Original Communication

Journal of Neurology

, Volume 260, Issue 1, pp 275-284

High-throughput classification of clinical populations from natural viewing eye movements

  • Po-He TsengAffiliated withDepartment of Computer Science, University of Southern California
  • , Ian G. M. CameronAffiliated withCentre for Neuroscience Studies, Queen’s University
  • , Giovanna PariAffiliated withCentre for Neuroscience Studies, Queen’s UniversityDepartment of Medicine, Queen’s University
  • , James N. ReynoldsAffiliated withCentre for Neuroscience Studies, Queen’s UniversityDepartment of Biomedical and Molecular Science, Queen’s University
  • , Douglas P. MunozAffiliated withCentre for Neuroscience Studies, Queen’s UniversityDepartment of Medicine, Queen’s UniversityDepartment of Biomedical and Molecular Science, Queen’s UniversityDepartment of Psychology, Queen’s University
  • , Laurent IttiAffiliated withDepartment of Computer Science, University of Southern CaliforniaNeuroscience Program, University of Southern California Email author 

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Abstract

Many high-prevalence neurological disorders involve dysfunctions of oculomotor control and attention, including attention deficit hyperactivity disorder (ADHD), fetal alcohol spectrum disorder (FASD), and Parkinson’s disease (PD). Previous studies have examined these deficits with clinical neurological evaluation, structured behavioral tasks, and neuroimaging. Yet, time and monetary costs prevent deploying these evaluations to large at-risk populations, which is critically important for earlier detection and better treatment. We devised a high-throughput, low-cost method where participants simply watched television while we recorded their eye movements. We combined eye-tracking data from patients and controls with a computational model of visual attention to extract 224 quantitative features. Using machine learning in a workflow inspired by microarray analysis, we identified critical features that differentiate patients from control subjects. With eye movement traces recorded from only 15 min of videos, we classified PD versus age-matched controls with 89.6 % accuracy (chance 63.2 %), and ADHD versus FASD versus control children with 77.3 % accuracy (chance 40.4 %). Our technique provides new quantitative insights into which aspects of attention and gaze control are affected by specific disorders. There is considerable promise in using this approach as a potential screening tool that is easily deployed, low-cost, and high-throughput for clinical disorders, especially in young children and elderly populations who may be less compliant to traditional evaluation tests.

Keywords

ADHD FASD Parkinson’s disease Attention deficits Eye tracking