2024
A spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 1-31. DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRI4D 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 ResearchConceptsBrain 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 networksGyrification 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 lifeAssociation 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 alterationsSchizophreniaDouble 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 abnormalities383. Symptom Severity for Psychosis Biotype 3 Shows Unique Associations With Environmental Exposures as Measured By the Exposome Score for Schizophrenia
Kromenacker B, Yassin W, Clementz B, Keshavan M, Ivleva E, Gershon E, McDowell J, Keedy S, Pearlson G, Hill S, Tamminga C. 383. Symptom Severity for Psychosis Biotype 3 Shows Unique Associations With Environmental Exposures as Measured By the Exposome Score for Schizophrenia. Biological Psychiatry 2024, 95: s256-s257. DOI: 10.1016/j.biopsych.2024.02.882.Peer-Reviewed Original ResearchFunctional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review
Mehta D, Siddiqui S, Ward H, Steele V, Pearlson G, George T. Functional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review. Schizophrenia Research 2024, 267: 86-98. PMID: 38531161, DOI: 10.1016/j.schres.2024.03.016.Peer-Reviewed Original ResearchAuditory verbal hallucinationsRepetitive transcranial magnetic stimulationVerbal hallucinationsTranscranial magnetic stimulationTreatment-resistant auditory verbal hallucinationsAVH patientsTreatment of auditory verbal hallucinationsImpact of repetitive transcranial magnetic stimulationEmotion regulation regionsLanguage processing regionsAberrant neural activityHigh-frequency repetitive transcranial magnetic stimulationMagnetic stimulationRTMS interventionNeural substratesNeural effectsNeural mechanismsSham-controlled studySchizophreniaBrain activityNeuroimaging dataProcessing regionsNeuroimaging analysisNeuroimaging outcomesBrain abnormalitiesAccelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis
Wannan C, Nelson B, Addington J, Allott K, Anticevic A, Arango C, Baker J, Bearden C, Billah T, Bouix S, Broome M, Buccilli K, Cadenhead K, Calkins M, Cannon T, Cecci G, Chen E, Cho K, Choi J, Clark S, Coleman M, Conus P, Corcoran C, Cornblatt B, Diaz-Caneja C, Dwyer D, Ebdrup B, Ellman L, Fusar-Poli P, Galindo L, Gaspar P, Gerber C, Glenthøj L, Glynn R, Harms M, Horton L, Kahn R, Kambeitz J, Kambeitz-Ilankovic L, Kane J, Kapur T, Keshavan M, Kim S, Koutsouleris N, Kubicki M, Kwon J, Langbein K, Lewandowski K, Light G, Mamah D, Marcy P, Mathalon D, McGorry P, Mittal V, Nordentoft M, Nunez A, Pasternak O, Pearlson G, Perez J, Perkins D, Powers A, Roalf D, Sabb F, Schiffman J, Shah J, Smesny S, Spark J, Stone W, Strauss G, Tamayo Z, Torous J, Upthegrove R, Vangel M, Verma S, Wang J, Rossum I, Wolf D, Wolff P, Wood S, Yung A, Agurto C, Alvarez-Jimenez M, Amminger P, Armando M, Asgari-Targhi A, Cahill J, Carrión R, Castro E, Cetin-Karayumak S, Chakravarty M, Cho Y, Cotter D, D’Alfonso S, Ennis M, Fadnavis S, Fonteneau C, Gao C, Gupta T, Gur R, Gur R, Hamilton H, Hoftman G, Jacobs G, Jarcho J, Ji J, Kohler C, Lalousis P, Lavoie S, Lepage M, Liebenthal E, Mervis J, Murty V, Nicholas S, Ning L, Penzel N, Poldrack R, Polosecki P, Pratt D, Rabin R, Eichi H, Rathi Y, Reichenberg A, Reinen J, Rogers J, Ruiz-Yu B, Scott I, Seitz-Holland J, Srihari V, Srivastava A, Thompson A, Turetsky B, Walsh B, Whitford T, Wigman J, Yao B, Yuen H, Ahmed U, Byun A, Chung Y, Do K, Hendricks L, Huynh K, Jeffries C, Lane E, Langholm C, Lin E, Mantua V, Santorelli G, Ruparel K, Zoupou E, Adasme T, Addamo L, Adery L, Ali M, Auther A, Aversa S, Baek S, Bates K, Bathery A, Bayer J, Beedham R, Bilgrami Z, Birch S, Bonoldi I, Borders O, Borgatti R, Brown L, Bruna A, Carrington H, Castillo-Passi R, Chen J, Cheng N, Ching A, Clifford C, Colton B, Contreras P, Corral S, Damiani S, Done M, Estradé A, Etuka B, Formica M, Furlan R, Geljic M, Germano C, Getachew R, Goncalves M, Haidar A, Hartmann J, Jo A, John O, Kerins S, Kerr M, Kesselring I, Kim H, Kim N, Kinney K, Krcmar M, Kotler E, Lafanechere M, Lee C, Llerena J, Markiewicz C, Matnejl P, Maturana A, Mavambu A, Mayol-Troncoso R, McDonnell A, McGowan A, McLaughlin D, McIlhenny R, McQueen B, Mebrahtu Y, Mensi M, Hui C, Suen Y, Wong S, Morrell N, Omar M, Partridge A, Phassouliotis C, Pichiecchio A, Politi P, Porter C, Provenzani U, Prunier N, Raj J, Ray S, Rayner V, Reyes M, Reynolds K, Rush S, Salinas C, Shetty J, Snowball C, Tod S, Turra-Fariña G, Valle D, Veale S, Whitson S, Wickham A, Youn S, Zamorano F, Zavaglia E, Zinberg J, Woods S, Shenton M. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophrenia Bulletin 2024, 50: 496-512. PMID: 38451304, PMCID: PMC11059785, DOI: 10.1093/schbul/sbae011.Peer-Reviewed Original ResearchClinical high-risk individualsClinical high riskNational Institute of Mental HealthInstitute of Mental HealthAttenuated positive symptomsPersistent negative symptomsTransition to psychosisCHR statusHigh riskNegative symptomsPositive symptomsAnxiety symptomsPsychosocial functioningCognitive dataOutcomes of individualsDigital health technologiesDaily surveysPsychosisSCZPublic health needsMental healthNovel pharmacological interventionsSchizophreniaClinical outcomesHealth needsMore 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
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 ResearchConceptsNetwork dysconnectivityFunctional dysconnectivityExecutive functioningState fMRI studyAttentional deficitsFMRI studyHypothesized modelSalience networkBrain networksConnectivity findingsBehavioral symptomsNeurodevelopmental modelSymptom severityDysconnectivityHypothesized mechanismsSchizophreniaDeficitsVital modelsSummaryThis paperMotor symptomsFunctioningSymptomsFindingsPurpose of ReviewOverThoughtIs There a Cannabis-Associated Psychosis Sub-type?
Pearlson G, Keshavan M. Is There a Cannabis-Associated Psychosis Sub-type? 2023, 91-106. DOI: 10.1017/9781108943246.011.Peer-Reviewed Original ResearchMental illnessMental health teamsHealth policy implicationsHigh-potency cannabisStages of neurodevelopmentPotential adverse effectsHealth teamsImpact of exposureCannabis dependenceAdverse effectsCannabisSynthetic cannabinoidsIllnessPsychosisCannabinoidsNew research findingsAssociationComplex associationPutative modelNeurodevelopmentSchizophreniaClinical and Cortical Similarities Identified Between Bipolar Disorder I and Schizophrenia: A Multivariate Approach
Rootes-Murdy K, Edmond J, Jiang W, Rahaman, Chen J, Perrone-Bizzozero N, Calhoun V, van Erp T, Ehrlich S, Agartz I, Jönsson E, Andreassen O, Westlye L, Wang L, Pearlson G, Glahn D, Hong E, Buchanan R, Kochunov P, Voineskos A, Malhotra A, Tamminga C, Liu J, Turner J. Clinical and Cortical Similarities Identified Between Bipolar Disorder I and Schizophrenia: A Multivariate Approach. Biological Psychiatry 2023, 93: s16. DOI: 10.1016/j.biopsych.2023.02.058.Peer-Reviewed Original ResearchCharacterization 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 studyGroupSeverity
2022
Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach
Rootes-Murdy K, Edmond J, Jiang W, Rahaman M, Chen J, Perrone-Bizzozero N, Calhoun V, van Erp T, Ehrlich S, Agartz I, Jönsson E, Andreassen O, Westlye L, Wang L, Pearlson G, Glahn D, Hong E, Buchanan R, Kochunov P, Voineskos A, Malhotra A, Tamminga C, Liu J, Turner J. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Frontiers In Human Neuroscience 2022, 16: 1001692. PMID: 36438633, PMCID: PMC9684186, DOI: 10.3389/fnhum.2022.1001692.Peer-Reviewed Original ResearchHealthy volunteersSymptom profilesGM patternsClinical symptom presentationGray matter deficitsGray matter alterationsSevere symptom profileDistinct symptom profilesUnique symptom profileStructural neuroimaging studiesCurrent diagnostic criteriaPattern of schizophreniaBipolar disorder IBrains of individualsGM alterationsPANSS scoresCingulate gyrusDiagnostic criteriaTemporal poleSymptom presentationBilateral insulaDiagnostic groupsDisorder ISchizophreniaNeuroimaging studiesWhat can clozapine’s effect on neural oscillations tell us about its therapeutic effects? A scoping review and synthesis
Raymond N, Lizano P, Kelly S, Hegde R, Keedy S, Pearlson G, Gershon E, Clementz B, Tamminga C, Keshavan M. What can clozapine’s effect on neural oscillations tell us about its therapeutic effects? A scoping review and synthesis. Biomarkers In Neuropsychiatry 2022, 6: 100048. DOI: 10.1016/j.bionps.2022.100048.Peer-Reviewed Original ResearchTherapeutic effectNeural oscillationsTreatment-resistant schizophreniaEffects of clozapineElectrical activityRate of seizuresResistant schizophreniaBrain electrical activityClozapinePotential mechanismsSlow wavesSynthesis of findingsSchizophreniaLiterature pertainingHypothesis-driven investigationsReviewElectroencephalogramSeizuresFindingsIndividualsBiomarkers
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