Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study

Topic: Risk, prevention and care in population studies

Accurate dementia risk prediction is challenging, and may be facilitated by better use of imaging and genetic data, including their complex interactions. We explored using deep survival neural networks to integrate these multi-modal, high-dimensional data. We included 3521 Rotterdam Study participants, 6340 magnetic resonance imaging (MRI) scans, with follow-up clinical diagnosis for dementia, and used 504 samples from Alzheimer's Disease Neuroimaging Initiative (ADNI) as an external validation. Genetic data included APOE-ε4 status and 76 additional SNPs. We developed models combining Convolutional Neural Networks (CNN) and Cox Proportional Hazards (CPH) models and provided post-hoc explanations. Our models outperformed CPH models including age, sex, and genetic inputs in both Rotterdam Study and ADNI by C-index of 0.88/0.63 V.S. 0.85/0.58, p-value of 0.02/0.002. Although their performance did not surpass CPH models also included MRI markers (0.89/0.66), additional predictability was obtained in age-stratified prediction in ADNI. Incorporating CNN image features in CPH models further increased performance to highest C-index of 0.90/0.69. Age and image had the highest importance in prediction, with age, image and genetic features showing the strongest interactions. Our approach indicates that imaging and genetic data can be feasibly integrated for dementia risk prediction, with informative extraction, reliable explanations and potential predictive gains.

Key findings

• Our approach indicates that imaging and genetic data can be feasibly integrated for dementia risk prediction, with informative extraction, reliable explanations and potential predictive gains.

Why it matters

Deep survival neural networks combining imaging and genetic data predicted dementia risk better than models using only age, sex, and genetics, but did not outperform models that also included MRI markers. The authors conclude that imaging and genetic data can feasibly be integrated for risk prediction, which is about prediction rather than treatment or prevention.

Study details

• Study designpost-hoc analysis
• Participants3,521
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-026-47047-y
• PubMed: 41041540
• 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).

@article{yu2026-41041540,
  author  = {Yu, Jing and Rosbergen, Mathijs T. and Wolters, Frank J. and Bron, Esther E. and Vernooij, Meike Willemijn and Ikram, M. Arfan and Roshchupkin, Gennady V.},
  title   = {{Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study}},
  journal = {Scientific Reports},
  year    = {2026},
  doi     = {10.1038/s41598-026-47047-y},
  pmid    = {41041540},
  url     = {https://doi.org/10.1038/s41598-026-47047-y}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

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• Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis Human brain mapping · 2018-11-01 · usefulness score 50/100
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• The impact of APOE genotype on survival: Results of 38,537 participants from six population-based cohorts (E2-CHARGE) PloS one · 2019-07-29 · usefulness score 71/100

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