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Neuronal encoding of social constructs during natural human dialogue
Poster Session D, Saturday, September 13, 5:00 - 6:30 pm, Field House
Analise Bottinger1,2,3, Irene Caprara*1,2, Jing Cai1,2, Emery Jacobowitz3, Elizabeth Mayer3, Mohsen Jamali✝1,2, Ziv Williams✝1,2; 1Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA, 2Harvard Medical School, Boston, MA, USA, 3Northeastern University, Boston, MA, USA
Social interaction is central to the human experience, allowing for humans to navigate dynamic, and often unpredictable interactions and make meaningful connections with one another. However, due to the stochasticity of these natural interactions, understanding the neural components that constitute social communication remains a major challenge. In this study, we leveraged a unique opportunity to perform semi-chronic recordings from frontotemporal neurons (n= 251) in participants (n = 5) engaged in natural dialogue to reveal intricate cellular representations of social interactions. To address the inherent variability in this dataset, we mapped single neuronal recordings to relevant social metrics using natural language processing (NLP) techniques such as topic modeling and sentiment analysis, using these metrics to uncover trends and patterns within subsequent neuronal recordings. We then identified neurons with selective responses to these constructs and modeled their ensemble activities. By mapping neuronal activities across hundreds of interaction events, we identified neurons that reflect various aspects of conversations, including clout, authenticity, conversational dynamics, analytical content, and social dimensions. Our findings reveal how distinct neurons represent the agency of interaction and transition across agents. Collectively, these findings begin to shed light on some of the basic cellular building blocks that underlie natural social communication in humans and provide a framework for understanding social behavioral and communication disorders from a neuronal standpoint.
Topic Areas: Computational Approaches,