Decoding brain basis of laughter and crying in natural scenes

Lauri Nummenmaa*, Tuulia Malèn, Sanaz Nazari-Farsani, Kerttu Seppälä, Lihua Sun, Severi Santavirta, Henry K. Karlsson, Matthew Hudson, Jussi Hirvonen, Mikko Sams, Sophie Scott, Vesa Putkinen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Laughter and crying are universal signals of prosociality and distress, respectively. Here we investigated the functional brain basis of perceiving laughter and crying using naturalistic functional magnetic resonance imaging (fMRI) approach. We measured haemodynamic brain activity evoked by laughter and crying in three experiments with 100 subjects in each. The subjects i) viewed a 20-minute medley of short video clips, and ii) 30 min of a full-length feature film, and iii) listened to 13.5 min of a radio play that all contained bursts of laughter and crying. Intensity of laughing and crying in the videos and radio play was annotated by independent observes, and the resulting time series were used to predict hemodynamic activity to laughter and crying episodes. Multivariate pattern analysis (MVPA) was used to test for regional selectivity in laughter and crying evoked activations. Laughter induced widespread activity in ventral visual cortex and superior and middle temporal and motor cortices. Crying activated thalamus, cingulate cortex along the anterior-posterior axis, insula and orbitofrontal cortex. Both laughter and crying could be decoded accurately (66–77% depending on the experiment) from the BOLD signal, and the voxels contributing most significantly to classification were in superior temporal cortex. These results suggest that perceiving laughter and crying engage distinct neural networks, whose activity suppresses each other to manage appropriate behavioral responses to others’ bonding and distress signals.

Original languageEnglish
Article number120082
Pages (from-to)1-9
Number of pages9
JournalNeuroImage
Volume273
DOIs
Publication statusPublished - Jun 2023
MoE publication typeA1 Journal article-refereed

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