| Matthew Goodwin PhD|
Department: Department of Health Sciences
Office: 312E RB
Post-Doctoral Fellow, Massachusetts Institute of Technology, Media Lab
Ph.D., University of Rhode Island (Behavioral Science)
M.A., University of Rhode Island (Experimental Psychology)
B.A., Wheaton College (Psychology)
Specializations/Research Interests: personal health informatics, computational behavioral science, Autism spectrum disorders
Selected Research/Scholarship Projects:
“Minimally Verbal Children with ASD: From Basic Mechanisms to Innovative Interventions”
This is a collaborative effort between Boston University, Harvard Medical School, and Northeastern University to better understand and support the 30% of children with ASD who remain minimally verbal in to the school years. Over the 5 years of this project our team of investigators, in partnership with primary stakeholders in the community (schools educating these children, major autism organizations, and most importantly, families of minimally verbal children) will complete a series of investigations that will: (1) Advance our knowledge of the heterogeneous phenotypes associated with minimally verbal ASD; (2) Develop and disseminate novel methods of assessing some cognitive, linguistic, and behavioral domains; (3) Propose and evaluate several mechanisms to explain why some children remain nonverbal; (4) Complete RCT of a novel intervention, specifically designed for this population, which has shown great promise in pilot studies; (5) Develop neurobiological markers that predict response to treatment in the RCTs, and that serve as measures of outcome success; and (6) Evaluate disruptions in excitatory and inhibitory neuronal pathways in critical language regions in brain tissue from children with ASD using novel molecular methods. This ACE will be the first systematic integrated approach to address these gaps in our knowledge on minimally verbal school-aged children with ASD.
Collaborator(s): Boston University, Massachusetts General Hospital
Funding: NIH P50, Autism Center of Excellence
“Toward Outcome Measurement of Anxiety in Youth with Autism Spectrum Disorders”
This four-year multisite project will advance outcome measurement of anxiety in future clinical trials in youth with ASDs. Our aims include: (1) Identify the manifestations of anxiety in children and adolescents with ASD through focus groups with parents of children with ASDs to guide the revision of a parent-rated anxiety scale (CASI-Anxiety scale) and a clinician-rated interview (Pediatric Anxiety Rating Scale; PARS); (2) Evaluate the distribution, internal consistency, factor structure, item analysis and online test-retest reliability of the revised parent-rated measure of anxiety in 900 children with ASDs; (3) Evaluate the validity and test-retest reliability of the revised parent-rated CASI-Anxiety scale and the Pediatric Anxiety Rating Scale (PARS) in 50 youth with ASDs via clinic-based in person assessments; (4) Evaluate the reliability and validity of the revised Pediatric Anxiety Rating Scale (PARS) in a separate sample of 40 subjects with ASDs accompanied by mild to severe anxiety; and (5) Compare heart rate, heart rate variability and electrodermal activity in 30 subjects with ASDs accompanied by elevated anxiety on the revised CASI Anxiety scale to 30 subjects with ASDs of similar age and gender distribution with low to mild anxiety.
Collaborator(s): Yale, U of Pennsylvania, Ohio State University
Funding: NIH R01, Outcome measures for use in Treatment Trials for Individuals with Intellectual and Developmental Disabilities“Toward Outcome Measurement of Anxiety in Youth with Autism Spectrum Disorders”
This project will define and explore a new research area we call Computational Behavior Science – integrated technologies for multimodal computational sensing and modeling to capture, measure, analyze, and understand human behaviors. Just as 20th century medical imaging technologies revolutionized internal medicine, we believe behavior imaging technologies will usher in a new era of quantitative understanding of behavior. Our motivating goal is to revolutionize the diagnosis and treatment of behavioral and developmental disorders. Our thesis is that emerging sensing and interpretation capabilities in vision, audition, and wearable computing technologies, when further developed and properly integrated, will transform this vision into reality. The need for this technology and its potential impact, both societal and scientific, are obvious and broad, ranging from the cognitive and brain sciences to the etiology, diagnosis, and treatment of developmental disorders such as autism. More specifically, we hope to: (1) Enable widespread screening of autism by allowing non-experts to easily collect high-quality behavioral data and perform an initial assessment of risk status; (2) Improve behavioral therapy through increased availability and improved quality, by making it easier to track the progress of an intervention and follow guidelines for maximizing learning progress; and (3) Enable longitudinal analysis of a child’s development based on quantitative behavioral data, using new tools for visualization.
Collaborator(s): Georgia Tech, MIT Media Lab, Boston University, University of Southern California, Carnegie Mellon University, Emory University, University of Illinois at Urbana Champaign
Funding: NSF Expeditions Computing Award
Boser, K, Wayland, S, Goodwin, MS(in progress). 21st Century Tools for Students with Autism and Related Disorders. Brookes.
Velicer, WF, Goodwin, MS, Hoppner, BB, & Molenaar, P (in progress). Single-Case Analysis for Behavior Science Research. Taylor & Francis/Routledge.
Levine, T, Goodwin, MS, & Sheinkopf, S (in press). Psychophysiologic arousal to social stress in Autism Spectrum Disorders. In VB Patel, VR Preedy, & C Martin (Eds.) Comprehensive Guide to Autism. Elsevier.
Goodwin, MS (2012). Passive telemetric monitoring: Novel methods for real-world behavioral assessment. In M.Mehl & T. Conner (Eds.) Handbook of Research Methods for Studying Daily Life. (pp. 251-266). Guilford.
Cohen, IL, Yoo, HY, Goodwin, MS, Moskowitz (2011). Assessing challenging behaviors in Autism Spectrum Disorders: Prevalence, rating scales, and autonomic indicators. In J. Matson & P. Sturmey (Eds.) International Handbook of Autism and Pervasive Developmental Disorders. (pp. 247-270). Springer.
Velicer, WF, Höppner BB, & Goodwin, MS (2010). Time series studies. In Neil J. Salkind, (Ed),Encyclopedia of Research Design. Thousand Oaks, CA: Sage Publications.
Walls, TA, Höppner BB, & Goodwin, MS (2007). Statistical issues in intensive longitudinal data analysis. In A. Stone,S. Shiffman, A. Atienza, L. Nebelling (Eds.) The Science of Real-time Data Capture. (pp. 338-360). New York: Oxford University Press.
Baron, MG, Lipsitt, LP, & Goodwin, MS (2006). Scientific foundations for research and practice. In G. Baron, J. Groden, G. Groden, & L. Lipsitt (Eds.) Stress and Coping in Autism. (pp. 53-92). New York: Oxford University Press.
Peer-Reviewed Journal Articles
Woodard, CR, Goodwin, MS, Zelazo, PR, Aube, D, Scrimgeour, M, Ostholthoff, T, & Brickley, M (2012). A comparison of autonomic, behavioral, and parent-report measures of sensory sensitivity in young children with autism. Research in Autism Spectrum Disorders, 6, 1234-1246.
Chen, GM, Yoder, KJ, Ganzel, BL, Goodwin, MS, & Belmonte, MK (2012). Harnessing repetitive behaviours to engage attention and learning in a novel therapy for autism: An exploratory analysis. Frontiers in Educational Psychology, 3, 1-16.
Albinali, F, Goodwin, MS, & Intille, SS (2012). Detecting stereotypical motor movements in the classroom using accelerometry and pattern recognition algorithms. Pervasive and Mobile Computing, 8, 103-114.
Oberleitner, R, Reischl, U, Lacy, T, Goodwin, MS, & Spitalnick, JS (2011). Emerging use of behavior imaging for autism and beyond. Future Visions on Biomedicine and Bioinformatics, 1, 93-104.
Goodwin, MS, Intille, SS, Albinali, & Velicer, WF (2011). Automated detection of stereotypical motor movements. Journal of Autism and Developmental Disorders, 41, 770-782.