AI-Generated Motifs Distinguish Altered Spatiotemporal Pain Response in the VTA of Mice With Chronic Pain

More than one fifth of American adults lives with chronic pain. As pain chronifies, the underlying neuronal circuitry undergoes maladaptive spatial and temporal changes. We previously developed and used an advanced CMOS sensor to record video of ventral tegmental area (VTA) activity in response to acute pain and pain chronification. Here we use both discriminative and generative AI approaches to spatiotemporally characterize the VTA's complex response to pain and quantify changes in its circuit dynamics murine chronic pain models. We trained a time-attention convolutional neural network (TA-CNN) and used its gradient-weighted class activation maps (Grad-CAMs) to spatially isolate activity which differentiates pre- and post-surgical responses to stimulation. Next, we implemented an unsupervised vector quantized variational autoencoder (VQ-VAE) to learn a dense, discrete representation of the VTA's response in terms of a codebook of spatiotemporal motifs. The TA-CNN's (test set accuracy 0.787) CAMs help isolate post-surgery activity differences to the inferior segments of the VTA for partial sciatic nerve ligation (PNL) subjects but not sham subjects. The VQ-VAE (validation mean squared error 0.00732) identifies distinct spatiotemporal motifs which spatially correspond to observed VTA sub-regions. Furthermore, these motifs show changes in both spatial organization and time-response to pain after PNL but not after sham operation. These motifs also exhibit intensified spatiotemporal responses to varying intensities of mechanical stimulation in post-PNL recordings. The use of AI to fit complex space-time dynamics to an ordered latent representation or code paves the way for nuanced analysis of previously difficult-to-approach problems.