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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.


Co-activation graphs for each emotion category.A) Force-directed graphs for each emotion category, based on the Fruchterman-Reingold spring algorithm (134). The nodes (circles) are regions or networks, color-coded by anatomical system. The edges (lines) reflect co-activation between pairs of regions or networks, assessed based on the joint distribution of activation intensity in the Bayesian model (Pearson’s r across all MCMC iterations) and thresholded at P <. 05 corrected based on a permutation test. The size of each circle reflects its betweenness-centrality (48, 49), a measure of how strongly it connects disparate networks. (B) The same connections in the anatomical space of the brain. One location is depicted for each cortical network for visualization purposes, though the networks were distributed across regions (see Fig 3A). C) Global network efficiency (see refs. (135, 136)) within (diagonal elements) and between (off-diagonals) brain systems. Global efficiency (135, 136) is defined as the inverse of the average minimum path length between all members of each group of regions/nodes. Minimum path length is the minimum number of intervening nodes that must be traversed to reach one node from another, counting only paths with statistically significant associations and with distance values proportional to (2—Pearson’s r), rather than binary values, to better reflect the actual co-activation values. Higher efficiency reflects more direct relationships among the systems. Values of 0 indicates disjoint systems, with no significant co-activation paths connecting any pair of regions/networks, and values of 1 indicate the upper bound of efficiency, with a perfect association between each pair of regions. Co-activation is related to connectivity and network integration, though all fMRI-based connectivity measures only indirectly reflect actual neural connections. Efficiency is related to the average correlation among regions (r = 0.76) but not the average intensity (r = 0.02; see S5 Fig).
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pcbi.1004066.g003: Co-activation graphs for each emotion category.A) Force-directed graphs for each emotion category, based on the Fruchterman-Reingold spring algorithm (134). The nodes (circles) are regions or networks, color-coded by anatomical system. The edges (lines) reflect co-activation between pairs of regions or networks, assessed based on the joint distribution of activation intensity in the Bayesian model (Pearson’s r across all MCMC iterations) and thresholded at P <. 05 corrected based on a permutation test. The size of each circle reflects its betweenness-centrality (48, 49), a measure of how strongly it connects disparate networks. (B) The same connections in the anatomical space of the brain. One location is depicted for each cortical network for visualization purposes, though the networks were distributed across regions (see Fig 3A). C) Global network efficiency (see refs. (135, 136)) within (diagonal elements) and between (off-diagonals) brain systems. Global efficiency (135, 136) is defined as the inverse of the average minimum path length between all members of each group of regions/nodes. Minimum path length is the minimum number of intervening nodes that must be traversed to reach one node from another, counting only paths with statistically significant associations and with distance values proportional to (2—Pearson’s r), rather than binary values, to better reflect the actual co-activation values. Higher efficiency reflects more direct relationships among the systems. Values of 0 indicates disjoint systems, with no significant co-activation paths connecting any pair of regions/networks, and values of 1 indicate the upper bound of efficiency, with a perfect association between each pair of regions. Co-activation is related to connectivity and network integration, though all fMRI-based connectivity measures only indirectly reflect actual neural connections. Efficiency is related to the average correlation among regions (r = 0.76) but not the average intensity (r = 0.02; see S5 Fig).

Mentions: Fig. 3 shows that each emotion category was associated with a qualitatively different configuration of co-activation between cortical networks and subcortical brain regions. In Fig. 3A, force-directed graphs of the relationships among the 49 anatomical regions/networks demonstrate very different topological configurations for the five emotion categories. In these graphs, regions and networks are represented by circles (nodes), with significant co-activations (edges) represented as lines. The size of each circle reflects the region/network’s betweeness centrality [48,49], a graph-theoretic measure of the degree to which a region/network is a ‘connector’ of multiple other regions. Colors reflect membership in six cerebral zones: Cortex, basal ganglia, cerebellum/brainstem, thalamus, amygdala, and hippocampus. Fig. 3B shows the same graph’s relationships in anatomical brain space. Finally, Fig. 3C shows estimates of average co-activation within (diagonals) and between (off-diagonals) the six cerebral zones. The co-activation metric reflects global efficiency, based on the average shortest path length, a graph theoretic measure of the shortest path connecting the regions in the co-activation graphs, and was calculated as the average (1/path length) between pairs of regions/networks.


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)

Co-activation graphs for each emotion category.A) Force-directed graphs for each emotion category, based on the Fruchterman-Reingold spring algorithm (134). The nodes (circles) are regions or networks, color-coded by anatomical system. The edges (lines) reflect co-activation between pairs of regions or networks, assessed based on the joint distribution of activation intensity in the Bayesian model (Pearson’s r across all MCMC iterations) and thresholded at P <. 05 corrected based on a permutation test. The size of each circle reflects its betweenness-centrality (48, 49), a measure of how strongly it connects disparate networks. (B) The same connections in the anatomical space of the brain. One location is depicted for each cortical network for visualization purposes, though the networks were distributed across regions (see Fig 3A). C) Global network efficiency (see refs. (135, 136)) within (diagonal elements) and between (off-diagonals) brain systems. Global efficiency (135, 136) is defined as the inverse of the average minimum path length between all members of each group of regions/nodes. Minimum path length is the minimum number of intervening nodes that must be traversed to reach one node from another, counting only paths with statistically significant associations and with distance values proportional to (2—Pearson’s r), rather than binary values, to better reflect the actual co-activation values. Higher efficiency reflects more direct relationships among the systems. Values of 0 indicates disjoint systems, with no significant co-activation paths connecting any pair of regions/networks, and values of 1 indicate the upper bound of efficiency, with a perfect association between each pair of regions. Co-activation is related to connectivity and network integration, though all fMRI-based connectivity measures only indirectly reflect actual neural connections. Efficiency is related to the average correlation among regions (r = 0.76) but not the average intensity (r = 0.02; see S5 Fig).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4390279&req=5

pcbi.1004066.g003: Co-activation graphs for each emotion category.A) Force-directed graphs for each emotion category, based on the Fruchterman-Reingold spring algorithm (134). The nodes (circles) are regions or networks, color-coded by anatomical system. The edges (lines) reflect co-activation between pairs of regions or networks, assessed based on the joint distribution of activation intensity in the Bayesian model (Pearson’s r across all MCMC iterations) and thresholded at P <. 05 corrected based on a permutation test. The size of each circle reflects its betweenness-centrality (48, 49), a measure of how strongly it connects disparate networks. (B) The same connections in the anatomical space of the brain. One location is depicted for each cortical network for visualization purposes, though the networks were distributed across regions (see Fig 3A). C) Global network efficiency (see refs. (135, 136)) within (diagonal elements) and between (off-diagonals) brain systems. Global efficiency (135, 136) is defined as the inverse of the average minimum path length between all members of each group of regions/nodes. Minimum path length is the minimum number of intervening nodes that must be traversed to reach one node from another, counting only paths with statistically significant associations and with distance values proportional to (2—Pearson’s r), rather than binary values, to better reflect the actual co-activation values. Higher efficiency reflects more direct relationships among the systems. Values of 0 indicates disjoint systems, with no significant co-activation paths connecting any pair of regions/networks, and values of 1 indicate the upper bound of efficiency, with a perfect association between each pair of regions. Co-activation is related to connectivity and network integration, though all fMRI-based connectivity measures only indirectly reflect actual neural connections. Efficiency is related to the average correlation among regions (r = 0.76) but not the average intensity (r = 0.02; see S5 Fig).
Mentions: Fig. 3 shows that each emotion category was associated with a qualitatively different configuration of co-activation between cortical networks and subcortical brain regions. In Fig. 3A, force-directed graphs of the relationships among the 49 anatomical regions/networks demonstrate very different topological configurations for the five emotion categories. In these graphs, regions and networks are represented by circles (nodes), with significant co-activations (edges) represented as lines. The size of each circle reflects the region/network’s betweeness centrality [48,49], a graph-theoretic measure of the degree to which a region/network is a ‘connector’ of multiple other regions. Colors reflect membership in six cerebral zones: Cortex, basal ganglia, cerebellum/brainstem, thalamus, amygdala, and hippocampus. Fig. 3B shows the same graph’s relationships in anatomical brain space. Finally, Fig. 3C shows estimates of average co-activation within (diagonals) and between (off-diagonals) the six cerebral zones. The co-activation metric reflects global efficiency, based on the average shortest path length, a graph theoretic measure of the shortest path connecting the regions in the co-activation graphs, and was calculated as the average (1/path length) between pairs of regions/networks.

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.