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Scientific Understanding of Consciousness |
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Bayesian Model of Category-Specific Emotional Brain Responses
PLoS Computational Biology, Published: April 8, 2015 https://doi.org/10.1371/journal.pcbi.1004066 A Bayesian Model of Category-Specific Emotional Brain Responses Tor D. Wager, et.al. Department of Psychology and Neuroscience and the Institute for Cognitive Science, University of Colorado, Boulder, Colorado, United States of America Department of Biostatistics and Bioinformatics, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, United States of America Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America Department of Statistics and Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom, Functional Magnetic Resonance Imaging of the Brain (FMRIB) Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom Department of Psychology, Northeastern University, Boston, Massachusetts, United States of America Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, United States of America [paraphrase] Neuroimaging provides a unique way of understanding the ‘emotional brain’ by identifying patterns across multiple systems that imbue each instance of emotion with its particular qualities. In this meta-analysis across 148 studies, we ask whether it is possible to identify patterns that differentiate five emotion categories—fear, anger, disgust, sadness, and happiness—in a way that is consistent across studies. Our analyses support this capability, paving the way for brain markers for emotion that can be applied prospectively in new studies and individuals. In addition, we investigate the anatomical nature of the patterns that are diagnostic of emotion categories, and find that they are distributed across many brain systems associated with diverse cognitive, perceptual, and motor functions. For example, among other systems, information diagnostic of emotion category was found in both large, multi-functional cortical networks and in the thalamus, a small region composed of functionally dedicated sub-nuclei. Thus, rather than relying on measures in single regions, capturing the distinctive qualities of different types of emotional responses will require integration of measures across multiple brain systems. Beyond this broad conclusion, our results provide a foundation for specifying the precise mix of activity across systems that differentiates one emotion category from another. Our goal was to develop a brain-based model of the five most common emotion categories—fear, anger, disgust, sadness, and happiness—based on findings across studies. Developing such a model would provide a rich characterization of the ‘core’ brain activation and co-activation patterns prototypical of each emotion category, which could be used to both make inferences about the distinctive features of emotion categories and their functional similarities across the brain or in specific systems. In addition, a useful model should be able to go beyond identifying significant differences across emotion categories and provide information that is actually diagnostic of the category based on observed patterns of brain activity. From a meta-analytic database of nearly 400 neuroimaging studies (6,827 participants) on affect and emotion, we used a subset of studies (148 studies) focused on the five emotion categories mentioned above to develop an integrated, hierarchical Bayesian model of the functional brain patterns underlying them. We used this model to address two broad questions that have been of sustained interest in emotion research, and which are fundamental to the development of a more complete model of emotional experience. First, we asked whether it is possible to identify patterns of brain activity diagnostic of emotion categories across contexts and studies. Second, we asked whether emotion categories can be localized to specific brain structures or circuits, or to more broadly distributed patterns of activity across multiple systems. For many decades, scientists have searched to no avail for the brain basis of emotion categories in specific anatomical regions—e.g., fear in the amygdala, disgust in the insula, etc. The amygdala and insula are involved in fear and disgust, but are neither sufficient nor necessary for their experience. Conversely, emotions in both categories engage a much wider array of systems assumed to have cognitive, perceptual, and sensory functions [12], and damage to these systems can profoundly affect emotionality [26,27]. This multi-system view of emotion is consistent with network-based theories of the brain’s functional architecture [28,29] that have gained substantial traction in recent years. Based on these findings, we predicted that anger, sadness, fear, disgust and happiness emerge from the interactions across distributed brain networks that are not specific to emotion per se, but that subserve other basic processes, including attention, memory, action, and perception, as well as autonomic, endocrine, and metabolic regulation of the body [30,31]. Empirical support for this network approach to emotion has begun to emerge in individual experiments (e.g., [14,32–36]), and in task-independent (“resting-state”) analyses [37]. In Kassam et al. [14], for example, emotion category-related fMRI activity was widely distributed across the brain; and the same is true for recent work predicting depression from brain activity [19], again illustrating the need for a network approach. Our analysis included 148 PET and fMRI studies published from 1993 to 2011 (377 maps, 2159 participants) that attempted to specifically cultivate one of the five most commonly studied categories of emotion—happiness, fear, sadness, anger, and disgust. The studies were relatively heterogeneous in their methods for eliciting emotion (the most common were visual, auditory, imagery, and memory recall), and in the stimuli used (faces, pictures, films, words, and others). There was some covariance between emotion categories and elicitation methods (S1 Table), and we assessed the impact of this in several analyses. Studies used both male and female participants, primarily of European descent. The goal of our analysis was to test whether each emotion category has a unique signature of activity across the brain that is consistent despite varying methodological conditions (ruling out the possibility that emotion activity maps differ systematically because of method variables), providing a provisional brain ‘signature’ for each emotion category. To develop a model for emotion categories and test its accuracy in diagnosing the emotions being cultivated in specific studies, we constructed a generative, Bayesian Spatial Point Process (BSPP) model of the joint posterior distribution of peak activation locations over the brain for each emotion category (see Methods and [38]). The BSPP model is a hierarchical Bayesian representation of the joint density of the number and locations of peak activations within a study (i.e., x, y, z coordinates) given its particular emotion category. 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. We applied the BSPP model to ‘decode’ each study’s emotion category from patterns of brain activity in our meta-analytic database. Once the Bayesian model is estimated, it can be inverted in a straightforward manner to estimate the posterior probability of each emotion category given a set of brain activation coordinates (see Methods). We used Bayes rule to obtain these probabilities, assuming no prior knowledge of the base-rate of studies in each category (i.e., flat priors), and used leave-one-study-out cross-validation so that predictions were always made about studies not used to train the model. The model performed the five-way classification of emotion categories with accuracy ranging from 43% for anger to 86% for fear to (mean balanced accuracy = 66%; Fig. 1B; S1 Table); chance was 20% for all categories, and absence of bias was validated by a permutation test. The BSPP model outperformed both a Naďve Bayes classifier (mean accuracy was 35%) and a nonlinear support-vector-machine based classifier (mean accuracy was 33%; see Supplementary Methods for details), confirming its utility in distinguishing different emotion categories. The results of our BSPP model indicate that emotion categories are associated with distinct patterns of activity and co-activation distributed across the brain, such that there is a reliable brain basis for diagnosing instances of each emotion category across the variety of studies within our meta-analytic database. The brain patterns are sufficient to predict the emotion category targeted in a study with moderate to high accuracy, depending on the category, in spite of substantial heterogeneity in the paradigms, imaging methods, and subject populations used. [end of paraphrase]
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