Eye movement changes as an indicator of mild cognitive impairment
BackgroundEarly identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by th...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1171417/full |
| _version_ | 1797756028448669696 |
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| author | Julius Opwonya Julius Opwonya Boncho Ku Kun Ho Lee Kun Ho Lee Kun Ho Lee Joong Il Kim Jaeuk U. Kim Jaeuk U. Kim |
| author_facet | Julius Opwonya Julius Opwonya Boncho Ku Kun Ho Lee Kun Ho Lee Kun Ho Lee Joong Il Kim Jaeuk U. Kim Jaeuk U. Kim |
| author_sort | Julius Opwonya |
| collection | DOAJ |
| description | BackgroundEarly identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data.MethodsWe collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics’ odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC).ResultsLR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840.ConclusionChanges in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline. |
| first_indexed | 2024-03-12T17:55:38Z |
| format | Article |
| id | doaj.art-8ad0bf7a6f4c449cb1dd582fce16cd3e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-12T17:55:38Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-8ad0bf7a6f4c449cb1dd582fce16cd3e2023-08-02T16:40:13ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-06-011710.3389/fnins.2023.11714171171417Eye movement changes as an indicator of mild cognitive impairmentJulius Opwonya0Julius Opwonya1Boncho Ku2Kun Ho Lee3Kun Ho Lee4Kun Ho Lee5Joong Il Kim6Jaeuk U. Kim7Jaeuk U. Kim8Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South KoreaKM Convergence Science, University of Science and Technology, Daejeon, South KoreaDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South KoreaGwangju Alzheimer’s Disease and Related Dementias (GARD) Cohort Research Center, Chosun University, Gwangju, South KoreaDepartment of Biomedical Science, Chosun University, Gwangju, South KoreaDementia Research Group, Korea Brain Research Institute, Daegu, South KoreaDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South KoreaDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South KoreaKM Convergence Science, University of Science and Technology, Daejeon, South KoreaBackgroundEarly identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data.MethodsWe collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics’ odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC).ResultsLR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840.ConclusionChanges in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.https://www.frontiersin.org/articles/10.3389/fnins.2023.1171417/fullAlzheimer’s diseasemild cognitive impairmenteye movement analysis and synthesismachine learning (ML)saccades |
| spellingShingle | Julius Opwonya Julius Opwonya Boncho Ku Kun Ho Lee Kun Ho Lee Kun Ho Lee Joong Il Kim Jaeuk U. Kim Jaeuk U. Kim Eye movement changes as an indicator of mild cognitive impairment Frontiers in Neuroscience Alzheimer’s disease mild cognitive impairment eye movement analysis and synthesis machine learning (ML) saccades |
| title | Eye movement changes as an indicator of mild cognitive impairment |
| title_full | Eye movement changes as an indicator of mild cognitive impairment |
| title_fullStr | Eye movement changes as an indicator of mild cognitive impairment |
| title_full_unstemmed | Eye movement changes as an indicator of mild cognitive impairment |
| title_short | Eye movement changes as an indicator of mild cognitive impairment |
| title_sort | eye movement changes as an indicator of mild cognitive impairment |
| topic | Alzheimer’s disease mild cognitive impairment eye movement analysis and synthesis machine learning (ML) saccades |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1171417/full |
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