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Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers
Alexander Y. Guo, John P Laporte, Kavita Singh, Jonghyun Bae, Keagan Bergeron, Alexandre Rouen, Noam Fox, Nathan Zhang et al.. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring, 2025-07-01
Topic: Amyloid, tau and blood-test diagnosis
MAP-RTAP (AUC = 0.78) and pTau/Aβ42 (AUC = 0.76) were the best performing individual biomarkers. Combining C-NDI derived using C-NODDI and Aβ42/Aβ40 achieved the highest performance (AUC = 0.84) and accuracy (0.84), while other combinations optimized either sensitivity (0.93) or specificity (0.88). dMRI biomarkers demonstrate comparable performance to CSF biomarkers, with notable improvements achieved when combined. This study highlights dMRI's effectiveness for enhancing early AD detection. Highlights: Advanced multishell diffusion MRI provides equivalent performance as CSF biomarkers in classifying MCICombining diffusion MRI and CSF biomarkers improves classification performanceStatistical diffusion MRI models perform best when used individually to classify MCIThe pTau/Aβ42 ratio outperforms other individual CSF biomarkers in MCI diagnosisBiophysical diffusion MRI models achieve the best performance when combined with CSF data.
Key findings
- MAP-RTAP (AUC = 0.78) and pTau/Aβ42 (AUC = 0.76) were the best performing individual biomarkers.
- Combining C-NDI derived using C-NODDI and Aβ42/Aβ40 achieved the highest performance (AUC = 0.84) and accuracy (0.84), while other combinations optimized either sensitivity (0.93) or specificity (0.88). dMRI biomarkers demonstrate comparable performance to CSF biomarkers, with notable improvements a
- This study highlights dMRI's effectiveness for enhancing early AD detection.
- Highlights: Advanced multishell diffusion MRI provides equivalent performance as CSF biomarkers in classifying MCICombining diffusion MRI and CSF biomarkers improves classification performanceStatistical diffusion MRI models perform best when used individually to classify MCIThe pTau/Aβ42 ratio outperforms other individual CSF biomarkers in MCI diagnosisBiophysical diffusion MRI models achieve the best performance when combined with CSF data.
Why it matters
Advanced diffusion MRI biomarkers performed comparably to CSF biomarkers for classifying mild cognitive impairment, and combining C-NDI with Aβ42/Aβ40 achieved the highest performance with an AUC of 0.84. This is about enhancing early detection, not treating, curing or preventing Alzheimer's disease.
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Study details
- Effect sizeAUC = 0.78; AUC = 0.76; AUC = 0.84
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 39 of the 96 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring) |
| Journal's citation record | 1 / 4 | Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring: journal h-index 86 (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 | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 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 | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | 1 / 7 | Cited 4 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.5× 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 | 1 / 4 | Cited 4 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 3 / 3 | National Institutes of Health: institution h-index 2,723 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40949843) |
| Free to read | 2 / 2 | Free to read |
| Points | 39 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 39 of the 96 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1002/dad2.70182
- PubMed: 40949843
- Original source →
- Free full text: Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring →
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Guo, A. Y., Laporte, J. P., Singh, K., Bae, J., Bergeron, K., Rouen, A., et al. (2025). Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. https://doi.org/10.1002/dad2.70182
Vancouver
Guo AY, Laporte JP, Singh K, Bae J, Bergeron K, Rouen A, et al. Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 2025. doi:10.1002/dad2.70182. PMID: 40949843.
BibTeX
@article{guo2025-40949843,
author = {Guo, Alexander Y. and Laporte, John P and Singh, Kavita and Bae, Jonghyun and Bergeron, Keagan and Rouen, Alexandre and Fox, Noam and Zhang, Nathan and others},
title = {{Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers}},
journal = {Alzheimer s \& Dementia Diagnosis Assessment \& Disease Monitoring},
year = {2025},
doi = {10.1002/dad2.70182},
pmid = {40949843},
url = {https://doi.org/10.1002/dad2.70182}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.