Administrative Supplement to The When to Worry about Language Study (W2W-L)

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: unknown

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Key facts

  • Disease

    N/A

  • Start & end year

    2018
    2023
  • Known Financial Commitments (USD)

    $197,084
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    ELIZABETH SPENCER NORTON
  • Research Location

    United States of America
  • Lead Research Institution

    NORTHWESTERN UNIVERSITY
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease susceptibility

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

PROJECT SUMMARY - Original SubmissionPrimary language impairment (PLI) begins early in life and affects 6-8% of children. Although languageintervention is maximally effective the earlier it is delivered, normative variation in language acquisition acrosstoddlerhood (here 24-36 months) impedes accurate identification of PLI prior to late preschool age. Theproposed study introduces a novel, theoretically- grounded, neurodevelopmental framework designedto generate a sensitive and specific model to identify PLI as early as possible. Our developmentally-sensitive, translational approach introduces multiple innovations including: (1) characterizing thedevelopmental patterning of toddler emergent language beginning at 24 mos. using state-of-the-art methods,within a large community sample; (2) incorporating EEG/ERP neural biomarkers of language into PLI riskassessment; (3) using a novel paradigm to assess the protective effects of both behavioral and neuralsynchronization within parent-child language transactions; and (4) consideration of irritability, a robustdevelopmental marker of early mental health risk, to enhance identification of those language delayed toddlersat highest risk for persistence. For the proposed When to Worry about Language Study (W2W-L), wecapitalize on our funded study of 350 infants (50% irritable and 50% non-irritable) (R01MH107652, Wakschlag,PI) and enrich it via recruitment of a sub-sample of 200 late talking toddlers. This will yield a large and diversesample of 550 24-month-olds. Our key predictors will be toddler emergent language patterns (24-36 months),their neural biomarkers and synchrony within the transactional language environment. Our central outcome isprimary language impairment (PLI) status at preschool age (54 mos., when PLI can be reliably evaluated),assessed via clinical gold standard expressive and receptive language abilities. SPECIFIC AIMS: AIM 1a.Evaluate accuracy of PLI prediction based on multi-component measures of language including intensivelongitudinal assessments of toddler developmental precursors of key language functions at older ages, neuralbiomarkers. We will assess neural and linguistic processing via quantitative EEG during parent-child interactionand ERPs to speech sounds as well as during eye tracking tasks and 1b. Evaluate feasibility of creating analgorithm for early identification of PLI that can be applied in clinical practice, using cross-validation andmachine learning. AIM 2: Test the hypothesis that parent-child dyadic synchrony buffers PLI risk For the firsttime, we combine behavioral and novel social EEG measures of parent-child synchrony during naturalinteraction and a custom-designed word learning task to directly test how observed (behavioral) and neural(EEG) dyadic synchrony impact word learning. AIM 3: Test whether consideration of toddler irritabilityenhances PLI prediction. In sum, PLI confers sustained negative effects on a variety of personal-social andacademic outcomes. Pinpointing children at highest risk for PLI is critical for reducing the public health burdenof PLI for children, families, and the systems supporting them, and enhancing targeted allocation of resources.