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Basic Science and Pathogenesis.

Yuhan Cui1, Dhivya Srinivasan1, Zhijian Yang1

  • 1Artificial Intelligence in Biomedical Imaging Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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Summary
This summary is machine-generated.

Deep learning identified distinct brain aging patterns, revealing genetic links to neurodegeneration. These findings offer new insights into the genetic factors influencing brain aging and Alzheimer's disease.

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Area of Science:

  • Neuroimaging
  • Genetics
  • Deep Learning

Background:

  • Deep learning models can identify heterogeneous brain aging patterns and neurodegeneration markers.
  • The genetic underpinnings of age-related brain atrophy patterns are complex and require further investigation.

Purpose of the Study:

  • To investigate the genetic associations of distinct brain aging patterns identified through deep learning.
  • To evaluate the reproducibility of previously reported associations between brain aging patterns and diagnostic groups.

Main Methods:

  • Analyzed regional brain volumes from T1 scans and whole-genome sequencing (WGS) data from the Alzheimer's Disease Sequencing Project (ADSP) (n=2401).
  • Investigated 5 established brain aging patterns (R-indices) and their association with diagnostic groups (cognitively normal vs. mild cognitive impairment/Alzheimer's disease).
  • Conducted genome-wide association analyses to identify genetic associations with R-indices, controlling for confounders.

Main Results:

  • Significant differences in brain aging patterns (R2, R3, R5) were observed between diagnostic groups.
  • Genome-wide association analyses identified 5 genes significantly associated with specific brain aging patterns (R1, R2, R3, R5).
  • Identified genes associated with R-indices have prior links to various traits including BMI, cognitive ability, cardiovascular risk factors, and blood cell measures.

Conclusions:

  • Established brain aging indices (R-indices) successfully captured structural brain aging heterogeneity in ADSP participants.
  • Replicated associations between R-indices and Alzheimer's disease groups, demonstrating model generalizability.
  • Discovered novel associations between brain aging patterns and specific genes, providing insights into neurodegeneration heterogeneity and genetic risk factors.