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Hippocampal surface analysis using discrete Ricci flow and Riemannian metrics for the diagnosis of Alzheimer's disease
Ahmadi F, Bidabad B.. Research Square, 2026-09-18
Topic: Brain imaging, connectivity and assessment
Alzheimer’s disease (AD) is a neurodegenerative disorder that is characterized by structural changes of the hippocampus, thus hippocampal morphology becomes an important biomarker for early diagnosis. We propose a geometry-aware framework for AD classification based on discrete Ricci flow and Symmetric Positive Definite (SPD) manifold learning. We conformally parameterize the hippocampus surface meshes from the MRI data using Ricci flow and retrieve three geometric descriptors, Heat Kernel Signature, Area Distortion and Conformal Factor, during the Ricci energy optimization process. These features are represented as covariance matrices, resulting in SPD descriptors per subject. To preserve intrinsic manifold structure, classification is performed by KNN with multiple SPD-aware metrics, including the Affine-Invariant Riemannian Metric (AIRM), Wasserstein Metric, Log-Euclidean Metric and so on. Experimental results on ADNI dataset show that AIRM achieves the best performance with 96\% accuracy, which outperforms other metrics evaluated. The results demonstrate that the combination of geometric analysis based on Ricci flow and manifold learning is effective for the diagnosis of Alzheimer’s disease.
Key findings
- Experimental results on ADNI dataset show that AIRM achieves the best performance with 96\% accuracy, which outperforms other metrics evaluated.
- The results demonstrate that the combination of geometric analysis based on Ricci flow and manifold learning is effective for the diagnosis of Alzheimer’s disease.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, 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 13 of the 62 points we could judge it on, which is 21 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 | 1 / 5 | Rated “low” 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 | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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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
Ahmadi, F., & Bidabad, B. (2026). Hippocampal surface analysis using discrete Ricci flow and Riemannian metrics for the diagnosis of Alzheimer's disease. Research Square. https://doi.org/10.21203/rs.3.rs-10382441/v1
Vancouver
Ahmadi F, Bidabad B. Hippocampal surface analysis using discrete Ricci flow and Riemannian metrics for the diagnosis of Alzheimer's disease. Research Square. 2026. doi:10.21203/rs.3.rs-10382441/v1.
BibTeX
@article{ahmadi2026-5d93c013,
author = {Ahmadi, F. and Bidabad, B.},
title = {{Hippocampal surface analysis using discrete Ricci flow and Riemannian metrics for the diagnosis of Alzheimer's disease}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-10382441/v1},
url = {https://doi.org/10.21203/rs.3.rs-10382441/v1}
}
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