CoChat
Sign Up
Cited by 14Dementia and Cognitive Impairment ResearchRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare

MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study

Open access
Hassan, Salma, Akaila, Dawlat, Arjemandi, Maryam, Papineni, Vijay, Yaqub, Mohammad·2025·Scientific Reports
DOI 10.1038/s41598-025-97674-0PMC PMC12056088
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-025-97674-0

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The MINDSETS model integrates radiomic features from longitudinal MRI scans with polygenic risk scores, proteomics, and clinical assessments, achieving 89% diagnostic accuracy in distinguishing AD from vascular dementia.

    “The MINDSETS model exemplifies this trend by integrating radiomic features from longitudinal MRI scans with polygenic risk scores, proteomics, and clinical assessments, achieving 89% diagnostic accuracy in distinguishing AD from vascular dementia255.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [255]

    The Proposed MINDSETS approach section describes combining longitudinal MRI radiomics with clinical, genotype, and cognitive-assessment data; the Quantitative results report 88.60% accuracy for AD versus vascular dementia using all longitudinal scans and multi-omics data, which rounds to 89%. Our copy does not identify polygenic risk scores or proteomics as model inputs, so those specifics are unsupported. (LLM, conf=0.89)

    Experimental — accuracy not yet measured

  • The MINDSETS model generates individualized risk profiles and cognitive decline trajectories by linking multi-omic embeddings with longitudinal Mini-Mental State Examination scores.

    “For example, the MINDSETS model generates individualized risk profiles and cognitive decline trajectories by linking multi-omic embeddings with longitudinal Mini-Mental State Examination scores255.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [255]

    The Proposed MINDSETS approach section (page unavailable) says the model combines MRI features with clinical and genotype data and MMSE scores. The Clinical significance section says it can track disease progression over time using MRI and multi-omics data. Our copy does not say it generates individualized risk profiles or cognitive-decline trajectories by linking learned features to longitudinal MMSE scores. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Alzheimer's disease subtypes identified from ROS/MAP differ in lipid metabolism and synaptic function.

    “These subtypes differ in lipid metabolism and synaptic function, highlighting the utility of combining molecular and clinical data to capture disease heterogeneity124,255,396,397.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [255]

    The Abstract and Background > Proposed MINDSETS approach describe combining MRI features with clinical, cognitive, and genetic data to distinguish dementia diagnoses. Searches for ROS/MAP, lipid metabolism, and synaptic function found no passage reporting that Alzheimer’s subtypes differ on those dimensions. The cited paper therefore does not verify the specific subtype claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Large-scale studies such as ROS/MAP have used multi-omic data from dorsolateral prefrontal cortex tissue to identify Alzheimer's disease subtypes that exhibit distinct cognitive trajectories.

    “Large-scale studies such as ROS/MAP project have utilized multi-omic data from dorsolateral prefrontal cortex tissue to identify AD subtypes that exhibit distinct cognitive trajectories.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [255]

    The Dataset section (page not provided) describes ANMerge, with longitudinal MRI, genomic, clinical, and cognitive data. A search for ROS/MAP, dorsolateral prefrontal cortex tissue, and AD subtypes with distinct cognitive trajectories found no passage addressing the attributed study or finding. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. [17]Chouliaras, L. & O’Brien, J. T. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Mol. Psychiatry 1–14 (2023). PMC10827668 37608222
  2. [36]Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631 (2019).

Cited by

Sorted by citations

Related

Authors
Hassan, S., Akaila, D., Arjemandi, M., Papineni, V., & Yaqub, M.
Affiliations
  • Mohamed bin Zayed University of Artificial Intelligence (AE)
  • Sheikh Shakhbout Medical City (AE)
Corresponding author
Hassan, Salma
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-05-06
Volume
15
Issue
1
Pages
15835-15835
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-97674-0
PMC
PMC12056088
OpenAlex
W4410120769
Cited by
14
References
36
Citations by year
2026: 3 · 2025: 10 · 2024: 1
FWCI
8.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
Alzheimer's disease, vascular dementia, differential diagnosis, multi-omics integration, radiomics, longitudinal MRI, ensemble learning, interpretable models, treatment efficacy assessment, early diagnosis
MeSH terms
Multiomics, Aged, Alzheimer Disease, Dementia, Female, Humans, Magnetic Resonance Imaging, Male, Dementia, Vascular, Genomics, Neuroimaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review