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Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence

Nature Neuroscience volume 26, pages 1256–1266 (2023)Cite this article

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An Author Correction to this article was published on 27 July 2023

This article has been updated

Abstract

Humans require a shared conceptualization of others’ emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5–15-year-old children (n = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence.

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Fig. 1: Mean activation (model coefficients or betas) to each emotion category across both videos.
Fig. 2: Activation classification analysis results.
Fig. 3: Similarity analysis results.
Fig. 4: Group-level dynamic synchrony results for Despicable Me.
Fig. 5: Dynamic synchrony analysis results in the oldest children for Despicable Me.
Fig. 6: Qualitative examination of the scenes eliciting higher synchrony in the oldest children for each video.

Data availability

MRI data: The first nine releases of the Healthy Brain Network Biobank, an open dataset, were used in these analyses. These data are available at http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/sharing_neuro.html.

Video codes: The codes for the videos obtained using the EmoCodes system are available at https://emocodes.org/datasets/.

Code availability

Pre-processing was carried out using the Human Connectome Project minimal processing pipeline (available at https://github.com/Washington-University/HCPpipelines). Additional processing, analyses and plotting were carried out using custom scripts written in Python 3.7.2 (available at https://github.com/catcamacho/hbn_analysis) using the numpy version 1.21.6, scipy version 1.7.3, nibabel version 3.2.1 and pandas version 1.1.2 libraries. Analyses were carried out using the pliers version 0.4.1, statsmodels version 0.13.2 and scikit-learn version 0.24.2 libraries. Plotting was carried out using the seaborn version 0.11.1 and matplotlib version 3.4.2 libraries.

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Acknowledgements

We thank A. Witherspoon for assistance with video coding, the families who participated in the Healthy Brain Network study and the Child Mind Institute for sharing the data publicly. This work was funded by the National Science Foundation (DGE-1745038 to M.C.C.) and the National Institutes of Health (HD102156 to M.C.C. and MH109589 to D.M.B.).

Author information

Authors and Affiliations

  1. Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA

    M. Catalina Camacho, Joseph P. Culver, Chad M. Sylvester & Deanna M. Barch

  2. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA

    Ashley N. Nielsen, Emily Furtado, Chad M. Sylvester & Deanna M. Barch

  3. Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

    Dori Balser, David C. Steinberger, Leah Fruchtman & Deanna M. Barch

  4. Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA

    Joseph P. Culver

  5. Department of Physics, Washington University in St. Louis, St. Louis, MO, USA

    Joseph P. Culver

  6. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA

    Joseph P. Culver

  7. Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA

    Joseph P. Culver

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  1. M. Catalina Camacho
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Contributions

M.C.C.: conceptualization, methodology, software, validation, formal analysis, data curation, writing—original draft, writing—review and editing, visualization and project administration. A.N.N.: conceptualization, methodology, supervision and writing—review and editing. D.B.: conceptualization, investigation and writing—review and editing. E.F.: conceptualization, writing—original draft and writing—review and editing. D.C.S.: conceptualization, investigation and writing—review and editing. L.F.: conceptualization, investigation and writing—review and editing. J.P.C.: conceptualization, supervision and writing—review and editing. C.M.S.: conceptualization, supervision and writing—review and editing. D.M.B.: conceptualization, methodology, resources, supervision, writing—review and editing and funding acquisition.

Corresponding author

Correspondence to M. Catalina Camacho.

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Nature Neuroscience thanks Erik Nook, Heini Saarimäki and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Emotion-specific activation differences maps.

(a) Difference maps for general emotions (column minus row). Mean activation maps are shown on the diagonal. (b) Difference maps for specific emotions (column minus row). Mean activation maps are shown on the diagonal.

Extended Data Fig. 2 Parcel-wise Pearson Correlations between chronological age and activation to each emotion.

Age explained at most 3.8% of the variance in parcel-level activation (Age-Activation r2 range: Negative 0-0.017, Positive 0-0.030, Anger 0-0.020, Happy 0-0.026, Sad 0-0.023, Excited 0-0.028, Fearful 0-0.023; Puberty-Activation r2 range: Negative 0-0.029, Positive 0-0.030, Anger 0-0.024, Happy 0-0.038, Sad 0-0.014, Excited 0-0.030, Fearful 0-0.021).

Extended Data Fig. 3 Significant parcels and similarity coefficients for each model of maturation after FDR correction.

(a) Average inter-subject correlations for each parcel for each movie. Significant parcels (one-sided Pearson’s r; permutation based and FDR-corrected p < 0.05) are outlined in black. (b) Significant parcels and their coefficient magnitudes for the Convergence similarity model of maturation. (c) Significant parcels for the Nearest Neighbor and (d) Divergence models of maturation across chronological age and puberty.

Extended Data Fig. 4 Full sample dynamic analysis results for The Present.

(a) Dynamic synchrony across the full sample with replicating peaks in synchrony shaded purple. Peaks at least 5 seconds wide and with a prominence higher than the 95th percentile value for that network (permutation-based p < 0.001). Parcels were limited to those that were significantly correlated across the sample at the group level after FDR correction. (b) Video feature means within the peaks were compared to features outside of the peaks using a two-sided t-test to test if specific video features elicited increased synchronization. Plotted features were those that were significantly different (FDR-corrected p < 0.05). Bars are plotted at the mean with dots indicating each measure used in computing the mean. (C) Violin plots of mean synchrony values by network. White dots indicate median value, the box the 50% interquartile range, and the whiskers each the upper and lower 25%. The dashed horizontal line indicates the value at permuted p < 0.05 after FDR correction.

Extended Data Fig. 5 Dynamic Synchrony analysis results in the oldest children for The Present.

(a) Network dynamic activation similarity (synchrony) for each the oldest and youngest children in the sample. Included parcels are shown to the left of each trace. Shaded areas denote significant increases in synchrony (1.5 standard deviations above the mean) in the oldest children. (b) Bar plots of the mean value show the results from the video feature analysis comparing portions of the video within peaks of inter-subject synchrony to outside the peaks. Dots overlaid on the bar plots indicate each measure used in computing the mean Only features that significantly differed after FDR-correction are plotted.

Extended Data Fig. 6 Network-wise dynamic similarity plots for 3 age groups: oldest, middle, and youngest, for each movie. Included parcels are shown to the left of each trace.

Shaded areas denote significant increases in synchrony (1.5 standard deviations above the mean) in the oldest children.

Supplementary information

Supplementary Information

Supplementary Methods and Analyses (Appendix A and Appendix B) and Figures and Tables (Appendix C).

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Camacho, M.C., Nielsen, A.N., Balser, D. et al. Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence. Nat Neurosci 26, 1256–1266 (2023). https://doi.org/10.1038/s41593-023-01358-9

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