Stanford Researchers Create Lab Replica of Pain Pathway and Develop AI to Predict Mice Neuronal Reactions to Visual Stimuli

The Science & Technology desk compiles a weekly digest of impactful and interesting research publications and developments at Stanford. Here's the latest in this week’s Research Roundup. Researchers at Stanford have successfully created a replica of a neuronal pathway responsible for transmitting pain, known as the ascending sensory pathway. This groundbreaking achievement was detailed in a recent study published in Nature. The replica, crafted in a lab dish called an “assembloid,” allows scientists to study and manipulate the chemical activities of neurons involved in pain transmission, from peripheral ganglions—often located on hands—to the cortex. This development is significant because chronic pain affects millions of people worldwide, and current treatments like opioids are highly addictive. By understanding how the ascending sensory pathway functions and can be controlled, researchers may pave the way for more targeted and less addictive pain management therapies. Sergiu Pasca, a professor of psychiatry and behavioral sciences and the senior author of the study, highlighted the potential applications of their research. "We believe that screening for drugs that can dampen sensory organoids’ ability to trigger excessive or inappropriate neuronal transmission in our assembloid, without impacting the brain’s reward circuitry as opioids do, could lead to better and safer treatments for chronic pain," Pasca explained in an interview with Stanford Medicine. AI and Digital Twins In another notable study, also published in Nature, Stanford researchers harnessed an AI algorithm to predict mice's neuronal responses to visual stimuli. The algorithm, similar to foundational models like ChatGPT, analyzed extensive video footage of mice watching action-packed movies. This choice of stimulating content was deliberate, given that mice, like humans, favor processing motion over fine details due to their low-resolution vision. The team collected a vast dataset of neuronal activity to train their model, enabling it to act as a "digital twin" and accurately predict how mice would react to new visual images. This predictive capability would have been much harder to achieve with a smaller dataset, emphasizing the importance of large-scale data in AI research. Andreas Tolias, a senior author of the study and professor of ophthalmology, spoke about the broader implications of this work. "Our goal is to demystify the brain’s inner workings, understanding how individual neurons or groups of neurons collaborate to process information," he said to Stanford Medicine. These advancements in neuroscience and AI not only enhance our comprehension of complex biological systems but also hold promise for developing innovative solutions to pressing medical issues. The creation of assembloids and the prediction of neuronal responses using digital twins could revolutionize pain treatment and deepen our insights into brain function, respectively.