2024
Categorical and Dimensional Approaches for Psychiatric Classification and Treatment Targeting: Considerations from Psychosis Biotypes
Clementz B, Assaf M, Sweeney J, Gershon E, Keedy S, Hill S, Ivleva E, Tamminga C, McDowell J, Keshavan M, Gibbons R, Carpenter W, Pearlson G. Categorical and Dimensional Approaches for Psychiatric Classification and Treatment Targeting: Considerations from Psychosis Biotypes. Advances In Neurobiology 2024, 40: 685-723. PMID: 39562461, DOI: 10.1007/978-3-031-69491-2_23.Peer-Reviewed Original ResearchConceptsIdiopathic psychosisBiotype-1Response to clozapineBipolar-Schizophrenia NetworkContinuum of severityReduced physiological responseDSM diagnosesNeurobiological distinctionsCategorical diagnosisSchizophrenia NetworkDiagnostic boundariesSalient stimuliPsychosis diagnosisPsychosis casesPsychiatric classificationPsychosis dataPsychosisBiotype-2Identified biotypesNeural activityIntermediate phenotypesTreatment targetElectrophysiological biomarkersElectrophysiological measurementsMedical modelLocal-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 prediction4D 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 networksNeurostructural 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 1SubgroupsA 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
Revisiting 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 ReviewOverThoughtSupervised 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 ResearchMeSH KeywordsBiomarkersBipolar DisorderBrainHumansMagnetic Resonance ImagingPhenotypePsychotic DisordersSchizophreniaConceptsPsychosis 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 implicationsMulti-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study
Meng X, Iraji A, Fu Z, Kochunov P, Belger A, Ford J, McEwen S, Mathalon D, Mueller B, Pearlson G, Potkin S, Preda A, Turner J, van Erp T, Sui J, Calhoun V. Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study. NeuroImage Clinical 2023, 38: 103434. PMID: 37209635, PMCID: PMC10209454, DOI: 10.1016/j.nicl.2023.103434.Peer-Reviewed Original ResearchConceptsIndependent component analysisData-driven approachData miningF1 scoreClassification modelReference algorithmNetwork connectivityMagnetic resonance imaging dataNetworkImaging dataPredictive resultsPatient dataFunctional magnetic resonance imaging (fMRI) dataData acquisition timeConnectivity networksFrameworkConnectivityPromising approachNew subjectMiningAnalytic approachAlgorithmDatasetAcquisition timeComponent analysisEmotional scene processing in biotypes of psychosis
Trotti R, Parker D, Sabatinelli D, Keshavan M, Keedy S, Gershon E, Pearlson G, Hill S, Tamminga C, McDowell J, Clementz B. Emotional scene processing in biotypes of psychosis. Psychiatry Research 2023, 324: 115227. PMID: 37121219, PMCID: PMC10175237, DOI: 10.1016/j.psychres.2023.115227.Peer-Reviewed Original ResearchMeSH KeywordsBrainElectroencephalographyEmotionsEvoked PotentialsHumansPsychotic DisordersSchizophreniaConceptsSocial-emotional deficitsEmotional scenesSelf-reported emotional experienceEvent-related potential studyEmotional scene processingEmotional processing deficitsPsychosis groupPsychosis subgroupsSocio-occupational functioningEmotional processingProcessing deficitsScene processingEmotional experienceNeurophysiological correlatesNeural responsesPotential studiesScalp locationsPsychosis biotypesERPCurrent studyDeficitsFirst variateFuture translational researchPsychosisPsychosis casesPeripheral inflammation is associated with impairments of inhibitory behavioral control and visual sensorimotor function in psychotic disorders
Zhang L, Lizano P, Xu Y, Rubin L, Lee A, Lencer R, Reilly J, Keefe R, Keedy S, Pearlson G, Clementz B, Keshavan M, Gershon E, Tamminga C, Sweeney J, Hill S, Bishop J. Peripheral inflammation is associated with impairments of inhibitory behavioral control and visual sensorimotor function in psychotic disorders. Schizophrenia Research 2023, 255: 69-78. PMID: 36965362, PMCID: PMC10175233, DOI: 10.1016/j.schres.2023.03.030.Peer-Reviewed Original ResearchMeSH KeywordsBehavior ControlBipolar DisorderHumansInflammationNeuropsychological TestsPsychotic DisordersSchizophreniaConceptsCognitive domainsInhibitory controlGeneral cognitive abilitySpecific cognitive domainsInhibitory behavioral controlC-reactive proteinPsychotic disordersPsychosis spectrum disordersCognitive abilitiesPeripheral inflammationInflammation factorsSpectrum disorderSensorimotor functionSensorimotor tasksNeurobehavioral domainsGreater deficitsSubgroup of individualsBehavioral controlPsychosis subgroupsCognitive impairmentPreliminary evidenceHigher inflammation scoresNeurobehavioral batteryBrain anatomyBehavioral monitoring
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
2020
Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA
Radua J, Vieta E, Shinohara R, Kochunov P, Quidé Y, Green M, Weickert C, Weickert T, Bruggemann J, Kircher T, Nenadić I, Cairns M, Seal M, Schall U, Henskens F, Fullerton J, Mowry B, Pantelis C, Lenroot R, Cropley V, Loughland C, Scott R, Wolf D, Satterthwaite T, Tan Y, Sim K, Piras F, Spalletta G, Banaj N, Pomarol-Clotet E, Solanes A, Albajes-Eizagirre A, Canales-Rodríguez E, Sarro S, Di Giorgio A, Bertolino A, Stäblein M, Oertel V, Knöchel C, Borgwardt S, du Plessis S, Yun J, Kwon J, Dannlowski U, Hahn T, Grotegerd D, Alloza C, Arango C, Janssen J, Díaz-Caneja C, Jiang W, Calhoun V, Ehrlich S, Yang K, Cascella N, Takayanagi Y, Sawa A, Tomyshev A, Lebedeva I, Kaleda V, Kirschner M, Hoschl C, Tomecek D, Skoch A, van Amelsvoort T, Bakker G, James A, Preda A, Weideman A, Stein D, Howells F, Uhlmann A, Temmingh H, López-Jaramillo C, Díaz-Zuluaga A, Fortea L, Martinez-Heras E, Solana E, Llufriu S, Jahanshad N, Thompson P, Turner J, van Erp T, collaborators E, Glahn D, Pearlson G, Hong E, Krug A, Carr V, Tooney P, Cooper G, Rasser P, Michie P, Catts S, Gur R, Gur R, Yang F, Fan F, Chen J, Guo H, Tan S, Wang Z, Xiang H, Piras F, Assogna F, Salvador R, McKenna P, Bonvino A, King M, Kaiser S, Nguyen D, Pineda-Zapata J. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA. NeuroImage 2020, 218: 116956. PMID: 32470572, PMCID: PMC7524039, DOI: 10.1016/j.neuroimage.2020.116956.Peer-Reviewed Original Research
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 measures
2014
Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia
Meda SA, Ruaño G, Windemuth A, O’Neil K, Berwise C, Dunn SM, Boccaccio LE, Narayanan B, Kocherla M, Sprooten E, Keshavan MS, Tamminga CA, Sweeney JA, Clementz BA, Calhoun VD, Pearlson GD. Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia. Proceedings Of The National Academy Of Sciences Of The United States Of America 2014, 111: e2066-e2075. PMID: 24778245, PMCID: PMC4024891, DOI: 10.1073/pnas.1313093111.Peer-Reviewed Original ResearchConceptsDefault mode networkPsychotic bipolar disorderUnaffected first-degree relativesFirst-degree relativesSZ probandsResting-state functional MRI scansBipolar disorderMode networkFunctional MRI scansLong-term potentiationBrain's default mode networkGlobal enrichment analysisSubset of controlsPatient groupHealthy controlsDMN modulationDrug treatmentImmune responsePsychiatric disordersStudy subjectsMRI scansDMN connectivityMultivariate analysisFunctional connectivitySchizophrenia
2013
Resting State Electroencephalogram Oscillatory Abnormalities in Schizophrenia and Psychotic Bipolar Patients and Their Relatives from the Bipolar and Schizophrenia Network on Intermediate Phenotypes Study
Narayanan B, O’Neil K, Berwise C, Stevens MC, Calhoun VD, Clementz BA, Tamminga CA, Sweeney JA, Keshavan MS, Pearlson GD. Resting State Electroencephalogram Oscillatory Abnormalities in Schizophrenia and Psychotic Bipolar Patients and Their Relatives from the Bipolar and Schizophrenia Network on Intermediate Phenotypes Study. Biological Psychiatry 2013, 76: 456-465. PMID: 24439302, PMCID: PMC5045030, DOI: 10.1016/j.biopsych.2013.12.008.Peer-Reviewed Original ResearchConceptsFirst-degree relativesSlow beta activityFast alpha activitySZ probandsAlpha activityHealthy control subjectsBeta activityRelative risk estimatesFrontal delta activityEEG spectral activityModerate relative riskPsychotic bipolar patientsLow-frequency activityPsychotic bipolar probandsGroup independent component analysisControl subjectsRelative riskPost-hoc pair-wise comparisonsBipolar patientsGenetic predispositionIntermediate Phenotypes (B-SNIP) studyOscillatory abnormalitiesAnalysis of covarianceUnique endophenotypesBipolar disorder
2012
Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia
Sui J, He H, Liu J, Yu Q, Adali T, Pearlson G, Calhoun V. Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2012, 2012: 2692-2695. PMID: 23366480, DOI: 10.1109/embc.2012.6346519.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsFemaleHumansMagnetic Resonance ImagingMaleMultivariate AnalysisSchizophreniaYoung AdultConceptsMulti-set canonical correlation analysisData fusionMulti-modal fusionDisparate data setsMultiple data typesJoint independent component analysisData typesFusion modelJoint informationData setsIndependent component analysisHigher decomposition accuracyEffective mannerCanonical correlation analysisDecomposition accuracyLimited viewEffective approachPromising approachBiomedical imagingFusionComponent analysisAccuracyIllness biomarkersInformationSet
2000
White matter hyperintensity volume in late‐onset and early‐onset schizophrenia
Rivkin P, Kraut M, Barta P, Anthony J, Arria A, Pearlson G. White matter hyperintensity volume in late‐onset and early‐onset schizophrenia. International Journal Of Geriatric Psychiatry 2000, 15: 1085-1089. PMID: 11180463, DOI: 10.1002/1099-1166(200012)15:12<1085::aid-gps250>3.0.co;2-x.Peer-Reviewed Original ResearchConceptsWhite matter hyperintensitiesMagnetic resonance imagingWhite matter hyperintensity volumeLate-onset psychosisEarly-onset schizophreniaEarly-onset schizophrenicsNeuro-imaging studiesNeuro-degenerative processesControl subjectsCase ascertainmentOnset psychosisHyperintensity volumeMatter hyperintensitiesOnset schizophreniaWMH volumeAnatomic correlatesResonance imagingStudy designSchizophreniaSignificant differencesWMH measurementsPresent studySchizophrenicsContinuous measureLatent vulnerabilityMRI findings differentiate between late‐onset schizophrenia and late‐life mood disorder
Rabins P, Aylward E, Holroyd S, Pearlson G. MRI findings differentiate between late‐onset schizophrenia and late‐life mood disorder. International Journal Of Geriatric Psychiatry 2000, 15: 954-960. PMID: 11044878, DOI: 10.1002/1099-1166(200010)15:10<954::aid-gps224>3.0.co;2-o.Peer-Reviewed Original ResearchConceptsLate-onset schizophreniaLate-life bipolar disorderBilateral cortical atrophyLarger third ventriclesRight temporal hornLate-life mood disordersLate-life depressionDegree of atrophyNormal control groupGender-matched controlsFunctional imaging studiesSulcal enlargementCortical atrophyMRI findingsTemporal hornFunctional abnormalitiesSylvian fissureMood disordersThird ventricleOutpatient servicesMRI scansPatientsAffective disordersControl groupBipolar disorderNeurobiology of schizophrenia
Pearlson G. Neurobiology of schizophrenia. Annals Of Neurology 2000, 48: 556-566. PMID: 11026439, DOI: 10.1002/1531-8249(200010)48:4<556::aid-ana2>3.0.co;2-2.Peer-Reviewed Original ResearchConceptsOngoing cell deathFunctional brain imaging studiesNeurobiology of schizophreniaBrain imaging studiesCommon chronicNeuropathological studiesUnknown etiologyGlutamate systemClinical expressionMigrational abnormalitiesBiochemical abnormalitiesFetal brainNicotinic receptorsEffective treatmentNumerous biological markersCerebral circuitsRegional abnormalitiesImaging studiesBrain diseasesSecond hitBiological markersAbnormal personality traitsSingle disorderSchizophreniaBiological susceptibility