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Eye movement behavior in a real-world virtual reality task reveals ADHD in children

Liya Merzon, Kati Pettersson, Eeva T. Aronen, Hanna Huhdanpää, Erik Seesjärvi, Linda Henriksson, Joe MacInnes Orcid Logo, Minna Mannerkoski, Emiliano Macaluso, Juha Salmi

Scientific Reports, Volume: 12, Issue: 1

Swansea University Author: Joe MacInnes Orcid Logo

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DOI (Published version): 10.1038/s41598-022-24552-4

Abstract

Eye movements and other rich data obtained in virtual reality (VR) environments resembling situations where symptoms are manifested could help in the objective detection of various symptoms in clinical conditions. In the present study, 37 children with attention deficit hyperactivity disorder and 36...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa63400
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last_indexed 2025-06-28T07:08:59Z
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spelling 2025-06-27T14:50:53.5779663 v2 63400 2023-05-11 Eye movement behavior in a real-world virtual reality task reveals ADHD in children 06dcb003ec50192bafde2c77bef4fd5c 0000-0002-5134-1601 Joe MacInnes Joe MacInnes true false 2023-05-11 MACS Eye movements and other rich data obtained in virtual reality (VR) environments resembling situations where symptoms are manifested could help in the objective detection of various symptoms in clinical conditions. In the present study, 37 children with attention deficit hyperactivity disorder and 36 typically developing controls (9–13 y.o) played a lifelike prospective memory game using head-mounted display with inbuilt 90 Hz eye tracker. Eye movement patterns had prominent group differences, but they were dispersed across the full performance time rather than associated with specific events or stimulus features. A support vector machine classifier trained on eye movement data showed excellent discrimination ability with 0.92 area under curve, which was significantly higher than for task performance measures or for eye movements obtained in a visual search task. We demonstrated that a naturalistic VR task combined with eye tracking allows accurate prediction of attention deficits, paving the way for precision diagnostics. Journal Article Scientific Reports 12 1 Springer Science and Business Media LLC 2045-2322 24 11 2022 2022-11-24 10.1038/s41598-022-24552-4 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee This work was supported by the Academy of Finland (grants #325981 and #328954 to JS), Finnish Cultural Foundation (grant #00210721 to LM), Psychiatry Research Foundation (grant #20210019 LM), Instrumentarium Science Foundation (grant #200005 to ES), and Aalto Brain Centre. 2025-06-27T14:50:53.5779663 2023-05-11T11:27:20.7284451 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Liya Merzon 1 Kati Pettersson 2 Eeva T. Aronen 3 Hanna Huhdanpää 4 Erik Seesjärvi 5 Linda Henriksson 6 Joe MacInnes 0000-0002-5134-1601 7 Minna Mannerkoski 8 Emiliano Macaluso 9 Juha Salmi 10 63400__27583__bbaa9882c3844c6daea22403e17e9108.pdf 63400.pdf 2023-05-23T15:22:09.4766036 Output 1421342 application/pdf Version of Record true © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title Eye movement behavior in a real-world virtual reality task reveals ADHD in children
spellingShingle Eye movement behavior in a real-world virtual reality task reveals ADHD in children
Joe MacInnes
title_short Eye movement behavior in a real-world virtual reality task reveals ADHD in children
title_full Eye movement behavior in a real-world virtual reality task reveals ADHD in children
title_fullStr Eye movement behavior in a real-world virtual reality task reveals ADHD in children
title_full_unstemmed Eye movement behavior in a real-world virtual reality task reveals ADHD in children
title_sort Eye movement behavior in a real-world virtual reality task reveals ADHD in children
author_id_str_mv 06dcb003ec50192bafde2c77bef4fd5c
author_id_fullname_str_mv 06dcb003ec50192bafde2c77bef4fd5c_***_Joe MacInnes
author Joe MacInnes
author2 Liya Merzon
Kati Pettersson
Eeva T. Aronen
Hanna Huhdanpää
Erik Seesjärvi
Linda Henriksson
Joe MacInnes
Minna Mannerkoski
Emiliano Macaluso
Juha Salmi
format Journal article
container_title Scientific Reports
container_volume 12
container_issue 1
publishDate 2022
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-022-24552-4
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Eye movements and other rich data obtained in virtual reality (VR) environments resembling situations where symptoms are manifested could help in the objective detection of various symptoms in clinical conditions. In the present study, 37 children with attention deficit hyperactivity disorder and 36 typically developing controls (9–13 y.o) played a lifelike prospective memory game using head-mounted display with inbuilt 90 Hz eye tracker. Eye movement patterns had prominent group differences, but they were dispersed across the full performance time rather than associated with specific events or stimulus features. A support vector machine classifier trained on eye movement data showed excellent discrimination ability with 0.92 area under curve, which was significantly higher than for task performance measures or for eye movements obtained in a visual search task. We demonstrated that a naturalistic VR task combined with eye tracking allows accurate prediction of attention deficits, paving the way for precision diagnostics.
published_date 2022-11-24T08:26:35Z
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