@article {MoayediENEURO.0151-15.2016, author = {M. Moayedi and G. Di Stefano and M. T. Stubbs and B. Djeugam and M. Liang and G. D. Iannetti}, title = {Nociceptive-Evoked Potentials Are Sensitive to Behaviorally Relevant Stimulus Displacements in Egocentric Coordinates}, volume = {3}, number = {3}, elocation-id = {ENEURO.0151-15.2016}, year = {2016}, doi = {10.1523/ENEURO.0151-15.2016}, publisher = {Society for Neuroscience}, abstract = {Feature selection has been extensively studied in the context of goal-directed behavior, where it is heavily driven by top-down factors. A more primitive version of this function is the detection of bottom-up changes in stimulus features in the environment. Indeed, the nervous system is tuned to detect fast-rising, intense stimuli that are likely to reflect threats, such as nociceptive somatosensory stimuli. These stimuli elicit large brain potentials maximal at the scalp vertex. When elicited by nociceptive laser stimuli, these responses are labeled laser-evoked potentials (LEPs). Although it has been shown that changes in stimulus modality and increases in stimulus intensity evoke large LEPs, it has yet to be determined whether stimulus displacements affect the amplitude of the main LEP waves (N1, N2, and P2). Here, in three experiments, we identified a set of rules that the human nervous system obeys to identify changes in the spatial location of a nociceptive stimulus. We showed that the N2 wave is sensitive to: (1) large displacements between consecutive stimuli in egocentric, but not somatotopic coordinates; and (2) displacements that entail a behaviorally relevant change in the stimulus location. These findings indicate that nociceptive-evoked vertex potentials are sensitive to behaviorally relevant changes in the location of a nociceptive stimulus with respect to the body, and that the hand is a particularly behaviorally important site.}, URL = {https://www.eneuro.org/content/3/3/ENEURO.0151-15.2016}, eprint = {https://www.eneuro.org/content/3/3/ENEURO.0151-15.2016.full.pdf}, journal = {eNeuro} }