The role of feedback in speech in autism: The missing perceptuomotor link

Specific Aim(s) of the project: to leverage ongoing studies to characterize and describe the locus of communication differences in individuals with mv-ASD, focusing on analysis of speech production during receptive and expressive language tasks while measuring attention and cognitive load of the subjects.  

Over the past few decades, there have been significant gains made in speech, language, auditory, and brain science, as well as digital signal processing, artificial intelligence, and human/brain-computer interfaces, resulting in breakthroughs in modern communication technology. Yet, there exists a huge gap in the application of these technologies to help individuals who are minimally verbal with autism spectrum disorder (mv-ASD) speak or communicate holistically through assistive devices. In our proposed effort, we will seek to use advanced signal sensing and analysis technologies to characterize both vocal and behavioral means of expression and to uncover the nature and physiological basis of the verbal deficit. Using advanced signal enhancement, human/brain-to-machine interfacing using AttentivU platform, we aim to promote the generation of speech, and, when speech is not possible, alternative forms of communication (e.g., manual, facial, eye gaze) that will help individuals with mv-ASD express themselves.

This project will bridge the communication technology gap by bringing together experts from leading institutions in the field of ASD and the neurophysiology of communication: (1) the Massachusetts General Hospital (MGH) Lurie Center for Autism/Martinos Center for Biomedical Imaging with clinical experience and expertise in recruiting and characterizing a large ASD population, and in ASD speech protocol design and brain imaging, (2) the Massachusetts Institution of Technology (MIT) Media Lab with its innovative real-time on-body sensing and interpreting of human neurophysiology using AttentivU platform, and (3) the MIT Lincoln Laboratory with its advanced speech and neuro-computational modeling and analysis methods and mobile off-body multi-modal platforms. 

We will work with 30 individuals with mv-ASD and age and sex-matched healthy control subjects, ages 16-40 years. The insights gained from the proposed project will help develop a set of tools for use by clinicians towards monitoring the efficacy of communication interventions for individuals with mv-ASD and the development of personalized treatments.