Significance Childhood poverty has been linked to emotion dysregulation, which is further associated with negative physical and psychological health in adulthood. The current study provides evidence of prospective associations between childhood poverty and adult neural activity during effortful attempts to regulate negative emotion. Adults with lower family income at age 9 exhibited reduced ventrolateral and dorsolateral prefrontal cortex activity and failure to suppress amygdala activation at age 24. Chronic stressor exposure across childhood mediated the relations between family income at age 9 and prefrontal cortex activity. The concurrent adult income, on the other hand, was not associated with neural activity. The information on the developmental timing of poverty effects and neural mechanisms may inform early interventions aimed at reducing health disparities.

Consistent with existing theoretical models, these results provide evidence that default network-ventral attention network interconnections are a key locus of dysfunction in ADHD. Moreover, these findings contribute to growing evidence that distributed dysconnectivity within and between large-scale networks is present in ADHD.

A recent wave of studies—over 100 conducted over the last decade—shows that exerting effort at controlling impulses or behavioral tendencies leaves a person depleted and less able to engage in subsequent rounds of regulation. Regulatory depletion is thought to play an important role in everyday problems (e.g., excessive spending, overeating) as well as psychiatric conditions, but its neurophysiological basis is poorly understood. Using a placebo-controlled, double-blind design, we demonstrate that the psychostimulant methylphenidate (commonly known as ‘Ritalin’), a catecholamine reuptake blocker that increases dopamine and norepinephrine at the synaptic cleft, fully blocks effort-induced depletion of regulatory control. Spectral analysis of trial-by-trial reaction times found specificity of methylphenidate effects on regulatory depletion in the slow-4 frequency band. This band is associated with the operation of resting state brain networks that produce mind wandering, raising potential connections between our results and recent brain network-based models of control over attention.

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations.

Individuals with generalized social anxiety disorder tend to make overly negative and distorted predictions about social events, which enhance perceptions of threat and contribute to excessive anxiety in social situations. Here, we coupled functional magnetic resonance imaging and a multiround economic exchange game ('trust game') to probe mentalizing, the social-cognitive ability to attribute mental states to others. Relative to interactions with a computer, those with human partners ('mentalizing') elicited less activation of medial prefrontal cortex in generalized social anxiety patients compared with matched healthy control participants. Diminished medial prefrontal cortex function may play a role in the social-cognitive pathophysiology of social anxiety.

Agents invariably face trade-offs between exploration, which increases informational stores and potentially opens up new opportunities, and exploitation, which utilizes existing informational stores to take advantage of known opportunities. This exploration/exploitation trade-off has been extensively studied in computer science and has been productively applied to multiple cognitive domains. In this chapter, this framework is extended to the ubiquitous alternation between two modes of serial thought: mind-wandering and goal-directed thought. The exploration/exploitation framework provides a new perspective on the functionality of mind-wandering and its pattern of regular switching with goal-directed thought. It also raises new hypotheses about the regulation of mind-wandering across time and differences in the propensity to mind-wander across individuals.

This study supports the view that stuttering is a complex neurodevelopmental disorder and provides comprehensive brain network maps that substantiate past theories emphasizing the importance of considering situational, emotional, attentional and linguistic factors in explaining the basis for stuttering onset, persistence, and recovery.

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.