MedicalResearch.com Interview with:
Di Wu, Msc
PhD candidate at Indiana University
Graduate Research Assistant
Department of Physics
Indiana University Bloomington
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Current clinical diagnosis and evaluations of Autism Spectrum Disorder (ASD). has remained subjective in nature. There is a need to have objective assessments for the disorder. We discovered in this study an important motion feature that was unknown before. This feature provides a clear screening of ASD. It gave a remarkable quantitative connection between the way children with ASD move and their psychiatric scores, like the IQ score and the Vineland Adaptive Behavior Scale. This connection we captured suggests that the motor feature may be an essential core feature characterizing ASD deficits, as well as neurodevelopment in general.
MedicalResearch.com: What should clinicians and patients take away from your report?
Response: Most of us usually take the easiness in the way we move for granted. Some natural motions, like reaching to a cup of coffee or walking, seems too simple to contain any internal relevant information. What we discovered is that by zooming into the motions at millisecond time scales we could get striking physiological information about the underlying neuronal system from analyzing simple natural motions. This information is not detectable from naked eyes observations or from traditional movement analyses. We illustrated the power of this motion feature by screening ASD subjects and quantifying their Autism Spectrum Disorder severity in our studies.
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Response: Our work may start a new way of assessing motion outputs in complementary way to traditional motor skill assessments. The measurements we introduced in our work is quantitative and precise in nature. It is easily-accessible with modern motion tracking technology by adding the novel analytic tools we developed. It may be applied more generally to other neurological disorders as well as to longitudinally track individualized neurodevelopment. Our work also suggests that with future design and implementation of the analytic tools developed, the widely used motion tracking sensors built in smart phones or fitness trackers may be able to provide more physiological information, beyond counting step by users.
MedicalResearch.com: Is there anything else you would like to add?
Response: This study was a joint collaboration between Indiana University and Rutgers University. Di Wu was lead author on the manuscript, with Dr. Jorge V. Jose at Indiana University and Dr. Elizabeth B. Torres at Rutgers University as principal investigators. Dr. John Nurnberger played an important role as the psychiatrist in the collaboration.
MedicalResearch.com: Thank you for your contribution to the MedicalResearch.com community.
Di Wu, Jorge V. José, John I. Nurnberger & Elizabeth B. Torres
Scientific Reports 8, Article number: 614 (2018)
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