Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features.

Introduction: Pain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a "black box", where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations. Method: We focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states. Results and discussion: The ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation.

Chemogenetic manipulation of parasympathetic neurons (DMV) regulates feeding behavior and energy metabolism.

Parasympathetic nervous system (PNS) innervates with several peripheral organs such as liver, pancreas and regulates energy metabolism. However, the direct role of PNS on food intake has been poorly understood. In the present study, we investigated the role of parasympathetic nervous system in regulation of feeding by chemogenetic methods. Adeno associated virus carrying DREADD (designer receptors exclusively activated by designer drugs) infused into the target brain region by stereotaxic surgery. The stimulatory hM3Dq or inhibitory hM4Di DREADD was over-expressed in selective population of dorsal motor nucleus of the vagus (DMV) neurons by Cre-recombinase-dependent manners. Activation of parasympathetic neuron by intraperitoneal injection of the M3-muscarinic receptor ligand clozapine-N-oxide (CNO) (1 mg/kg) suppressed food intake and resulted in body weight loss in ChAT-Cre mice. Parasympathetic neurons activation resulted in improved glucose tolerance while inhibition of the neurons resulted in impaired glucose tolerance. Stimulation of parasympathetic nervous system by injection of CNO (1 mg/kg) increased oxygen consumption and energy expenditure. Within the hypothalamus, in the arcuate nucleus (ARC) changed AGRP/POMC neurons. These results suggest that direct activation of parasympathetic nervous system decreases food intake and body weight with improved glucose tolerance.