Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer’s Disease: A Review

Beltrán, Jessica; García-Vázquez, Mireya S.; Benois-Pineau, Jenny; Robledo, Luis Miguel Gutiérrez; Dartigues, Jean‐François

doi:10.1155/2018/2676409

Cited by 35 publications

(18 citation statements)

References 113 publications

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“…Previous work from our group examined a similar set of features, extending this finding to MCI (Fraser et al, 2017). More generally, Beltrán et al (2018) propose that the analysis of eye movements (in reading, as well as other paradigms) could support the early diagnosis of AD, and Pereira et al (2014) suggest that eye movements may be able to predict the conversion from MCI to AD, as eye-movements can be sensitive to subtle changes in memory, visual, and executive processes.…”

Section: Related Workmentioning

confidence: 99%

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Fraser

Fors

Eckerström

et al. 2019

Front. Aging Neurosci.

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Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the “early fusion” approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (ρ = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).

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Section: Related Workmentioning

confidence: 99%

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Fraser

Fors

Eckerström

et al. 2019

Front. Aging Neurosci.

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“…Indeed, cognitive processes can be observed through eye movements and offer a wealth of information related to internal processes (Itti, 2015;Coutrot, Hsiao, & Chan, 2018). Inference from gaze data consists in deducing subjective characteristics solely from ocular data, such as age (Le Meur et al, 2017b), gender (Coutrot, Binetti, Harrison, Mareschal, & Johnston, 2016;Sammaknejad, Pouretemad, Eslahchi, Salahirad, & Alinejad, 2017), mental states and traits (Liao, Zhang, Zhu, & Ji, 2005;Hoppe, Loetscher, Morey, & Bulling, 2015;Yamada & Kobayashi, 2017;Hoppe, Loetscher, Morey, & Bulling, 2018), expertise and skill proficiency (Eivazi & Bednarik, 2011;Boccignone, Ferraro, Crespi, Robino, & de'Sperati, 2014;Tien et al, 2014;Kolodziej, Majkowski, Francuz, Rak, & Augustynowicz, 2018), and neurological disorders (Kupas, Harangi, Czifra, & Andrassy, 2017;Terao, Fukuda, & Hikosaka, 2017).It has proven useful in identifying autism spectrum disorder (Pierce et al, 2016), fetal alcohol spectrum disorder (Tseng, Paolozza, Munoz, Reynolds, & Itti, 2013), dementia (Zhang et al, 2016;Beltrán, García-Vázquez, Benois-Pineau, Gutierrez-Robledo, & Dartigues, 2018), dyslexia (Benfatto et al, 2016), anxiety (Abbott, Shirali, Haws, & Lack, 2017), mental fatigue (Yamada & Kobayashi, 2017), and other disorders. It has also been applied to task detection (Borji & Itti, 2014;Haji-Abolhassani & Clark, 2014;Kanan, Ray, Bseiso, Hsiao, & Cottrell, 2014;Boisvert & Bruce, 2016).…”

Section: Inference From Gaze Datamentioning

confidence: 99%

Predicting artificial visual field losses: A gaze-based inference study

et al. 2019

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Visual field defects are a worldwide concern, and the proportion of the population experiencing vision loss is ever increasing. Macular degeneration and glaucoma are among the four leading causes of permanent vision loss. Identifying and characterizing visual field losses from gaze alone could prove crucial in the future for screening tests, rehabilitation therapies, and monitoring. In this experiment, 54 participants took part in a free-viewing task of visual scenes while experiencing artificial scotomas (central and peripheral) of varying radii in a gaze-contingent paradigm. We studied the importance of a set of gaze features as predictors to best differentiate between artificial scotoma conditions. Linear mixed models were utilized to measure differences between scotoma conditions. Correlation and factorial analyses revealed redundancies in our data. Finally, hidden Markov models and recurrent neural networks were implemented as classifiers in order to measure the predictive usefulness of gaze features. The results show separate saccade direction biases depending on scotoma type. We demonstrate that the saccade relative angle, amplitude, and peak velocity of saccades are the best features on the basis of which to distinguish between artificial scotomas in a free-viewing task. Finally, we discuss the usefulness of our protocol and analyses as a gaze-feature identifier tool that discriminates between artificial scotomas of different types and sizes.

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“…AD seems to affect smooth pursuit as well, in which slower initiation of eye movements to follow a moving target has been reported in AD compared with healthy individuals [165]. In sum, eye tracking has been suggested to be used to explore the traces of AD that may appear many years prior to the clinical diagnosis of AD, which may offer remarkable benefits for early diagnosis or treatment of the disease [168].…”

Section: Oculometrics As Objective Biomarkers In Aging Researchmentioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.

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Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer’s Disease: A Review

Cited by 35 publications

References 113 publications

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Predicting artificial visual field losses: A gaze-based inference study

Aging and Eye Tracking: in the Quest for Objective Biomarkers

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