The brain contains both generalized and stimulus-type-specific representations of aversive events, but models of how these are integrated and related to subjective experience are lacking. We combined functional magnetic resonance imaging with predictive modeling to identify representations of generalized (common) and stimulus-type-specific negative affect across mechanical pain, thermal pain, aversive sounds and aversive images of four intensity levels each. This allowed us to examine how generalized and stimulus-specific representations jointly contribute to aversive experience. Stimulus-type-specific negative affect was largely encoded in early sensory pathways, whereas generalized negative affect was encoded in a distributed set of midline, forebrain, insular and somatosensory regions. All models specifically predicted negative affect rather than general salience or arousal and accurately predicted negative affect in independent samples, demonstrating robustness and generalizability. Common and stimulus-type-specific models were jointly important for predicting subjective experience. Together, these findings offer an integrated account of how negative affect is constructed in the brain and provide predictive neuromarkers for future studies.
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Brain patterns generated and analyzed during the current study, as well as source data for figures are freely available via . The dataset used in study 6 is available at https://github.com/cocoanlab/interpret_ml_neuroimaging/.
Code for analysis and for generating figures is openly shared at https://github.com/canlab/2021_Ceko_MPA2_Aversive/. Analyses reported in this paper were performed using code release v1.0.1 (https://doi.org/10.5281/zenodo.6452244).
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We thank D. Ott, J. Griffin, E. Biringen and T. Wilkes for assistance with data collection; P. Gianaros for sharing data included in study 4 and R. Stark for sharing data included in study 6; and R. Botvinik-Nezer, K. Zorina-Lichtenwalter and B. Petre for helpful comments on earlier versions of the manuscript. This work was funded by NIH R01DA035484 (to T.D.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Extended Data Fig. 1 PLS-R models trained to predict normative ratings to negative (aversive) and positive (pleasant) IAPS images.
(a) PLS-R procedure to estimate brain patterns for ‘arousal’ (common across stimuli) and for stimulus type-specific outcomes (IAPS norm ratings) simultaneously (b) Behavior plots. Left: normative ratings shown for each individual stimulus (that is, IAPS image); original IAPS scales (1–9 scales for Valence (higher score = less negative / more positive; 0 is neutral) and Arousal (higher score = more arousing). Right: norm ratings averaged per bin (‘stimulus intensity level’, used for PLS-R training) and shown on a 0–4 split scale (higher score = more negative / more positive; 0 is neutral); Pattern response plots. Relationship between observed and predicted ratings. Circles reflect mean values across participants for each stimulus type, error bars reflect within-participant SEM. ‘Arousal’ model (panel 1), trained on all stimuli, significantly predicted ratings across stimulus types. Stimulus type-specific models (panels 2–3) significantly predicted ratings to target (color-matched), but not off-target stimulus type. r, mean within-participant Pearson correlation between predicted and observed ratings; two-sided P-values based on a 10,000 samples bootstrap test of within-participant r values. (c) Left: PLS-R model weight maps showing which brain areas make a reliable contribution to each model’s prediction (based on bootstrapping with 10,000 samples and displayed here at t > 3, retaining positive values). Right: Model encoding maps showing where in the brain voxel-wise activity correlates with PLS model outcomes, corrected for multiple comparisons using q < 0.05 FDR and thresholded at t > 3, retaining positive values. (d) Violin plots showing average BOLD response per stimulus intensity (x-axis) in bilateral ventral striatum (vStr) and amygdala ROIs (Supplementary Table 7), * p = 0.047, ** p = 0.002, * p < 0.001 (left panels); Mean structure coefficient values for each model, averaged across in-ROI voxels across both hemispheres, * p < 0.001, only p-values associated with positive t-values are marked and interpreted, each dot is a participant (right panels); one-sample t-test on n = 55 participants, treating participant as random effect, bars reflect mean values across participants for each stimulus type, error bars reflect within-participant SEM.(e) 3D surface maps of vStr and amy are displaying FDR-corrected model encoding maps for PLS ‘norm’ models of positive and negative images, and for the PLS model trained on participants’ ratings of negative images (Analysis 1).
Supplementary Methods, Figs. 1–4 and Tables 1–10
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Čeko, M., Kragel, P.A., Woo, CW. et al. Common and stimulus-type-specific brain representations of negative affect. Nat Neurosci 25, 760–770 (2022). https://doi.org/10.1038/s41593-022-01082-w
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