Research Highlights

Promoting Implementation of Behavioral Classroom Interventions for Children with ADHD in Urban Schools

Gwen Lawson, PhD, has been awarded a K23 award from the National Institute of Mental Health (NIMH).  This project aims to iteratively develop and pilot test a set of key implementation strategies that promote teachers' use of behavioral classroom interventions for children with symptoms of ADHD. During the first year of this four-year project, Dr. Lawson will examine barriers and facilitators to teachers' use of behavioral classroom interventions; during the following school year, she will iteratively develop and refine an implementation strategy resource package in partnership with stakeholders. Finally, the package will be pilot tested in a small-scale randomized controlled trial across the final two years of the project. As a mentored patient-oriented research career development award, the research proposal also supports a training plan to increase Dr. Lawson's expertise in three areas: (1) School-based interventions for ADHD; (2) Developing tailored implemenation stategies using mixed-methods; and (3) School-based hybrid implementation-effectiveness trials.

A Computer Vision Toolbox for Computational Analysis of Nonverbal Social Communication

The National Institute of Mental Health is funding a five year study led by Birkan Tunc, PhD. Recent advances in computer vision and machine learning promise to rapidly advance research on human behavior, by introducing reliable and granular measurement tools within a new paradigm: computational behavior analysis. Such tools capture and quantify with extraordinary precision all observable human behavior such as facial and bodily expressions, head gestures, and body posture. Unfortunately, there is a gap between promised possibilities and their translation into behavioral and mental health research.  While numerous successful computational approaches for quantifying facial expressions and body movements exist, their use is nearly always restricted to specialized engineers, rendering computational behavior analysis out of reach for those most interested in studying human behavior-clinical scientists. As a result, there are currently no well-established tools for quantifying facial communication or effectively capturing individual differences in social functioning.  With this grant, we aim to develop and validate easy-to-use computational tools that a reseacher with no engineering background can easily and reliably deploy and interpret. This would democratize computational behavior analysis and significantly advance science in this area.

Digitizing Human Vocal Interactions to Understand and Diagnose Autism

The National Institute on Deafness and Other Communication Disorders (NIDCD) is funding a 5-year study entitled, "Digitizing Human Vocal Interactions to Understand and Diagnose Autism". Julia Parish-Morris, PhD is the Principal Investigator.  This new project is focused on identifying sensitive and specific language-based markers of autism specturm disorder that can be detected during social interaction. Using machine learning and natural language processing, this study will lay the foundation for personalized approaches to social communication intervention and support. This project is slated to start on July 1, 2020.