Three new research projects have been funded through the Vanderbilt Kennedy Center’s (VKC) IDD-Reads (Intellectual and Developmental Disability REsearch And Data Science) Awards. IDD-Reads awards are a mechanism of the VKC’s Intellectual and Developmental Disabilities Research Center (IDDRC) and support collaborations between IDD researchers and data scientists.
The awards are available to teams consisting of VKC members and investigators and faculty who are advisors to students of the Vanderbilt Data Science Institute (VDSI), CHIPI (Center for Improving the Public’s Health Using Informatics), or Department of Health Policy.
“Vanderbilt has become a national leader in the development of complex, analyzable datasets from electronic health records,” said Jeffrey Neul, M.D., Ph.D., Annette Schaffer Eskind Chair and VKC director. “These data provide exciting new opportunities to answer questions of importance to the IDD field, such as examining patterns of disease or health care utilization for populations of individuals with IDD. With these awards, we are increasing the access of IDD researchers to large datasets and cutting-edge data science methodologies to analyze the data. And we also are facilitating non-traditional research by familiarizing future ‘big data’ scientists with issues of importance to the field of IDD. So, the true goal is to spur a long-term interest in IDD for trainees, thus increasing the workforce of researchers focused on issues of importance to IDD.”
2021-22 IDD-Reads Awards
Project Title: A machine-learning approach to characterize effects of specific sensory abnormalities on social communication and language ability in autism and predict symptom severity
Mark Wallace, Ph.D.
Investigators: Mark T. Wallace, Ph.D. (VKC faculty), Sarah Vassall (graduate student), VDSI faculty TBD
Changes in sensory function (e.g., hearing, touch) are frequently seen in autism. How such sensory features relate to challenges in social interactions and language and communication, some of the more common clinical problems reported in autism, remains unknown. Advances in artificial intelligence (AI) now allow us to take a “big data” approach to such a problem, enabling the analysis of data from a large group of individuals with autism. In the current work we are using AI-based machine-learning based approaches to do such analyses, with the goal of identifying how sensory characteristics relate to the different aspects of social communication. The ultimate goal of this work is to provide a better picture of how the various characteristics that define autism cluster together, with the hope that such knowledge will allow the development of more personalized treatments directed toward improving quality of life.
Project Title: Investigating the Real-world Efficacy of Selective Serotonin Reuptake Inhibitors for Treating Anxiety in Autistic Youth
Lana Jeradeh Boursoulian, M.D.
Investigators: Lana Jeradeh Boursoulian, M.D. (VKC faculty), Zachary J. Williams (VKC trainee), Tonnar Castellano (VDSI trainee), J. Christopher Slaughter, DrPH (VDSI faculty)
Anxiety disorders are among the most common mental health problems present in children on the autism spectrum. Current first-line treatments for anxiety in both autistic and non-autistic children include psychotherapy and selective serotonin reuptake inhibitor medications (SSRIs), but importantly, the clinical trials that showed these treatments were effective for anxiety in children frequently excluded autistic participants. Although recent studies have demonstrated that various psychotherapies are effective in helping autistic children with anxiety disorders, there is currently minimal evidence to support the use of SSRIs to treat anxiety in this population.
To address this gap in the literature, researchers will analyze a large observational dataset of well-characterized autistic children seeking medical care at one of 12 academic centers in the Autism Speaks Autism Care Network (ACNet). By examining anxiety scores from autistic children at times when they were on or off SSRI medication, the real-world effect of SSRIs on anxiety can be precisely quantified. Potential side effects of SSRI medications, including sleep disturbances and gastrointestinal symptoms, will also be quantified. This study will provide clinicians with crucial data about both the benefits and potential harms of SSRIs in the autistic population, allowing clinicians to make more informed decisions when treating anxiety in autistic children.
Project Title: Using Natural Language Processing to Understand the Employment and Postsecondary Experiences of Autistic Youth who are Transitioning to Adulthood
Julie Lounds Taylor, Ph.D.
Personnel: Julie Lounds Taylor, Ph.D. (VKC faculty), Sarah Carlson (VKC trainee), Jessie Spencer-Smith, Ph.D. (VDSI faculty), Soyeon Park (VDSI trainee)
Researchers at Vanderbilt University Medical Center’s Transitions Lab have recently been awarded grant funding to support a new, cutting-edge project, “Using Natural Language Processing to Understand the Employment and Postsecondary Experiences of Autistic Youth who are Transitioning to Adulthood.” In collaboration with faculty and students at the Vanderbilt Data Science Institute, researchers will utilize Natural Language Query models, a type of Natural Language Processing, to analyze 185 structured interviews conducted with autistic youth and their parents addressing youth employment and educational experiences. Analyses will focus on identifying influential aspects of youth employment and post-secondary education experiences among autistic youth, such as independence, self-determination, and fit. Researchers expect that this project will support the identification of important components of employment and post-secondary education experiences not regularly reported in the literature, leading to more careful measurement of these activities in the future
To learn more about IDD-Reads awards, see https://vkc.vumc.org/vkc/iddreads/.
Top photo by Getty Images
