Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing - Lin, Fa-Hsuan; Lee, Hsin-Ju; Kuo, Wen-Jui; Jääskeläinen, Iiro P. (Frontiers in Psychology, 2021)
While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized Partial Least Squares analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during first-time and repeated watching of three ~5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including ones associated with self-rated experienced humorousness that were composed of frontal, parietal and temporal areas acting in concert. In conclusion, Partial Least Squares method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify functionally relevant brain network activity without the need for explicit temporal models.