Discourse-level comprehension engages medial frontal Theory of Mind brain regions even for expository texts.

In addition to understanding individual word meanings and processing the syntactic and semantic dependencies among those words within a sentence, language comprehension often requires constructing a higher-order discourse structure based on the relationships among clauses and sentences in the extended context. Prior fMRI studies of discourse-level comprehension have reported greater activation for texts than unconnected sentences in what-appear-to-be regions of the Theory of Mind (ToM) network. However, those studies have generally used narratives rich in mental state content, thus confounding coherence and content. We report an fMRI experiment where ToM regions were defined functionally in each participant, and their responses were examined to texts vs. sentence lists. Critically, we used expository texts to minimize mental state content. Medial frontal but not posterior ToM regions exhibited small but reliable increases in their responses to texts relative to unconnected sentences, suggesting a role for these regions in discourse comprehension independent of content.

Analysis of task-based functional MRI data preprocessed with fMRIPrep.

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

Denying humanity: The distinct neural correlates of blatant dehumanization.

Recent behavioral work demonstrates that many people view low-status groups as less "evolved and civilized" than high-status groups. Are these people using blatant expressions of dehumanization simply to express strong dislike toward other groups? Or is blatant dehumanization a process distinct from other negative assessments? We tested these competing hypotheses using functional neuroimaging. Participants judged 10 groups (e.g., Europeans, Muslims, rats) on four scales: blatant dehumanization, dislike, dissimilarity and perceived within-group homogeneity. Consistent with expectations, neural responses when making ratings of dehumanization diverged from those when judging the same targets on the other related dimensions. Specifically, we found regions in the left inferior parietal cortex (IPC) and left inferior frontal cortex (IFC) that were selectively parametrically modulated by dehumanization ratings. The pattern of responses in the left IFC was also consistent with animalistic dehumanization: high responses to low-status human groups and animals, and lower responses to high-status human groups. By contrast, a region in the posterior cingulate cortex was parametrically sensitive specifically to liking. We therefore demonstrate a double dissociation between brain activity associated with judgments of blatant dehumanization and judgments of dislike. (PsycINFO Database Record

Directed network discovery with dynamic network modelling.

Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modelling technique for network discovery (Dynamic Network Modelling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modelling introducing three key changes: (1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, (2) the tests take into account the magnitude and sign of each influence, and (3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face.

Decoding task and stimulus representations in face-responsive cortex.

Observers can deliberately attend to some aspects of a face (e.g. emotional expression) while ignoring others. How do internal goals influence representational geometry in face-responsive cortex? Participants watched videos of naturalistic dynamic faces during MRI scanning. We measured multivariate neural response patterns while participants formed an intention to attend to a facial aspect (age, or emotional valence), and then attended to that aspect, and responses to the face's emotional valence, independent of attention. Distinct patterns of response to the two tasks were found while forming the intention, in left fronto-lateral but not face-responsive regions, and while attending to the face, in almost all face-responsive regions. Emotional valence was represented in right posterior superior temporal sulcus and medial prefrontal cortex, but could not be decoded when unattended. Shifting the focus of attention thus alters cortical representation of social information, probably reflecting neural flexibility to optimally integrate goals and perceptual input.

Localizing Pain Matrix and Theory of Mind networks with both verbal and non-verbal stimuli.

Functional localizer tasks allow researchers to identify brain regions in each individual's brain, using a combination of anatomical and functional constraints. In this study, we compare three social cognitive localizer tasks, designed to efficiently identify regions in the "Pain Matrix," recruited in response to a person's physical pain, and the "Theory of Mind network," recruited in response to a person's mental states (i.e. beliefs and emotions). Participants performed three tasks: first, the verbal false-belief stories task; second, a verbal task including stories describing physical pain versus emotional suffering; and third, passively viewing a non-verbal animated movie, which included segments depicting physical pain and beliefs and emotions. All three localizers were efficient in identifying replicable, stable networks in individual subjects. The consistency across tasks makes all three tasks viable localizers. Nevertheless, there were small reliable differences in the location of the regions and the pattern of activity within regions, hinting at more specific representations. The new localizers go beyond those currently available: first, they simultaneously identify two functional networks with no additional scan time, and second, the non-verbal task extends the populations in whom functional localizers can be applied. These localizers will be made publicly available.

Empathic control through coordinated interaction of amygdala, theory of mind and extended pain matrix brain regions.

Brain regions in the "pain matrix", can be activated by observing or reading about others in physical pain. In previous research, we found that reading stories about others' emotional suffering, by contrast, recruits a different group of brain regions mostly associated with thinking about others' minds. In the current study, we examined the neural circuits responsible for deliberately regulating empathic responses to others' pain and suffering. In Study 1, a sample of college-aged participants (n=18) read stories about physically painful and emotionally distressing events during functional magnetic resonance imaging (fMRI), while either actively empathizing with the main character or trying to remain objective. In Study 2, the same experiment was performed with professional social workers, who are chronically exposed to human suffering (n=21). Across both studies activity in the amygdala was associated with empathic regulation towards others' emotional pain, but not their physical pain. In addition, psychophysiological interaction (PPI) analysis and Granger causal modeling (GCM) showed that amygdala activity while reading about others' emotional pain was preceded by and positively coupled with activity in the theory of mind brain regions, and followed by and negatively coupled with activity in regions associated with physical pain and bodily sensations. Previous work has shown that the amygdala is critically involved in the deliberate control of self-focused distress - the current results extend the central importance of amygdala activity to the control of other-focused empathy, but only when considering others' emotional pain.