Disorders of attention, social, and communicative functioning have become a significant public health concern yet we lack a systematic data base characterizing the typical development of basic building blocks that support optimal outcomes. We developed the first two individual difference measures assessing these building blocks for infants and children. We now propose to implement the measures in 13 research labs to collectively build a well-planned, large-scale, shared database, mine the database to advance knowledge and theory in developmental science, and, at the same time, forge a new model for collaborative research.
Faces, voices, and infant-directed speech are highly salient to typically developing infants and scaffold language, social, and cognitive development. In contrast, children with autism show social-orienting impairments and deficits in social-communicative functioning. These capabilities depend on early multisensory attention skills (integrating and attending to information across the senses) particularly in dynamically changing faces, voices, and audiovisual speech. Without measures to assess individual differences in these fundamental skills, the pathways by which they affect later language, social, and cognitive development remain poorly understood. Our new protocols, now adapted for remote testing, assess individual differences in attention maintenance, disengagement, and speed, and accuracy of intersensory processing for audiovisual social and nonsocial events in preverbal children. Preliminary findings from my current R01 reveal exciting relations between these multisensory skills and language and social outcomes in 3- to 36-month-olds. The present proposal builds on this foundation. The Multisensory Data Network brings together a group of experts in developmental science. Using remote platforms we will 1) integrate our new protocols into each of their research labs, and 2) implement an overall data collection plan, with each lab testing participants using both protocols along with standardized language and social outcomes. This will create a shared database of more than 1600 3- to 60-month-old children. 3) Data will be uploaded to Databrary, an online data-sharing library. 4) We will guide and standardize data collection across sites and create a multisite aggregate dataset for easy sharing across investigators. 5) Capitalizing on advantages of large datasets, we will derive the first preliminary norms for multisensory attention skills across 3 to 60 months and, using cutting-edge, SEM-based analyses, develop models characterizing developmental cascades from multisensory attention skills to more complex language and social capabilities that rely on this foundation. 6) We then develop portable protocols, expanding potential applications to classroom and home settings. This project will provide the first dataset of multisensory attention skills and effects on later outcomes across the first 5 years of life, with implications for identifying atypical trajectories and infants at risk for delays and guiding interventions. This collaborative model will catalyze new research directions and is more time efficient, cost effective, and will generate a larger, more diverse dataset than possible in individual research labs.
Aim 1: Characterize preliminary norms for MAAP and IPEP and relations with language and social functioning: 1A. From the cross-sectional sample of 200 children at each of 8 ages (3, 6, 12, 18, 24, 36, 48, 60 months) characterize norms for typical development across age for MAAP and IPEP multisensory attention measures. 1B. From the cross-sectional sample, identify cut-off points for norms that indicate concurrent delays in language and social functioning. 1C. From the longitudinal subsets, identify cut-off points for predicting future risk for delays in language and social functioning (6 or more months later).
Aim 2: Determine relations of the MAAP and IPEP with language and social and outcomes and test conceptual models: From the longitudinal data, test models relating developmental pathways of basic skills assessed on MAAP and IPEP, to language and social outcomes using state-of-the-art structural equation modeling, including longitudinal panel models and modern robust methods.
Aim 3: Technical development and standardization of MAAP and IPEP: Standardize MAAP and IPEP data collection with stand-alone programs to more easily share with other labs. Standardize data management and automate data export and analysis with major statistical packages. Create touch screen tablet versions for older children and integrate eye-tracking with a portable laptop to extend data collection to homes and schools.
Aim 4: Generate, standardize, and manage large shared database: Implement MAAP and IPEP in collaborating labs and provide training for uniform data collection. Generate a large database across 13 labs (including ours) for MAAP, IPEP and outcomes. There will be more than 1600 cross-sectional participants (at least 200 at each of 8 ages: 3, 6, 12, 18, 24, 36, 48, and 60 months) and 3 longitudinal subgroups of 150 each (at 6, 12, 18 months; 18, 24, 36 months; 36, 48, 60 months) plus an independent longitudinal group (N = 100 spanning 3, 6, 12, 18, 24, 36, 48, 60 months) from our ongoing study. Starting at 12 months, there will be language and social outcomes at each age as well as a cognitive subscale to characterize the nonverbal functioning of the sample. Data will be shared across sites using Databrary. We will standardize, maintain and manage the shared database, aggregate data across sites, and ensure data quality and ease of data mining across sites.
Accomplishing these aims will forge a new model for collaborative research that has high added value. By pooling data collection across sites according to an overarching research design, we will achieve a much larger and more diverse sample, with internal replication and more efficient use of resources, catalyze new research collaborations, and accelerate the progress of scientific discovery.
Project Title: Multisensory Development: New Measures and a Collaborative Database
Funding Agency: NIH/NICHD
Project Dates: 4/19/2019 to 3/31/2024
Grant Number: 1 R01 HD094803-01A1
Awardee Institution: Florida International University (FIU)
PIs and Co-Is (FIU):
Lorraine E. Bahrick (PI)
James Torrence Todd (Co-I)
Shannon Pruden (Co-I)
Primary Performance Site and PI
Lorraine E. Bahrick (Florida International University) Infant Development Lab
Collaborating Sites and Site PIs:
Aaron Buss (University of Tennessee, Knoxville)
Marianella Casasola (Cornell University)
Janet Frick (University of Georgia)
Scott Johnson (University of California, Los Angeles)
Kerry Jordan (Utah State University)
Robin Panneton (Virginia Tech)
Lynn Perry (University of Miami)
Shannon Pruden (Florida International University)
Greg Reynolds (University of Tennessee, Knoxville)
Shannon Ross-Sheehy (University of Tennessee, Knoxville)
Ryan Stevenson (University of Western Ontario)
Consultants:
Kasey Soska (Data Management Consultant)
James Jaccard (Statistical/Methodological Consultant)
Rick Gilmore (Data Sharing Consultant/Databrary Expert)
Amin Sarafraz (Computer Programmer)