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.

Background Neuroimaging studies of emotion in schizophrenia have reported abnormalities in amygdala and other regions, although divergent results and heterogeneous paradigms complicate conclusions from single experiments. To identify more consistent patterns of dysfunction, a meta-analysis of functional imaging studies of emotion was undertaken. Methods Searching Medline and PsycINFO databases up through January of 2011, 88 potential articles were identified, of which 26 met inclusion criteria, comprising 450 patients with schizophrenia and 422 healthy comparison subjects. Contrasts were selected to include emotion perception and emotion experience. Foci from individual studies were subjected to a voxel-wise meta-analysis using multi-level kernel density analysis. Results For emotional experience, comparison subjects showed greater activation in the left occipital pole. For emotional perception, schizophrenia subjects showed reduced activation in bilateral amygdala, visual processing areas, anterior cingulate cortex (ACC), dorsolateral frontal cortex, medial frontal cortex and subcortical structures. Schizophrenia subjects showed greater activation in the cuneus, parietal lobule, precentral gyrus and superior temporal gyrus. Combining across studies and eliminating studies that did not balance on effort and stimulus complexity eliminated most differences in visual processing regions as well as most areas where schizophrenia subjects showed a greater signal. Reduced reactivity of the amygdala appeared primarily in implicit studies of emotion, whereas deficits in ACC activity appeared throughout all contrasts. Conclusions Processing emotional stimuli, schizophrenia patients show reduced activation in areas engaged by emotional stimuli, although in some conditions, schizophrenia patients exhibit increased activation in areas outside those traditionally associated with emotion, possibly representing compensatory processing.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.

As the discipline of functional neuroimaging grows there is an increasing interest in meta analysis of brain imaging studies. A typical neuroimaging meta analysis collects peak activation coordinates (foci) from several studies and identifies areas of consistent activation. Most imaging meta analysis methods only produce null hypothesis inferences and do not provide an interpretable fitted model. To overcome these limitations, we propose a Bayesian spatial hierarchical model using a marked independent cluster process. We model the foci as offspring of a latent study center process, and the study centers are in turn offspring of a latent population center process. The posterior intensity function of the population center process provides inference on the location of population centers, as well as the inter-study variability of foci about the population centers. We illustrate our model with a meta analysis consisting of 437 studies from 164 publications, show how two subpopulations of studies can be compared and assess our model via sensitivity analyses and simulation studies. Supplemental materials are available online.

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods.

A growing body of work suggests that sensory processes may also contribute to affective experience. In this study, we performed a meta-analysis of affective experiences driven through visual, auditory, olfactory, gustatory, and somatosensory stimulus modalities including study contrasts that compared affective stimuli to matched neutral control stimuli. We found, first, that limbic and paralimbic regions, including the amygdala, anterior insula, pre-supplementary motor area, and portions of orbitofrontal cortex were consistently engaged across two or more modalities. Second, early sensory input regions in occipital, temporal, piriform, mid-insular, and primary sensory cortex were frequently engaged during affective experiences driven by visual, auditory, olfactory, gustatory, and somatosensory inputs. A classification analysis demonstrated that the pattern of neural activity across a contrast map diagnosed the stimulus modality driving the affective experience. These findings suggest that affective experiences are constructed from activity that is distributed across limbic and paralimbic brain regions and also activity in sensory cortical regions.

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SUMMARY This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian proess prior leads to the posterior consistency for parameter estimation and variable selection for scalar-on-image regression, even when the number of predictors is larger than the sample size. The proposed method is compared to alternatives via simulation and applied to an electroen-cephalography study of alcoholism.