Frontoparietal and Default Mode Network Contributions to Self-Referential Processing in Social Anxiety Disorder.

Social anxiety disorder (SAD) is characterized by negative self-referential processing, which triggers excessive emotional reactivity. In healthy individuals, positive self-views typically predominate and are supported by regions of the default mode network (DMN) that represent self-related information and regions of the frontoparietal control network (FPCN) that contribute to metacognitive awareness and emotion regulation. The current study used functional magnetic resonance imaging (fMRI) to examine patterns of DMN and FPCN activation during positive and negative self-referential judgments in SAD patients (N = 97) and controls (N = 34). As expected, SAD patients demonstrated a striking difference in self-beliefs compared with non-anxious healthy controls, endorsing fewer positive traits and more negative traits. However, SAD patients and controls demonstrated largely similar patterns of DMN and FPCN recruitment during self-referential judgements. No significant group differences were observed. However, equivalence testing identified numerous regions demonstrating effect sizes that were not small enough to conclude that they were practically equivalent to zero, despite the nonsignificant null hypothesis test. These regions may be key targets to investigate in future studies using larger samples.

fMRIPrep: a robust preprocessing pipeline for functional MRI.

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

The relationship between early and recent life stress and emotional expression processing: A functional connectivity study.

The aim of this study was to characterize neural activation during the processing of negative facial expressions in a non-clinical group of individuals characterized by two factors: the levels of stress experienced in early life and in adulthood. Two models of stress consequences were investigated: the match/mismatch and cumulative stress models. The match/mismatch model assumes that early adversities may promote optimal coping with similar events in the future through fostering the development of coping strategies. The cumulative stress model assumes that effects of stress are additive, regardless of the timing of the stressors. Previous studies suggested that stress can have both cumulative and match/mismatch effects on brain structure and functioning and, consequently, we hypothesized that effects on brain circuitry would be found for both models. We anticipated effects on the neural circuitry of structures engaged in face perception and emotional processing. Hence, the amygdala, fusiform face area, occipital face area, and posterior superior temporal sulcus were selected as seeds for seed-based functional connectivity analyses. The interaction between early and recent stress was related to alterations during the processing of emotional expressions mainly in to the cerebellum, middle temporal gyrus, and supramarginal gyrus. For cumulative stress levels, such alterations were observed in functional connectivity to the middle temporal gyrus, lateral occipital cortex, precuneus, precentral and postcentral gyri, anterior and posterior cingulate gyri, and Heschl's gyrus. This study adds to the growing body of literature suggesting that both the cumulative and the match/mismatch hypotheses are useful in explaining the effects of stress.

The neural bases of cognitive emotion regulation: The roles of strategy and intensity.

When confronted with unwanted negative emotions, individuals use a variety of cognitive strategies for regulating these emotions. The brain mechanisms underlying these emotion regulation strategies have not been fully characterized, and it is not yet clear whether these mechanisms vary as a function of emotion intensity. To address these issues, 30 community participants (17 females, 13 males, Mage = 24.3 years) completed a picture-viewing emotion regulation task with neutral viewing, reacting to negative stimuli, cognitive reappraisal, attentional deployment, and self-distancing conditions. Brain and behavioral data were simultaneously collected in a 3T GE MRI scanner. Findings indicated that prefrontal regions were engaged by all three regulation strategies, but reappraisal showed the least amount of increase in activity as a function of intensity. Overall, these results suggest that there are both brain and behavioral effects of intensity and that intensity is useful for probing strategy-specific effects and the relationships between the strategies. Furthermore, while these three strategies showed significant overlap, there also were specific strategy-intensity interactions, such as frontoparietal control regions being preferentially activated by reappraisal and self-distancing. Conversely, self-referential and attentional regions were preferentially recruited by self-distancing and distraction as intensity increased. Overall, these findings are consistent with the notion that there is a continuum of cognitive emotion regulation along which all three of these strategies lie.

Acceptance versus reappraisal: Behavioral, autonomic, and neural effects.

Emotion regulation (ER) is an important skill for well-being. Cognitive reappraisal is a goal-oriented cognitive change strategy. Acceptance involves decentering from immediate habits of reactivity, observing moment-to-moment shifts in thoughts, emotions, and sensations. These two regulation strategies are thought to have different effects on emotion; however, no study has examined the differential effects of reappraisal and acceptance on behavioral, autonomic, and brain responses in the context of ideographic personally salient negative self-beliefs. Thirty-five right-handed, healthy adults were presented idiographic negative self-beliefs embedded in autobiographical scripts. We measured negative emotion ratings, autonomic psychophysiology, and functional magnetic resonance imaging blood oxygen-level dependent responses while participants read neutral statements, reacted to their own negative self-beliefs, and implemented reappraisal and acceptance strategies. Compared with react, reappraisal resulted in significantly lesser negative emotion and respiration rate; no differences in heart rate and skin conductance level; greater brain responses implicated in cognitive control, language, and social cognition; and lesser amygdala responses. Compared with react, acceptance resulted in significantly lesser negative emotion, respiration rate, and heart rate; no difference in skin conductance level; and greater brain responses in networks implicated in cognitive control and attention. Compared with acceptance, reappraisal resulted in significantly lesser negative emotion; no difference in respiration rate and skin conductance level; higher heart rate; greater brain responses in brain regions implicated in cognitive control; and lesser brain responses in amygdala. Reappraisal is more effective than acceptance in down-regulating negative emotion, but may require greater recruitment of autonomic, cognitive, and brain resources. ClinicalTrials.gov identifier: NCT02036658.

Characteristics of canonical intrinsic connectivity networks across tasks and monozygotic twin pairs.

Intrinsic connectivity networks (ICNs) are becoming more prominent in the analyses of in vivo brain activity as the field of neurometrics has revealed their importance for augmenting traditional cognitive neuroscience approaches. Consequently, tools that assess the coherence, or connectivity, and morphology of ICNs are being developed to support inferences and assumptions about the dynamics of the brain. Recently, we reported trait-like profiles of ICNs showing reliability over time and reproducibility across different contexts. This study further examined the trait-like and familial nature of ICNs by utilizing two divergent task paradigms in twins. The study aimed to identify stable network phenotypes that exhibited sensitivity to individual differences and external perturbations in task demands. Analogous ICNs were detected in each task and these ICNs showed consistency in morphology and intranetwork coherence across tasks, whereas the ICN timecourse dynamics showed sensitivity to task demands. Specifically, the timecourse of an arm/hand sensorimotor network showed the strongest correlation with the timeline of a hand imitation task, and the timecourse of a language-processing network showed the strongest temporal association with a verb generation task. The area V1/simple visual stimuli network exhibited the most consistency in morphology, coherence, and timecourse dynamics within and across tasks. Similarly, this network exhibited familiality in all three domains as well. Hence, this experiment is a proof of principle that the morphology and coherence of ICNs can be consistent both within and across tasks, that ICN timecourses can be differentially and meaningfully modulated by a task, and that these domains can exhibit familiality.

Emotion Regulation in Social Anxiety Disorder: Reappraisal and Acceptance of Negative Self-beliefs.

BACKGROUND: Social anxiety disorder (SAD) is characterized by negative self-beliefs (NSBs) that are thought to maintain symptom severity-at least in part-by impairing emotion regulation. Few studies to date have investigated the neural basis of emotion regulation during NSBs in SAD. Moreover, different regulation strategies have not been directly compared, leaving open questions about the generality of emotion regulation deficits in SAD. METHODS: Patients with SAD (n = 113) and healthy control subjects (n = 35) underwent functional magnetic resonance imaging while reacting to NSBs or attempting to downregulate negative emotions occasioned by NSBs using either reappraisal (reinterpreting negative beliefs) or acceptance (nonjudgmentally experiencing thoughts and emotions). Ratings of negative emotion were collected after each trial. RESULTS: When cued to do so, patients with SAD were able to downregulate negative emotions using both reappraisal and acceptance and demonstrated effective recruitment of frontoparietal regulatory regions. Patients with SAD demonstrated greater activation of default mode network and somatomotor regions for the react versus accept contrast. Both groups demonstrated reductions in frontoparietal and default mode network activation during acceptance relative to reappraisal. Greater SAD symptom severity was associated with lower activation in frontoparietal regions during both regulation conditions. CONCLUSIONS: There were no group differences in frontoparietal recruitment during two distinct emotion regulation strategies. However, individual differences in symptom severity within the SAD group were associated with frontoparietal regulation-related activation. Patients with SAD were differentiated from control subjects in default mode network recruitment patterns, suggesting that acceptance may be a useful task condition for revealing altered neural activity in SAD.

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.

The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance.

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.