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MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI
Yao N, Wang Z, Sun D, Han C, Li Y, Nan J, Zhu F, Zhao C et al.. Research Square, 2026-10-23
Topic: Brain imaging, connectivity and assessment
Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, and early diagnosis is essential for timely intervention. Structural magnetic resonance imaging (sMRI) provides valuable information for characterizing AD-related brain alterations. However, existing sMRI-based brain graph methods often rely on single graph construction strategies, limiting their ability to capture complementary spatial and morphological relationships among brain regions. Moreover, multi-view approaches may introduce redundant features and noisy connections without effective selection and fusion mechanisms.To address these issues, we propose a Multi-View Masked Graph Neural Network (MVMGNN) for AD diagnosis using sMRI data. Brain regions are represented as graph nodes with radiomics features, and two complementary graph views are constructed based on spatial proximity and feature similarity. A joint node–edge masking mechanism is introduced to adaptively select informative feature dimensions and structural connections. In addition, a patient-level cross-view gated fusion module is developed to integrate multi-view representations. Experimental results on the ADNI dataset demonstrate that MVMGNN achieves competitive performance compared with existing methods across AD-related classification tasks. Interpretability analysis further reveals discriminative brain regions and structural connections associated with AD, providing insights into sMRI-based brain network analysis.
Key findings
- Experimental results on the ADNI dataset demonstrate that MVMGNN achieves competitive performance compared with existing methods across AD-related classification tasks.
- Interpretability analysis further reveals discriminative brain regions and structural connections associated with AD, providing insights into sMRI-based brain network analysis.
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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Preprint — not yet peer-reviewed.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. |
What each of these 20 components measures, and what this score is not →
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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
Yao, N., Wang, Z., Sun, D., Han, C., Li, Y., Nan, J., et al. (2026). MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI. Research Square. https://doi.org/10.21203/rs.3.rs-11185794/v1
Vancouver
Yao N, Wang Z, Sun D, Han C, Li Y, Nan J, et al. MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI. Research Square. 2026. doi:10.21203/rs.3.rs-11185794/v1.
BibTeX
@article{yao2026-d17fad06,
author = {Yao, N. and Wang, Z. and Sun, D. and Han, C. and Li, Y. and Nan, J. and Zhu, F. and Zhao, C. and others},
title = {{MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-11185794/v1},
url = {https://doi.org/10.21203/rs.3.rs-11185794/v1}
}