High-throughput classification of clinical populations from natural viewing eye movements.
Identifieur interne : 000D63 ( Main/Exploration ); précédent : 000D62; suivant : 000D64High-throughput classification of clinical populations from natural viewing eye movements.
Auteurs : Po-He Tseng [États-Unis] ; Ian G M. Cameron ; Giovanna Pari ; James N. Reynolds ; Douglas P. Munoz ; Laurent IttiSource :
- Journal of neurology [ 1432-1459 ] ; 2013.
English descriptors
- KwdEn :
- Adolescent, Aged, Attention (physiology), Attention Deficit Disorder with Hyperactivity (complications), Biometry, Child, Female, Fetal Alcohol Spectrum Disorders (physiopathology), Fixation, Ocular (physiology), Humans, Male, Middle Aged, Ocular Motility Disorders (classification), Ocular Motility Disorders (etiology), Parkinson Disease (complications), Photic Stimulation, Pregnancy, Young Adult.
- MESH :
- classification : Ocular Motility Disorders.
- complications : Attention Deficit Disorder with Hyperactivity, Parkinson Disease.
- etiology : Ocular Motility Disorders.
- physiology : Attention, Fixation, Ocular.
- physiopathology : Fetal Alcohol Spectrum Disorders.
- Adolescent, Aged, Biometry, Child, Female, Humans, Male, Middle Aged, Photic Stimulation, Pregnancy, Young Adult.
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.
DOI: 10.1007/s00415-012-6631-2
PubMed: 22926163
Affiliations:
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Cameron</name></author><author><name sortKey="Pari, Giovanna" sort="Pari, Giovanna" uniqKey="Pari G" first="Giovanna" last="Pari">Giovanna Pari</name></author><author><name sortKey="Reynolds, James N" sort="Reynolds, James N" uniqKey="Reynolds J" first="James N" last="Reynolds">James N. Reynolds</name></author><author><name sortKey="Munoz, Douglas P" sort="Munoz, Douglas P" uniqKey="Munoz D" first="Douglas P" last="Munoz">Douglas P. Munoz</name></author><author><name sortKey="Itti, Laurent" sort="Itti, Laurent" uniqKey="Itti L" first="Laurent" last="Itti">Laurent Itti</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:22926163</idno><idno type="pmid">22926163</idno><idno type="doi">10.1007/s00415-012-6631-2</idno><idno type="wicri:Area/PubMed/Corpus">000982</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000982</idno><idno type="wicri:Area/PubMed/Curation">000982</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000982</idno><idno type="wicri:Area/PubMed/Checkpoint">000982</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000982</idno><idno type="wicri:Area/Ncbi/Merge">001231</idno><idno type="wicri:Area/Ncbi/Curation">001231</idno><idno type="wicri:Area/Ncbi/Checkpoint">001231</idno><idno type="wicri:Area/Main/Merge">000D74</idno><idno type="wicri:Area/Main/Curation">000D63</idno><idno type="wicri:Area/Main/Exploration">000D63</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">High-throughput classification of clinical populations from natural viewing eye movements.</title><author><name sortKey="Tseng, Po He" sort="Tseng, Po He" uniqKey="Tseng P" first="Po-He" last="Tseng">Po-He Tseng</name><affiliation wicri:level="4"><nlm:affiliation>Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, University of Southern California, Los Angeles, CA 90089</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Los Angeles</settlement></placeName><orgName type="university">Université de Californie du Sud</orgName></affiliation></author><author><name sortKey="Cameron, Ian G M" sort="Cameron, Ian G M" uniqKey="Cameron I" first="Ian G M" last="Cameron">Ian G M. Cameron</name></author><author><name sortKey="Pari, Giovanna" sort="Pari, Giovanna" uniqKey="Pari G" first="Giovanna" last="Pari">Giovanna Pari</name></author><author><name sortKey="Reynolds, James N" sort="Reynolds, James N" uniqKey="Reynolds J" first="James N" last="Reynolds">James N. Reynolds</name></author><author><name sortKey="Munoz, Douglas P" sort="Munoz, Douglas P" uniqKey="Munoz D" first="Douglas P" last="Munoz">Douglas P. Munoz</name></author><author><name sortKey="Itti, Laurent" sort="Itti, Laurent" uniqKey="Itti L" first="Laurent" last="Itti">Laurent Itti</name></author></analytic><series><title level="j">Journal of neurology</title><idno type="eISSN">1432-1459</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Aged</term><term>Attention (physiology)</term><term>Attention Deficit Disorder with Hyperactivity (complications)</term><term>Biometry</term><term>Child</term><term>Female</term><term>Fetal Alcohol Spectrum Disorders (physiopathology)</term><term>Fixation, Ocular (physiology)</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Ocular Motility Disorders (classification)</term><term>Ocular Motility Disorders (etiology)</term><term>Parkinson Disease (complications)</term><term>Photic Stimulation</term><term>Pregnancy</term><term>Young Adult</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Ocular Motility Disorders</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Attention Deficit Disorder with Hyperactivity</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Ocular Motility Disorders</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Attention</term><term>Fixation, Ocular</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Fetal Alcohol Spectrum Disorders</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Aged</term><term>Biometry</term><term>Child</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Photic Stimulation</term><term>Pregnancy</term><term>Young Adult</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>Los Angeles</li></settlement><orgName><li>Université de Californie du Sud</li></orgName></list><tree><noCountry><name sortKey="Cameron, Ian G M" sort="Cameron, Ian G M" uniqKey="Cameron I" first="Ian G M" last="Cameron">Ian G M. Cameron</name><name sortKey="Itti, Laurent" sort="Itti, Laurent" uniqKey="Itti L" first="Laurent" last="Itti">Laurent Itti</name><name sortKey="Munoz, Douglas P" sort="Munoz, Douglas P" uniqKey="Munoz D" first="Douglas P" last="Munoz">Douglas P. Munoz</name><name sortKey="Pari, Giovanna" sort="Pari, Giovanna" uniqKey="Pari G" first="Giovanna" last="Pari">Giovanna Pari</name><name sortKey="Reynolds, James N" sort="Reynolds, James N" uniqKey="Reynolds J" first="James N" last="Reynolds">James N. Reynolds</name></noCountry><country name="États-Unis"><region name="Californie"><name sortKey="Tseng, Po He" sort="Tseng, Po He" uniqKey="Tseng P" first="Po-He" last="Tseng">Po-He Tseng</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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