Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

Frontiers in Psychology

Hsin-Ju Lee¹  Fa-Hsuan Lin²  Iiro P. Jääskeläinen³  Wen-Jui Kuo⁴ 

[1] Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada;Sunnybrook Research Institute, Toronto, ON, Canada;Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada;Sunnybrook Research Institute, Toronto, ON, Canada;Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland;Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland;International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia;Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan;Brain Research Center, National Yang-Ming University, Taipei, Taiwan;

关键词: functional connectivity;    comedy;    movie;    networks;    humor;   

DOI  :  10.3389/fpsyg.2020.547353

来源: Frontiers

【 摘 要 】

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 the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the 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 the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

【 授权许可】

CC BY