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Diagnostic Labels and Measurement Timing Drive Systematic Inconsistency in ADNI Neuroimaging Data
Smith PJ, Richardson HW, Robertson KA, Dibble AJ, Dalby C, Svanera M.. bioRxiv, 2026-09-22
Topic: Brain imaging, connectivity and assessment
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is widely used to train machine learning models for Alzheimer’s disease, yet whether it provides consistent ground truth for predictive modeling has not been systematically tested. In this paper, we showed that ADNI contains three interacting sources of bias with direct implications for machine learning: (a) diagnostic label inconsistency, (b) technical measurement drift, and (c) longitudinal survivor bias. A substantial proportion of cases, particularly within intermediate stages, fall outside ADNI’s own diagnostic thresholds. MRI field strength and evolving processing pipelines introduce significant technical variability in hippocampal volume, while cohort survivor bias arising from differential retention of participants across phases further distorts longitudinal estimates of disease progression. These findings indicated that ADNI does not provide the stable, internally consistent labels often required in machine learning applications. We proposed a practical framework for diagnostic validation, feature harmonization, and cohort accounting, offering guidance for building more robust and biologically meaningful predictive models from large-scale neuroimaging cohorts. Graphical Abstract Summary: of the three interacting sources of bias identified in ADNI (diagnostic label inconsistency, technical measurement drift, and longitudinal survivor bias), their key quantitative findings, and corresponding mitigation strategies for machine learning pipelines.
Key findings
- In this paper, we showed that ADNI contains three interacting sources of bias with direct implications for machine learning: (a) diagnostic label inconsistency, (b) technical measurement drift, and (c) longitudinal survivor bias.
- MRI field strength and evolving processing pipelines introduce significant technical variability in hippocampal volume, while cohort survivor bias arising from differential retention of participants across phases further distorts longitudinal estimates of disease progression.
- These findings indicated that ADNI does not provide the stable, internally consistent labels often required in machine learning applications.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 35 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for sample size, effect reported, studied in people 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 22 of the 62 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| 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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| 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 | 22 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 22 of the 62 points we could judge it on is 35 out of 100. |
| Total | 35 / 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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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
Smith, P. J., Richardson, H. W., Robertson, K. A., Dibble, A. J., Dalby, C., & Svanera, M. (2026). Diagnostic Labels and Measurement Timing Drive Systematic Inconsistency in ADNI Neuroimaging Data. bioRxiv. https://doi.org/10.64898/2026.09.16.752036
Vancouver
Smith PJ, Richardson HW, Robertson KA, Dibble AJ, Dalby C, Svanera M. Diagnostic Labels and Measurement Timing Drive Systematic Inconsistency in ADNI Neuroimaging Data. bioRxiv. 2026. doi:10.64898/2026.09.16.752036.
BibTeX
@article{smith2026-132c6603,
author = {Smith, P. J. and Richardson, H. W. and Robertson, K. A. and Dibble, A. J. and Dalby, C. and Svanera, M.},
title = {{Diagnostic Labels and Measurement Timing Drive Systematic Inconsistency in ADNI Neuroimaging Data}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.09.16.752036},
url = {https://doi.org/10.64898/2026.09.16.752036}
}