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Multi-regional interaction among neuronal populations underlies the brain'sprocessing of rich sensory information in our daily lives. Recent neuroscienceand neuroimaging studies have increasingly used naturalistic stimuli andexperimental design to identify such realistic sensory computation in thebrain. However, existing methods for cross-areal interaction analysis withdimensionality reduction, such as reduced-rank regression and canonicalcorrelation analysis, have limited applicability and interpretability innaturalistic settings because they usually do not appropriately 'demix' neuralinteractions into those associated with different types of task parameters orstimulus features (e.g., visual or audio). In this paper, we develop a newmethod for cross-areal interaction analysis that uses the rich task or stimulusparameters to reveal how and what types of information are shared by differentneural populations. The proposed neural demixed shared component analysiscombines existing dimensionality reduction methods with a practical neuralnetwork implementation of functional analysis of variance with latentvariables, thereby efficiently demixing nonlinear effects of continuous andmultimodal stimuli. We also propose a simplifying alternative under theassumptions of linear effects and unimodal stimuli. To demonstrate our methods,we analyzed two human neuroimaging datasets of participants watchingnaturalistic videos of movies and dance movements. The results demonstrate thatour methods provide new insights into multi-regional interaction in the brainduring naturalistic sensory inputs, which cannot be captured by conventionaltechniques.

Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments