Characterizing Resting Brain Activity to Predict the Amplitude of Pain-Evoked Potentials in the Human Insula

How the perception of pain emerges from human brain activity remains largely unknown. Apart from inter-individual variations, this perception depends not only on the physical characteristics of the painful stimuli, but also on other psycho-physiological aspects. Indeed a painful stimulus applied to an individual can sometimes evoke very distinct sensations from one trial to the other. Hence the state of a subject receiving such a stimulus should (at least partly) explain the intensity of pain elicited by that stimulus. Using intracranial electroencephalography (iEEG) from the insula to measure this cortical "state", our goal is to study to which extent ongoing brain activity in the human insula, an area thought to play a key role in pain perception, may predict the magnitude of pain-evoked potentials and, more importantly, whether it may predict the perception intensity. To this aim, we summarize the ongoing insular activity by defining frequency-dependent features, derived using continuous wavelet and Fourier transforms. We then take advantage of this description to predict the amplitude of the insular local field potentials (LFPs) elicited by painful (heat) and non-painful (auditory, visual and vibrotactile) stimuli, as well as to predict the intensity of perception.

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Mulders, D., Liberati, G., Verleysen, M., & Mouraux, A. (2017). Characterizing Resting Brain Activity to Predict the Amplitude of Pain-Evoked Potentials in the Human Insula. The Benelearn 2017 Proceedings, 89-91. https://hdl.handle.net/2078.5/222430