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 controlsEnlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders.
Guimond S, Alftieh A, Devenyi G, Mike L, Chakravarty M, Shah J, Parker D, Sweeney J, Pearlson G, Clementz B, Tamminga C, Keshavan M. Enlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders. Psychological Medicine 2024, 54: 1835-1843. PMID: 38357733, PMCID: PMC11132920, DOI: 10.1017/s003329172300380x.Peer-Reviewed Original ResearchPituitary gland volumePsychotic disordersSymptom severityAntipsychotic doseIllness durationCognitive functionAssociated with greater symptom severitySignificant effect of diagnosisAssociated with symptom severityHigher antipsychotic dosePituitary volumeBipolar-Schizophrenia NetworkIntermediate Phenotypes consortiumStructural magnetic resonance imagingProgression of psychosisGreater symptom severityEffect of diagnosisLower cognitive functionMore severe symptomsSample of individualsTransdiagnostic sampleMAGeT Brain algorithmGland volumeTrait markerBetween-group differences
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 accuracyEvaluation 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 DifferencesLongitudinal Stability of Psychosis Biomarkers: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP)
Trotti R, Parker D, McDowell J, Pearlson G, Keshavan M, Keedy S, Gershon E, Hill S, Ivleva E, Tamminga C, Clementz B. Longitudinal Stability of Psychosis Biomarkers: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP). Biological Psychiatry 2021, 89: s124. DOI: 10.1016/j.biopsych.2021.02.321.Peer-Reviewed Original ResearchBipolar-Schizophrenia NetworkIntermediate phenotypes
2020
O3.4. PSYCHOSIS PHENOTYPES FROM B-SNIP FOR CLINICAL ADVANCES: BIOTYPE CHARACTERISTICS AND TARGETS
Clementz B, Trotti R, Pearlson G, Keshavan M, Gershon E, Keedy S, Ivleva E, McDowell J, Tamminga C. O3.4. PSYCHOSIS PHENOTYPES FROM B-SNIP FOR CLINICAL ADVANCES: BIOTYPE CHARACTERISTICS AND TARGETS. Schizophrenia Bulletin 2020, 46: s7-s7. PMCID: PMC7234003, DOI: 10.1093/schbul/sbaa028.015.Peer-Reviewed Original ResearchClinical featuresPsychosis biotypesPsychosis casesPsychosis syndromeTreatment targetsSpecific treatment targetsB-SNIPClinical manifestationsBiomarker profilesPsychosis subgroupsB-SNIP consortiumDeep phenotyping approachPathophysiological mechanismsPsychosis patientsBipolar-Schizophrenia NetworkPsychosis diagnosisBiological subtypesHealthy personsClinical practiceClinical advancesNegative symptomsClinical phenomenologySyndromeAuditory paired-stimuli responses across the psychosis and bipolar spectrum and their relationship to clinical features
Parker D, Trotti R, McDowell J, Keedy S, Gershon E, Ivleva E, Pearlson G, Keshavan M, Tamminga C, Sweeney J, Clementz B. Auditory paired-stimuli responses across the psychosis and bipolar spectrum and their relationship to clinical features. Biomarkers In Neuropsychiatry 2020, 3: 100014. PMID: 36644018, PMCID: PMC9837793, DOI: 10.1016/j.bionps.2020.100014.Peer-Reviewed Original ResearchSchizoaffective disorderGroup differencesBipolar disorderAuditory paired-stimulus paradigmPaired-stimulus paradigmNeural responsesIdentification of biomarkersB-SNIPClinical featuresSignificant group differencesPsychosis subjectsBipolar-Schizophrenia NetworkPsychosis casesHealthy subjectsPreparatory periodPsychosis syndromeFrequency principal components analysisMania symptomsP50 responsePositive symptomsClinical phenotypeAffective syndromeBipolar spectrumPsychosisPutative biomarkersEffects of Stimulus Repetition on Emotional Processing in Psychosis Biotypes: Findings From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium
Abdelmageed S, Trotti R, Parker D, Sabatinelli D, Tamminga C, Gershon E, Keedy S, Sweeney J, Keshavan M, Pearlson G, McDowell J, Clementz B. Effects of Stimulus Repetition on Emotional Processing in Psychosis Biotypes: Findings From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium. Biological Psychiatry 2020, 87: s389. DOI: 10.1016/j.biopsych.2020.02.996.Peer-Reviewed Original ResearchNeuroanatomical Sex Differences in the Visual Cortex of Individuals With Psychosis: Findings From the Bipolar-Schizophrenia Network on Intermediate Phenotypes
Turkozer H, Lizano P, Adhan I, Ivleva E, Lutz O, Zeng A, Raymond N, Clementz B, Pearlson G, Sweeney J, Gershon E, Keshavan M, Tamminga C. Neuroanatomical Sex Differences in the Visual Cortex of Individuals With Psychosis: Findings From the Bipolar-Schizophrenia Network on Intermediate Phenotypes. Biological Psychiatry 2020, 87: s205. DOI: 10.1016/j.biopsych.2020.02.533.Peer-Reviewed Original ResearchNeuroanatomical sex differencesBipolar-Schizophrenia NetworkVisual cortexSex differencesIntermediate phenotypesCortexPsychosisLongitudinal Stability of EEG Psychosis Biomarkers: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP)
Trotti R, Parker D, Tamminga C, Pearlson G, Keshavan M, Keedy S, Gershon E, McDowell J, Clementz B. Longitudinal Stability of EEG Psychosis Biomarkers: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP). Biological Psychiatry 2020, 87: s409-s410. DOI: 10.1016/j.biopsych.2020.02.1045.Peer-Reviewed Original ResearchBipolar-Schizophrenia NetworkIntermediate phenotypes
2019
Intrinsic neural activity differences in psychosis biotypes: Findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium
Thomas O, Parker D, Trotti R, McDowell J, Gershon E, Sweeney J, Keshavan M, Keedy S, Ivleva E, Tamminga C, Pearlson G, Clementz B. Intrinsic neural activity differences in psychosis biotypes: Findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Biomarkers In Neuropsychiatry 2019, 1: 100002. PMID: 36643612, PMCID: PMC9837786, DOI: 10.1016/j.bionps.2019.100002.Peer-Reviewed Original ResearchPsychosis biotypesHealthy personsBipolar-Schizophrenia NetworkSingle-trial powerFirst-degree biological relativesNeural activity differencesInter-stimulus intervalBiotype 2Intermediate Phenotypes (B-SNIP) consortiumPsychosis casesEffective treatmentIntrinsic activityPsychosis subgroupsNeural modulationLow intrinsic activityDSM groupNeural activitySocial functioningBiotype characterizationBiological relativesConventional diagnosisIntermediate phenotypesDifferentiating featuresDSM syndromesProbands
2018
145. Diagnosis and Biotype Comparisons Across the Psychosis Spectrum: Investigating Amygdala-Hippocampal Differences From the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Study
Guimond S, Kelly S, Mike L, Chakravarty M, Sweeney J, Pearlson G, Clementz B, Tamminga C, Keshavan M. 145. Diagnosis and Biotype Comparisons Across the Psychosis Spectrum: Investigating Amygdala-Hippocampal Differences From the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Study. Biological Psychiatry 2018, 83: s59. DOI: 10.1016/j.biopsych.2018.02.163.Peer-Reviewed Original ResearchBipolar-Schizophrenia NetworkIntermediate Phenotypes (B-SNIP) studyPsychosis spectrumPhenotype studiesDiagnosis148. Auditory and Visual EEG Validators of Psychosis Biotypes, Findings From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium
Parker D, Trotti R, McDowell J, Keedy S, Sweeney J, Gershon E, Pearlson G, Keshavan M, Tamminga C, Clementz B. 148. Auditory and Visual EEG Validators of Psychosis Biotypes, Findings From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium. Biological Psychiatry 2018, 83: s60-s61. DOI: 10.1016/j.biopsych.2018.02.166.Peer-Reviewed Original Research9.3 PSYCHOSIS BIOTYPES VERSUS CLINICAL SYNDROMES THROUGH THE PRISM OF INTRINSIC NEURAL ACTIVITY
Clementz B, Pearlson G, Tamminga C, Sweeney J, Keshavan M. 9.3 PSYCHOSIS BIOTYPES VERSUS CLINICAL SYNDROMES THROUGH THE PRISM OF INTRINSIC NEURAL ACTIVITY. Schizophrenia Bulletin 2018, 44: s14-s14. PMCID: PMC5888365, DOI: 10.1093/schbul/sby014.031.Peer-Reviewed Original ResearchInter-trial intervalNeural activityIntrinsic neural activityPsychosis biotypesDSM diagnosesSingle-trial powerOngoing neural activityBipolar-Schizophrenia NetworkHealthy personsStimulus salienceNeural responsesNeural oscillationsB-SNIPNeurobiological similaritiesNeurophysiological modelFirst-degree relativesEEG measuresTreatment developmentSensory cortexTranslational research programClinical featuresDistinct physiological mechanismsHealthy groupPsychosis diagnosisPsychosis casesT22. 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
10. Brain Structure Biomarkers at the Psychosis/Nonpsychosis Interphase: Findings From the Bipolar–Schizophrenia Network for Intermediate Phenotypes
Ivleva E, Clementz B, Dutcher A, Aslan S, Witte B, Poudyal G, Lu H, Meda S, Pearlson G, Sweeney J, Keshavan M, Tamminga C. 10. Brain Structure Biomarkers at the Psychosis/Nonpsychosis Interphase: Findings From the Bipolar–Schizophrenia Network for Intermediate Phenotypes. Schizophrenia Bulletin 2017, 43: s10-s10. PMCID: PMC5475460, DOI: 10.1093/schbul/sbx021.029.Peer-Reviewed Original Research205. Machine Learning to Further Improve Classification of Psychotic Disorders Using Clinical and Biological Stratification: Updates From the Bipolar Schizophrenia Network for Intermediate Phenotypes (BSNIP)
Tandon N, Sudarshan M, Mothi S, Clementz B, Pearlson G, Sweeney J, Tamminga C, Keshavan M. 205. Machine Learning to Further Improve Classification of Psychotic Disorders Using Clinical and Biological Stratification: Updates From the Bipolar Schizophrenia Network for Intermediate Phenotypes (BSNIP). Schizophrenia Bulletin 2017, 43: s105-s105. PMCID: PMC5475741, DOI: 10.1093/schbul/sbx021.283.Peer-Reviewed Original ResearchAPPLYING MULTIVARIATE TECHNIQUES, INCLUDING PARALLEL ICA TO COMMON COMPLEX PSYCHIATRIC ENDOPHENOTYPES
Pearlson G, Meda, Khadka S, Tamminga C, Keshavan M, Clementz B, Sweeney J, Gershon E, Raskin S, Fallahi C. APPLYING MULTIVARIATE TECHNIQUES, INCLUDING PARALLEL ICA TO COMMON COMPLEX PSYCHIATRIC ENDOPHENOTYPES. European Neuropsychopharmacology 2017, 27: s519. DOI: 10.1016/j.euroneuro.2016.09.636.Peer-Reviewed Original ResearchNeuron differentiationMultiple risk genesState functional MRIMolecular biological pathwaysComplex psychiatric diseasesSynaptic contactsBipolar-Schizophrenia NetworkEnrichment analysisUnivariate analysisMedical disordersIntermediate Phenotypes (B-SNIP) studyPsychiatric diseasesLarger sample sizeDisease riskEEG phenotypesFunctional MRISchizophrenia NetworkState EEG dataElectrophysiological phenotypeInterneuron development