Classification of MCI patients using vergence eye movements and pupil responses obtained during a visual oddball test

Hashemi, Amin; Leonovych, Oleksii; Jiménez, Elizabeth Carolina; Sierra-Marcos, Alba; Romeo, August; Valenzuala, Patricia Bustos; Puig, Maria Solé; Moliner, Joan López; Tubau, Elisabet; Supèr, Hans

doi:10.1016/j.ahr.2023.100121

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…In patients with MCI, attended stimuli are accompanied by a weak enhancement, whereas Alzheimer patients show no difference in vergence responses to attended and unattended stimuli. Such models can even predict the risk of patients with MCI developing Alzheimer's disease [19].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population

Romeo,

Leonovych,

Solé Puig

et al. 2024

Sensors

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(1) Background: Our previous research provides evidence that vergence eye movements may significantly influence cognitive processing and could serve as a reliable measure of cognitive issues. The rise of consumer-grade eye tracking technology, which uses sophisticated imaging techniques in the visible light spectrum to determine gaze position, is noteworthy. In our study, we explored the feasibility of using webcam-based eye tracking to monitor the vergence eye movements of patients with Mild Cognitive Impairment (MCI) during a visual oddball paradigm. (2) Methods: We simultaneously recorded eye positions using a remote infrared-based pupil eye tracker. (3) Results: Both tracking methods effectively captured vergence eye movements and demonstrated robust cognitive vergence responses, where participants exhibited larger vergence eye movement amplitudes in response to targets versus distractors. (4) Conclusions: In summary, the use of a consumer-grade webcam to record cognitive vergence shows potential. This method could lay the groundwork for future research aimed at creating an affordable screening tool for mental health care.

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Section: Introductionmentioning

confidence: 99%

Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population

Romeo,

Leonovych,

Solé Puig

et al. 2024

Sensors

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“…Current imaging tools for diagnosing and classifying MCI and AD mainly include MRI and PET. MRI can identify brain structural and functional changes caused by AD at different stages (such as hippocampal volume, cortical thickness) (Hashemi et al 2023 ); 18F-FDG PET evaluates brain metabolism by measuring local glucose consumption in brain tissue (Qiu et al 2022 ). Recent research shows that, in the MCI phase, 18F-FDG PET may show features of neurodegeneration earlier than MRI (Kang et al 2021 ; Cahill and Huang 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics

Mu,

Cui,

Liao

et al. 2024

EJNMMI Rep.

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Background This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Methods In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression. Results Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD. Conclusion Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.

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“…In MCI patients, attended stimuli are accompanied by a weak enhancement whereas Alzheimer patients show no difference in vergence responses to attended and unattended stimuli. Such models can even predict the risk of MCI patients developing Alzheimer's disease (Hashemi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population

Romeo,

Leonovych,

Solé Puig

et al. 2023

Preprint

View full text Add to dashboard Cite

Our previous research provides evidence that vergence eye movements may significantly influence cognitive processing and could serve as a reliable measure of cognitive issues. The rise of consumer-grade eye tracking technology, which uses sophisticated imaging techniques in the visible light spectrum to determine gaze position, is noteworthy. In our study, we explored the feasibility of using webcam-based eye tracking to monitor the vergence eye movements of patients with Mild Cognitive Impairment (MCI) during a visual oddball paradigm. We simultaneously recorded eye positions using a remote infrared-based pupil eye tracker. Both tracking methods demonstrated more pronounced cognitive vergence responses for targets as opposed to distractors. In summary, the use of a consumer-grade webcam to record cognitive vergence shows potential. This method could potentially lay the groundwork for future research aimed at creating an affordable screening tool for mental health care.

show abstract

Classification of MCI patients using vergence eye movements and pupil responses obtained during a visual oddball test

Cited by 4 publications

References 18 publications

Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population

Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population

Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics

Cognitive Vergence Recorded with a Webcam-Based Eye-Tracker during an Oddball Task in an Elderly Population

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