2024
The mechanisms underlying conditioning of phantom percepts differ between those with hallucinations and synesthesia
del Rio M, Kafadar E, Fisher V, D’Costa R, Powers A, Ward J. The mechanisms underlying conditioning of phantom percepts differ between those with hallucinations and synesthesia. Scientific Reports 2024, 14: 5607. PMID: 38453946, PMCID: PMC10920618, DOI: 10.1038/s41598-024-53663-3.Peer-Reviewed Original Research
2023
Barriers and solutions to the adoption of translational tools for computational psychiatry
Benrimoh D, Fisher V, Mourgues C, Sheldon A, Smith R, Powers A. Barriers and solutions to the adoption of translational tools for computational psychiatry. Molecular Psychiatry 2023, 28: 2189-2196. PMID: 37280282, PMCID: PMC10611570, DOI: 10.1038/s41380-023-02114-y.Peer-Reviewed Original ResearchConceptsMainstream research directionIntegration of tasksComputational psychiatryComputational tasksComputational expertiseGame platformFormal modelDevelopment of tasksTaskLimited ecological validityResearch directionsEcological validityComputational methodsTest-retest reliabilityMore positive impactsInformation processingLarge-scale research projectProcessingHuman brainResearch projectEffects of exercise intervention on psychotic symptoms: A meta-analysis and hypothetical model of neurobiological mechanisms
Oliva H, Monteiro-Junior R, Oliva I, Powers A. Effects of exercise intervention on psychotic symptoms: A meta-analysis and hypothetical model of neurobiological mechanisms. Progress In Neuro-Psychopharmacology And Biological Psychiatry 2023, 125: 110771. PMID: 37075881, DOI: 10.1016/j.pnpbp.2023.110771.Peer-Reviewed Original ResearchConceptsExercise interventionGeneral symptomsPsychotic symptomsEfficacy of exerciseEffects of exerciseSpecific brain areasWeb of ScienceCochrane CENTRALSymptom improvementTemporal lobeBrain areasLarge effect sizesPsychotic patientsPositive symptomsNegative symptomsSymptomsNeurobiological mechanismsNeurophysiology studiesInterventionExerciseEffect sizeDatabase searchSignificant improvementNeurobiological modelsPatientsSampling from different populations: Sociodemographic, clinical, and functional differences between samples of first episode psychosis individuals and clinical high-risk individuals who progressed to psychosis
Hagler M, Ferrara M, Yoviene Sykes L, Li F, Addington J, Bearden C, Cadenhead K, Cannon T, Cornblatt B, Perkins D, Mathalon D, Seidman L, Tsuang M, Walker E, Powers A, Allen A, Srihari V, Woods S. Sampling from different populations: Sociodemographic, clinical, and functional differences between samples of first episode psychosis individuals and clinical high-risk individuals who progressed to psychosis. Schizophrenia Research 2023, 255: 239-245. PMID: 37028205, PMCID: PMC10207144, DOI: 10.1016/j.schres.2023.03.047.Peer-Reviewed Original ResearchConceptsFirst-episode psychosis servicesClinical high riskClinical high-risk individualsEarly detectionFirst-episode psychosis individualsRecent psychiatric hospitalizationCourse of illnessHigh-risk individualsAttenuated positive symptomsCHR researchGeographic catchmentSyndromal psychosisPsychosis individualsPsychiatric hospitalizationEarly intervention effortsHigh riskPsychosis servicesPositive symptomsGlobal functioningClinical resourcesProtective factorsDifferent populationsFE participantsGeneralizability of findingsFES programSelf-reported Gesture Interpretation and Performance Deficits in Individuals at Clinical High Risk for Psychosis
Karp E, Williams T, Ellman L, Strauss G, Walker E, Corlett P, Woods S, Powers A, Gold J, Schiffman J, Waltz J, Silverstein S, Mittal V. Self-reported Gesture Interpretation and Performance Deficits in Individuals at Clinical High Risk for Psychosis. Schizophrenia Bulletin 2023, 49: 746-755. PMID: 36939086, PMCID: PMC10154698, DOI: 10.1093/schbul/sbac197.Peer-Reviewed Original ResearchConceptsClinical high riskGesture deficitsInternalizing disordersCHR groupGesture interpretationLower verbal learningViable assessment toolVerbal learningNeurocognitive tasksGeneral intelligencePerformance deficitsNonverbal behaviorProcessing speedCHR participantsSimilar deficitsGreater deficitsHigh riskClinical InterviewFull psychotic disorderDeficitsSpecific subdomainsSRGPPsychotic disordersPsychosisGesturesThe reliability and validity of the revised Green et al. paranoid thoughts scale in individuals at clinical high‐risk for psychosis
Williams T, Walker E, Strauss G, Woods S, Powers A, Corlett P, Schiffman J, Waltz J, Gold J, Silverstein S, Ellman L, Zinbarg R, Mittal V. The reliability and validity of the revised Green et al. paranoid thoughts scale in individuals at clinical high‐risk for psychosis. Acta Psychiatrica Scandinavica 2023, 147: 623-633. PMID: 36905387, PMCID: PMC10463775, DOI: 10.1111/acps.13545.Peer-Reviewed Original ResearchConceptsCHR individualsClinical controlFull psychosisHealthy controlsGeneral populationPsychosis symptomsCHR participantsPoor social functioningGreen Paranoid Thoughts ScalePsychosisGroup differencesSocial functioningConfirmatory factor analysisParanoid Thoughts ScaleInterview measuresSeverity continuumTwo-factor structureCritical populationSelf-report measuresPresent studyDiscriminant validityPsychometric indicesParanoid thoughtsIndividualsParticipantsLearning to Discern the Voices of Gods, Spirits, Tulpas, and the Dead
Luhrmann T, Alderson-Day B, Chen A, Corlett P, Deeley Q, Dupuis D, Lifshitz M, Moseley P, Peters E, Powell A, Powers A. Learning to Discern the Voices of Gods, Spirits, Tulpas, and the Dead. Schizophrenia Bulletin 2023, 49: s3-s12. PMID: 36840538, PMCID: PMC9959996, DOI: 10.1093/schbul/sbac005.Peer-Reviewed Original ResearchA novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing
Drusko A, Baumeister D, McPhee Christensen M, Kold S, Fisher V, Treede R, Powers A, Graven-Nielsen T, Tesarz J. A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing. Scientific Reports 2023, 13: 3196. PMID: 36823292, PMCID: PMC9950064, DOI: 10.1038/s41598-023-29758-8.Peer-Reviewed Original ResearchConceptsHierarchical Gaussian FilterPrior expectationsRelevant individual differencesPain perceptionLearning-based interventionsTesting paradigmCognitive processesSensory evidenceIndividual differencesPsychophysical paradigmInferential processesVisual cuesElectrical cutaneous stimulusPrior weightingPerceptionPain stimuliPrior beliefsIndividual levelNociceptive inputBeliefsGreater relianceStimuliStrong weightingAcute pain stimuliParadigm
2022
Accelerated cortical thinning precedes and predicts conversion to psychosis: The NAPLS3 longitudinal study of youth at clinical high-risk
Collins M, Ji J, Chung Y, Lympus C, Afriyie-Agyemang Y, Addington J, Goodyear B, Bearden C, Cadenhead K, Mirzakhanian H, Tsuang M, Cornblatt B, Carrión R, Keshavan M, Stone W, Mathalon D, Perkins D, Walker E, Woods S, Powers A, Anticevic A, Cannon T. Accelerated cortical thinning precedes and predicts conversion to psychosis: The NAPLS3 longitudinal study of youth at clinical high-risk. Molecular Psychiatry 2022, 28: 1182-1189. PMID: 36434057, PMCID: PMC10005940, DOI: 10.1038/s41380-022-01870-7.Peer-Reviewed Original ResearchConceptsCHR-NCPercent thickness changeCortical thinningPsychosis onsetHealth controlsProgressive grey matter lossNorth American Prodrome Longitudinal StudyClinical high-risk individualsGray matter lossHigh-risk individualsLongitudinal studyGreater percent decreaseNovel treatment targetsParietal cortical regionsTargeted early interventionsCortical thicknessCortical areasMRI scansCHR individualsTreatment targetsEarly interventionCortical regionsLeft hemisphere regionsROC analysisHC participantsMathematical nosology: Computational approaches to understanding psychosis
Powers AR. Mathematical nosology: Computational approaches to understanding psychosis. Schizophrenia Research 2022, 245: 1-4. PMID: 35697570, DOI: 10.1016/j.schres.2022.05.025.Peer-Reviewed Original ResearchHumansPsychotic DisordersThree prominent self-report risk measures show unique and overlapping utility in characterizing those at clinical high-risk for psychosis
Williams TF, Powers AR, Ellman LM, Corlett PR, Strauss GP, Schiffman J, Waltz JA, Silverstein SM, Woods SW, Walker EF, Gold JM, Mittal VA. Three prominent self-report risk measures show unique and overlapping utility in characterizing those at clinical high-risk for psychosis. Schizophrenia Research 2022, 244: 58-65. PMID: 35597134, PMCID: PMC9829103, DOI: 10.1016/j.schres.2022.05.006.Peer-Reviewed Original ResearchConceptsProdromal Questionnaire-BriefPositive symptomsSelf-report questionnairesSpecific positive symptomsStructured Clinical InterviewClinical high riskCriterion validityHealthy controlsSpecific symptomsHigh riskDiscriminant validityPsychosis symptomsClinical InterviewCHR individualsStrong convergent validitySymptomsPsychosis riskNeuropsychological testsConsistent significant correlationLimited specificitySignificant correlationConvergent validityPsychosisConstruct validityQuestionnaireConditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility
Kafadar E, Fisher VL, Quagan B, Hammer A, Jaeger H, Mourgues C, Thomas R, Chen L, Imtiaz A, Sibarium E, Negreira AM, Sarisik E, Polisetty V, Benrimoh D, Sheldon AD, Lim C, Mathys C, Powers AR. Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility. Biological Psychiatry 2022, 92: 772-780. PMID: 35843743, PMCID: PMC10575690, DOI: 10.1016/j.biopsych.2022.05.007.Peer-Reviewed Original ResearchConceptsCH rateIncoming sensory evidenceSensory evidencePerceptual statesTask performanceComputational psychiatrySubset of participantsPrior expectationsHallucination severityBehavioral dataSymptom severityPast experienceStable measureHallucinationsPsychotic symptomsHallucination frequencyTaskSymptom expressionBayesian modelState markerHallucinatorsNonhallucinatorsOverweightingPerceptionSymptom riskRelating Glutamate, Conditioned, and Clinical Hallucinations via 1H-MR Spectroscopy
Leptourgos P, Bansal S, Dutterer J, Culbreth A, Powers A, Suthaharan P, Kenney J, Erickson M, Waltz J, Wijtenburg SA, Gaston F, Rowland LM, Gold J, Corlett P. Relating Glutamate, Conditioned, and Clinical Hallucinations via 1H-MR Spectroscopy. Schizophrenia Bulletin 2022, 48: 912-920. PMID: 35199836, PMCID: PMC9212089, DOI: 10.1093/schbul/sbac006.Peer-Reviewed Original ResearchConceptsGlutamate levelsAnterior insulaPrior beliefsCognitive modelPathophysiological theoriesAuditory cortexClinical hallucinationsAnterior cingulateComputational psychiatryNew treatmentsDorsolateral prefrontalVisual cuesPrior expectationsSame participantsHallucinationsInsulaNegative relationshipCurrent experienceMagnetic resonance spectroscopyBeliefsComputational modelingTonePerceptual pathways to hallucinogenesis
Sheldon AD, Kafadar E, Fisher V, Greenwald MS, Aitken F, Negreira AM, Woods SW, Powers AR. Perceptual pathways to hallucinogenesis. Schizophrenia Research 2022, 245: 77-89. PMID: 35216865, PMCID: PMC9232894, DOI: 10.1016/j.schres.2022.02.002.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeasuring Voluntary Control Over Hallucinations: The Yale Control Over Perceptual Experiences (COPE) Scales
Mourgues C, Hammer A, Fisher V, Kafadar E, Quagan B, Bien C, Jaeger H, Thomas R, Sibarium E, Negreira AM, Sarisik E, Polisetty V, Eken H, Imtiaz A, Niles H, Sheldon AD, Powers AR. Measuring Voluntary Control Over Hallucinations: The Yale Control Over Perceptual Experiences (COPE) Scales. Schizophrenia Bulletin 2022, 48: 673-683. PMID: 35089361, PMCID: PMC9077437, DOI: 10.1093/schbul/sbab144.Peer-Reviewed Original ResearchConceptsAuditory verbal hallucinationsPositive clinical outcomesClinical outcomesPilot studyQuality of lifeConvergent validityFrequent auditory verbal hallucinationsExperiences ScaleClinical measuresConventional treatmentNovel interventionsSymptom severitySignificant distressClinical scalesComprehensive batteryPsychosis-spectrum diagnosisVerbal hallucinationsSound psychometric propertiesAVH contentVoluntary controlPsychometric propertiesHallucinationsControl ScaleValidation studyIntervention
2021
Navigating the Benefits and Pitfalls of Online Psychiatric Data Collection
Quagan B, Woods SW, Powers AR. Navigating the Benefits and Pitfalls of Online Psychiatric Data Collection. JAMA Psychiatry 2021, 78: 1185-1186. PMID: 34431976, PMCID: PMC9205608, DOI: 10.1001/jamapsychiatry.2021.2315.Peer-Reviewed Original ResearchRacial and ethnic differences in perception of provider cultural competence among patients with depression and anxiety symptoms: a retrospective, population-based, cross-sectional analysis
Eken HN, Dee EC, Powers AR, Jordan A. Racial and ethnic differences in perception of provider cultural competence among patients with depression and anxiety symptoms: a retrospective, population-based, cross-sectional analysis. The Lancet Psychiatry 2021, 8: 957-968. PMID: 34563316, PMCID: PMC10688309, DOI: 10.1016/s2215-0366(21)00285-6.Peer-Reviewed Original ResearchConceptsProvider cultural competenceAdjusted odds ratioHispanic ethnicityDepression symptomsHealth statusMultivariable ordinal logistic regressionUS National Health Interview SurveyNational Health Interview SurveySelf-reported health statusOutcome variablesAlaskan NativesPresence of symptomsAssociation of raceCross-sectional studyHealth Interview SurveyMental health statusMental health providersAfrican AmericansCross-sectional analysisMean ageSubgroup analysisOrdinal logistic regressionOdds ratioPatients' viewsCultural competenceRegression dynamic causal modeling for resting‐state fMRI
Frässle S, Harrison SJ, Heinzle J, Clementz BA, Tamminga CA, Sweeney JA, Gershon ES, Keshavan MS, Pearlson GD, Powers A, Stephan KE. Regression dynamic causal modeling for resting‐state fMRI. Human Brain Mapping 2021, 42: 2159-2180. PMID: 33539625, PMCID: PMC8046067, DOI: 10.1002/hbm.25357.Peer-Reviewed Original Research
2020
Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience
Gold JM, Corlett PR, Strauss GP, Schiffman J, Ellman LM, Walker EF, Powers AR, Woods SW, Waltz JA, Silverstein SM, Mittal VA. Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience. Schizophrenia Bulletin 2020, 46: 1346-1352. PMID: 32648913, PMCID: PMC7707066, DOI: 10.1093/schbul/sbaa091.Peer-Reviewed Original ResearchConceptsClinical high riskComputational cognitive neuroscienceNew behavioral measureCognitive neuroscienceBehavioral measuresPsychosis risk predictionCognitive mechanismsTrait vulnerabilityDisorganization symptomsNeural systemsPsychosis symptomsPsychosis riskSpecialized interviewsPhenotype measuresNeuroscienceCHR assessmentTreatment targetsPsychotic disordersCourse of illnessInterview methodPsychosisNew treatment targetsIllness progressionPositive predictive valueMeasuresModeling perception and behavior in individuals at clinical high risk for psychosis: Support for the predictive processing framework
Kafadar E, Mittal VA, Strauss GP, Chapman HC, Ellman LM, Bansal S, Gold JM, Alderson-Day B, Evans S, Moffatt J, Silverstein SM, Walker EF, Woods SW, Corlett PR, Powers AR. Modeling perception and behavior in individuals at clinical high risk for psychosis: Support for the predictive processing framework. Schizophrenia Research 2020, 226: 167-175. PMID: 32593735, PMCID: PMC7774587, DOI: 10.1016/j.schres.2020.04.017.Peer-Reviewed Original ResearchConceptsClinical high riskCHR participantsDegraded speech stimuliPredictive processing frameworkUtility of interventionsSample of participantsPerceptual inferenceSensory evidencePsychotic spectrum disordersSpeech stimuliSpeech taskComputational underpinningsBehavioral tasksEfficacy of interventionsSpectrum disorderTarget tonesParticipants' performanceComputational modelingHigh riskPoor recognitionLatent factorsSuch tasksPrior beliefsTaskAppropriate risk stratification