Disorder-specific cingulo-opercular network hyperconnectivity in pediatric OCD relative to pediatric anxiety

In the face of ambiguity: intrinsic brain organization in development predicts one's bias toward positivity or negativity

Exacerbated negativity bias, including in responses to ambiguity, represents a common phenotype of internalizing disorders. Individuals differ in their propensity toward positive or negative appraisals of ambiguity. This variability constitutes one's valence bias, a stable construct linked to mental health. Evidence suggests an initial negativity in response to ambiguity that updates via regulatory processes to support a more positive bias. Previous work implicates the amygdala and prefrontal cortex, and regions of the cingulo-opercular system, in this regulatory process. Nonetheless, the neurodevelopmental origins of valence bias remain unclear. The current study tests whether intrinsic brain organization predicts valence bias among 119 children and adolescents (6 to 17 years). Using whole-brain resting-state functional connectivity, a machine-learning model predicted valence bias (r = 0.20, P = 0.03), as did a model restricted to amygdala and cingulo-opercular system features (r = 0.19, P = 0.04). Disrupting connectivity revealed additional intra-system (e.g. fronto-parietal) and inter-system (e.g. amygdala to cingulo-opercular) connectivity important for prediction. The results highlight top-down control systems and bottom-up perceptual processes that influence valence bias in development. Thus, intrinsic brain organization informs the neurodevelopmental origins of valence bias, and directs future work aimed at explicating related internalizing symptomology.

Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

Background

Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development.

Methods

110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations.

Results

In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables.

Conclusions

The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

Hypoconnectivity within the cingulo-opercular network in patients with mild cognitive impairment in Chinese communities

INTRODUCTION

At rest, the brain's higher cognitive systems engage in correlated activity patterns, forming networks. With mild cognitive impairment (MCI), it is essential to understand how functional connectivity within and between resting-state networks changes. This study used resting-state functional connectivity to identify significant differences within and between the cingulo-opercular network (CON) and default mode network (DMN).

METHODS

We assessed cognitive function in patients using the Chinese version of the Alzheimer's disease assessment scale-Cognitive subscale (ADAS-Cog). A group of MCI subjects (ages 60-83 years, n = 45) was compared to age-matched healthy controls (n = 70). Resting-state functional connectivity was used to determine functional connectivity strength within and between the CON and DMN.

RESULTS

Compared to healthy controls, the MCI group showed significantly lower functional connectivity within the CON (F = 10.76, df = 1, p = 0.001, FDR adjusted p = 0.003). Additionally, the MCI group displayed no distinct differences in functional connectivity within DMN (F = 0.162, df = 1, p = 0.688, FDR adjusted p = 0.688) and between CON and DMN (F = 2.270, df = 1, p = 0.135, FDR adjusted p = 0.262). Moreover, we found no correlation between ADAS-Cog and within- or between-connectivity metrics among subjects with MCI.

CONCLUSIONS

Our findings indicate that specific patterns of hypoconnectivity within CON circuitry may characterize MCI relative to healthy controls. This work improves our understanding of network dysfunction underlying MCI and could inform more targeted treatment.

Defining brain-based OCD patient profiles using task-based fMRI and unsupervised machine learning

While much research has highlighted phenotypic heterogeneity in obsessive compulsive disorder (OCD), less work has focused on heterogeneity in neural activity. Conventional neuroimaging approaches rely on group averages that assume homogenous patient populations. If subgroups are present, these approaches can increase variability and can lead to discrepancies in the literature. They can also obscure differences between various subgroups. To address this issue, we used unsupervised machine learning to identify subgroup clusters of patients with OCD who were assessed by task-based fMRI. We predominantly focused on activation of cognitive control and performance monitoring neurocircuits, including three large-scale brain networks that have been implicated in OCD (the frontoparietal network, cingulo-opercular network, and default mode network). Participants were patients with OCD (n = 128) that included both adults (ages 24-45) and adolescents (ages 12-17), as well as unaffected controls (n = 64). Neural assessments included tests of cognitive interference and error processing. We found three patient clusters, reflecting a "normative" cluster that shared a brain activation pattern with unaffected controls (65.9% of clinical participants), as well as an "interference hyperactivity" cluster (15.2% of clinical participants) and an "error hyperactivity" cluster (18.9% of clinical participants). We also related these clusters to demographic and clinical correlates. After post-hoc correction for false discovery rates, the interference hyperactivity cluster showed significantly longer reaction times than the other patient clusters, but no other between-cluster differences in covariates were detected. These findings increase precision in patient characterization, reframe prior neurobehavioral research in OCD, and provide a starting point for neuroimaging-guided treatment selection.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.