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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Pain Research

Background:

  • Brain age predicted differences (brain-PAD) are reportedly larger in chronic pain patients.
  • Previous studies show conflicting results, potentially due to mixed pain type samples.
  • Methodological issues like sample size and sex effects require further investigation.

Purpose of the Study:

  • To investigate differences in brain-PAD between musculoskeletal pain types and controls.
  • To explore the utility of a novel convolutional neural network, DeepBrainNet, for brain age prediction.
  • To address methodological limitations in previous brain age prediction studies concerning chronic pain.

Main Methods:

  • Utilized DeepBrainNet, a novel convolutional neural network, for predicting brain-PAD.
  • Employed a large, multi-institutional dataset (n=660) with diverse scanners and age ranges (19-83 years).
  • Included participants with chronic low back pain (CBP), osteoarthritis (OA) pain, and healthy controls, controlling for scanner variability.

Main Results:

  • Osteoarthritis (OA) pain participants exhibited significantly higher brain-PAD (3-4.7 years) compared to chronic low back pain (CBP) and controls.
  • No significant difference in brain-PAD was observed between controls and CBP.
  • Brain-PAD was significantly associated with multidimensional aspects of the pain experience.

Conclusions:

  • This study provides evidence for distinct effects of chronic pain types on brain aging.
  • DeepBrainNet offers a robust method for brain age prediction, sensitive to neuropathological changes.
  • Findings contribute to clarifying the debate on brain aging mechanisms in chronic pain conditions.