PROJECT SUMMARY
Primary language impairment (PLI) begins early in life and affects 6-8% of children. Language intervention
is maximally effective the earlier it is delivered. However, normative variation in language acquisition across
toddlerhood (here, 24-36 months) contributes to a high rate of false positives, impeding accurate identification
of PLI prior to late preschool age. The proposed study introduces a novel, theoretically- grounded,
neurodevelopmental framework designed to generate a sensitive and specific model of toddler PLI
risk. Innovations introduced in this developmentally-sensitive, translational approach include: (1) a
developmental precursor model using state-of-the-art methods to characterize multiple features and growth
patterns of toddler emergent language patterns, within a large community sample; (2) incorporating EEG/ERP
neural biomarkers of language and transactional synchrony into PLI predictive models; and (3) considering
emergent mental health risk. Mental health risk is captured via multi-method measures of irritability, a
developmentally meaningful marker of risk for internalizing and externalizing problems that are common
correlates of PLI. The proposed When to Worry about Language Study (W2W-L) will capitalize on the
team's existed funded study of 350 infants (50% irritable and 50% non irritable) (R01MH107652, Wakschlag,
PI) and enrich it via recruitment of a new sub-sample of 200 late talking toddlers. This will yield a large and
diverse sample of 550 24 month olds, followed to age 54 months (when PLI can be reliably evaluated). The
key predictor will be toddler emergent language patterns measured via language skill, language processing,
and corollary neural biomarkers. The central outcome is primary language impairment (PLI) status at
preschool age, assessed via clinical gold standard measures. Key risk modifiers are distal and proximal
features of the transactional language environment, and longitudinal patterns of irritability.
SPECIFIC AIMS: Aim 1. Specify the contribution of language skills, processing, neural biomarkers, and
their growth to early PLI prediction. Hypotheses: 1a. Language skills, processing, and neural biomarkers will
each contribute incrementally to PLI prediction. 1b. Considering longitudinal patterns will enhance prediction.
Aim 2. Identify the distal risk- and proximal protective- features of the transactional language environment that
provide greatest explanatory power for individual differences in PLI. Hypothesis 2: Family history and poor
parental language ability will increase PLI risk, and features of parental input, and behavioral and neural
synchrony will decrease PLI risk. Aim 3. Examine the mutual influences of toddler irritability, proximal language
environment, and emergent language patterns on PLI pathways. Hypothesis 3: A model specifying these
reciprocal influences over time will sharpen PLI prediction beyond variance explained by their individual
influences. Aim 4. Evaluate feasibility of a clinical algorithm for earlier PLI risk identification. We will use
machine learning approaches to generate a sensitive/specific, feasible clinical model building on Aims 1-3.
