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Deep learning-based brain age prediction in normal aging and dementia.

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A deep learning model predicts brain age using imaging, finding a larger brain age gap correlates with cognitive decline and Alzheimer's disease. This gap predicts future clinical changes, aiding neurodegenerative disease research.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Gerontology

Background:

  • Brain aging involves functional and structural changes.
  • Alzheimer's disease (AD) is associated with accelerated brain aging.
  • Understanding brain age is crucial for neurodegenerative disease research.

Purpose of the Study:

  • To develop a deep learning model for brain age prediction.
  • To investigate the relationship between brain age gap and neurodegenerative syndromes.
  • To explore the predictive power of brain age gap in clinical progression.

Main Methods:

  • Utilized fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) data.
  • Developed and applied a deep learning-based brain age prediction model.
  • Performed occlusion analysis for model interpretability.

Main Results:

  • An elevated brain age gap strongly correlated with cognitive impairment and AD biomarkers.
  • The brain age gap demonstrated longitudinal predictive capability across clinical stages.
  • Brain aging patterns, as identified by the model, varied across diagnostic groups, with the AD continuum showing similarities to normal aging.

Conclusions:

  • Deep learning-based brain age prediction is a valuable tool for assessing neurodegenerative diseases.
  • The brain age gap serves as a significant indicator of cognitive decline and disease progression.
  • Model interpretability revealed modality-specific aging patterns relevant to different neurodegenerative conditions.