Combining fMRI during resting state and an attention bias task in children

Threat- and reward-related brain circuitry, perceived stress, and anxiety in adolescents during the COVID-19 pandemic: a longitudinal investigation

The Coronavirus disease (COVID-19) pandemic led to heightened anxiety in adolescents. The basolateral amygdala (BLA) and the nucleus accumbens (NAcc) are implicated in response to stress and may contribute to anxiety. The role of threat- and reward-related circuitry in adolescent anxiety during the COVID-19 pandemic, however, is not clear. Ninety-nine adolescents underwent resting-state fMRI ∼1 year before the pandemic. Following shelter-in-place orders, adolescents reported their perceived stress and, 1 month later, their anxiety. Generalized multivariate analyses identified BLA and NAcc seed-based whole-brain functional connectivity maps with perceived stress. In the resulting significant clusters, we examined the association between seed-based connectivityand subsequent anxiety. Perceived stress was associated with bilateral BLA and NAcc connectivity across distributed clusters that included prefrontal, limbic, temporal, and cerebellar regions. Several NAcc connectivity clusters located in ventromedial prefrontal, parahippocampal, and temporal cortices were positively associated with anxiety; NAcc connectivity with the inferior frontal gyrus was negatively associated. BLA connectivity was not associated with anxiety. These results underscore the integrative role of the NAcc in responding to acute stressors and its relation to anxiety in adolescents. Elucidating the involvement of subcortical-cortical circuitry in adolescents' capacity to respond adaptively to environmental challenges can inform treatment for anxiety-related disorders.

Task-Rest Reconfiguration Efficiency of the Reward Network Across Adolescence and Its Association With Early Life Stress and Depressive Symptoms

OBJECTIVE

Adolescents face significant changes in many domains of their daily lives that require them to flexibly adapt to changing environmental demands. To shift efficiently among various goals, adolescents must reconfigure their brains, disengaging from previous tasks and engaging in new activities.

METHOD

To examine this reconfiguration, we obtained resting-state and task-based functional magnetic resonance imaging (fMRI) scans in a community sample of 164 youths. We assessed the similarity of functional connectivity (FC) of the reward network between resting state and a reward-processing state, indexing the degree of reward network reconfiguration required to meet task demands. Given research documenting relations among reward network function, early life stress (ELS), and adolescent depression, we examined the association of reconfiguration efficiency with age across adolescence, the moderating effect of ELS on this association, and the relation between reconfiguration efficiency and depressive symptoms.

RESULTS

We found that older adolescents showed greater reconfiguration efficiency than younger adolescents and, furthermore, that this age-related association was moderated by the experience of ELS.

CONCLUSION

These findings suggest that reconfiguration efficiency of the reward network increases over adolescence, a developmental pattern that is attenuated in adolescents exposed to severe ELS. In addition, even after controlling for the effects of age and exposure to ELS, adolescents with higher levels of depressive symptoms exhibited greater reconfiguration efficiency, suggesting that they have brain states at rest that are more strongly optimized for reward processing than do asymptomatic youth.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. 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.

Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice

Introduction: In resting-state functional magnetic resonance imaging (rs-fMRI) studies, global signal regression (GSR) is a controversial preprocessing strategy. It effectively eliminates global noise driven by motion and respiration but also can introduce artifacts and remove functionally relevant metabolic information. Most preclinical rs-fMRI studies are performed in anesthetized animals, and anesthesia will alter both metabolic and neuronal activity. Methods: In this study, we explored the effect of GSR on rs-fMRI data collected under anesthetized and awake state in mice (n = 12). We measured global signal amplitude, and also functional connectivity (FC), functional connectivity density (FCD) maps, and brain modularity, all commonly used data-driven analysis methods to quantify connectivity patterns. Results: We found that global signal amplitude was similar between the awake and anesthetized states. However, GSR had a different impact on connectivity networks and brain modularity changes between states. We demonstrated that GSR had a more prominent impact on the anesthetized state, with a greater decrease in functional connectivity and increased brain modularity. We classified mice using the change in amplitude of brain modularity coefficient (ΔQ) before and after GSR processing. The results revealed that, when compared with the largest ΔQ group, the smallest ΔQ group had increased FCD in the cortex region in both the awake and anesthetized states. This suggests differences in individual mice may affect how GSR differentially affects awake versus anesthetized functional connectivity. Discussion: This study suggests that, for rs-fMRI studies which compare different physiological states, researchers should use GSR processing with caution.

Mindfulness-based therapy improves brain functional network reconfiguration efficiency

Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants' brains closer to a state required for mindful awareness.

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.

Attentional biases in human anxiety

Across clinical and subclinical samples, anxiety has been associated with increased attentional capture by cues signaling danger. Various cognitive models attribute the onset and maintenance of anxiety symptoms to maladaptive selective information processing. In this brief review, we 1) describe the evidence for the relations between anxiety and attention bias toward threat, 2) discuss the neurobiology of anxiety-related differences in threat bias, 3) summarize work investigating the developmental origins of attention bias toward threat, and 4) examine efforts to translate threat bias research into clinical intervention. Future directions in each area are discussed, including the use of novel analytic approaches improving characterization of threat-processing-related brain networks, clarifying the role of cognitive control in the development of attention bias toward threat, and the need for larger, well-controlled randomized clinical trials examining moderators and mediators of treatment response. Ultimately, this work has important implications for understanding the etiology of and for intervening on anxiety difficulties among children and adults.

Flexible brain transitions between hierarchical network segregation and integration associated with cognitive performance during a multisource interference task

Cognition involves locally segregated and globally integrated processing. This process is hierarchically organized and linked to evidence from hierarchical modules in brain networks. However, researchers have not clearly determined how flexible transitions between these hierarchical processes are associated with cognitive behavior. Here, we designed a multisource interference task (MSIT) and introduced the nested-spectral partition (NSP) method to detect hierarchical modules in brain functional networks. By defining hierarchical segregation and integration across multiple levels, we showed that the MSIT requires higher network segregation in the whole brain and most functional systems but generates higher integration in the control system. Meanwhile, brain networks have more flexible transitions between segregated and integrated configurations in the task state. Crucially, higher functional flexibility in the resting state, less flexibility in the task state and more efficient switching of the brain from resting to task states were associated with better task performance. Our hierarchical modular analysis was more effective at detecting alterations in functional organization and the phenotype of cognitive performance than graph-based network measures at a single level.

Leveraging big data to map neurodevelopmental trajectories in pediatric anxiety

Anxiety disorders are the most prevalent psychiatric condition among youth, with symptoms commonly emerging prior to or during adolescence. Delineating neurodevelopmental trajectories associated with anxiety disorders is important for understanding the pathophysiology of pediatric anxiety and for early risk identification. While a growing literature has yielded valuable insights into the nature of brain structure and function in pediatric anxiety, progress has been limited by inconsistent findings and challenges common to neuroimaging research. In this review, we first discuss these challenges and the promise of ‘big data’ to map neurodevelopmental trajectories in pediatric anxiety. Next, we review evidence of age-related differences in neural structure and function among anxious youth, with a focus on anxiety-relevant processes such as threat and safety learning. We then highlight large-scale cross-sectional and longitudinal studies that assess anxiety and are well positioned to inform our understanding of neurodevelopment in pediatric anxiety. Finally, we detail relevant challenges of ‘big data’ and propose future directions through which large publicly available datasets can advance knowledge of deviations from normative brain development in anxiety. Leveraging ‘big data’ will be essential for continued progress in understanding the neurobiology of pediatric anxiety, with implications for identifying markers of risk and novel treatment targets.

Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study

INTRODUCTION

There has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common.

METHODS

Inspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns.

RESULTS

We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation.

CONCLUSION

Nonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.

Attentional bias of subliminal emotional faces in adolescents with obsessive-compulsive disorder

Attentional bias to threatening information may play a vital role in the onset and maintenance of obsessive-compulsive disorder (OCD). This study aimed to explore whether adolescents with OCD exhibited attentional bias toward faces that express disgust or fear. Participants were 27 adolescents with a first-time primary diagnosis of OCD and 27 healthy controls. To assess OCD, depression, and anxiety symptoms, all participants completed the Yale-Brown Obsessive Compulsive Scale, the Hamilton Depression Scale, and the Hamilton Anxiety Scale, respectively, followed by the modified dot probe task. Repeated measures ANOVA revealed a main effect of validity type and a significant group × validity type interaction effect. The results of one sample t-tests showed that participants in the OCD group had an attentional bias toward both disgusted and fearful faces. Further analysis indicated that adolescents in the OCD group showed facilitated attention toward the fearful faces and difficulty disengaging from disgusted faces. Adolescents with OCD exhibited facilitated attention toward threat stimuli, and when they allocated attention to threat, they experienced difficulty disengaging from it. Treatment procedures to modify the attentional bias may be effective.

Converging Multi-modal Evidence for Implicit Threat-Related Bias in Pediatric Anxiety Disorders

This report examines the relationship between pediatric anxiety disorders and implicit bias evoked by threats. To do so, the report uses two tasks that assess implicit bias to negative-valence faces, the first by eye-gaze and the second by measuring body-movement parameters. The report contrasts task performance in 51 treatment-seeking, medication-free pediatric patients with anxiety disorders and 36 healthy peers. Among these youth, 53 completed an eye-gaze task, 74 completed a body-movement task, and 40 completed both tasks. On the eye-gaze task, patients displayed longer gaze duration on negative relative to non-negative valence faces than healthy peers, F(1, 174) = 8.27, p = .005. In contrast, on the body-movement task, patients displayed a greater tendency to behaviorally avoid negative-valence faces than healthy peers, F(1, 72) = 4.68, p = .033. Finally, implicit bias measures on the two tasks were correlated, r(38) = .31, p = .049. In sum, we found an association between pediatric anxiety disorders and implicit threat bias on two tasks, one measuring eye-gaze and the other measuring whole-body movements. Converging evidence for implicit threat bias encourages future research using multiple tasks in anxiety.