UCSF

Chronic pain disorders are among the top causes of global disability, presenting unique therapeutic challenges due to their complex pathophysiology and inherently subjective nature. Recent advances in non-invasive imaging, particularly magnetic resonance imaging (MRI), offer unprecedented opportunities to investigate the mechanisms underlying chronic pain conditions. This dissertation advances our understanding through three interconnected studies employing functional connectivity approaches: First, we develop a novel graph- theoretical model that predicts brain functional connectivity patterns from white matter structural architecture, providing insights into both healthy and diseased brain function. Second, using cross-decomposition analysis, we reveal previously uncharacterized relationships between distributed somatosensory patterns extracted from body map data and resting-state functional brain networks in chronic lower back pain patients. Finally, we extend functional connectivity principles to the knee, identifying distinct patterns of cartilage thickness change over 8 years that correlate with osteoarthritis progression and risk factors. Together, these works demonstrate the utility of connectivity-based approaches in understanding chronic pain across multiple biological scales.