Developing a Community-Based ASD Research Registry This application addresses Broad Challenge Area 05 (Comparative Effectiveness Research) and Specific Challenge Topic 05-MH-104 (Building ASD Registries for Use in Comparative Effectiveness Research). Enrolling large samples in research studies is the most significant barrier to better understanding the etiology and treatment of ASD. A growing body of research portrays ASD as polygenic, phenotypically heterogeneous, and highly variable in treatment response. Large samples are therefore critical to unraveling the multiple """"""""autisms"""""""" that comprise this puzzling disorder. At the same time, the low prevalence of ASD and the high burden of many studies make enrollment challenging. We propose a novel strategy to create an ASD research registry that complements those adopted by national registries such as IAN and AGRE. We propose to build on existing community-academic partnerships to: 1) Enroll 7500 families of individuals with ASD into a research registry through population-based recruitment. We will contact >15,000 Pennsylvania families of children diagnosed with ASD in the Medicaid system through a statewide needs assessment distributed by Pennsylvania's Bureau of Autism Services (BAS). We will also pursue alternative strategies that build on a rich array of academic-community partnerships and initiatives already in place. Caregivers will be asked to enroll in an ASD research registry and asked about specific types of studies in which they might participate. Based on response from our previous needs assessment and other research efforts, we expect to enroll at least 7500 individuals with ASD. 2) Estimate the sample bias in our respondents. We will assess sample bias with regard to geography, sex, race, ethnicity and service needs, and how bias is associated with willingness to participate in research. We will estimate bias using Medicaid claims and special education data for the population from which we are recruiting. This will be invaluable to partnering studies that make use of this registry. 3) Determine the accuracy of caregiver-reported clinical characterization. Enrolled caregivers will complete the Social Communication Questionnaire (SCQ) and the Social Responsiveness Scale (SRS). We will invite a stratified random samplee200 respondents to receive more extensive phenotyping;comparison of brief and gold standard measures will allow us to refine our strategy for efficiently identifying individuals with ASD. 4) Use bioinformatics and local community support to rapidly create and sustain the registry. The registry will use a web-based data management system that will create an electronic directory with tracking and email follow-up, and enable secure online data collection. Through a program of training and support, and involvement in an Advisory Committee, community partners will both shape and benefit from the registry. The registry will also maximize access to other researchers while maintaining scientific and ethical standards. The proposed activities will result in a large-scale, geographically proximal laboratory for the biological and behavioral characterization and treatment of individuals with ASD. We also will develop and disseminate a model for creating community registries that has the potential to result in a national network for intervention effectiveness research.
The difficulty of enrolling large samples in research studies is perhaps the most significant barrier to better understanding the causes and treatment of Autism Spectrum Disorder, or ASD. We propose a new approach to quickly and efficiently create an ASD research registry that includes at least 7500 individuals with ASD within the state of Pennsylvania interested in participating in research. We will establish that members of the registry are as diverse as the broader population from which they are drawn, test a cost-effective way of verifying their diagnosis, and demonstrate how to build effective partnerships between universities and local and state agencies to advance research.
Kohls, Gregor; Antezana, Ligia; Mosner, Maya G et al. (2018) Altered reward system reactivity for personalized circumscribed interests in autism. Mol Autism 9:9 |
Ratto, Allison B; Kenworthy, Lauren; Yerys, Benjamin E et al. (2018) What About the Girls? Sex-Based Differences in Autistic Traits and Adaptive Skills. J Autism Dev Disord 48:1698-1711 |
SPARK Consortium. Electronic address: pfeliciano@simonsfoundation.org; SPARK Consortium (2018) SPARK: A US Cohort of 50,000 Families to Accelerate Autism Research. Neuron 97:488-493 |
Zhang, Fan; Savadjiev, Peter; Cai, Weidong et al. (2018) Whole brain white matter connectivity analysis using machine learning: An application to autism. Neuroimage 172:826-837 |
Yerys, Benjamin E; Herrington, John D; Bartley, Gregory K et al. (2018) Arterial spin labeling provides a reliable neurobiological marker of autism spectrum disorder. J Neurodev Disord 10:32 |
Maddox, Brenna B; Cleary, Patrick; Kuschner, Emily S et al. (2018) Lagging skills contribute to challenging behaviors in children with autism spectrum disorder without intellectual disability. Autism 22:898-906 |
Koberstein, John N; Poplawski, Shane G; Wimmer, Mathieu E et al. (2018) Learning-dependent chromatin remodeling highlights noncoding regulatory regions linked to autism. Sci Signal 11: |
Yerys, Benjamin E; Herrington, John D; Satterthwaite, Theodore D et al. (2017) Globally weaker and topologically different: resting-state connectivity in youth with autism. Mol Autism 8:39 |
Fortin, Jean-Philippe; Parker, Drew; Tunç, Birkan et al. (2017) Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161:149-170 |
Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan et al. (2017) On characterizing population commonalities and subject variations in brain networks. Med Image Anal 38:215-229 |
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