AUTHOR=Xiao Zhengdong , Martinez Erik , Kulkarni Prathamesh M. , Zhang Qiaosheng , Hou Qianning , Rosenberg David , Talay Robert , Shalot Leor , Zhou Haocheng , Wang Jing , Chen Zhe Sage 

TITLE=Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

JOURNAL=Frontiers in Cellular Neuroscience

VOLUME=13

YEAR=2019

URL=https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2019.00165

DOI=10.3389/fncel.2019.00165

ISSN=1662-5102

ABSTRACT=<p>Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded <italic>in vivo</italic> extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (<italic>n</italic> = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (<italic>n</italic> = 10 sessions), in both evoked (<italic>p</italic> = 0.058) and spontaneous pain-like behaviors (<italic>p</italic> = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.</p>