AUTHOR=Bak Myeong Seong , Park Haney , Yoon Heera , Chung Geehoon , Shin Hyunjin , Shin Soonho , Kim Tai Wan , Lee Kyungjoon , Nägerl U. Valentin , Kim Sang Jeong , Kim Sun Kwang 

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

JOURNAL=Frontiers in Molecular Neuroscience

VOLUME=Volume 17 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2024.1356453

DOI=10.3389/fnmol.2024.1356453

ISSN=1662-5099

ABSTRACT=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. 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.