A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain

Abstract

Neuronal populations in the brain are engaged in a temporally coordinated manner at rest. Here we show that spontaneous transitions between large-scale resting-state networks are altered in chronic neuropathic pain. We applied an approach based on the Hidden Markov Model to magnetoencephalography data to describe how the brain moves from one activity state to another. This identified 12 fast transient (~80 ms) brain states including the sensorimotor, ascending nociceptive pathway, salience, visual, and default mode networks. Compared to healthy controls, we found that people with neuropathic pain exhibited abnormal alpha power in the right ascending nociceptive pathway state, but higher power and coherence in the sensorimotor network state in the beta band, and shorter time intervals between visits of the sensorimotor network, indicating more active time in this state. Conversely, the neuropathic pain group showed lower coherence and spent less time in the frontal attentional state. Therefore, this study reveals a temporal imbalance and dysregulation of spectral frequency-specific brain microstates in patients with neuropathic pain. These findings can potentially impact the development of a mechanism-based therapeutic approach by identifying brain targets to stimulate using neuromodulation to modify abnormal activity and to restore effective neuronal synchrony between brain states.