Limits...
A Bayesian model of category-specific emotional brain responses.

Wager TD, Kang J, Johnson TD, Nichols TE, Satpute AB, Barrett LF - PLoS Comput. Biol. (2015)

Bottom Line: Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures.The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks.Such brain-based models of emotion provide a foundation for new translational and clinical approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology and Neuroscience and the Institute for Cognitive Science, University of Colorado, Boulder, Colorado, United States of America.

ABSTRACT
Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories--fear, anger, disgust, sadness, or happiness--is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.

No MeSH data available.


Related in: MedlinePlus

Emotion-predictive patterns of activity across cortical networks and subcortical regions.A) Left: Seven resting-state connectivity networks from the Buckner Lab with cortical, basal ganglia, and cerebellar components. Colors reflect the network membership. Right: Published anatomical parcellations were used to supplement the resting-state networks to identify sub-regions in amygdala (131), hippocampus (131, 132), and thalamus (133). dAN: dorsal attention network; Def: default mode network; FPN: fronto-parietal network; Limbic: limbic network; SMN: somatomotor network; vAN: ventral attention network; Vis: visual network. B) The profile of activation intensity across the 7 cortical and basal ganglia resting-state networks, and anatomical amygdalar and thalamic regions. Colors indicate different emotion categories, as in Fig. 1. Red: anger; green: disgust; purple: fear; yellow: happiness; blue: sadness. Values farther toward the solid circle indicate greater average intensity in the network (i.e., more expected study centers). C) Two canonical patterns estimated using non-negative matrix factorization, and the distribution of intensity values for each emotion across the two canonical patterns. The colored area shows the 95% joint confidence interval (confidence ellipsoids) derived from the 10,000 Markov chain Monte Carlo samples in the Bayesian model. Non-overlapping confidence ellipsoids indicate significant differences across categories in the expression of each profile.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4390279&req=5

pcbi.1004066.g002: Emotion-predictive patterns of activity across cortical networks and subcortical regions.A) Left: Seven resting-state connectivity networks from the Buckner Lab with cortical, basal ganglia, and cerebellar components. Colors reflect the network membership. Right: Published anatomical parcellations were used to supplement the resting-state networks to identify sub-regions in amygdala (131), hippocampus (131, 132), and thalamus (133). dAN: dorsal attention network; Def: default mode network; FPN: fronto-parietal network; Limbic: limbic network; SMN: somatomotor network; vAN: ventral attention network; Vis: visual network. B) The profile of activation intensity across the 7 cortical and basal ganglia resting-state networks, and anatomical amygdalar and thalamic regions. Colors indicate different emotion categories, as in Fig. 1. Red: anger; green: disgust; purple: fear; yellow: happiness; blue: sadness. Values farther toward the solid circle indicate greater average intensity in the network (i.e., more expected study centers). C) Two canonical patterns estimated using non-negative matrix factorization, and the distribution of intensity values for each emotion across the two canonical patterns. The colored area shows the 95% joint confidence interval (confidence ellipsoids) derived from the 10,000 Markov chain Monte Carlo samples in the Bayesian model. Non-overlapping confidence ellipsoids indicate significant differences across categories in the expression of each profile.

Mentions: To characterize the BSPP intensity maps, we calculated the mean intensity for each emotion category in 49 a priori regions and networks, which together covered the entire brain (Fig. 2A). For cortical, basal ganglia, and cerebellar networks, we used results from Buckner and colleagues [42–44], who identified seven networks with coherent resting-state connectivity across 1,000 participants. Each of the seven included a cortical network and correlated areas within the basal ganglia (BG) and cerebellum. We supplemented these networks with anatomical sub-regions within the amygdala, hippocampus, thalamus, and the brainstem and hypothalamus. We tested whether a) broad anatomical divisions (e.g., cortex, amygdala) showed different overall intensity values across the five emotion categories; and b) whether the ‘signature’ of activity across networks within each division differed significantly across emotions (S3 Table). Our broad goal, however, was not to exhaustively test all emotion differences in all regions, but to provide a broad characterization of each emotion category and which brain divisions are important in diagnosing them.


A Bayesian model of category-specific emotional brain responses.

Wager TD, Kang J, Johnson TD, Nichols TE, Satpute AB, Barrett LF - PLoS Comput. Biol. (2015)

Emotion-predictive patterns of activity across cortical networks and subcortical regions.A) Left: Seven resting-state connectivity networks from the Buckner Lab with cortical, basal ganglia, and cerebellar components. Colors reflect the network membership. Right: Published anatomical parcellations were used to supplement the resting-state networks to identify sub-regions in amygdala (131), hippocampus (131, 132), and thalamus (133). dAN: dorsal attention network; Def: default mode network; FPN: fronto-parietal network; Limbic: limbic network; SMN: somatomotor network; vAN: ventral attention network; Vis: visual network. B) The profile of activation intensity across the 7 cortical and basal ganglia resting-state networks, and anatomical amygdalar and thalamic regions. Colors indicate different emotion categories, as in Fig. 1. Red: anger; green: disgust; purple: fear; yellow: happiness; blue: sadness. Values farther toward the solid circle indicate greater average intensity in the network (i.e., more expected study centers). C) Two canonical patterns estimated using non-negative matrix factorization, and the distribution of intensity values for each emotion across the two canonical patterns. The colored area shows the 95% joint confidence interval (confidence ellipsoids) derived from the 10,000 Markov chain Monte Carlo samples in the Bayesian model. Non-overlapping confidence ellipsoids indicate significant differences across categories in the expression of each profile.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390279&req=5

pcbi.1004066.g002: Emotion-predictive patterns of activity across cortical networks and subcortical regions.A) Left: Seven resting-state connectivity networks from the Buckner Lab with cortical, basal ganglia, and cerebellar components. Colors reflect the network membership. Right: Published anatomical parcellations were used to supplement the resting-state networks to identify sub-regions in amygdala (131), hippocampus (131, 132), and thalamus (133). dAN: dorsal attention network; Def: default mode network; FPN: fronto-parietal network; Limbic: limbic network; SMN: somatomotor network; vAN: ventral attention network; Vis: visual network. B) The profile of activation intensity across the 7 cortical and basal ganglia resting-state networks, and anatomical amygdalar and thalamic regions. Colors indicate different emotion categories, as in Fig. 1. Red: anger; green: disgust; purple: fear; yellow: happiness; blue: sadness. Values farther toward the solid circle indicate greater average intensity in the network (i.e., more expected study centers). C) Two canonical patterns estimated using non-negative matrix factorization, and the distribution of intensity values for each emotion across the two canonical patterns. The colored area shows the 95% joint confidence interval (confidence ellipsoids) derived from the 10,000 Markov chain Monte Carlo samples in the Bayesian model. Non-overlapping confidence ellipsoids indicate significant differences across categories in the expression of each profile.
Mentions: To characterize the BSPP intensity maps, we calculated the mean intensity for each emotion category in 49 a priori regions and networks, which together covered the entire brain (Fig. 2A). For cortical, basal ganglia, and cerebellar networks, we used results from Buckner and colleagues [42–44], who identified seven networks with coherent resting-state connectivity across 1,000 participants. Each of the seven included a cortical network and correlated areas within the basal ganglia (BG) and cerebellum. We supplemented these networks with anatomical sub-regions within the amygdala, hippocampus, thalamus, and the brainstem and hypothalamus. We tested whether a) broad anatomical divisions (e.g., cortex, amygdala) showed different overall intensity values across the five emotion categories; and b) whether the ‘signature’ of activity across networks within each division differed significantly across emotions (S3 Table). Our broad goal, however, was not to exhaustively test all emotion differences in all regions, but to provide a broad characterization of each emotion category and which brain divisions are important in diagnosing them.

Bottom Line: Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures.The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks.Such brain-based models of emotion provide a foundation for new translational and clinical approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology and Neuroscience and the Institute for Cognitive Science, University of Colorado, Boulder, Colorado, United States of America.

ABSTRACT
Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories--fear, anger, disgust, sadness, or happiness--is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.

No MeSH data available.


Related in: MedlinePlus