2024
Illusory generalizability of clinical prediction models
Chekroud A, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, Kambeitz J, Corlett P, Koutsouleris N, Krumholz H, Krystal J, Paulus M. Illusory generalizability of clinical prediction models. Science 2024, 383: 164-167. PMID: 38207039, DOI: 10.1126/science.adg8538.Peer-Reviewed Original Research
2023
Broadening the Use of Machine Learning in Psychiatry
Adkinson B, Chekroud A. Broadening the Use of Machine Learning in Psychiatry. Biological Psychiatry 2023, 93: 4-5. PMID: 36456077, DOI: 10.1016/j.biopsych.2022.10.006.Peer-Reviewed Original ResearchPersonalized symptom clusters that predict depression treatment outcomes: A replication of machine learning methods
Chen Y, Stewart J, Ge J, Cheng B, Chekroud A, Hellerstein D. Personalized symptom clusters that predict depression treatment outcomes: A replication of machine learning methods. Journal Of Affective Disorders Reports 2023, 11: 100470. DOI: 10.1016/j.jadr.2023.100470.Peer-Reviewed Original ResearchHamilton Rating ScaleSymptom clustersTreatment trajectoriesQuick InventoryTreatment outcomesDouble-blind clinical trialDepressive Symptomatology-Self ReportRating ScaleBest baseline predictorsHAM-D scoresDepression treatment outcomesAnxiety disorder diagnosisQIDS-SR16Depressive illnessEscitalopram monotherapyBaseline variablesClinical trialsBaseline predictorsTreatment efficacyUnipolar depressionDifferent treatment trajectoriesTreatment planLogistic regressionDisorder diagnosisDual treatment
2022
Clinical and Financial Outcomes Associated With a Workplace Mental Health Program Before and During the COVID-19 Pandemic
Bondar J, Morrow C, Gueorguieva R, Brown M, Hawrilenko M, Krystal JH, Corlett PR, Chekroud AM. Clinical and Financial Outcomes Associated With a Workplace Mental Health Program Before and During the COVID-19 Pandemic. JAMA Network Open 2022, 5: e2216349. PMID: 35679044, PMCID: PMC9185188, DOI: 10.1001/jamanetworkopen.2022.16349.Peer-Reviewed Original ResearchConceptsMental health programsHealth programsCohort studyLarge clinical effect sizesPatient Health Questionnaire-9Common mental health conditionsSheehan Disability ScaleClinical effect sizeMental health conditionsMental health symptomsCost of treatmentGeneralized anxiety disorderMental health difficultiesMental health benefitsWorkplace wellness programsCare navigationClinical improvementPrimary outcomeClinical benefitQuestionnaire-9Disability ScaleMedication managementLeast moderate anxietyMixed-effects regressionPerson psychotherapy
2021
Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99–2019/20: an observational time series analysis
Degli Esposti M, Ziauddeen H, Bowes L, Reeves A, Chekroud A, Humphreys D, Ford T. Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99–2019/20: an observational time series analysis. Social Psychiatry And Psychiatric Epidemiology 2021, 57: 993-1006. PMID: 34951652, PMCID: PMC8705084, DOI: 10.1007/s00127-021-02215-5.Peer-Reviewed Original ResearchConceptsObservational time series analysisPsychiatric disordersBed daysAdmission ratesHospital activityAge groupsNHS hospitalsBinomial regression modelsNegative binomial regression modelsInpatient psychiatric careMental health needsInpatient hospital activityHospital admissionRegression modelsInpatient carePsychiatric careHealth needsHospitalDisordersCareDepressionAdultsPronounced increaseSame periodChildrenParsing the antidepressant effects of non-invasive brain stimulation and pharmacotherapy: A symptom clustering approach on ELECT-TDCS
Goerigk S, Padberg F, Chekroud A, Kambeitz J, Bühner M, Brunoni A. Parsing the antidepressant effects of non-invasive brain stimulation and pharmacotherapy: A symptom clustering approach on ELECT-TDCS. Brain Stimulation 2021, 14: 906-912. PMID: 34048940, DOI: 10.1016/j.brs.2021.05.008.Peer-Reviewed Original ResearchConceptsTranscranial direct current stimulationNon-invasive brain stimulationDepressive symptomsBrain stimulationEfficacy of tDCSCore depressive symptomsLinear mixed regression modelsDepressive symptom clustersConsiderable inter-individual variabilityDirect current stimulationEscitalopram 20Antidepressant effectsAntidepressant efficacyInter-individual variabilityAtypical symptomsDepressed patientsMixed regression modelsInsomnia symptomsTDCS sessionsPharmacotherapyEscitalopramSymptomsPlaceboSymptom clustersCurrent stimulationThe promise of machine learning in predicting treatment outcomes in psychiatry
Chekroud A, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021, 20: 154-170. PMID: 34002503, PMCID: PMC8129866, DOI: 10.1002/wps.20882.Peer-Reviewed Original Research
2020
Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder
Anderson KM, Collins MA, Kong R, Fang K, Li J, He T, Chekroud AM, Yeo BTT, Holmes AJ. Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder. Proceedings Of The National Academy Of Sciences Of The United States Of America 2020, 117: 25138-25149. PMID: 32958675, PMCID: PMC7547155, DOI: 10.1073/pnas.2008004117.Peer-Reviewed Original ResearchMeSH KeywordsAstrocytesAutopsyBrainCerebral CortexDepressive Disorder, MajorFemaleGene Expression ProfilingGene Expression RegulationGene OntologyGene Regulatory NetworksGenome-Wide Association StudyGenomicsHumansInterneuronsMaleMultifactorial InheritanceNeuroimagingSignal TransductionSingle-Cell AnalysisSomatostatinConceptsGenome-wide association studiesTranscriptional dataTranscriptional correlatesMajor depressive disorderCorrelates of depressionGene transcriptionSpecific genesGene expressionGene dysregulationIntegrative analysisBiological pathwaysAssociation studiesExpression dataGenesMolecular pathwaysCortical gene expressionDepressive disorderEx vivo geneCells associatesIntegrated analysisVivo geneBiological systemsRegulationPathwayUK BiobankPrecision non-implantable neuromodulation therapies: a perspective for the depressed brain
Borrione L, Bellini H, Razza L, Avila A, Baeken C, Brem A, Busatto G, Carvalho A, Chekroud A, Daskalakis Z, Deng Z, Downar J, Gattaz W, Loo C, Lotufo P, da Graça M. Martin M, McClintock S, O’Shea J, Padberg F, Passos I, Salum G, Vanderhasselt M, Fraguas R, Benseñor I, Valiengo L, Brunoni A. Precision non-implantable neuromodulation therapies: a perspective for the depressed brain. Brazilian Journal Of Psychiatry 2020, 42: 403-419. PMID: 32187319, PMCID: PMC7430385, DOI: 10.1590/1516-4446-2019-0741.Peer-Reviewed Original ResearchConceptsMajor depressive disorderSide effectsCurrent first-line treatmentMultiple medication trialsMagnetic seizure therapyFirst-line treatmentCognitive side effectsTranscranial direct current stimulationTranscranial magnetic stimulationDirect current stimulationCognitive behavioral therapyCost-effectiveness analysisClinical respondersSeizure therapyElectroconvulsive therapyMedication trialsPathophysiological mechanismsClinical trialsDepressed patientsDepressive disorderDepressed brainMagnetic stimulationNeuromodulation techniquesCurrent stimulationTherapySymptom clusters in adolescent depression and differential response to treatment: a secondary analysis of the Treatment for Adolescents with Depression Study randomised trial
Bondar J, Caye A, Chekroud A, Kieling C. Symptom clusters in adolescent depression and differential response to treatment: a secondary analysis of the Treatment for Adolescents with Depression Study randomised trial. The Lancet Psychiatry 2020, 7: 337-343. PMID: 32199509, DOI: 10.1016/s2215-0366(20)30060-2.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBayes TheoremChildCognitive Behavioral TherapyCombined Modality TherapyDepressive Disorder, MajorDiagnostic and Statistical Manual of Mental DisordersFemaleFluoxetineHumansMalePsychiatric Status Rating ScalesSelective Serotonin Reuptake InhibitorsTreatment OutcomeUnited StatesConceptsChildren's Depression Rating Scale-RevisedCognitive behavioral therapyCDRS-R scoresSymptom clustersDepression StudyTreatment efficacySecondary analysisMajor depressive disorderDSM-IV diagnosisCo-occurring symptomsSpecific symptom clustersPlacebo groupAcute phaseClinical profileActive treatmentSymptom scoresSleep disturbancesClinical trialsDepressive disorderPill placeboTreatment responseTherapeutic modalitiesSpecific symptomsNew therapiesPhysical complaintsRealizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019
Browning M, Carter CS, Chatham C, Den Ouden H, Gillan CM, Baker JT, Chekroud AM, Cools R, Dayan P, Gold J, Goldstein RZ, Hartley CA, Kepecs A, Lawson RP, Mourao-Miranda J, Phillips ML, Pizzagalli DA, Powers A, Rindskopf D, Roiser JP, Schmack K, Schiller D, Sebold M, Stephan KE, Frank MJ, Huys Q, Paulus M. Realizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019. Biological Psychiatry 2020, 88: e5-e10. PMID: 32113656, DOI: 10.1016/j.biopsych.2019.12.026.Peer-Reviewed Original Research
2019
The Opportunity for Exercise to Improve Population Mental Health
Chekroud A, Trugerman A. The Opportunity for Exercise to Improve Population Mental Health. JAMA Psychiatry 2019, 76: 1206-1207. PMID: 31483446, DOI: 10.1001/jamapsychiatry.2019.2282.Peer-Reviewed Original ResearchA Mendelian Randomization Approach for Assessing the Relationship Between Physical Activity and Depression
Chekroud A. A Mendelian Randomization Approach for Assessing the Relationship Between Physical Activity and Depression. JAMA Psychiatry 2019, 76: 361-362. PMID: 30673064, DOI: 10.1001/jamapsychiatry.2018.3870.Peer-Reviewed Original ResearchRepetitive transcranial magnetic stimulation for depression
Chekroud A, Cristea I. Repetitive transcranial magnetic stimulation for depression. The Lancet 2019, 393: 403. PMID: 30712895, DOI: 10.1016/s0140-6736(18)32760-0.Peer-Reviewed Original ResearchAltered functional connectivity and low-frequency signal fluctuations in early psychosis and genetic high risk
Tang Y, Zhou Q, Chang M, Chekroud A, Gueorguieva R, Jiang X, Zhou Y, He G, Rowland M, Wang D, Fu S, Yin Z, Leng H, Wei S, Xu K, Wang F, Krystal JH, Driesen NR. Altered functional connectivity and low-frequency signal fluctuations in early psychosis and genetic high risk. Schizophrenia Research 2019, 210: 172-179. PMID: 30685394, DOI: 10.1016/j.schres.2018.12.041.Peer-Reviewed Original ResearchConceptsFunctional connectivityHigh-risk individualsAltered functional connectivityHealthy comparison subjectsGenetic high riskGenetic high-risk individualsLow-frequency signal fluctuationsFunctional magnetic resonanceALFF abnormalitiesALFF measuresFunctional connectivity measuresBasal gangliaFirst episodeHigh riskEarly psychosisComparison subjectsSchizophrenia diathesisSchizophrenia vulnerabilityFESzGenetic riskLow-frequency fluctuationsIllnessSchizophreniaVoxel connectivityGHR
2018
Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia
Plis SM, Amin MF, Chekroud A, Hjelm D, Damaraju E, Lee HJ, Bustillo JR, Cho K, Pearlson GD, Calhoun VD. Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia. NeuroImage 2018, 181: 734-747. PMID: 30055372, PMCID: PMC6321628, DOI: 10.1016/j.neuroimage.2018.07.047.Peer-Reviewed Original ResearchConceptsGray matter patternsIntrinsic connectivity networksPosterior cingulate cortexHealthy controlsDynamic functional connectivityStructural MRIFunctional MRIDFNC statesGray matter densitySignificant group differencesTemporal lobeTemporal cortexCingulate cortexSame brainFunctional connectivityBrain structuresCognitive scoresStrong associationLinkage/associationMultimodal brain imaging dataImaging dataGroup differencesSignificant correlationSMRI dataCortexT107. WHY VALIDATION MATTERS: A DEMONSTRATION PREDICTING ANTIPSYCHOTIC RESPONSE USING 5 RCTS
Chekroud A. T107. WHY VALIDATION MATTERS: A DEMONSTRATION PREDICTING ANTIPSYCHOTIC RESPONSE USING 5 RCTS. Schizophrenia Bulletin 2018, 44: s157-s157. PMCID: PMC5888612, DOI: 10.1093/schbul/sby016.383.Peer-Reviewed Original ResearchMultivariate Pattern Analysis of Genotype–Phenotype Relationships in Schizophrenia
Zheutlin AB, Chekroud AM, Polimanti R, Gelernter J, Sabb FW, Bilder RM, Freimer N, London ED, Hultman CM, Cannon TD. Multivariate Pattern Analysis of Genotype–Phenotype Relationships in Schizophrenia. Schizophrenia Bulletin 2018, 44: 1045-1052. PMID: 29534239, PMCID: PMC6101611, DOI: 10.1093/schbul/sby005.Peer-Reviewed Original ResearchConceptsMultivariate pattern analysisIndependent samplesVisual memoryCognitive endophenotypesPredictive strengthSchizophreniaMemoryIndividual variationPattern analysisSingle predictorCertain domainsDiscovery samplePsychiatric patientsPolygenic risk scoresPredictive powerScoresEndophenotypesPotential relationshipRelationshipRandom forestGenetic risk variantsLimited setPredictorsComprehensive setSamples
2017
Bigger Data, Harder Questions—Opportunities Throughout Mental Health Care
Chekroud A. Bigger Data, Harder Questions—Opportunities Throughout Mental Health Care. JAMA Psychiatry 2017, 74: 1183-1184. PMID: 29094160, DOI: 10.1001/jamapsychiatry.2017.3333.Peer-Reviewed Original ResearchThe perilous path from publication to practice
Chekroud A, Koutsouleris N. The perilous path from publication to practice. Molecular Psychiatry 2017, 23: 24-25. PMID: 29112192, DOI: 10.1038/mp.2017.227.Peer-Reviewed Original Research