Show simple item record

dc.contributor.authorSadria, Mehrshad
dc.contributor.authorKarimi, Soroush
dc.contributor.authorLayton, Anita T. 19:14:00 (GMT) 19:14:00 (GMT)
dc.descriptionThe final publication is available at Elsevier via © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.description.abstractIndividuals suffering from autism spectrum disorder (ASD) exhibit impaired social communication, the manifestations of which include abnormal eye contact and gaze. In this study, we first seek to characterize the spatial and temporal attributes of this atypical eye gaze. To achieve that goal, we analyze and compare eye-tracking data of ASD and typical development (TD) children. A fixation time analysis indicates that ASD children exhibit a distinct gaze pattern when looking at faces, spending significantly more time at the mouth and less at the eyes, compared with TD children. Another goal of this study is to identify an analytic approach that can better reveal differences between the face scanning patterns of ASD and TD children. Face scanning involves transitioning from one area of interest (AOI) to another and is not taken into account by the traditional fixation time analysis. Instead, we apply four network analysis approaches that measure the “importance” of a given AOI: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Degree centrality and eignevector centrality yield statistically significant difference in the mouth and right eye, respectively, between the ASD and TD groups, whereas betweenness centrality reveals statistically significant between-group differences in four AOIs. Closeness centrality yields statistically meaningful differences in three AOIs, but those differences are negligible. Thus, our results suggest that betweenness centrality is the most effective network analysis approach in distinguishing the eye gaze patterns between ASD and TD children.en
dc.description.sponsorshipThis research was supported by the Canada 150 Research Chair program.en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectfixation timeen
dc.subjectdegree centralityen
dc.subjectbetweenness centralityen
dc.subjectcloseness centralityen
dc.subjectEigenvector centralityen
dc.subjecteye trackingen
dc.titleNetwork centrality analysis of eye-gaze data in autism spectrum disorderen
dcterms.bibliographicCitationM. Sadria, S. Karimi, A.T. Layton, Network centrality analysis of eye-gaze data in autism spectrum disorder, Computers in Biology and Medicine (2019), doi: j.compbiomed.2019.103332.en
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation1Faculty of Scienceen
uws.contributor.affiliation2Applied Mathematicsen
uws.contributor.affiliation2School of Pharmacyen

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages