Approximation to pain-signaling network in humans by means of migraine

Abstract

Nociceptive signals are processed within a pain-related network of the brain. Migraine is a rather specific model to gain insight into this system. Brain networks may be described by white matter tracts interconnecting functionally defined gray matter regions. Here, we present an overview of the migraine-related pain network revealed by this strategy. Based on diffusion tensor imaging data from subjects in the Human Connectome Project (HCP) database, we used a global tractography approach to reconstruct white matter tracts connecting brain regions that are known to be involved in migraine-related pain signaling. This network includes an ascending nociceptive pathway, a descending modulatory pathway, a cortical processing system, and a connection between pain-processing and modulatory areas. The insular cortex emerged as the central interface of this network. Direct connections to visual and auditory cortical association fields suggest a potential neural basis of phono- or photophobia and aura phenomena. The intra-axonal volume (Vintra) as a measure of fiber integrity based on diffusion microstructure was extracted using an innovative supervised machine learning approach in form of a Bayesian estimator. Self-reported pain levels of HCP subjects were positively correlated with tract integrity in subcortical tracts. No correlation with pain was found for the cortical processing systems.