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Background <p>Predicting cognitive decline from brain MRI is a central question in neuroscience. Hippocampal volume (HV) is a key cognitive biomarker, and normative models can be augmented with multimodal information. Here we augment normative models with genetic information and show improvements in cognitive decline prediction across multiple experimental setups.</p> Methods <p>We improve normative models for HV by integrating multi-threshold polygenic scores (PGS) with demographic and imaging data using Gaussian Process Regression (GPR). Models were trained on 23,997 participants from UK Biobank (UKBB) and validated on 3,000 out-of-sample participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European Prevention of Alzheimer's Disease (EPAD) cohorts.</p> Results <p>Our genetically-informed models significantly strengthened associations across six experimental designs and 13 key neurocognitive measures, including Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Alzheimer’s Disease Assessment Scale (ADAS), while enhancing prediction of future cognitive decline.</p> Conclusions <p>Together, these findings underscore the promise of integrating multi-threshold PGS with neuroimaging-based predictive models to improve prognostication and early intervention strategies for neurodegenerative diseases.</p>

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Multi-threshold polygenic risk improves hippocampal-based cognitive decline prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Mohammed Janahi,
  • Luigi Lorenzini,
  • Neil P. Oxtoby,
  • Frederik Barkhof,
  • Younes Mokrab,
  • Jonathan M. Schott,
  • Andre Altmann,
  • for the Alzheimer’s Disease Neuroimaging Initiative

摘要

Background

Predicting cognitive decline from brain MRI is a central question in neuroscience. Hippocampal volume (HV) is a key cognitive biomarker, and normative models can be augmented with multimodal information. Here we augment normative models with genetic information and show improvements in cognitive decline prediction across multiple experimental setups.

Methods

We improve normative models for HV by integrating multi-threshold polygenic scores (PGS) with demographic and imaging data using Gaussian Process Regression (GPR). Models were trained on 23,997 participants from UK Biobank (UKBB) and validated on 3,000 out-of-sample participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European Prevention of Alzheimer's Disease (EPAD) cohorts.

Results

Our genetically-informed models significantly strengthened associations across six experimental designs and 13 key neurocognitive measures, including Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Alzheimer’s Disease Assessment Scale (ADAS), while enhancing prediction of future cognitive decline.

Conclusions

Together, these findings underscore the promise of integrating multi-threshold PGS with neuroimaging-based predictive models to improve prognostication and early intervention strategies for neurodegenerative diseases.