Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.

Associations Between Neighborhood Resources and Youths' Response to Reward Omission in a Task Modeling Negatively Biased Environments

OBJECTIVE

Neighborhoods provide essential resources (eg, education, safe housing, green space) that influence neurodevelopment and mental health. However, we need a clearer understanding of the mechanisms mediating these relationships. Limited access to neighborhood resources may hinder youths from achieving their goals and, over time, shape their behavioral and neurobiological response to negatively biased environments blocking goals and rewards.

METHOD

To test this hypothesis, 211 youths (aged ∼13.0 years, 48% boys, 62% identifying as White, 75% with a psychiatric disorder diagnosis) performed a task during functional magnetic resonance imaging. Initially, rewards depended on performance (unbiased condition); but later, rewards were randomly withheld under the pretense that youths did not perform adequately (negatively biased condition), a manipulation that elicits frustration, sadness, and a broad response in neural networks. We investigated associations between the Childhood Opportunity Index (COI), which quantifies access to youth-relevant neighborhood features in 1 metric, and the multimodal response to the negatively biased condition, controlling for age, sex, medication, and psychopathology.

RESULTS

Youths from less-resourced neighborhoods responded with less anger (p < .001, marginal R2 = 0.42) and more sadness (p < .001, marginal R2 = 0.46) to the negatively biased condition than youths from well-resourced neighborhoods. On the neurobiological level, lower COI scores were associated with a more localized processing mode (p = .039, marginal R2 = 0.076), reduced connectivity between the somatic-motor-salience and the control network (p = .041, marginal R2 = 0.040), and fewer provincial hubs in the somatic-motor-salience, control, and default mode networks (all pFWE < .05).

CONCLUSION

The present study adds to a growing literature documenting how inequity may affect the brain and emotions in youths. Future work should test whether findings generalize to more diverse samples and should explore effects on neurodevelopmental trajectories and emerging mood disorders during adolescence.

DIVERSITY & INCLUSION STATEMENT

One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

Responding to threat: Associations between neural reactivity to and behavioral avoidance of threat in pediatric anxiety

BACKGROUND

Despite broad recognition of the central role of avoidance in anxiety, a lack of specificity in its operationalization has hindered progress in understanding this clinically significant construct. The current study uses a multimodal approach to investigate how specific measures of avoidance relate to neural reactivity to threat in youth with anxiety disorders.

METHODS

Children with anxiety disorders (ages 6-12 years; n = 65 for primary analyses) completed laboratory task- and clinician-based measures of avoidance, as well as a functional magnetic resonance imaging task probing neural reactivity to threat. Primary analyses examined the ventral anterior insula (vAI), amygdala, and ventromedial prefrontal cortex (vmPFC).

RESULTS

Significant but distinct patterns of association with task- versus clinician-based measures of avoidance emerged. Clinician-rated avoidance was negatively associated with right and left vAI reactivity to threat, whereas laboratory-based avoidance was positively associated with right vAI reactivity to threat. Moreover, left vAI-right amygdala and bilateral vmPFC-right amygdala functional connectivity were negatively associated with clinician-rated avoidance but not laboratory-based avoidance.

LIMITATIONS

These results should be considered in the context of the restricted range of our treatment-seeking sample, which limits the ability to draw conclusions about these associations across children with a broader range of symptomatology. In addition, the limited racial and ethnic diversity of our sample may limit the generalizability of findings.

CONCLUSION

These findings mark an important step towards bridging neural findings and behavioral patterns using a multimodal approach. Advancing understanding of behavioral avoidance in pediatric anxiety may guide future treatment optimization by identifying individual-specific targets for treatment.

Altered neural associations with cognitive and emotional functions in cannabis dependence

Negative emotional state has been found to correlate with poor cognitive performance in cannabis-dependent (CD) individuals, but not healthy controls (HCs). To examine the neural substrates underlying such unusual emotion-cognition coupling, we analyzed the behavioral and resting state fMRI data from the Human Connectome Project and found opposite brain-behavior associations in the CD and HC groups: (i) although the cognitive performance was positively correlated with the within-network functional connectivity strength and segregation (i.e. clustering coefficient and local efficiency) of the cognitive network in HCs, these correlations were inversed in CDs; (ii) although the cognitive performance was positively correlated with the within-network Granger effective connectivity strength and integration (i.e. characteristic path length) of the cognitive network in CDs, such associations were not significant in HCs. In addition, we also found that the effective connectivity strength within cognition network mediated the behavioral coupling between emotional state and cognitive performance. These results indicate a disorganization of the cognition network in CDs, and may help improve our understanding of substance use disorder.

Congenital Adrenal Hyperplasia and Brain Health: A Systematic Review of Structural, Functional, and Diffusion Magnetic Resonance Imaging (MRI) Investigations

BACKGROUND

Congenital adrenal hyperplasia (CAH) is a group of genetic disorders that affects the adrenal glands and is the most common cause of primary adrenal insufficiency in children. In the past few decades, magnetic resonance imaging (MRI) has been implemented to investigate how the brain may be affected by CAH. A systematic review was conducted to evaluate and synthesize the reported evidence of brain findings related to CAH using structural, functional, and diffusion-weighted MRI.

METHODS

We searched bibliographical databases through July 2021 for brain MRI studies in individuals with CAH.

RESULTS

Twenty-eight studies were identified, including 13 case reports or series, 10 studies that recruited and studied CAH patients vs unaffected controls, and 5 studies without a matched control group. Eleven studies used structural MRI to identify structural abnormalities or quantify brain volumes, whereas 3 studies implemented functional MRI to investigate brain activity, and 3 reported diffusion MRI findings to assess white matter microstructure. Some commonly reported findings across studies included cortical atrophy and differences in gray matter volumes, as well as white matter hyperintensities, altered white matter microstructure, and distinct patterns of emotion and reward-related brain activity.

CONCLUSIONS

These findings suggest differences in brain structure and function in patients with CAH. Limitations of these studies highlight the need for CAH neuroimaging studies to incorporate larger sample sizes and follow best study design and MRI analytic practices, as well as clarify potential neurologic effects seen across the lifespan and in relation to clinical and behavioral CAH phenotypes.

Trajectory of rich club properties in structural brain networks

Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.

The Task Pre-Configuration Is Associated With Cognitive Performance Evidence From the Brain Synchrony

Although many resting state and task state characteristics have been studied, it is still unclear how the brain network switches from the resting state during tasks. The current theory shows that the brain is a complex dynamic system and synchrony is defined to measure brain activity. The study compared the changes of synchrony between the resting state and different task states in healthy young participants (N = 954). It also examined the ability to switch from the resting state to the task-general architecture of synchrony. We found that the synchrony increased significantly during the tasks. And the results showed that the brain has a task-general architecture of synchrony during different tasks. The main feature of task-based reasoning is that the increase in synchrony of high-order cognitive networks is significant, while the increase in synchrony of sensorimotor networks is relatively low. In addition, the high synchrony of high-order cognitive networks in the resting state can promote task switching effectively and the pre-configured participants have better cognitive performance, which shows that spontaneous brain activity and cognitive ability are closely related. These results revealed changes in the brain network configuration for switching between the resting state and task state, highlighting the consistent changes in the brain network between different tasks. Also, there was an important relationship between the switching ability and the cognitive performance.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Attention networks and the intrinsic network structure of the human brain

Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large‐scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti‐grayordinates. Resulting group maps were compared to the group‐level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto‐parietal and midcingulo‐insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

Gray matter structures associated with neuroticism: A meta-analysis of whole-brain voxel-based morphometry studies

Neuroticism is major higher-order personality trait and has been robustly associated with mental and physical health outcomes. Although a growing body of studies have identified neurostructural markers of neuroticism, the results remained highly inconsistent. To characterize robust associations between neuroticism and variations in gray matter (GM) structures, the present meta-analysis investigated the concurrence across voxel-based morphometry (VBM) studies using the anisotropic effect size signed differential mapping (AES-SDM). A total of 13 studies comprising 2,278 healthy subjects (1,275 females, 29.20 ± 14.17 years old) were included. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Meta-regression analyses indicated that the neuroticism-GM associations were not confounded by age and gender. Overall, our study is the first whole-brain meta-analysis exploring the brain structural correlates of neuroticism, and the findings may have implications for the intervention of high-neuroticism individuals, who are at risk of mental disorders, by targeting the dACC/mPFC.