Published January 20, 2026 | Version v3
Dataset Open

VR-Based Cognitive Screening (VR-CS) Eye-Tracking Metrics for Early Cognitive Decline (AD, MCI, Controls; N=60)

Authors/Creators

  • 1. Curtin Malaysia Research Institute, Curtin University Malaysia

Contributors

Contact person:

Supervisor:

  • 1. ROR icon Curtin University Sarawak
  • 2. Curtin University

Description

Overview

This record provides an ethics-compliant, reproducible package supporting a VR-based cognitive screening study with integrated eye tracking (Alzheimer’s disease, mild cognitive impairment, and cognitively normal controls; N=60, 20 per group). The open materials enable verification and reproduction of all aggregated statistics (Tables 4–6) and figures (e.g., Figure 3) reported in the manuscript. The package also includes the full analysis pipeline and parameter settings used for leakage-safe cross-validated ROC analyses; re-running the complete ROC pipeline from participant-level predictors requires controlled access to human research data (see below).

Open-access contents (this Zenodo record)

  1. Analysis code (Python) with exact parameters and random seeds.
  2. ROI dictionary / mapping rules used to group ROI names into ROI types (e.g., KW/INST/BG) and task-specific ROI segmentation.
  3. Aggregated, de-identified summary tables and figure source data sufficient to reproduce Tables 4–6 and Figure 3 without exposing participant-level data.
  4. Aggregated ROC/CV outputs (AUC, confidence intervals, fold-level summaries) enabling transparent verification of manuscript performance claims.
  5. Reproducibility instructions (README) and environment specification.

Controlled-access contents (not publicly released here)

Participant-level derived feature tables and any raw/near-raw eye-tracking files contain potentially re-identifiable human research data and are therefore not publicly available. Access may be granted to qualified researchers for research purposes upon reasonable request and execution of a data use agreement, subject to ethics constraints. A Curtin Research Data Collection (Curtin RDC) controlled-access record is currently under institutional review/in preparation; the landing page DOI/URL will be added when available.

Licensing

Data and documentation in this Zenodo record are released under CC BY 4.0. Source code is additionally available under the MIT License (see LICENSE_CODE_MIT.txt in the package).

Hardware/Software

Eye-tracking was collected using a Pico 4 Pro HMD with integrated eye tracking (HMD refresh 90 Hz; eye-tracking sampled at 60 Hz). The VR task was developed in Unity3D.

Intended use

These materials support method validation, benchmarking, and transparent re-analysis of VR-based eye-tracking markers for early cognitive decline. External validation on independent cohorts is required before clinical deployment.

Contact: haiwei.zuo@postgrad.curtin.edu.my

Files

VR_MMSE_Zenodo_Open_v2_0_FINAL.zip

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Additional details

Related works

Is supplemented by
Software: https://github.com/HaiweiZuo/vr-cs-eye-tracking-analysis (URL)

Dates

Created
2026-01-20

Software

Repository URL
https://github.com/HaiweiZuo/vr-cs-eye-tracking-analysis
Programming language
Python
Development Status
Active

References

  • Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189-198. doi:10.1016/0022-3956(75)90026-6.
  • Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol. 2005;62(7):1160-1163. doi:10.1001/archneur.62.7.1160.
  • Tarnanas I, Tsolaki M, Nef T, Müri RM, Mosimann UP. Can a novel computerized cognitive screening test provide additional information for early detection of Alzheimer's disease? Alzheimers Dement. 2014;10(6):790-798. doi:10.1016/j.jalz.2014.01.002.
  • Biondi J, Fernandez G, Castro S, Agamennoni O. Eye movement behavior identification for Alzheimer's disease diagnosis. J Integr Neurosci. 2018;17(4):349-354. doi:10.31083/j.jin.2018.04.0416.
  • Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2020;16(9):1305-1311. doi:10.1016/j.jalz.2018.07.219.
  • Kim SY, Park J, Choi H, et al. Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study. J Med Internet Res. 2023;25:e48093. doi:10.2196/48093.
  • Xu Y, Zhang C, Pan B, Yuan Q, Zhang X. A portable and efficient dementia screening tool using eye tracking technology, machine learning and virtual reality. npj Digit Med. 2024;7:219. doi:10.1038/s41746-024-01206-5.