Whole-Brain Resting-State Functional Connectivity Patterns Associated With Pediatric Anxiety and Involuntary Attention Capture

Structure-function coupling in network connectivity and associations with negative affectivity in a group of transdiagnostic adolescents

The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated 'domain-specific' factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.

Resting-State Cortical Network and Subcortical Hyperconnectivity in Youth With Generalized Anxiety Disorder in the ABCD Study

BACKGROUND

Generalized anxiety disorder (GAD) frequently emerges during childhood or adolescence, yet few studies have examined functional connectivity differences in youth with GAD. Functional magnetic resonance imaging (fMRI) studies of adults with GAD have implicated multiple brain regions; however, frequent examination of individual brain seed regions and/or networks has limited a holistic view of GAD-associated differences. The current study therefore used resting-state fMRI data from the Adolescent Brain Cognitive Development (ABCD) Study to investigate connectivity in youths with GAD across multiple cortical networks and subcortical regions implicated in adult GAD, considering diagnosis changes across 2 assessment periods.

METHODS

In 164 youths with GAD and 3158 healthy control participants, within- and between-network connectivity for 6 cortical networks and 6 subcortical regions was assessed using linear mixed-effect models. Changes in GAD-associated connectivity between baseline and 2-year follow-up were then compared for participants with continuous GAD, GAD at baseline and not follow-up (GAD remitters), and GAD at follow-up and not baseline (GAD converters) versus control participants.

RESULTS

Youths with GAD showed greater within-ventral attention network (VAN) connectivity and hyperconnectivity between the amygdala and cingulo-opercular network and between striatal regions and the cingulo-opercular, default mode, and salience networks (false discovery rate p < .05). Within-VAN connectivity decreased for GAD remitters between baseline and follow-up. Sensitivity analyses revealed that these hyperconnectivity patterns were not observed in youths with major depressive disorder (n = 19), separation anxiety (n = 33), or social anxiety disorder (n = 111) who did not have GAD.

CONCLUSIONS

Results indicate that GAD in childhood and adolescence is associated with altered subcortical to cortical network connectivity and that within-VAN hyperconnectivity, in particular, is associated with clinically significant GAD-specific symptoms.

Network-level enrichment provides a framework for biological interpretation of machine learning results

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

Neural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study

BACKGROUND

Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. However, it is challenging to administer and score these assessments alongside high-quality infant neuroimaging data. In the current study, we aimed to identify infant resting-state functional connectivity associated with both parent report and observed behavioral estimates of infant novelty-evoked distress.

METHODS

Using data from the OIT (Origins of Infant Temperament) study, which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress. These parent-report measures were then summarized into a composite score used for imaging analysis. Our infant magnetic resonance imaging sample was a synthetic cohort, harmonizing data from 2 functional magnetic resonance imaging studies of 4-month-old infants (OIT and BCP [Baby Connectome Project]; n = 101), both of which included measures of parent-reported temperament. Brain-behavior associations were evaluated using enrichment, a statistical approach that quantifies the clustering of brain-behavior associations within network pairs.

RESULTS

Results demonstrated that parent-report composites of novelty-evoked distress were significantly associated with 3 network pairs: dorsal attention-salience/ventral attention, dorsal attention-default mode, and dorsal attention-control. These network pairs demonstrated negative associations with novelty-evoked distress, indicating that less connectivity between these network pairs was associated with greater novelty-evoked distress. Additional analyses demonstrated that dorsal attention-control network connectivity was associated with observed novelty-evoked distress in the OIT sample (n = 38).

CONCLUSIONS

Overall, this work is broadly consistent with existing work and implicates dorsal attention network connectivity in novelty-evoked distress. This study provides novel data on the neural basis of infant novelty-evoked distress.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Novel mechanism-based treatments for pediatric anxiety and depressive disorders

Pediatric anxiety and depressive disorders are common, can be highly impairing, and can persist despite the best available treatments. Here, we review research into novel treatments for childhood anxiety and depressive disorders designed to target underlying cognitive, emotional, and neural circuit mechanisms. We highlight three novel treatments lying along a continuum relating to clinical impact of the disorder and the intensity of clinical management required. We review cognitive training, which involves the lowest risk and may be applicable for problems with mild to moderate impact; psychotherapy, which includes a higher level of clinical involvement and may be sufficient for problems with moderate impact; and brain stimulation, which has the highest potential risks and is therefore most appropriate for problems with high impact. For each treatment, we review the specific underlying cognitive, emotional, and brain circuit mechanisms that are being targeted, whether treatments modify those underlying mechanisms, and efficacy in reducing symptoms. We conclude by highlighting future directions, including the importance of work that leverages developmental windows of high brain plasticity to time interventions to the specific epochs in childhood that have the largest and most enduring life-long impact.

A Novel Cognitive Training Program Targets Stimulus-Driven Attention to Alter Symptoms, Behavior, and Neural Circuitry in Pediatric Anxiety Disorders: Pilot Clinical Trial

Objective: Pediatric anxiety disorders are associated with increased stimulus-driven attention (SDA), the involuntary capture of attention by salient stimuli. Increased SDA is linked to increased activity in the right ventrolateral prefrontal cortex (rVLPFC), especially in the portion corresponding to the ventral attention network (VAN). In this study, we present a small clinical trial using a novel attention training program designed to treat pediatric anxiety by decreasing SDA and activity in the rVLPFC. Methods: Children ages 8-12 with anxiety disorders (n = 18) participated in eight sessions of attention training over a 4-week period. At baseline and after completing training, participants completed clinical anxiety measures and a battery of cognitive tasks designed to measure three different aspects of attention: SDA, goal-oriented attention, and threat bias. A subset of participants (n = 12) underwent baseline and post-training neuroimaging while engaged in an SDA task. Brain analyses focused on activity within the rVLPFC. Results: Parent (p < 0.001)-, child (p < 0.002)-, and clinician-rated (p < 0.02) anxiety improved significantly over the course of training. Training significantly altered SDA [F(1,92) = 8.88, corrected p-value (pcor) < 0.012, uncorrected p-value (puncor) < 0.004]. Anxiety improvement correlated with improvements in goal-directed attention [r(10) = 0.60, pcor < 0.12 puncor < 0.04]. Within an area of the rVLPFC corresponding to the cingulo-opercular network (CON), there was a main effect of training [F(1,20) = 6.75, pcor < 0.16, puncor < 0.02], with decreasing signal across training. There was a significant interaction between training and anxiety on this region's activity [F(1,20) = 9.48, pcor < 0.048, puncor < 0.006]. Post hoc testing revealed that post-training activity within this CON area correlated with residual anxiety [r(10) = 0.68, p < 0.02]. Conclusions: SDA and rVLPFC neural activity may be novel therapeutic targets in pediatric anxiety. After undergoing a training paradigm aimed at modifying this aspect of attention and its underlying neural circuitry, patients showed lower anxiety, changes in SDA and goal-oriented attention, and decreased activity in the CON portion of the rVLPFC.

Neurobiology of Treatment in Pediatric Anxiety Disorders

Both pharmacologic and psychotherapeutic treatment-related changes increase activity in brain regions implicated in prefrontal regulatory circuits, and the functional connectivity of these regions with the amygdala is enhanced following pharmacological treatment. This may suggest overlapping mechanisms of action across therapeutic modalities. The existing literature is best viewed as a partially constructed scaffold on which to construct a vigorous understanding of biomarkers in pediatric anxiety syndromes. As the field approaches leveraging "fingerprints" in neuroimaging with "outputs" in neuropsychiatric tasks and scale, we can move beyond one-size-fits-all selection of psychiatric interventions toward more nuanced therapeutic strategies that recognize individual differences.

Network-specific selectivity of functional connections in the neonatal brain

The adult human brain is organized into functional brain networks, groups of functionally connected segregated brain regions. A key feature of adult functional networks is long-range selectivity, the property that spatially distant regions from the same network have higher functional connectivity than spatially distant regions from different networks. Although it is critical to establish the status of functional networks and long-range selectivity during the neonatal period as a foundation for typical and atypical brain development, prior work in this area has been mixed. Although some studies report distributed adult-like networks, other studies suggest that neonatal networks are immature and consist primarily of spatially isolated regions. Using a large sample of neonates (n = 262), we demonstrate that neonates have long-range selective functional connections for the default mode, fronto-parietal, and dorsal attention networks. An adult-like pattern of functional brain networks is evident in neonates when network-detection algorithms are tuned to these long-range connections, when using surface-based registration (versus volume-based registration), and as per-subject data quantity increases. These results help clarify factors that have led to prior mixed results, establish that key adult-like functional network features are evident in neonates, and provide a foundation for studies of typical and atypical brain development.