Predicting whole-brain neural dynamics from prefrontal cortex fNIRS signal during movie-watching

Functional near-infrared spectroscopy (fNIRS) offers a portable, cost-effective alternative to functional magnetic resonance imaging (fMRI) for non-invasively measuring neural activity. However, fNIRS measurements are limited to cortical regions near the scalp, missing important medial and deeper brain areas. We introduce a predictive model that maps prefrontal fNIRS signals to whole-brain fMRI activity during movie-watching. By aligning neural responses to a common audiovisual stimulus, our approach leverages shared dynamics across imaging modalities to map fNIRS signals to broader neural activity patterns. We scanned participants with fNIRS and utilized a publicly available fMRI dataset of participants watching the same TV episode. The model was trained on the first half of the episode and tested on a held-out participant watching the second half to assess cross-individual and cross-stimulus generalizability. The model significantly predicted fMRI time courses in 66 out of 122 brain regions, including in areas otherwise inaccessible to fNIRS. The predicted fMRI time course also replicated intersubject functional connectivity patterns and retained semantic information about the movie content. Our publicly available model enables researchers to infer broader neural dynamics from localized fNIRS data, offering new opportunities for studying the neural basis of complex cognitive processes during naturalistic tasks.