Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Shared and distinctive dysconnectivity patterns underlying pure generalized anxiety disorder (GAD) and comorbid GAD and depressive symptoms

The resting-state connectivity features underlying pure generalized anxiety disorder (GAD, G1) and comorbid GAD and depressive symptoms (G2) have not been directly compared. Furthermore, it is unclear whether these features might serve as potential prognostic biomarkers and change with treatment. Degree centrality (DC) in G1 (40 subjects), G2 (58 subjects), and healthy controls (HCs, 54 subjects) was compared before treatment, and the DC of G1 or G2 at baseline was compared with that after 4 weeks of paroxetine treatment. Using support vector regression (SVR), voxel-wise DC across the entire brain and abnormal DC at baseline were employed to predict treatment response. At baseline, G1 and G2 exhibited lower DC in the left mid-cingulate cortex and vermis IV/V compared to HCs. Additionally, compared to HCs, G1 had lower DC in the left middle temporal gyrus, while G2 showed higher DC in the right inferior temporal/fusiform gyrus. However, there was no significant difference in DC between G1 and G2. The SVR based on abnormal DC at baseline could successfully predict treatment response in responders in G2 or in G1 and G2. Notably, the predictive performance based on abnormal DC at baseline surpassed that based on DC across the entire brain. After treatment, G2 responders showed lower DC in the right medial orbital frontal gyrus, while no change in DC was identified in G1 responders. The G1 and G2 showed common and distinct dysconnectivity patterns and they could potentially serve as prognostic biomarkers. Furthermore, DC in patients with GAD could change with treatment.

Paternal Prenatal and Lactation Exposure to a High-Calorie Diet Shapes Transgenerational Brain Macro- and Microstructure Defects, Impacting Anxiety-Like Behavior in Male Offspring Rats

Prenatal exposure to high-energy diets (HED) increases the susceptibility to behavioral alterations in the male offspring. We addressed whether prenatal HED primes the transgenerational inheritance of structural brain changes impacting anxiety/depression-like behavior in the offspring. For this, we used female Wistar rats exposed to a HED [cafeteria (CAF) diet, n = 6] or chow [control (CON) n = 6] during development. Anxiety and depression-like behavior were evaluated in filial 1 (F1), filial 2 (F2), and filial 3 (F3) male offspring using the open field (OFT), elevated plus maze, novelty suppressed feeding (NSFT), tail suspension (TST), and forced swimming tests. Structural brain changes were identified by deformation-based morphometry (DBM) and diffusion tensor imaging using ex vivo MRI. We found that the F1, F2, and F3 offspring exposed to CAF diet displayed higher anxious scores including longer feeding latency during the NSFT, and in the closed arms, only F1 offspring showed longer stay on edges during the OFT versus control offspring. DBM analysis revealed that CAF offspring exhibited altered volume in the cerebellum, hypothalamus, amygdala, and hippocampus preserved up to the F3 generation of anxious individuals. Also, F3 CAF anxious exhibited greater fractional anisotropy and axial diffusivity (AD) in the amygdala, greater apparent diffusion coefficient in the corpus callosum, and greater AD in the hippocampus with respect to the control. Our results suggest that prenatal and lactation exposure to HED programs the transgenerational inheritance of structural brain changes related to anxiety-like behavior in the male offspring.

Effects of Hyperdirect Pathway Theta Burst Transcranial Magnetic Stimulation on Inhibitory Control, Craving, and Smoking in Adults With Nicotine Dependence: A Double-Blind, Randomized Crossover Trial

BACKGROUND

Nicotine dependence is associated with dysregulated hyperdirect pathway (HDP)-mediated inhibitory control (IC). However, there are currently no evidence-based treatments that have been shown to target the HDP to improve IC and reduce cigarette cravings and smoking.

METHODS

Following a baseline nonstimulation control session, this study (N = 37; female: n = 17) used a double-blind, randomized crossover design to examine the behavioral and neural effects of intermittent theta burst stimulation (iTBS) and continuous TBS (cTBS) to the right inferior frontal gyrus (rIFG)-a key cortical node of the HDP. Associations between treatment effects were also explored.

RESULTS

At baseline, HDP IC task-state functional connectivity was positively associated with IC task performance, which confirmed the association between HDP circuit function and IC. Compared with iTBS, rIFG cTBS improved IC task performance. Compared with the baseline nonstimulation control session, both TBS conditions reduced cigarette craving and smoking; however, although craving and smoking were lower for cTBS, no differences were found between the two active conditions. In addition, although HDP IC task-state functional connectivity was greater following cTBS than iTBS, there was no significant difference between conditions. Finally, cTBS-induced improvement in IC task performance was associated with reduced craving, and cTBS-induced reduction in craving was associated with reduced smoking.

CONCLUSIONS

These findings warrant further investigation into the effects of rIFG cTBS for increasing IC and reducing craving and smoking among individuals with nicotine dependence. Future sham-controlled cTBS studies may help further elucidate the mechanisms by which rIFG cTBS mediates IC and smoking behavior.

A systematic review and meta-analysis of resting-state fMRI in anxiety disorders: Need for data sharing to move the field forward

Anxiety disorders are among the most prevalent psychiatric disorders. Neuroimaging findings remain uncertain, and resting state functional magnetic resonance (rs-fMRI) connectivity is of particular interest since it is a scalable functional imaging modality. Given heterogeneous past findings for rs-fMRI in anxious individuals, we characterize patterns across anxiety disorders by conducting a systematic review and meta-analysis. Studies were included if they contained at the time of scanning both a healthy group and a patient group. Due to insufficient study numbers, the quantitative meta-analysis only included seed-based studies. We performed an activation likelihood estimation (ALE) analysis that compared patients and healthy volunteers. All analyses were corrected for family-wise error with a cluster-level threshold of p < .05. Patients exhibited hypo-connectivity between the amygdala and the medial frontal gyrus, anterior cingulate cortex, and cingulate gyrus. This finding, however, was not robust to potential file-drawer effects. Though limited by strict inclusion criteria, our results highlight the heterogeneous nature of reported findings. This underscores the need for data sharing when attempting to detect reliable patterns of disruption in brain activity across anxiety disorders.

Feasibility of applying graph theory to diagnosing generalized anxiety disorder using machine learning models

The aim of this study was to investigate whether the alterations of topological properties can facilitate the diagnosis of generalized anxiety disorder (GAD). Twenty first-episode drug-naive Chinese individuals with GAD and twenty age-sex-education-matched healthy controls (HCs) were included in the primary training set, and the results of which were validated using nineteen drug-free patients with GAD and nineteen unmatched HCs. Two 3 T scanners were used to acquire T1, diffusion tensor, and resting-state functional images. Topological properties were altered in the functional cerebral networks among patients with GAD, but not in the structural networks. Using the nodal topological properties in the anti-correlated functional networks, machine learning models distinguished drug-naive GADs from their matched HCs independent of the type of kernels and the amount of features. Although the models built with drug-naive GADs failed to distinguish drug-free GADs from HCs, the features selected for those models could be used to build new models for distinguishing drug-free GADs from HCs. Our findings suggested that it is feasible to utilize the topological characteristics of brain network to facilitate the diagnosis of GAD. However, further research with decent sample sizes, multimodal features, and improved modeling methods are needed to build more robust models.

Disrupted resting-state functional connectivity of frontal network in opium use disorder

Opioid use disorder (OUD) as a chronic relapsing disorder is initially driven by dysfunction of brain reward networks and associated with several psychiatric disorders. Resting-state EEG was recorded in 24 healthy participants as well as 31 patients with OUD. Healthy participants do not meet OUD criteria. After pre-processing of the raw EEG, functional connectivity in the frontal network using eLORETA and all networks using graph analysis method were calculated. Patients with OUD had higher electrical neuronal activity compared to healthy participants in higher frequency bands. The statistical analysis revealed that patients with OUD had significantly decreased phase synchronization in β1 and β2 frequency bands compared with the healthy group in the frontal network. Regarding global network topology, we found a significant decrease in the characteristic path length and an increase in global efficiency, clustering coefficient, and transitivity in patients compared with the healthy group. These changes indicated that local specialization and global integration of the brain were disrupted in OUD and it suggests a tendency toward random network configuration of functional brain networks in patients with OUD. Disturbances in EEG-based brain network indices might reflect an altered cortical functional network in OUD. These findings might provide useful biomarkers to understand cortical brain pathology in opium use disorder.

Network analysis reveals abnormal functional brain circuitry in anxious dogs

Anxiety is a common disease within human psychiatric disorders and has also been described as a frequently neuropsychiatric problem in dogs. Human neuroimaging studies showed abnormal functional brain networks might be involved in anxiety. In this study, we expected similar changes in network topology are also present in dogs. We performed resting-state functional MRI on 25 healthy dogs and 13 patients. The generic Canine Behavioral Assessment & Research Questionnaire was used to evaluate anxiety symptoms. We constructed functional brain networks and used graph theory to compare the differences between two groups. No significant differences in global network topology were found. However, focusing on the anxiety circuit, global efficiency and local efficiency were significantly higher, and characteristic path length was significantly lower in the amygdala in patients. We detected higher connectivity between amygdala-hippocampus, amygdala-mesencephalon, amygdala-thalamus, frontal lobe-hippocampus, frontal lobe-thalamus, and hippocampus-thalamus, all part of the anxiety circuit. Moreover, correlations between network metrics and anxiety symptoms were significant. Altered network measures in the amygdala were correlated with stranger-directed fear and excitability; altered degree in the hippocampus was related to attachment/attention seeking, trainability, and touch sensitivity; abnormal frontal lobe function was related to chasing and familiar dog aggression; attachment/attention seeking was correlated with functional connectivity between amygdala-hippocampus and amygdala-thalamus; familiar dog aggression was related to global network topology change. These findings may shed light on the aberrant topological organization of functional brain networks underlying anxiety in dogs.

Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France

Background

Prior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19 or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders has yet to be evaluated.

Methods

We performed an observational, multicenter, retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals.

Results

Of 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up period of 14.6 days (SD = 17.9), 326 of 857 (38.0%) patients with a diagnosis of psychiatric disorder died compared with 1276 of 14,311 (8.9%) patients without such a diagnosis (odds ratio 6.27, 95% CI 5.40-7.28, p < .01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (adjusted odds ratio 3.27, 95% CI 2.78-3.85, p < .01). However, additional adjustments for obesity and number of medical conditions resulted in a nonsignificant association (adjusted odds ratio 1.02, 95% CI 0.84-1.23, p = .86). Exploratory analyses after the same adjustments suggested that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants.

Conclusions

These findings suggest that the increased risk of COVID-19-related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population and not by the underlying psychiatric disease.

The structural connectome and internalizing and externalizing symptoms at 7 and 13 years in individuals born very preterm and full-term

BACKGROUND

Children born very preterm (VP) are at higher risk of emotional and behavioral problems compared with full-term (FT) children. We investigated the neurobiological basis of internalizing and externalizing symptoms in individuals born VP and FT by applying a graph theory approach.

METHODS

Structural and diffusion MRI data were combined to generate structural connectomes and calculate measures of network integration and segregation at 7 (VP:72; FT:17) and 13 years (VP:125; FT:44). Internalizing and externalizing were assessed at 7 and 13 years using the Strengths and Difficulties Questionnaire. Linear regression models were used to relate network measures and internalizing and externalizing symptoms concurrently at 7 and 13 years.

RESULTS

Lower network integration (characteristic path length and global efficiency) was associated with higher internalizing symptoms in VP and FT children at 7 years, but not at 13 years. The association between network integration (characteristic path length) and externalizing symptoms at 7 years was weaker, but there was some evidence for differential associations between groups, with lower integration in the VP and higher integration in the FT group associated with higher externalizing symptoms. At 13 years, there was some evidence that associations between network segregation (average clustering coefficient, transitivity, local efficiency) and externalizing differed between the VP and FT groups, with stronger positive associations in the VP group.

CONCLUSIONS

This study provides insights into the neurobiological basis of emotional and behavioral problems following preterm birth, highlighting the role of the structural connectome in internalizing and externalizing symptoms in childhood and adolescence.

Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder

Background: Generalized anxiety disorder (GAD) and panic disorder (PD) are the two severe subtypes of anxiety disorders (ADs), which are similar in clinical manifestation, pathogenesis, and treatment. Earlier studies have taken a whole-brain perspective on GAD and PD in the assumption that intrinsic fluctuations are static throughout the entire scan. However, it has recently been suggested that the dynamic alternations in functional connectivity (FC) may reflect the changes in macroscopic neural activity patterns underlying the critical aspects of cognition and behavior, and thus may act as biomarkers of disease. Methods: In this study, the resting-state functional MRI (fMRI) data were collected from 26 patients with GAD, 22 patients with PD, and 26 healthy controls (HCs). We investigated dynamic functional connectivity (DFC) by using the group spatial independent component analysis, a sliding window approach, and the k-means clustering methods. For group comparisons, the temporal properties of DFC states were analyzed statistically. Results: The dynamic analysis demonstrated two discrete connectivity "States" across the entire group, namely, a more segregated State I and a strongly integrated State II. Compared with HCs, patients with both GAD and PD spent more time in the weakly within-network State I, while performing fewer transitions and dwelling shorter in the integrated State II. Additionally, the analysis of DFC strength showed that connections associated with ADs were identified including the regions that belonged to default mode (DM), executive control (EC), and salience (SA) networks, especially the connections between SA and DM networks. However, no significant difference was found between the GAD and PD groups in temporal features and connection strength. Conclusions: More common but less specific alterations were detected in the GAD and PD groups, which implied that they might have similar state-dependent neurophysiological mechanisms and, in addition, could hopefully help us better understand their abnormal affective and cognitive performances in the clinic.

Disruption of functional and structural networks in first-episode, drug-naïve adolescents with generalized anxiety disorder

BACKGROUND

Individuals with generalized anxiety disorder (GAD) tend to worry exaggeratedly and uncontrollably about various daily routines. Previous studies have demonstrated that the GAD patients exhibited widespread alternations in both functional networks (FN) and structural networks (SN). However, the simultaneous alternations of the topological organization of FN, SN, as well as their couplings in GAD still remain unknown.

METHODS

Using multimodal approach, we constructed FN from resting-state functional magnetic imaging (R-fMRI) data and SN from diffusion magnetic resonance imaging (dMRI) data of 32 adolescent GAD patients and 25 healthy controls (HC). Graph theory analysis was employed to investigate the topological properties of FN, SN, and FN-SN coupling.

RESULTS

Compared to HC, the GAD patients showed disruptions in global (i.e., decreased clustering coefficient, global, and local efficiency) and subnetwork (i.e., reduced intermodular connections, rich club, and feeder connections) levels in FN. Abnormal global level properties (i.e., increased characteristic path length and reduced global efficiency) were also observed in SN. Altered FN-SN couplings in normalized characteristic path length and feeder connections were identified in the GAD patients. The identified network measures were correlated with anxiety severity in the GAD patients.

LIMITATIONS

The sample size of the current study is small and the cross-sectional nature can not infer causal relationship.

CONCLUSIONS

Our findings identified GAD-related topological alternations in both FN and SN, together with the couplings between FN and SN, providing us with a novel perspective for understanding the pathophysiological mechanisms of GAD.

White matter structural network disturbances in first-episode, drug-naïve adolescents with generalized anxiety disorder

BACKGROUND

Previous studies have suggested that individuals with generalized anxiety disorder (GAD) would show inefficient whole-brain communication and dysconnectivity in the fronto-parietal-subcortical sub-networks in the white matter (WM) structural network. However, these hypotheses have yet to be tested.

METHODS

Individual WM structural networks were constructed based on diffusion MRI data and deterministic tractography in 34 first-episode, medication-naïve adolescents with GAD and 27 healthy controls (HCs). Graph theory was applied to investigate the topological organization alterations of the structural network.

RESULTS

GAD patients showed disrupted small-world configurations (i.e., increased path length and decreased clustering coefficient) and hub organization (i.e., less connection strength in the feeder and local connections). A decreased connection strength was found in a GAD-related sub-network (mainly involving the frontal-subcortical circuits), which was able to distinguish GAD patients from HCs with higher accuracy (area under the curve of 0.96, sensitivity of 94%, specificity of 89%) than clinical scores and other topological alternations.

LIMITATIONS

The current study just compared GAD patients with HCs based on a small sample, leaving whether the alternations found here are specific to GAD still an open question. Future studies are recommended to recruit patients with other anxiety disorders (e.g., social anxiety disorder) and/or comorbid mood disorders to identify the GAD-specific WM alterations using a larger sample.

CONCLUSIONS

Our findings highlight the disruption of the topological organization of the whole-brain WM structural network (especially the frontal-subcortical circuits) in GAD, and suggest the potential of using structural connectivity of the GAD-related sub-network as a biomarker for GAD patients.

Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample

BACKGROUND

The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development.

METHODS

We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimaging-based machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women).

RESULTS

Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R2 of -.04, permutation test p > .05).

CONCLUSIONS

In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.

A preliminary study of disrupted functional network in individuals with Internet gaming disorder: Evidence from the comparison with recreational game users

Although online gaming may lead to Internet gaming disorder (IGD), most players are recreational game users (RGU) who do not develop IGD. So far, the topological organization of whole-brain functional networks in IGD remains poorly understood. The inclusion of RGU as a control group could minimize the potential effects of gaming experience and gaming-related cue familiarity on the neural characteristics of IGD subjects. In the present study, we applied graph theoretical analysis to preliminarily explore the topological organization of intrinsic functional brain networks in IGD. 61 IGD participants and 61 matched RGU participants were recruited to undergo a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and graph-based approaches were applied to analysis their topological attributes, including small-world, efficiency, and nodal centralities. Both of IGD and RGU groups showed efficient and economic small-world topology in brain functional networks. Although there was no significant group difference in global properties, subjects with IGD as compared to those with RGU showed increased nodal centralities in the reward, craving, emotional memory and sensory-motor processing regions. These results suggest that the functional network dysfunction, characterizing by heightened incentive motivation and sensory-motor coordination, may provide a new perspective for understanding the neural characteristics underlying IGD.

White Matter Structural Brain Connectivity of Young Healthy Individuals With High Trait Anxiety

Background: Although functional brain connectivity in anxiety-related disorders has been studied, brain connectivity in non-clinical populations with high trait anxiety has been rarely reported. Whether structural brain connectivity changes in young healthy individuals with high trait anxiety remains unknown.

Methods: Thirty-eight young healthy individuals with high anxiety levels and 34 healthy subjects with low anxiety levels who were matched by age, gender, and educational level were recruited. Diffusion tensor images were acquired to analyze white matter connectivity. A two-sample t-test was used for group comparison of weighted networks and graph properties.

Results: Different connections were detected in fractional anisotropy- and fiber number-weighted networks. These connections were widely distributed in various regions, where relative significance was located in the inter-hemispheric frontal lobe, the frontal-limbic lobe in the right intra-hemisphere, and the frontal-temporal lobe in the ipsilateral hemisphere. However, no significant difference was found in fiber length-weighted network and in graph properties among the three networks.

Conclusions: The structural connectivity of white matter may be a vulnerability marker. Hence, healthy individuals with high trait anxiety levels are susceptible to anxiety-related psychopathology. The findings may help elucidate the pathological mechanism of anxiety and establish interventions for populations susceptible to anxiety disorders.