AI-Based Localized Latent Neural Representations of Acute and Chronic Pain in Rats

Chronic pain is a widespread phenomenon affecting over 21% of the United States population. Despite the significant impact of pain on a patient's quality of life, the detection and identification of pain relies on subjective methods such as self-reporting. To address the challenges in identifying and treating chronic pain, a quantifiable biomarker for pain is needed. Here we present novel AI-driven method for the identification and isolation of localized pain signals in the brain during both acute and chronic pain. By using Matching Pursuit (MP) to decompose Local Field Potential (LFP) recordings from the Anterior Cingulate Cortex, the Nucleus Accumbens, and the Prelimbic Cortex, we can learn a latent representation with a conditional variational autoencoder (CVAE) and track changes in latent signal components in response to acute, sub-chronic, and chronic pain after injury. This method allows for both the identification of LFP signal components which are the primary drivers of observed aggregate changes in brain activity during pain, as well as for the tracking of said components over time. The model achieves an average per-feature RMSE of 0.130 on validation data and produces functionally separable latent representations of input MP atoms. The combination of MP for feature extraction and CVAE for latent space development allows for the extraction of both generalized and subject-specific pain motifs involved in chronic pain. These AI-driven biomarkers provide a basis for precision identification and quantitative monitoring of pain over time.