Predicting Alzheimer progression using EEG-based digital twins
Lorenzo Gaetano Amato, Elliz P. Scheijbeler, Anne M van Nifterick, Michael Lassi, Willem de Haan, Alida A. Gouw, Alberto Mazzoni. npj Digital Medicine, 2026-08-03
Topic: Amyloid, tau and blood-test diagnosis
Early and accurate prognosis of Alzheimer’s disease (AD) remains limited by current biomarkers, which are often invasive, require expensive equipment, or fail to predict individual trajectories of cognitive decline. In this study, we evaluated the Digital Alzheimer’s Disease Diagnosis (DADD) model, a digital twin framework that extracts neurodegeneration-related digital biomarkers from non-invasive recordings such as electroencephalography (EEG), mapping early functional neural alterations to latent parameters linked to structural AD mechanisms. DADD was applied to a cohort of 459 participants with Subjective Cognitive Decline (SCD) or Mild Cognitive Impairment (MCI), evaluating its performance against established AD biomarkers like APOE genotype, magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) markers in predicting prognostic outcomes over a 10-year follow-up. Digital biomarkers demonstrated strong prognostic value, predicting participants who converted at follow-up, significantly outperforming standard EEG metrics. They also robustly forecast both risk and timing of clinical conversion, matching the performance of established AD biomarkers and even outperforming them in SCD participants. Moreover, combining established AD biomarkers with digital biomarkers led to a significant increase in prognostic accuracy. DADD represents a robust clinical tool, enabling scalable, affordable, non-invasive AD detection and prognostic stratification across diverse clinical settings.
Key findings
- Digital biomarkers demonstrated strong prognostic value, predicting participants who converted at follow-up, significantly outperforming standard EEG metrics.
- Moreover, combining established AD biomarkers with digital biomarkers led to a significant increase in prognostic accuracy.
Why it matters
This study evaluated a digital twin framework that extracts biomarkers from non-invasive EEG recordings in 459 participants with subjective cognitive decline or mild cognitive impairment over a 10-year follow-up. The digital biomarkers predicted conversion and matched or outperformed established Alzheimer disease biomarkers, and combining them with established biomarkers increased prognostic accuracy.
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Study details
- Participants459
- Evidence strengthModerate
How this study scored 39/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (npj Digital Medicine) |
| Journal's citation record | 2 / 4 | Npj Digital Medicine: journal h-index 167 (OpenAlex, 2026-10-29) |
| 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 | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 2 / 4 | 459 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Alberto Mazzoni: h-index 33 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Amsterdam UMC Location Vrije Universiteit Amsterdam: institution h-index 938 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 39 / 100 | — |
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Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Amato, L. G., Scheijbeler, E. P., van Nifterick, A. M., Lassi, M., de Haan, W., Gouw, A. A., et al. (2026). Predicting Alzheimer progression using EEG-based digital twins. npj Digital Medicine. https://doi.org/10.1038/s41746-026-02997-5
Vancouver
Amato LG, Scheijbeler EP, van Nifterick AM, Lassi M, de Haan W, Gouw AA, et al. Predicting Alzheimer progression using EEG-based digital twins. npj Digital Medicine. 2026. doi:10.1038/s41746-026-02997-5.
BibTeX
@article{amato2026-4670ee3a,
author = {Amato, Lorenzo Gaetano and Scheijbeler, Elliz P. and van Nifterick, Anne M and Lassi, Michael and de Haan, Willem and Gouw, Alida A. and Mazzoni, Alberto},
title = {{Predicting Alzheimer progression using EEG-based digital twins}},
journal = {npj Digital Medicine},
year = {2026},
doi = {10.1038/s41746-026-02997-5},
url = {https://doi.org/10.1038/s41746-026-02997-5}
}
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