Chronic pain is experienced by millions worldwide, with highest prevalence in older adults, leading to disability. Biomarkers of healthy brain aging, derived from machine learning models that map MRI patterns to chronological age, offer a look at these pathological changes since deviations from healthy brain aging indicate potential underlying pathologies. The Predicted Brain Age Difference (brain-PAD; predicted brain age minus chronological age), proposed as a biomarker of disease. However, because it is based on a ‘global’ brain age measure, the brain-PAD is limited to signal a ‘poorer health’ state without specifying the type of underlying pathology. This project proposes to develop novel spatially distributed brain age measures (brain age maps) able to capture the brain atrophy signature of different chronic pain conditions like chronic back pain, osteoarthritis, and neck or shoulder pain. These brain age maps will be obtained from MRIs via innovative convolutional neural networks, which will then be used to develop biomarkers specific to chronic pain types. By accomplishing these goals, this project will reveal useful information about distinct neurobiological mechanisms of different chronic pain types and their determinants (e.g., aberrant sensory testing or resting functional networks), and how brain age is associated with the multidimensional experience of pain. This could be particularly useful to understand the causes of the high chronic pain prevalence in older adults. Finally, we evaluate the brain age maps’ ability to prognosticate chronic pain chronification and pain-related functional decline. With this significant study, the applicant will boost the understanding of the neurobiological mechanisms of pain and aging.
