The primary somatosensory (S1) cortex plays a key role in distinguishing different sensory stimuli. Vibrotactile touch information is conveyed from the periphery to the S1 cortex through three major classes of mechanoreceptors: slowly adapting type 1 (SA1), rapidly adapting (RA), and Pacinian (PC) afferents. It has been a long-standing question whether specific populations in the S1 cortex preserve the peripheral segregation by the afferent submodalities. Here, we investigated whether S1 neurons exhibit specific responses to two distinct vibrotactile stimuli, which excite different types of mechanoreceptors (e.g., SA1 and PC afferents). Using in vivo two-photon microscopy and genetically encoded calcium indicator, GCaMP6s, we recorded calcium activities of S1 L2/3 neurons. At the same time, static (<1 Hz) and dynamic (150 Hz) vibrotactile stimuli, which are known to excite SA1 and PC, respectively, were pseudorandomly applied to the right hind paw in lightly anesthetized mice. We found that most active S1 neurons responded to both static and dynamic stimuli, but more than half of them showed preferred responses to either type of stimulus. Only a small fraction of the active neurons exhibited specific responses to either static or dynamic stimuli. However, the S1 population activity patterns by the two stimuli were markedly distinguished. These results indicate that the vibrotactile inputs driven by excitation of distinct submodalities are converged on the single cells of the S1 cortex, but are well discriminated by population activity patterns composed of neurons that have a weighted preference for each type of stimulus.

The superficial dorsal horn of the spinal cord plays an important role in pain transmission and opioid activity. Several studies have demonstrated that opioids modulate pain transmission, and the activation of µ-opioid receptors (MORs) by opioids contributes to analgesic effects in the spinal cord. However, the effect of the activation of MORs on GABAergic interneurons and the contribution to the analgesic effect are much less clear. In this study, using transgenic mice, which allow the identification of GABAergic interneurons, we investigated how the activation of MORs affects the excitability of GABAergic interneurons and synaptic transmission between primary nociceptive afferent and GABAergic interneurons. We found that a selective µ-opioid agonist, [D-Ala², NMe-Phe⁴, Gly-ol]-enkephanlin (DAMGO), induced an outward current mediated by K⁺ channels in GABAergic interneurons. In addition, DAMGO reduced the amplitude of evoked excitatory postsynaptic currents (EPSCs) of GABAergic interneurons which receive monosynaptic inputs from primary nociceptive C fibers. Taken together, we found that DAMGO reduced the excitability of GABAergic interneurons and synaptic transmission between primary nociceptive C fibers and GABAergic interneurons. These results suggest one possibility that suppression of GABAergic interneurons by DMAGO may reduce the inhibition on secondary GABAergic interneurons, which increase the inhibition of the secondary GABAergic interneurons to excitatory neurons in the spinal dorsal horn. In this circumstance, the sum of excitation of the entire spinal network will control the pain transmission.
Analgesics, Opioid ; Animals ; Enkephalin, Ala(2)-MePhe(4)-Gly(5)- ; Excitatory Postsynaptic Potentials ; GABAergic Neurons* ; Interneurons ; Mice ; Mice, Transgenic ; Nerve Fibers, Unmyelinated ; Neurons ; Spinal Cord ; Spinal Cord Dorsal Horn* ; Substantia Gelatinosa ; Synaptic Transmission

Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.
Acupuncture Points ; Acupuncture Therapy ; Humans ; Neural Networks (Computer) ; Republic of Korea ; Syndrome