  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 six months, the
randomness no longer varies with the pain task. The study demonstrates the
effectiveness of random matrix theory in differentiating between resting state
and two distinct task states within the same patient. Further, it demonstrates
that random matrix theory is effective in measuring systematic changes
occurring in functional connectivity and offers new insights on how acute and
chronic pain are processed in the brain at a network level.
