Principal component analysis reveals multiple consistent responses to naturalistic stimuli in children and adults.

Functional MRI (fMRI) study of naturalistic conditions, for example, movie watching, usually focuses on shared responses across subjects. However, individual differences have been attracting increasing attention in search of group differences or associations with behavioral outcomes. Individual differences are typically studied by directly modeling the pair-wise intersubject correlation matrix or projecting the relations onto a single dimension. We contend that it is critical to examine whether there are one or more consistent responses underlying the whole sample, because multiple components, if exist, may undermine the intersubject relations using the previous methods. We propose to use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects and project the individual variability into higher dimensions. By analyzing an fMRI dataset of children and adults watching a cartoon movie, we showed evidence of two consistent responses in the supramarginal gyrus and other regions. While the first components in many regions represented a response pattern mostly in older children and adults, the second components mainly represented the younger children. The second components in the supramarginal network resembled a delayed version of the first PCs for 4 s (2 TR), indicating slower responses in the younger children than the older children and adults. The analyses highlight the importance of identifying multiple consistent responses in responses to naturalistic stimuli. This PCA-based approach could be complementary to the commonly used intersubject correlation to analyze movie-watching data.