Spontaneous back-pain alters randomness in functional connections in large scale brain networks

Abstract

We use randomness as a measure to assess the impact of evoked pain on brain networks. Randomness is defined here as the intrinsic correlations that exist between different brain regions when the brain is in a task-free state. We use fMRI data of three brain states in a set of back pain patients monitored over a period of 6 months. We find that randomness in the task-free state closely follows the predictions of Gaussian orthogonal ensemble of random matrices. However, the randomness decreases when the brain is engaged in attending to painful inputs in patients suffering with early stages of back pain. A persistence of this pattern is observed in the patients that develop chronic back pain, while the patients who recover from pain after 6 months, the randomness reverts back to a normal level.\n\nAuthor SummaryBack-pain is a salient percept known to affect brain regions. We studied random correlations in brain networks using random matrix theory. The brain networks were generated by fMRI scans obtained from a longitudinal back-pain study. Without modelling the neuronal interactions, we studied universal and subject-independent properties of brain networks in resting state and two distinct task states. Specifically, we hypothesized that relative to the resting state, random correlations would decrease when the brain is engaged in a task and found that the random correlations showed a maximum decrease when the brain is engaged in detecting back pain than performing a visual task.