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MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study
Salma Hassan, Dawlat Akaila, Maryam Arjemandi, Vijay Ram Kumar Papineni, Mohammad K. Yaqub. Scientific Reports, 2025-05-06
Topic: Risk, prevention and care in population studies
In the complex realm of cognitive disorders, Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms yet requiring distinct treatment approaches. The crux of effective treatment in slowing neurodegeneration lies in early, accurate diagnosis, as this significantly assists doctors in determining the appropriate course of action. However, current diagnostic practices often delay VaD diagnosis, impeding timely intervention and adversely affecting patient prognosis. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%. The proposed method segments the longitudinal MRI scans and extracts advanced radiomics features. Subsequently, it synergistically integrates the radiomics features with an ensemble of clinical, cognitive, and genetic data to provide state-of-the-art diagnostic accuracy, setting a new benchmark in classification accuracy on a large public dataset. The paper's primary contribution is proposing a comprehensive methodology utilizing multi-omics data to provide a nuanced understanding of dementia subtypes. Additionally, the paper introduces an interpretable model to enhance clinical decision-making coupled with a novel model architecture for evaluating treatment efficacy. These advancements lay the groundwork for future work not only aimed at improving differential diagnosis but also mitigating and preventing the progression of dementia.
Key findings
- The crux of effective treatment in slowing neurodegeneration lies in early, accurate diagnosis, as this significantly assists doctors in determining the appropriate course of action.
Why it matters
This study presents a multi-omics approach to differentiate Alzheimer's disease from vascular dementia with a diagnostic accuracy of 89.25%, which could assist doctors in determining appropriate treatment. Early, accurate diagnosis is described as crucial for slowing neurodegeneration.
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Study details
- Study designlongitudinal study
- Effect size89.25%
- Evidence strengthModerate
How this study scored 51/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific Reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29) |
| 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 | 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 2 / 7 | Cited 12 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 7.6× 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 12 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Mohammad K. Yaqub: h-index 26 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Mohamed bin Zayed University of Artificial Intelligence: institution h-index 162 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40328937) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-025-97674-0
- PubMed: 40328937
- Original source →
- Free full text: Scientific Reports →
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. Check the citation against the original before you use it.
APA 7th
Hassan, S., Akaila, D., Arjemandi, M., Papineni, V. R. K., & Yaqub, M. K. (2025). MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study. Scientific Reports. https://doi.org/10.1038/s41598-025-97674-0
Vancouver
Hassan S, Akaila D, Arjemandi M, Papineni VRK, Yaqub MK. MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study. Scientific Reports. 2025. doi:10.1038/s41598-025-97674-0. PMID: 40328937.
BibTeX
@article{hassan2025-40328937,
author = {Hassan, Salma and Akaila, Dawlat and Arjemandi, Maryam and Papineni, Vijay Ram Kumar and Yaqub, Mohammad K.},
title = {{MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study}},
journal = {Scientific Reports},
year = {2025},
doi = {10.1038/s41598-025-97674-0},
pmid = {40328937},
url = {https://doi.org/10.1038/s41598-025-97674-0}
}
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