterior portionGrouping of patientsMedial temporal lobe structuresTemporal lobe structuresBiotype 1Biotype 3T MRI dataBiotype 2Right subiculumHealthy controlsSubfield volumesFreeSurfer 6.0Whole hippocampusMorphometric findingsClinical ratingsHealthy populationHippocampusBipolar disorderAnatomical divisionsPosterior divisionSA disordersCognitive functionVolume decreaseFunctional MRI Findings in Schizophrenia
Pearlson G. Functional MRI Findings in Schizophrenia. 2020, 113-124. DOI: 10.1007/978-3-030-35206-6_6.Peer-Reviewed Original ResearchSchizophrenia patientsDrug-naïve patientsFunctional MRI findingsFunctional MRI literatureTask-related paradigmsMRI findingsMedication treatmentPredisposing causeHealthy controlsFunctional abnormalitiesMRI reportsPsychotic bipolarNeurodevelopmental anomaliesPatientsBrain differencesUnaffected relativesSynaptic efficiencySchizophreniaMRI literatureAbnormalitiesArticle reviewsLocal manifestationsReviewSymptomsNeurotransmitters
2018
T22. PITUITARY GLAND VOLUME DIFFERENCES IN INDIVIDUALS WITH PSYCHOSIS: RESULTS FROM THE BIPOLAR-SCHIZOPHRENIA NETWORK ON INTERMEDIATE PHENOTYPES (B-SNIP) STUDY
Guimond S, Tingue S, Devenyi G, Tang Y, Mike L, Chakravarty M, Sweeney J, Pearlson G, Clementz B, Tamminga C, Keshavan M. T22. PITUITARY GLAND VOLUME DIFFERENCES IN INDIVIDUALS WITH PSYCHOSIS: RESULTS FROM THE BIPOLAR-SCHIZOPHRENIA NETWORK ON INTERMEDIATE PHENOTYPES (B-SNIP) STUDY. Schizophrenia Bulletin 2018, 44: s121-s121. PMCID: PMC5888564, DOI: 10.1093/schbul/sby016.298.Peer-Reviewed Original ResearchPituitary gland volumeLarger pituitary glandsDuration of illnessHealthy controlsGland volumePituitary glandPsychotic bipolar disorderSymptom severityBipolar-Schizophrenia NetworkPituitary volumeSchizoaffective disorderLarger pituitary gland volumesClinical diagnosisLarger pituitary volumeSubgroup of patientsSignificant main effectGreater symptom severitySignificant subgroup differencesIndependent t-testAdrenal axisPatient groupIntermediate Phenotypes (B-SNIP) consortiumMAGeT Brain algorithmStructural magnetic resonance imagesIntermediate Phenotypes (B-SNIP) study
2017
Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group
Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, Andreassen O, Arango C, Banaj N, Bouix S, Bousman C, Brouwer R, Bruggemann J, Bustillo J, Cahn W, Calhoun V, Cannon D, Carr V, Catts S, Chen J, Chen J, Chen X, Chiapponi C, Cho K, Ciullo V, Corvin A, Crespo-Facorro B, Cropley V, De Rossi P, Diaz-Caneja C, Dickie E, Ehrlich S, Fan F, Faskowitz J, Fatouros-Bergman H, Flyckt L, Ford J, Fouche J, Fukunaga M, Gill M, Glahn D, Gollub R, Goudzwaard E, Guo H, Gur R, Gur R, Gurholt T, Hashimoto R, Hatton S, Henskens F, Hibar D, Hickie I, Hong L, Horacek J, Howells F, Hulshoff Pol H, Hyde C, Isaev D, Jablensky A, Jansen P, Janssen J, Jönsson E, Jung L, Kahn R, Kikinis Z, Liu K, Klauser P, Knöchel C, Kubicki M, Lagopoulos J, Langen C, Lawrie S, Lenroot R, Lim K, Lopez-Jaramillo C, Lyall A, Magnotta V, Mandl R, Mathalon D, McCarley R, McCarthy-Jones S, McDonald C, McEwen S, McIntosh A, Melicher T, Mesholam-Gately R, Michie P, Mowry B, Mueller B, Newell D, O'Donnell P, Oertel-Knöchel V, Oestreich L, Paciga S, Pantelis C, Pasternak O, Pearlson G, Pellicano G, Pereira A, Pineda Zapata J, Piras F, Potkin S, Preda A, Rasser P, Roalf D, Roiz R, Roos A, Rotenberg D, Satterthwaite T, Savadjiev P, Schall U, Scott R, Seal M, Seidman L, Shannon Weickert C, Whelan C, Shenton M, Kwon J, Spalletta G, Spaniel F, Sprooten E, Stäblein M, Stein D, Sundram S, Tan Y, Tan S, Tang S, Temmingh H, Westlye L, Tønnesen S, Tordesillas-Gutierrez D, Doan N, Vaidya J, van Haren N, Vargas C, Vecchio D, Velakoulis D, Voineskos A, Voyvodic J, Wang Z, Wan P, Wei D, Weickert T, Whalley H, White T, Whitford T, Wojcik J, Xiang H, Xie Z, Yamamori H, Yang F, Yao N, Zhang G, Zhao J, van Erp T, Turner J, Thompson P, Donohoe G. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Molecular Psychiatry 2017, 23: 1261-1269. PMID: 29038599, PMCID: PMC5984078, DOI: 10.1038/mp.2017.170.Peer-Reviewed Original ResearchConceptsSchizophrenia patientsFractional anisotropyWM skeletonWhite matter microstructural differencesWhite matter abnormalitiesWidespread WM abnormalitiesOnset of schizophreniaAnterior corona radiataEffect sizeDiffusion tensor imaging (DTI) dataTensor imaging dataHealthy controlsMeta-analyzed effectsCorpus callosumMedication dosageCorona radiataPsychiatric disordersLarge effect sizesWM microstructural differencesPatientsWM abnormalitiesRadial diffusivitySchizophreniaSignificant decreaseDiffusivity measures5.3 Endophenotypes Guide Psychosis Neurobiology Formulations
Tamminga C, Clementz B, Keshavan M, Gershon E, Pearlson G, Ivleva E. 5.3 Endophenotypes Guide Psychosis Neurobiology Formulations. Schizophrenia Bulletin 2017, 43: s7-s7. DOI: 10.1093/schbul/sbx021.019.Peer-Reviewed Original ResearchConventional diagnosisFunctional brain biomarkersBiomarker compositesGray matter changesPsychosis researchHealthy controlsAdditional biomarkersClear pathophysiologyBrain biomarkersMatter changesAxis IProband groupsBiotype 1Higher negative symptomsLifetime ratesPsychiatric conditionsNegative symptomsPsychosis groupPsychotic conditionsSchizophrenia NetworkDistinct molecular markersDiagnosisAdolescent cannabisBiomarkersEEG characteristicsSA64. Hallucination Severity Predicted by Auditory Cortex Resting State Connectivity in Bipolar and Schizophrenia Network on Intermediate Phenotypes Study
Okuneye V, Meda S, Keshavan M, Sweeney J, Tamminga C, Pearlson G, Gershon E, Keedy S. SA64. Hallucination Severity Predicted by Auditory Cortex Resting State Connectivity in Bipolar and Schizophrenia Network on Intermediate Phenotypes Study. Schizophrenia Bulletin 2017, 43: s136-s136. PMCID: PMC5475960, DOI: 10.1093/schbul/sbx023.063.Peer-Reviewed Original ResearchAuditory cortexHallucination severityState connectivityPsychosis patientsSignificant negative associationIntermediate Phenotypes (B-SNIP) studySchizophrenia NetworkResting-state connectivityAbnormal activation patternsBrodmann area 22Primary auditory cortexSecondary auditory cortexRight orbitofrontal cortexPrevious functional imaging studiesPhenotype studiesClinical rating scalesAuditory processing areasPossible common pathwayFunctional imaging studiesMiddle temporal cortexNegative associationPsychosis subjectsHealthy controlsHealthy subjectsConnectivity dysfunction
2016
Subcortical volumetric abnormalities in bipolar disorder
Hibar DP, Westlye LT, van Erp TG, Rasmussen J, Leonardo CD, Faskowitz J, Haukvik UK, Hartberg CB, Doan NT, Agartz I, Dale AM, Gruber O, Krämer B, Trost S, Liberg B, Abé C, Ekman CJ, Ingvar M, Landén M, Fears SC, Freimer NB, Bearden CE, Sprooten E, Glahn D, Pearlson G, Emsell L, Kenney J, Scanlon C, McDonald C, Cannon D, Almeida J, Versace A, Caseras X, Lawrence N, Phillips M, Dima D, Delvecchio G, Frangou S, Satterthwaite T, Wolf D, Houenou J, Henry C, Malt U, Bøen E, Elvsåshagen T, Young A, Lloyd A, Goodwin G, Mackay C, Bourne C, Bilderbeck A, Abramovic L, Boks M, van Haren N, Ophoff R, Kahn R, Bauer M, Pfennig A, Alda M, Hajek T, Mwangi B, Soares J, Nickson T, Dimitrova R, Sussmann J, Hagenaars S, Whalley H, McIntosh A, Thompson P, Andreassen O. Subcortical volumetric abnormalities in bipolar disorder. Molecular Psychiatry 2016, 21: 1710-1716. PMID: 26857596, PMCID: PMC5116479, DOI: 10.1038/mp.2015.227.Peer-Reviewed Original ResearchConceptsLateral ventricleBipolar disorderBDII patientsBD patientsIntracranial volumeLarger thalamic volumesSubcortical volumetric abnormalitiesLarger lateral ventriclesSignificant differencesSubcortical brain measuresCase-control differencesDevelopment of biomarkersMean hippocampusVolumetric abnormalitiesIllness onsetThalamic volumeBDI patientsGlobus pallidusSmaller hippocampiClinical subtypesDisease progressionHealthy controlsBrain changesNucleus accumbensPatients