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We use randomness as a measure to assess the impact of evoked pain on brainnetworks. Randomness is defined here as the intrinsic correlations that existbetween different brain regions when the brain is in a task-free state. We usefMRI data of three brain states in a set of back pain patients monitored over aperiod of 6 months. We find that randomness in the task-free state closelyfollows the predictions of Gaussian orthogonal ensemble of random matrices.However, the randomness decreases when the brain is engaged in attending topainful inputs in patients suffering with early stages of back pain. Apersistence of this pattern is observed in the patients that develop chronicback pain, while the patients who recover from pain after six months, therandomness no longer varies with the pain task. The study demonstrates theeffectiveness of random matrix theory in differentiating between resting stateand two distinct task states within the same patient. Further, it demonstratesthat random matrix theory is effective in measuring systematic changesoccurring in functional connectivity and offers new insights on how acute andchronic pain are processed in the brain at a network level.

Spontaneous back-pain alters randomness in functional connections in large scale brain networks: A random matrix perspective