2022
Three 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 validityQuestionnaire
2021
Computerized Assessment of Psychosis Risk †
Mittal VA, Ellman LM, Strauss GP, Walker EF, Corlett PR, Schiffman J, Woods SW, Powers AR, Silverstein SM, Waltz JA, Zinbarg R, Chen S, Williams T, Kenney J, Gold JM. Computerized Assessment of Psychosis Risk †. Journal Of Psychiatry And Brain Science 2021, 6: e210011. PMID: 34307899, PMCID: PMC8302046, DOI: 10.20900/jpbs.20210011.Peer-Reviewed Original ResearchClinical high riskComputerized assessmentPsychosis riskPsychosis risk calculatorHelp-seeking individualsBehavioral tasksComputational mechanismsNeurobiological systemsCHR participantsCHR groupCHR researchGroup differencesIllness mechanismsClinical InterviewCutting-edge computational methodsOutcomes two yearsHealthy controlsYoung peoplePrevention effortsMinimal trainingPsychosisTrainingRisk individualsLearning methodsIndividuals
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 valueMeasures
2019
Does hallucination perceptual modality impact psychosis risk?
Niles H, Walsh B, Woods S, Powers A. Does hallucination perceptual modality impact psychosis risk? Acta Psychiatrica Scandinavica 2019, 140: 360-370. PMID: 31355420, PMCID: PMC6752971, DOI: 10.1111/acps.13078.Peer-Reviewed Original ResearchConceptsClinical high riskPerceptual abnormalitiesPsychosis riskNon-verbal contentAuditory perceptual abnormalitiesIndividuals ages 12Verbal experienceAuditory experienceCHR individualsThought contentCHR sampleUnusual thought contentPredictive validityAuditory scoresGustatory componentsAge 12Meeting criteriaPsychotic disordersPhenomenological aspectsInterview notesPsychosisConversion riskScoresIndividualsExperience