Chronic pain as a state-constrained brain network disorder: a dynamical systems model integrating physiological...

Chronic pain is increasingly understood as a disorder of large-scale brain network organisation rather than a direct consequence of persistent nociceptive input. Neuroimaging studies have consistently identified alterations in the salience network (SN), default mode network (DMN), and central executive network (CEN), suggesting that persistent pain involves disturbances in the dynamic coordination of networks that integrate bodily signals, cognition, and self-related processing. However, prevailing models largely interpret these findings as intrinsic network dysfunction and provide limited explanation for the persistence and variability of pain states. In this narrative review, we propose a state-constrained triple-network model of self-organisation in which interactions among the SN, DMN, and CEN are continuously shaped by organism-level physiological regulation, including sleep organisation, autonomic balance, and interoceptive signalling. Within this framework, chronic pain may emerge when disturbances in physiological regulation bias salience processing toward bodily threat signals and stabilise network configurations that reinforce self-referential pain processing. We further interpret these dynamics within a dynamical systems framework in which persistent pain reflects a stable attractor configuration of triple-network interactions maintained by physiological regulatory constraints. Neuromodulatory interventions such as repetitive transcranial magnetic stimulation (rTMS) may act as perturbations capable of increasing network flexibility, while durable recovery may require restoration of physiological regulation and experiential integration. This framework links network neuroscience, physiological regulation, and the phenomenology of self-experience, providing a multilevel model for understanding the persistence of chronic pain and informing future approaches to integrated treatment.