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This paper introduces an independent component (IC)-based encoding framework to improve the analysis of fMRI data during story comprehension. The method decomposes fMRI data into ICs and trains encoding models to predict IC time series from large language model representations of linguistic input. Results show that a subset of ICs exhibited consistently high predictivity across subjects, corresponding to known cognitive networks, while noise-related components showed poor predictive performance.
Denoising fMRI data with independent component analysis reveals interpretable, subject-invariant cognitive networks that correlate with large language model representations of stories.
Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxelwise 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 series 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 cognitive networks known to respond during story listening (auditory and language). Auditory component time series were strongly correlated with acoustic stimulus features, highlighting the interpretability of identified component time series. 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.
