Prediction of depression symptoms in individual subjects with face and eye movement tracking.
Background: Depression is a challenge to diagnose reliably and the current gold standard for trials of DSM-5 has been in agreement between two or more medical specialists. Research studies aiming to objectively predict depression have typically used brain scanning. Less expensive methods from cognit...
| Publicado en: | Psychological Medicine Vol. 52; no. 9; pp. 1784 - 1793 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Cambridge University Press
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157954178&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157954178 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00332917 6Q3 jtl: Psychological Medicine issn: 00332917 maglogo: N pubinfo: dt: Jul2022 vid: 52 iid: 9 pid: 15979 pub: Cambridge University Press artinfo: ui: 157954178 157954178 157954178 10.1017/S0033291720003608 157954178 ppf: 1784 ppct: 9 formats: tig: atl: Prediction of depression symptoms in individual subjects with face and eye movement tracking. aug: au: Stolicyn, Aleks Steele, J. Douglas Seriès, Peggy affil: Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, UK sug: subj: Depression Diagnosis Depression Symptoms Facial Expression Evaluation Eye Movements Evaluation Cognition Task Performance and Analysis Human Male Female Young Adult Adult Affect Distraction Case Control Studies Machine Learning Descriptive Statistics Sensitivity and Specificity Evaluation Adult: 19-44 years Male Female ab: Background: Depression is a challenge to diagnose reliably and the current gold standard for trials of DSM-5 has been in agreement between two or more medical specialists. Research studies aiming to objectively predict depression have typically used brain scanning. Less expensive methods from cognitive neuroscience may allow quicker and more reliable diagnoses, and contribute to reducing the costs of managing the condition. In the current study we aimed to develop a novel inexpensive system for detecting elevated symptoms of depression based on tracking face and eye movements during the performance of cognitive tasks. Methods: In total, 75 participants performed two novel cognitive tasks with verbal affective distraction elements while their face and eye movements were recorded using inexpensive cameras. Data from 48 participants (mean age 25.5 years, standard deviation of 6.1 years, 25 with elevated symptoms of depression) passed quality control and were included in a case-control classification analysis with machine learning. Results: Classification accuracy using cross-validation (within-study replication) reached 79% (sensitivity 76%, specificity 82%), when face and eye movement measures were combined. Symptomatic participants were characterised by less intense mouth and eyelid movements during different stages of the two tasks, and by differences in frequencies and durations of fixations on affectively salient distraction words. Conclusions: Elevated symptoms of depression can be detected with face and eye movement tracking during the cognitive performance, with a close to clinically-relevant accuracy (~80%). Future studies should validate these results in larger samples and in clinical populations. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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