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Comparative Analysis of Individual Graph Construction Methods from Static FDG-PET Images: an ADNI study in Alzheimer’s Disease Subjects
HOROWITZ T, PHAM MT, WOJAK J, Parlak IB, Trung NL, GUEDJ E.. Research Square, 2026-09-10
Topic: Brain imaging, connectivity and assessment
Our findings indicate that all methods captured AD disconnection patterns but exhibited variability in preserving connectivity profiles, which seemed non-random but rather systematically linked to clinical measures and predictive power for AD classification. In which, the Effect Size-based (ES) method best preserves group-level graph structure (≥ 80% similarity for both CN and AD graphs), but showed the lowest predictive power for AD. PDF-based graphs, despite lower group-level similarity (≤ 70% for CN, ≤ 50% for AD), demonstrated superior AD classification performance (up to 7% gain over ES) and stronger correlations with clinical scores—particularly DTW, which showed robust positive correlations with MMSE (𝑟 = 0.48 with the first PCA component). There is no one-size-fits-all method; the choice depends on the classification task. PDF-based approaches like KLS and DTW emerged as strong candidates, with DTW excelling in AD tasks and KLS in MCI tasks. This analysis reveals the distinct strengths and limitations of each method, underscoring the essential need for careful selection aligned with specific research goals. Moreover, our findings propose a framework for optimal method choice, with broader implications for advancing both research and clinical applications.
Key findings
- Our findings indicate that all methods captured AD disconnection patterns but exhibited variability in preserving connectivity profiles, which seemed non-random but rather systematically linked to clinical measures and predictive power for AD classification.
- In which, the Effect Size-based (ES) method best preserves group-level graph structure (≥ 80% similarity for both CN and AD graphs), but showed the lowest predictive power for AD.
- PDF-based graphs, despite lower group-level similarity (≤ 70% for CN, ≤ 50% for AD), demonstrated superior AD classification performance (up to 7% gain over ES) and stronger correlations with clinical scores—particularly DTW, which showed robust positive correlations with MMSE (𝑟 = 0.48 with the first PCA component).
- There is no one-size-fits-all method; the choice depends on the classification task.
- PDF-based approaches like KLS and DTW emerged as strong candidates, with DTW excelling in AD tasks and KLS in MCI tasks.
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. |
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
HOROWITZ, T., PHAM, M. T., WOJAK, J., Parlak, I. B., Trung, N. L., & GUEDJ, E. (2026). Comparative Analysis of Individual Graph Construction Methods from Static FDG-PET Images: an ADNI study in Alzheimer’s Disease Subjects. Research Square. https://doi.org/10.21203/rs.3.rs-10905253/v1
Vancouver
HOROWITZ T, PHAM MT, WOJAK J, Parlak IB, Trung NL, GUEDJ E. Comparative Analysis of Individual Graph Construction Methods from Static FDG-PET Images: an ADNI study in Alzheimer’s Disease Subjects. Research Square. 2026. doi:10.21203/rs.3.rs-10905253/v1.
BibTeX
@article{horowitz2026-07c512f1,
author = {HOROWITZ, T. and PHAM, M. T. and WOJAK, J. and Parlak, I. B. and Trung, N. L. and GUEDJ, E.},
title = {{Comparative Analysis of Individual Graph Construction Methods from Static FDG-PET Images: an ADNI study in Alzheimer’s Disease Subjects}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-10905253/v1},
url = {https://doi.org/10.21203/rs.3.rs-10905253/v1}
}
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