Quantification of Tics in Tourette Syndrome

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PROJECT SUMMARY/ABSTRACT Tourette Syndrome (TS) is a chronic, childhood-onset neurodevelopmental disorder that affects 1-3% of people and is associated with adverse functional impacts. TS is characterized by tics, which are involuntary, repetitive movements and vocalizations. A current challenge in clinical care for TS is the lack of objective, quantitative, scalable tools to measure tics for the purposes of diagnosis and symptom severity monitoring. The overall objective of the proposed study is to use video-based methods in a large, diverse community sample to inform quantitative and automated phenotyping of tics. This study builds on prior work, including: 1) video-based observational methods with trained human raters to quantify tics for research purposes, 2) computer vision and machine learning techniques for movement analysis and medical diagnostic aids, and 3) preliminary data indicating supervised learning methods can be used to automate detection of eye tics with high accuracy. In Aim 1, videos and clinical data from N = 1,000 individuals with tics will be collected using remote and internet-based methods. A deep phenotyping approach will be used to quantitatively describe the phenotypic spectrum of observable motor and vocal tics, empirically derive tic severity benchmarks, and identify patient subgroups. In Aim 2, our computer vision team will apply supervised machine learning methods to Aim 1 data to create an algorithm capable of detecting the most common tics. Aim 3 will prospectively test the Aim 2 algorithm in N = 60 patients who completed TS treatment in a separate clinical trial to establish the algorithm’s sensitivity to change and convergent validity with current gold-standard tic severity measurement. This project will enable us, for the first time, to quantify the spectrum of observable tics in a large community sample, knowledge that will have immediate clinical relevance for diagnostic decision making and patient education. Aim 2 will yield a computer algorithm capable of autonomously quantifying the most common motor tics, a critical next step toward developing accurate, clinically valid, and scalable assessments for tic screening, diagnosis, treatment decision making, and symptom quantification in clinical trials.