  Multi-regional interaction among neuronal populations underlies the brain's
processing of rich sensory information in our daily lives. Recent neuroscience
and neuroimaging studies have increasingly used naturalistic stimuli and
experimental design to identify such realistic sensory computation in the
brain. However, existing methods for cross-areal interaction analysis with
dimensionality reduction, such as reduced-rank regression and canonical
correlation analysis, have limited applicability and interpretability in
naturalistic settings because they usually do not appropriately 'demix' neural
interactions into those associated with different types of task parameters or
stimulus features (e.g., visual or audio). In this paper, we develop a new
method for cross-areal interaction analysis that uses the rich task or stimulus
parameters to reveal how and what types of information are shared by different
neural populations. The proposed neural demixed shared component analysis
combines existing dimensionality reduction methods with a practical neural
network implementation of functional analysis of variance with latent
variables, thereby efficiently demixing nonlinear effects of continuous and
multimodal stimuli. We also propose a simplifying alternative under the
assumptions of linear effects and unimodal stimuli. To demonstrate our methods,
we analyzed two human neuroimaging datasets of participants watching
naturalistic videos of movies and dance movements. The results demonstrate that
our methods provide new insights into multi-regional interaction in the brain
during naturalistic sensory inputs, which cannot be captured by conventional
techniques.
