2024
Evidence 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 ResearchMeSH KeywordsAdolescentAdultBiomarkersBipolar DisorderCase-Control StudiesFemaleHumansMaleMiddle AgedPsychotic DisordersPursuit, SmoothYoung AdultConceptsSmooth 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 analysisA 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
2019
Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol‐Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort
Meda SA, Narayanan B, Chorlian D, Meyers JL, Gelernter J, Hesselbrock V, Bauer L, Calhoun VD, Porjesz B, Pearlson G. Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol‐Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort. Alcohol Clinical And Experimental Research 2019, 43: 1462-1477. PMID: 31009096, DOI: 10.1111/acer.14063.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAlcoholismCase-Control StudiesCohort StudiesElectroencephalographyFemaleGenetic Association StudiesGenome-Wide Association StudyGenotypeHumansMaleMiddle AgedMultigene FamilyNeuronsPhenotypePolymorphism, Single NucleotideSignal TransductionSubstance-Related DisordersWhite PeopleYoung AdultConceptsGenetic clustersSingle nucleotide polymorphism dataSignificant genotype-phenotype associationsNucleotide polymorphism dataLipid/cholesterol metabolismLinkage-based analysisGenotype-phenotype relationshipsGenotype-phenotype associationsGene clusterCell signalingPolymorphism dataMolecular mechanismsAlcoholism datasetGenomewide associationTop hitsGenetic componentNeuronal signalingGeneticsSignalingBiological componentsRelationship pairsCholesterol metabolismNeurogenesisSNP componentParallel independent component analysis
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 ResearchMeSH KeywordsAdultBipolar DisorderBrainCase-Control StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedOrgan SizeRetrospective StudiesConceptsLateral 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
2000
MRI 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 disorder
1999
Lack of normal pattern of cerebral asymmetry in familial schizophrenic patients and their relatives — The Maudsley Family Study
Sharma T, Lancaster E, Sigmundsson T, Lewis S, Takei N, Gurling H, Barta P, Pearlson G, Murray R. Lack of normal pattern of cerebral asymmetry in familial schizophrenic patients and their relatives — The Maudsley Family Study. Schizophrenia Research 1999, 40: 111-120. PMID: 10593451, DOI: 10.1016/s0920-9964(99)00143-7.Peer-Reviewed Original ResearchConceptsSchizophrenic patientsOccipitoparietal regionsControl subjectsNormal patternCortical regionsLack of asymmetryFamilial schizophrenic patientsNormal cerebral asymmetryHealthy control subjectsFirst-degree relativesObligate carriersCerebral asymmetryRegional brain volumesUnrelated control subjectsDegree relativesRight sensorimotorNormal brain asymmetryBrain volumeMaudsley Family StudyPatientsOccipital asymmetryUnaffected relativesSchizophreniaBrain asymmetryNormal asymmetry