Journal of Neurology

, Volume 260, Issue 1, pp 275–284 | Cite as

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

  • Po-He Tseng
  • Ian G. M. Cameron
  • Giovanna Pari
  • James N. Reynolds
  • Douglas P. Munoz
  • Laurent IttiEmail author
Original Communication


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.


ADHD FASD Parkinson’s disease Attention deficits Eye tracking 



We thank the National Science Foundation (CRCNS grant number BCS-0827764), the Army Research Office (grant nos. W911NF-08-1-0360 and W911NF-11-1-0046), the Human Frontier Science Program (grant RGP0039/2005-C), and the Canadian Institutes of Health Research (grant no. ELA 80227) for supporting this study. IGMC was supported by a scholarship from the Canadian Institutes for Health Research, and DPM was supported by the Canada Research Chair program.

Conflicts of interest


Supplementary material

415_2012_6631_MOESM1_ESM.pdf (1.4 mb)
Supplementary material 1 (PDF 1399 kb)


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Po-He Tseng
    • 1
  • Ian G. M. Cameron
    • 2
  • Giovanna Pari
    • 2
    • 3
  • James N. Reynolds
    • 2
    • 4
  • Douglas P. Munoz
    • 2
    • 3
    • 4
    • 5
  • Laurent Itti
    • 1
    • 6
    Email author
  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
  3. 3.Department of MedicineQueen’s UniversityKingstonCanada
  4. 4.Department of Biomedical and Molecular ScienceQueen’s UniversityKingstonCanada
  5. 5.Department of PsychologyQueen’s UniversityKingstonCanada
  6. 6.Neuroscience ProgramUniversity of Southern CaliforniaLos AngelesUSA

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