S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

Abstract

The primary somatosensory (S1) cortex plays an important role in the perception and discrimination of touch and pain mechanosensations. Conventionally, neurons in the somatosensory system including S1 cortex have been classified into low/high threshold (HT; non-nociceptive/nociceptive) or wide dynamic range (WDR; convergent) neurons by their electrophysiological responses to innocuous brush-stroke and noxious forceps-pinch stimuli. Besides this “noxiousness” (innocuous/noxious) feature, each stimulus also includes other stimulus features: “texture” (brush hairs/forceps-steel arm), “dynamics” (dynamic stroke/static press) and “intensity” (weak/strong). However, it remains unknown how S1 neurons inclusively process such diverse features of brushing and pinch at the single-cell and population levels. Using in vivo two-photon Ca 2+ imaging in the layer 2/3 neurons of the mouse S1 cortex, we identified clearly separated response patterns of the S1 neural population with distinct tuning properties of individual cells to texture, dynamics and noxiousness features of cutaneous mechanical stimuli. Among cells other than broadly tuned neurons, the majority of the cells showed a highly selective response to the difference in texture, but low selectivity to the difference in dynamics or noxiousness. Between the two low selectivity features, the difference in dynamics was slightly more specific, yet both could be decoded using the response patterns of neural populations. In addition, more neurons are recruited and stronger Ca 2+ responses are evoked as the intensity of forceps-pinch is gradually increased. Our results suggest that S1 neurons encode various features of mechanosensations with feature-dependent differential selectivity of single cells and distributed response patterns of populations. Moreover, we raise a caution about describing neurons by a single stimulus feature ignoring other aspects of the sensory stimuli.

Multiplexed Processing of Vibrotactile Information in the Mouse Primary Somatosensory Cortex

“…All analysis protocols are consistent with those in our previous study [19]. Regions of interest (ROIs) were manually marked in circles by detecting fluorescence of individual cell bodies in the recorded time-lapse movie.…”

“…All analysis protocols are consistent with those in our previous study [19]. Regions of interest (ROIs) were traces.…”

Decoding spontaneous pain from brain cellular calcium signals using deep learning

“…1f). These negative results may not be surprising, considering that S1 neurons process touch and proprioception as well as pain 7,10 . Hence, we developed a deep learning algorithm to detect distinct features of Ca 2+ activity that represent spontaneous pain.…”

“…The signals below the 70 th percentile in each ROI were averaged and used as a baseline fluorescence signal (F0). All signals were transformed to dF/F0 in each ROI to normalize the scale range 7 . The data length was fixed to 497 frames.…”

“…The neural processing of pain information involves multiple brain areas. Among these, the primary somatosensory (S1) cortex plays a key role in the perception and discrimination of pain sensation by encoding its intensity, location, and temporal course [5][6][7] . This led us to hypothesize that neuronal Ca 2+ activity patterns in the mouse S1 cortex differ between spontaneous pain and non-pain conditions, and that spontaneous pain could be measured quantitatively based on this discrepancy.…”