Home › Research › Brain scans and imaging
Structural, Functional and Cognitive Validation of a Deep-Learning MCI-to-AD Conversion Model in OASIS-3
Fin, S.; Moayedikia, A.. medRxiv (preprint), 2026-09-10
Topic: Risk, prevention and care in population studies
BackgroundDeep-learning models predict mild cognitive impairment (MCI)-to-Alzheimers disease (AD) conversion from structural MRI with high accuracy. However, they are typically validated within a single cohort and judged on discrimination alone. Whether their risk scores are biologically grounded, and whether they generalise to independent data, remains unclear. MethodsWe applied the ADNI-trained Temporal Adaptive Fusion Network (TAF-Net), without retraining, to 101 MCI participants from OASIS-3 (25 converters, 76 stable) and tested whether its conversion-risk scores were grounded in independent structural, functional, and cognitive markers of AD. Regional atrophy rates were derived from longitudinal FreeSurfer, cognition from longitudinal MMSE and CDR-Sum-of-Boxes, and baseline resting-state functional connectivity from fMRIPrep, in structural and functional subsamples of 41 and 40 participants. Associations used rank-based statistics with false-discovery-rate correction. ResultsHigher TAF-Net risk tracked faster atrophy in medial-temporal AD-signature regions but not in AD-spared cortex -- an anatomically specific coupling that survived adjustment for global atrophy. In external validation, risk discriminated converters (AUC = 0.72), comparable to native atrophy and strongly concordant with it; atrophy statistically accounted for the models prognostic signal. Discrimination transferred but calibration did not: the two lowest tertiles of risk were assigned near-zero probability yet converted at 15%. Risk also tracked the multi-year rate of cognitive decline and, at baseline, was associated with reduced within-network functional-connectivity integrity, concentrated in salience and default-mode hubs; longitudinal functional analyses were underpowered. ConclusionsA conversion model trained on one cohort produced risk scores that, in an independent cohort, were grounded in the structural, functional, and cognitive hallmarks of Alzheimers disease -- supporting biological validity and external generalisation. The score, however, largely re-expresses the neurodegenerative substrate captured by structural atrophy. Rank ordering transferred across cohorts but absolute risk did not, so the score requires recalibration before its values can be interpreted as individual probabilities.
Key findings
- Risk also tracked the multi-year rate of cognitive decline and, at baseline, was associated with reduced within-network functional-connectivity integrity, concentrated in salience and default-mode hubs; longitudinal functional analyses were underpowered.
Why it matters
This study tested a deep-learning model that predicts conversion from mild cognitive impairment to Alzheimer's disease using MRI, and found its risk scores tracked brain atrophy, cognitive decline, and functional connectivity changes. It is a validation of a prediction model, not a treatment, cure, or prevention.
New to this subject? Start with our plain-English guide: Brain scans & tests →
Study details
- Study designlongitudinal study
- Participants40
- Effect sizeAUC = 0.72
- 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 randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 32 of the 94 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 (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| 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 | 40 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| 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 | 1 / 4 | Last-listed author Alireza Moayedikia: h-index 13 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Swinburne University of Technology: institution h-index 509 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 32 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 32 of the 94 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 →
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. Check the citation against the original before you use it.
APA 7th
Fin, S., & Moayedikia, A. (2026). Structural, Functional and Cognitive Validation of a Deep-Learning MCI-to-AD Conversion Model in OASIS-3. medRxiv (preprint). https://doi.org/10.64898/2026.09.05.26362318
Vancouver
Fin S, Moayedikia A. Structural, Functional and Cognitive Validation of a Deep-Learning MCI-to-AD Conversion Model in OASIS-3. medRxiv (preprint). 2026. doi:10.64898/2026.09.05.26362318.
BibTeX
@article{fin2026-bb8c3259,
author = {Fin, S. and Moayedikia, A.},
title = {{Structural, Functional and Cognitive Validation of a Deep-Learning MCI-to-AD Conversion Model in OASIS-3}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.09.05.26362318},
url = {https://doi.org/10.64898/2026.09.05.26362318}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 15.
Related studies in Brain scans and imaging
Also tagged “assessment”
840 studies in the library carry the topic tag “assessment”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 840 tagged studies.