Meta-analytic activation maps can help identify affective processes captured by contrast-based task fMRI: the case of threat-related facial expressions

Abstract

Meta-analysis of functional magnetic resonance imaging (fMRI) data is an effective method for capturing the distributed patterns of brain activity supporting discrete cognitive and affective processes. One opportunity presented by the resulting meta-analysis maps (MAMs) is as a reference for better understanding the nature of individual contrast maps (ICMs) derived from specific task fMRI data. Here, we compared MAMs from 148 neuroimaging studies representing the broad emotion categories of fear, anger, disgust, happiness, and sadness with ICMs from fearful > neutral and angry > neutral facial expressions from an independent dataset of task fMRI (n = 1263). Analyses revealed that both fear and anger ICMs exhibited the greatest pattern similarity to fear MAMs. As the number of voxels included for the computation of pattern similarity became more selective, the specificity of MAM-ICM correspondence decreased. Notably, amygdala activity long considered critical for processing threat-related facial expressions was neither sufficient nor necessary for detecting MAM-ICM pattern similarity effects. Our analyses suggest that both fearful and angry facial expressions are best captured by distributed patterns of brain activity associated with fear. More generally, our analyses demonstrate how MAMs can be leveraged to better understand affective processes captured by ICMs in task fMRI data.

Neural indicators of food cue reactivity, regulation, and valuation and their associations with body composition and daily eating behavior

“…To complement these univariate regions of interest (ROIs), we leveraged whole-brain patterns associated with reactivity, regulation, and valuation, and calculated individual expression of these patterns. Distributed patterns of neural activity associated with target psychological processes may be more sensitive than univariate indicators (Chang et al, 2015), and this approach has been shown to reliably index people's conscious decisions to regulate negative emotions (Doré et al, 2017), as well as capture negative affective states during a threat processing task (Kim et al, 2019). Another study showed that whole-brain patterns of activation that more closely resembled a meta-analytic map of cognitive reappraisal (Buhle et al, 2014) during exposure to emotionally evocative stimuli were linked with lower stress during the task, even when participants were not instructed to regulate (Shahane et al, 2019).…”

Neural indicators of food cue reactivity, regulation, and valuation and their associations with body composition and daily eating behavior

“…To complement these univariate regions of interest (ROIs), we leveraged whole-brain patterns associated with reactivity, regulation, and valuation and calculated the individual expression of these patterns. Distributed patterns of neural activity associated with target psychological processes may be more sensitive than univariate indicators ( Chang et al , 2015 ), and this approach has been shown to reliably index people’s conscious decisions to regulate negative emotions ( Doré et al , 2017 ) as well as capture negative affective states during a threat-processing task ( Kim et al , 2019 ). Another study showed that whole-brain patterns of activation that more closely resembled a meta-analytic map of cognitive reappraisal ( Buhle et al , 2014 ) during exposure to emotionally evocative stimuli were linked with lower stress during the task, even when participants were not instructed to regulate ( Shahane et al , 2019 ).…”