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.
Primary language impairment (PLI) emerges early and is responsive to intervention; however, identification in toddlers is not currently possible because of the high rate of false positives reflecting transient language delays. We use a novel, theoretically-grounded, neurodevelopmental approach to generate earlier, more accurate identification of toddler risk for persistent PLI via: (a) multi-faceted, longitudinal assessment of toddler emergent language patterns; (b) detailed consideration of the transactional language environment; and (c) accounting for emergent health risk in predictive models. Earlier, reliable identification of toddlers at highest risk for PLI will optimize early intervention to prevent developmentally- cascading effects.