Functional grouping and cortical–subcortical interactions in emotion: A meta-analysis of neuroimaging studies
Introduction
In recent years, the number of neuroimaging studies of emotion has risen dramatically, providing new information on how the human brain creates emotion. At the time of writing, upwards of 200 neuroimaging studies have investigated the brain correlates of emotional processing; the general approach is to induce an affective state and then identify associated patterns of reliable signal increases in the brain. Affective states are most often categorized into one of several “discrete emotions” that correspond to English words such as “anger,” “fear,” “happiness,” and “disgust” or according to broader affective dimensions, such as hedonic valence (positive/negative), arousal (high/low), or approach/withdrawal. These studies have been summarized by several meta-analyses, which have served to localize the most consistent findings across studies and their specificity for particular affective states (e.g., Murphy et al., 2003, Phan et al., 2002, Wager et al., 2003).
One important limiting factor for individual studies and meta-analyses alike is that the brain–psychological mappings that they investigate are only as reliable as the categories they employ. Whether, and under what conditions, affective states can be grouped meaningfully into discrete categories and/or dimensions is currently debated (Barrett, 2006a). Perhaps in part because of this limitation, meta-analyses have not yielded strong evidence that human-defined categories of emotion can be consistently discerned from neuroimaging studies, and there is some inconsistency on this point across meta-analyses (for a discussion, see Barrett and Wager, 2006).
In this paper, we take a fundamentally different approach: In an updated meta-analysis of 162 neuroimaging studies of emotion (1990–2005), we use multivariate analyses to identify consistent patterns of co-activation across studies – which we herein refer to as functional groups – without reference to their particular affective labels. While our analyses are still constrained by the nature of the individual studies that largely employ these affective or emotional labels, the functional groups that emerge from our analyses are not defined based on identification with particular psychological categories. Thus, we can identify the most consistently activated areas in studies of emotion and the large-scale functional groups to which they belong, even if the mappings between psychological categories and functional groups is not a simple one. This inductive (data-driven) analytic approach can identify meaningful units of analysis at both the level of brain region and brain network that are far less limited by the construct validity of psychological categories of emotion, compared to individual studies and traditional meta-analyses. Such brain-based analyses of multi-study neuroimaging datasets may ultimately help to provide a physiological basis against which to evaluate the quality of psychological taxonomies of emotion. In specific follow-up analyses, we focus on identifying frontal–subcortical pathways, particularly those involving the amygdala, periaqueductal gray (PAG) and hypothalamus (Hy), regions critical for emotion in animal models (see Table 1 for a list of abbreviations of all brain region names).
Section snippets
The need for data-driven, brain-based analyses
It has long been recognized that emotion is a complex organism-level response to a situation or stimulus. This makes defining emotion difficult, with the result that there is no single, agreed upon definition of an emotional response (e.g., Ekman and Davidson, 1994). Furthermore, many models of emotion are grounded in particular kinds of experiences (either one's own feelings or the experience of seeing someone else's behavior as emotional (for a review, see Barrett et al., 2007b). For example,
A constructivist view: Emotion as combinations of basic brain-psychological processes
This strategy is consistent with a psychological constructionist approach to emotion, where the assumption is that the mental states called “anger,” “sadness,” and “fear” result from the interplay of more basic psychological processes that may not, themselves, be specific to emotion, but may combine in various ways (recipes, if you will) to produce varied emotional and affective states (e.g., Barrett, 2006b, Barrett et al., 2007c, Russell, 2003). Just what these more basic psychologist
The present approach: methodological overview and neuroscientific questions
Using newly developed meta-analytic techniques, we first identified the brain regions most consistently activated in studies of emotion, following which we identified canonical patterns of co-occurring activations, or “functional groups.” Co-occurrence in this context means that across studies, contrasts (e.g., anger vs. neutral) that activate one particular brain region also tend to activate other regions in the group; and while these groups may be indicative of functional networks with
Study selection
To collect as many published reports as possible we employed a three-pronged approach. First, we searched peer-reviewed journals (as indexed in large databases such as MEDLINE) for English-language manuscripts of neuroimaging studies of emotion induction or emotion experience published between January 1990 and December 2005. Second, we mined the articles found by the first method for other studies by searching through their reference sections. Third, we searched publications by known emotion
Identification of suprathreshold voxels
The results of the MKDA analysis were reported earlier in an abbreviated form (Wager et al., 2008) and are discussed and interpreted below in fuller detail in the context of the functional group analysis. Panels A–C in Fig. 4 show the (unweighted) activation peaks from all the 437 CIMs plotted on the orbital, lateral, and medial surfaces on the brain (respectively). As can be seen, activations across studies are distributed throughout the cortex; to achieve significance in the MKDA analysis,
Limitations
An important limitation of our approach, shared by the meta-analytic methods most frequently applied to neuroimaging data, is that it relies on peak coordinates reported by individual studies. Importantly, the use of peak coordinates involves the assumption that peak coordinates are representative of the activation maps from which they came, which may not always be the case; indeed, activation “blobs” may not be captured in size or shape by mere peak-reporting. Further, peak coordinates may be
Conclusions
Taken as a whole, these results suggest that human neuroimaging studies of emotion activate a wide range of areas across the brain including cortical regions such as dmPFC, ACC, OFC, IFG, Ins, OCC. In addition, like animal studies, these studies reliably activate subcortical areas such as Thal, vStr Amy, PAG and Hy. PAG and Hy are of particular interest because they are often neglected in discussions of human neuroimaging studies. The functional roles of PAG and Hy in emotional responding
Acknowledgments
We would like to thank Matthew Davidson, Seth Duncan, Jennifer Mize, Heepeel Chang, and Sara Steele for assistance with the preparation of the database and other materials, and Martin Lindquist, Niall Bolger, Tom Nichols, Sam Gershman, and Bryan Denny for helpful discussion. We would also like to thank the authors of SPM at the Functional Imaging Laboratory and Caret at the Van Essen Lab for making their software freely available. This research and the preparation of this manuscript were
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