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Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension

Kamya Hari¹, Taha Osama A Binhuraib¹, Jin Li¹, Cory Shain², Anna A Ivanova¹; ¹Georgia Institute of Technology, ²Stanford University

Presenter: Anna A Ivanova

Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxel-wise approaches are limited by measurement noise, inter-subject variability, and redundancy arising from spatially correlated voxels encoding overlapping neural signals. Here, we propose an independent component (IC) based encoding framework that dissociates stimulus-driven and noise-driven signals in fMRI data. We decompose continuous fMRI data from naturalistic story listening into ICs using one subset of the data, and train encoding models on independent data to predict IC time courses from large language model representations of linguistic input. Across subjects, a subset of ICs exhibited consistently high predictivity. These ICs were spatially and temporally consistent across subjects and included brain networks known to respond during story listening (auditory and language). The auditory component time courses were strongly correlated with acoustic stimulus features, highlighting the interpretability of identified component time courses. Components identified as noise or motion-related artifacts by ICA-AROMA showed uniformly poor predictive performance, confirming that highly predicted components reflect genuine stimulus-related neural signals rather than confounds. Overall, IC-based encoding models enable analyses at the level of functional networks, accommodating the variability in network locations across individuals and providing interpretable results that are easy to compare across subjects. Code provided at: https://github.com/kamyahari/IC-Encoding-Models.git

Topic Area: Methods, Tools, Theory & Neural Coding