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Multi-Domain Machine Learning of VR-synchronised directional EEG phase connectivity reveals candidate Alzheimer's continuum biomarkers
Park¹ EJ, Kim E, Kim DH, Kim NY.. Research Square, 2026-07-23
Topic: Brain imaging, connectivity and assessment
Early, scalable detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a global health priority, but non-invasive biomarkers that capture network-level dysfunction during standardized cognitive challenge remain limited. We coupled immersive virtual reality (VR)-synchronised 64-channel electroencephalography (EEG) with phase transfer entropy (PTE), a directional connectivity measure, to identify interpretable electrophysiological signatures across the AD continuum. In a quality-controlled cohort of 37 participants (AD n = 13, MCI n = 13, normal cognition n = 11; 72 recruited, 60 completed the protocol), 16,892 features across five computational domains were evaluated using a six-classifier pipeline with leave-one-out cross-validation, permutation testing, bootstrap resampling, and SHAP-based interpretation. The best-performing model achieved 86.5% three-class accuracy with 100% MCI recall (macro-F1 = 0.859) and identified 11 candidate biomarkers supported by cross-validated stability and effect-size evidence. Key signatures included attenuated posterior alpha net flow in AD, VR-elicited parieto-occipital gamma connectivity in MCI, and preserved central gamma hub structure in normal cognition. These findings suggest that VR-EEG directional connectivity provides a reproducible framework for identifying candidate electrophysiological biomarkers of the AD continuum, warranting validation in larger biomarker-confirmed cohorts.
Key findings
- These findings suggest that VR-EEG directional connectivity provides a reproducible framework for identifying candidate electrophysiological biomarkers of the AD continuum, warranting validation in larger biomarker-confirmed cohorts.
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Study details
- Participants13
- Effect size86.5%
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 34 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 21 of the 62 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 1 / 4 | 13 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 21 of the 62 points we could judge it on is 34 out of 100. |
| Total | 34 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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APA 7th
Park¹, E. J., Kim, E., Kim, D. H., & Kim, N. Y. (2026). Multi-Domain Machine Learning of VR-synchronised directional EEG phase connectivity reveals candidate Alzheimer's continuum biomarkers. Research Square. https://doi.org/10.21203/rs.3.rs-9925123/v1
Vancouver
Park¹ EJ, Kim E, Kim DH, Kim NY. Multi-Domain Machine Learning of VR-synchronised directional EEG phase connectivity reveals candidate Alzheimer's continuum biomarkers. Research Square. 2026. doi:10.21203/rs.3.rs-9925123/v1.
BibTeX
@article{park2026-7d2b87bc,
author = {Park¹, E. J. and Kim, E. and Kim, D. H. and Kim, N. Y.},
title = {{Multi-Domain Machine Learning of VR-synchronised directional EEG phase connectivity reveals candidate Alzheimer's continuum biomarkers}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-9925123/v1},
url = {https://doi.org/10.21203/rs.3.rs-9925123/v1}
}