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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.


Related in: MedlinePlus

Classification of emotion category using the Bayesian Spatial Point Process model.A) A schematic of the method, which models the population density of activation across the brain with a sparse set of multivariate Gaussian distributions at two levels (study center and population center). The intensity function map summarizes the expected frequency of activation conditional on an emotion category. The model also represents the joint activation across multiple brain regions, which is not captured in the intensity map. The model can also be used for classification by calculating the conditional likelihood of each emotion category given a set of foci using Bayes’ rule. B) Confusion matrix for the 5-way classification of emotion category based on the model. Diagonal entries reflect classification accuracy. C) The intensity maps for each of the 5 emotion categories. Intensity maps are continuous over space, and their integral over any portion of the brain reflects the expected number of activation centers in that area for all studies with a particular emotion. The maps are thresholded for display at a voxel-wise intensity of 0.001 or above.
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pcbi.1004066.g001: Classification of emotion category using the Bayesian Spatial Point Process model.A) A schematic of the method, which models the population density of activation across the brain with a sparse set of multivariate Gaussian distributions at two levels (study center and population center). The intensity function map summarizes the expected frequency of activation conditional on an emotion category. The model also represents the joint activation across multiple brain regions, which is not captured in the intensity map. The model can also be used for classification by calculating the conditional likelihood of each emotion category given a set of foci using Bayes’ rule. B) Confusion matrix for the 5-way classification of emotion category based on the model. Diagonal entries reflect classification accuracy. C) The intensity maps for each of the 5 emotion categories. Intensity maps are continuous over space, and their integral over any portion of the brain reflects the expected number of activation centers in that area for all studies with a particular emotion. The maps are thresholded for display at a voxel-wise intensity of 0.001 or above.

Mentions: The model parameters—including the number and locations of population centers and spatial variation at study and peak levels—were estimated by fitting the model to peak activation coordinates from our database using Markov Chain Monte Carlo (MCMC) sampling with a generative birth-and-death algorithm for population centers. The MCMC procedure draws samples from the joint posterior distribution of the number and locations of peak activations in the brain given an emotion category. The posterior distribution is summarized in part by the intensity function map representing the spatial posterior expected number of activation or population centers in each area across the brain given the emotion category; this can be used to interpret the activation pattern characteristic of an emotion category (Fig. 1A). Since the BSPP models the joint distribution of the number and locations of a set of peak coordinates, the posterior distribution also includes information about the co-activation across voxels; thus, MCMC samples drawn from it can be used to infer on the co-activation patterns and network properties for each emotion category (discussed below).


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)

Classification of emotion category using the Bayesian Spatial Point Process model.A) A schematic of the method, which models the population density of activation across the brain with a sparse set of multivariate Gaussian distributions at two levels (study center and population center). The intensity function map summarizes the expected frequency of activation conditional on an emotion category. The model also represents the joint activation across multiple brain regions, which is not captured in the intensity map. The model can also be used for classification by calculating the conditional likelihood of each emotion category given a set of foci using Bayes’ rule. B) Confusion matrix for the 5-way classification of emotion category based on the model. Diagonal entries reflect classification accuracy. C) The intensity maps for each of the 5 emotion categories. Intensity maps are continuous over space, and their integral over any portion of the brain reflects the expected number of activation centers in that area for all studies with a particular emotion. The maps are thresholded for display at a voxel-wise intensity of 0.001 or above.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004066.g001: Classification of emotion category using the Bayesian Spatial Point Process model.A) A schematic of the method, which models the population density of activation across the brain with a sparse set of multivariate Gaussian distributions at two levels (study center and population center). The intensity function map summarizes the expected frequency of activation conditional on an emotion category. The model also represents the joint activation across multiple brain regions, which is not captured in the intensity map. The model can also be used for classification by calculating the conditional likelihood of each emotion category given a set of foci using Bayes’ rule. B) Confusion matrix for the 5-way classification of emotion category based on the model. Diagonal entries reflect classification accuracy. C) The intensity maps for each of the 5 emotion categories. Intensity maps are continuous over space, and their integral over any portion of the brain reflects the expected number of activation centers in that area for all studies with a particular emotion. The maps are thresholded for display at a voxel-wise intensity of 0.001 or above.
Mentions: The model parameters—including the number and locations of population centers and spatial variation at study and peak levels—were estimated by fitting the model to peak activation coordinates from our database using Markov Chain Monte Carlo (MCMC) sampling with a generative birth-and-death algorithm for population centers. The MCMC procedure draws samples from the joint posterior distribution of the number and locations of peak activations in the brain given an emotion category. The posterior distribution is summarized in part by the intensity function map representing the spatial posterior expected number of activation or population centers in each area across the brain given the emotion category; this can be used to interpret the activation pattern characteristic of an emotion category (Fig. 1A). Since the BSPP models the joint distribution of the number and locations of a set of peak coordinates, the posterior distribution also includes information about the co-activation across voxels; thus, MCMC samples drawn from it can be used to infer on the co-activation patterns and network properties for each emotion category (discussed below).

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