The regulation of cognition and emotion is thought to depend on the top-down modulation of multiple neural circuits by the prefrontal cortex and, in particular, the dorsolateral prefrontal cortex (DLPFC).^3-7 Prefrontal-dependent cognitive control mechanisms that regulate attention and mood likely play a key role in mental health.^7,8 There is ample evidence of attenuation or failure of top-down control mechanisms in adults with depression,^9-11 anxiety,¹² and attention-deficit/hyperactivity disorder (ADHD).¹³ Given that these prevalent mental health problems often emerge during childhood and adolescence,^14-16 it is important to know whether dysregulated top-down control can be detected even before behavioral symptoms are evident.

We present a Human Connectome Project study tailored toward adolescent anxiety and depression. This study is one of the first studies of the Connectomes Related to Human Diseases initiative and is collecting structural, functional, and diffusion-weighted brain imaging data from up to 225 adolescents (ages 14-17 years), 150 of whom are expected to have a current diagnosis of an anxiety and/or depressive disorder. Comprehensive clinical and neuropsychological evaluations and longitudinal clinical data are also being collected. This article provides an overview of task functional magnetic resonance imaging (fMRI) protocols and preliminary findings (N = 140), as well as clinical and neuropsychological characterization of adolescents. Data collection is ongoing for an additional 85 adolescents, most of whom are expected to have a diagnosis of an anxiety and/or depressive disorder. Data from the first 140 adolescents are projected for public release through the National Institutes of Health Data Archive (NDA) with the timing of this manuscript. All other data will be made publicly-available through the NDA at regularly scheduled intervals. This article is intended to serve as an introduction to this project as well as a reference for those seeking to clinical, neurocognitive, and task fMRI data from this public resource.

Scholastic performance is the key metric by which schools measure student’s academic success, and it is important to understand the neural-correlates associated with greater scholastic performance. This study examines resting-state functional connectivity (RsFc) associated with scholastic performance (reading and mathematics) in preadolescent children (7–9 years) using an unbiased whole-brain connectome-wide multi-voxel pattern analysis (MVPA). MVPA revealed four clusters associated with reading composite score, these clusters were then used for whole-brain seed-based RsFc analysis. However, no such clusters were found for mathematics composite score. Post hoc analysis found robust associations between reading and RsFc dynamics with areas involved with the somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. These findings indicate that reading ability may be associated with a wide range of RsFc networks. Of particular interest, anticorrelations were observed between the default mode network and the somatomotor, dorsal attention, ventral attention, and frontoparietal networks. Previous research has demonstrated the importance of anticorrelations between the default mode network and frontoparietal network associated with cognition. These results extend the current literature exploring the role of network connectivity in scholastic performance of children.

Background: There is burgeoning interest in the application of neuroscientific technology to facilitate meditation and lead to beneficial psychological outcomes. One popular approach is using consumer-grade neurofeedback devices to deliver feedback on brain targets during meditation (mindfulness-based neurofeedback). It is hypothesized that optimizing brain targets like alpha and theta band activity may allow meditators to experience deeper mindfulness and thus beneficial outcomes. Objective: This study aimed to systematically review and meta-analyze the impacts of consumer-grade mindfulness-based neurofeedback compared with control conditions. Included studies involved mindfulness practice operationalized as open monitoring or focused attention meditation. This study was preregistered. Methods: A total of 16 randomized controlled training trials, as well as 5 randomized within-participant designs were included, encompassing 763 and 167 unique participants, respectively. Effects were categorized outcomes (ie, psychological distress, cognitive function, and physiological health) and process variables (ie, state mindfulness and brain measures). Study risk of bias, reporting bias, and publication bias were assessed. Results: Samples were typically small (n=30-50), and the majority of studies used mindfulness apps as controls. To deliver neurofeedback, most studies used the Muse device (11/16 randomized controlled trials [RCTs]). There was a modest effect for decreases in psychological distress compared with controls (k=11, g=–0.16, P=.03), and heterogeneity was low (I2< 0.25). However, there was no evidence for improvements in cognition (k=7, g=0.07, P=.48), mindfulness (k=9, g=0.02, P=.83), and physiological health (k=7, g=0.11, P=.57) compared to controls. Mechanistic modulation of brain targets was not found in RCTs or within-participant designs. Sex (male or female), age, clinical status, study quality, active or passive controls, sample size, and neurofeedback duration did not moderate effects. There was some evidence for reporting bias, but no evidence of publication bias. Adverse effects were not assessed in 19 out of 21 studies and not found in the 2 studies that assessed them. Conclusions: Assertions that consumer-grade devices can allow participants to modulate their brains and deepen their meditations are not currently supported. It is possible that neurofeedback effects may rely on “neurosuggestion” (placebo effects of neurotechnology). Future research should examine more extensive calibration and individualization of devices, larger sample sizes, and gold-standard sham-controlled RCTs.

Prior research indicates that lower resting-state functional coupling between two brain networks, lateral frontoparietal network (LFPN) and default mode network (DMN), relates to better cognitive test performance. However, most study samples skew towards wealthier individuals—and what is adaptive for one population may not be for another. In a pre-registered study, we analyzed resting-state fMRI from 6839 children ages 9-10 years. For children above poverty, we replicated the prior finding: better cognitive performance correlated with weaker LFPN-DMN coupling. For children in poverty, the slope of the relation was instead positive. This significant interaction related to several features of a child’s environment. Future research should investigate the possibility that leveraging internally guided cognition is a mechanism of resilience for children in poverty. In sum, “optimal” brain function depends in part on the external pressures children face, highlighting the need for more diverse samples in research on the human brain and behavior.