Decoding Chronic Pain States from Distributed Intracranial Recordings

Abstract

Chronic pain engages distributed cortical and subcortical circuits, and large-scale intracranial recordings in humans offer a valuable opportunity to characterize its neural signatures. Here, we recorded multi-day stereoelectroencephalography (sEEG) from six participants with refractory chronic neuropathic pain, each implanted with sEEG electrodes spanning dozens of cortical and subcortical structures. Using simultaneous chronic pain ratings, we decoded spontaneous high versus low pain states within individuals (median area under the curve = 0.72; five of six participants performed above chance). Pain-predictive signals were broadly distributed and highly participant-specific. However, mapping the spatial distribution of pain-predictive features revealed preferential representation within canonical macroscale networks: beta-band activity in the default mode network and high-gamma activity in the salience network. These results demonstrate that intracranial recordings can capture distributed, network-organized representations of spontaneous chronic pain states.