grant

Towards equitable early identification of autism spectrum disorders in females

Organization UNIVERSITY OF MINNESOTALocation MINNEAPOLIS, UNITED STATESPosted 11 Aug 2023Deadline 31 Dec 2026
NIHUS FederalResearch GrantFY20250-11 years oldASDAgeAlgorithmsAssessment instrumentAssessment toolAutismAutistic DisorderBrainBrain Nervous SystemChildChild YouthChildren (0-21)ClinicalCohort StudiesCommunitiesComputer ModelsComputerized ModelsConcurrent StudiesDataData CollectionDecision TreesDetectionDevelopmentDiagnosisDiagnosticDiagnostic FindingsDifferences between sexesDiffers between sexesDimensionsEarly Infantile AutismEarly InterventionEarly identificationEncephalonEnvironmentEquityEvaluationFactor AnalysesFactor AnalysisFamilyFemaleFundingFutureGeneral PopulationGeneral PublicGoalsHeterogeneityInfantile AutismInfrastructureInterventionInterviewK12K12 AwardK12 MechanismK12 ProgramKanner's SyndromeKnowledgeLifeM-CHATMachine LearningMeasurementMeasuresMedicalMental disordersMental health disordersMentored Clinical Scientist Development ProgramMentorsMentorshipMethodologyMethodsMinnesotaModelingModified Checklist for Autism in ToddlersNIMHNational Institute of Mental HealthOutcomeParentsPerceptionPerformancePhasePositionPositioning AttributePrimary CareProviderPsychiatric DiseasePsychiatric DisorderPsychologistQOLQuality of lifeQuestionnairesRegistriesReportingResearchRiskSamplingScreening procedureSex DifferencesSexual differencesSigns and SymptomsSiteStrategic PlanningSubgroupSymptomsTestingToddlerTrainingTraining SupportTranslational Research EnterpriseUniversitiesVisitWaiting ListsWorkagesautism attributesautism indicatorautism spectral disorderautism spectrum disorderautism spectrum disorder featuresautism spectrum disorder indicatorautism spectrum disorder symptomsautism symptomologyautism symptomsautism-like symptomsautism-related attributesautistic childrenautistic featuresautistic spectrum disorderautistic symptomsautistic traitsautistic-like symptomscareer developmentchild health care providerchildren on the autism spectrumchildren with ASDchildren with autismchildren with autism spectrum disorderclinical relevanceclinical translationclinically actionableclinically relevantclinically translatablecohortcommunity researchcomputational modelingcomputational modelscomputer based modelscomputerized modelingdevelopmentaldisease riskdisorder riskdissemination scienceearly childhoodearly screeninggender equitygradient boostingimplementation scienceimprovedinformation gatheringkidslenslenseslife spanlifespanmachine based learningmalemental illnessnovelparentpediatric care providerpediatric departmentpediatric health care providerpediatric providerpediatricianprecision medicineprecision-based medicineprofessorpsychiatric illnesspsychological disorderrandom forestrecruitrepetitive behaviorscreeningscreening toolsscreeningsservice interventionsexsex based differencessex-dependent differencessex-related differencessex-specific differencesskillssocial communicationtraining opportunitytraittranslation research enterprisetranslational research programwaitlistyoungster
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Full Description

Screening tools for autism spectrum disorder (ASD) show poor predictive performance in practice, particularly for females, which may arise due to sex-related measurement bias of screening questionnaires, and lack of precision in capturing the variability in early symptom profiles of ASD. Computational approaches to characterize heterogeneity and assess and account for sex-related measurement bias in early ASD symptoms may identify ASD risk profiles that can be clinically actionable in practice. The candidate's long-term goals are to enhance goals quality of life for children with ASD and their families by lowering the age of diagnosis, especially in females missed by traditional screening methods. The research and training described in this K23 application will build on the candidate's existing expertise, adding conceptual and methodological skills needed to develop and implement a novel screening approach that will more precisely identify ASD risk in a community-based sample.

Aim 1 evaluates the extent of sex-based measurement bias in measures shown to capture clinically-relevant variability in early ASD traits in a sample of 3,000 children between 17-25 months recruited from a community research registry. Aim 2 applies computational approaches to model dimensional variability in early ASD symptoms and identify subgroups of risk in the same sample that are hypothesized to vary on clinical outcomes at 36 months. Aim 3 takes a dissemination and implementation (D&I) science lens to assess parent and provider views on screening practices to identify facilitators and barriers to change via qualitative interviews (Pediatrician N=20; Parent N=40). This project is in line with NIMH Strategic Plan Goal 2 to “examining mental illness trajectories across the lifespan.” The candidate is a clinical psychologist and Assistant Professor at the University of Minnesota, with expertise in characterizing sex differences in early ASD trajectories.

The proposed K23 application will provide the candidate with the training needed to develop new knowledge and skills in conducting community-based screening for ASD, computational modeling of heterogeneity, and dissemination and implementation science. Mentors Dr. Damien Fair, Jed Elison, and Timothy Beebe possess the expertise and mentoring skills to support these training and scientific aims. This will position the candidate to build an independent clinical-translational research program focused on improving the precision of early screening for ASD to enable precision medicine for early ASD concerns that are equitable by sex.

Training will occur in an exceptional scientific environment in the Department of Pediatrics at the University of Minnesota and the newly established Masonic Institute of the Developing Brain.

Grant Number: 5K23HD112507-03
NIH Institute/Center: NIH

Principal Investigator: Catherine Burrows

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