Altered Topological Properties of Brain Networks in Social Anxiety Disorder: A Resting-state Functional MRI Study

Brain function abnormalities and neuroinflammation in people living with HIV-associated anxiety disorders

Background

People living with HIV (PLWH) exhibits an increased susceptibility to anxiety disorders, concomitant with heightened vulnerability to aberrant immune activation and inflammatory responses, and endocrine dysfunction. There exists a dearth of scholarly investigations pertaining to the neurological, immune, and endocrine dimensions of HIV-associated anxiety disorders.

Method

This study aimed to compare a group of 16 individuals diagnosed with HIV-associated anxiety disorders (HIV ANXs) according to the Diagnostic and statistical manual of mental disorders (5th ed.), with a HIV individual control group (HIV control) of 49 PLWH without mental disorders. Muti-modal magnetic resonance was employed to assess the brain function and structure of both groups. Seed-based functional connectivity (FC) was used to assess the regional intrinsic brain activity and the influence of regional disturbances on FC with other brain regions. Peripheral blood cytokines and chemokines concentrations were measured using liquid chip and ELISA.

Results

Amplitude of low-frequency fluctuations in the right inferior temporal gyrus (ITG) was increased. There is a significant decreased regional homogeneity in HIV ANXs in the right superior occipital gyrus (SOG). The right ITG and the right SOG were separately set as the seed brain region of interest (ROI 1 and ROI 2) to be analyzed the FC. FC decreased in HIV ANXs between ROI1 and the right middle occipital gyrus, the right SOG, FC between ROI2 and left ITG increased in HIV ANXs. No significant structural difference was found between two groups. Pro-inflammatory chemokines showed higher levels in the HIV ANXs. Pro-inflammatory cytokines, neurotrophic factors, and endocrine factors were significantly correlated with alterations in brain function.

Conclusion

This study suggests that patients with HIV-associated anxiety disorders may exhibit abnormalities in neurologic, immune, and endocrine functioning. Consequently, it is imperative to implement additional screening and intervention measures for anxiety disorders among PLWH.

Age-dependent alterations in the coordinated development of subcortical regions in adolescents with social anxiety disorder

Subcortical brain regions play essential roles in the pathology of social anxiety disorder (SAD). While adolescence is the peak period of SAD, the relationships between altered development of the subcortical regions during this period and SAD are still unclear. This study investigated the age-dependent alterations in structural co-variance among subcortical regions and between subcortical and cortical regions, aiming to reflect aberrant coordination during development in the adolescent with SAD. High-resolution T1-weighted images were obtained from 76 adolescents with SAD and 67 healthy controls (HC), ranging from 11 to 17.9 years. Symptom severity was evaluated with the Social Anxiety Scale for Children (SASC) and the Depression Self Rating Scale for Children (DSRS-C). Structural co-variance and sliding age-window analyses were used to detect age-dependent group differences in inter-regional coordination patterns among subcortical regions and between subcortical and cortical regions. The volume of the striatum significantly correlated with SAD symptom severity. The SAD group exhibited significantly enhanced structural co-variance among key regions of the striatum (putamen and caudate). While the co-variance decreased with age in healthy adolescents, the co-variance in SAD adolescents stayed high, leading to more apparent group differences in middle adolescence. Moreover, the striatum's mean structural co-variance with cortical regions decreased with age in HC but increased with age in SAD. Adolescents with SAD suffer aberrant developmental coordination among the key regions of the striatum and between the striatum and cortical regions. The degree of incoordination is age-dependent, which may represent a neurodevelopmental trait of SAD.

EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task

Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.

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.

Alterations in the topological organization of the default-mode network in Tourette syndrome

BACKGROUND

The exact pathophysiology of TS is still elusive. Previous studies have identified default mode networks (DMN) abnormalities in patients with TS. However, these literatures investigated the neural activity during the tic suppression, not a true resting-state. Therefore, this study aimed to reveal the neural mechanism of Tourette's syndrome (TS) from the perspective of topological organization and functional connectivity within the DMN by electroencephalography (EEG) in resting-state.

METHODS

The study was conducted by analyzing the EEG data of TS patients with graph theory approaches. Thirty children with TS and thirty healthy controls (HCs) were recruited, and all subjects underwent resting-state EEG data acquisition. Functional connectivity within the DMN was calculated, and network properties were measured.

RESULTS

A significantly lower connectivity in the neural activity of the TS patients in the β band was found between the bilateral posterior cingulate cortex/retrosplenial cortex (t = -3.02, p < 0.05). Compared to HCs, the TS patients' local topological properties (degree centrality) in the left temporal lobe in the γ band were changed, while the global topological properties (global efficiency and local efficiency) in DMN exhibited no significant differences. It was also demonstrated that the degree centrality of the left temporal lobe in the γ band was positively related to the Yale Global Tic Severity Scale scores (r = 0.369, p = 0.045).

CONCLUSIONS

The functional connectivity and topological properties of the DMN of TS patients were disrupted, and abnormal DMN topological property alterations might affect the severity of tic in TS patients. The abnormal topological properties of the DMN in TS patients may be due to abnormal functional connectivity alterations. The findings provide novel insight into the neural mechanism of TS patients.

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 topological organization of white matter structural networks in high myopia patients revealed by diffusion kurtosis imaging and tractography

Introduction

High myopia (HM) is a public health issue that can lead to severe visual impairment. Previous studies have exhibited widespread white matter (WM) integrity damage in HM patients. However, how these WM damages are topologically related, and the network-level structural disruptions underlying HM has not been fully defined. We aimed to assess the alterations of brain WM structural networks in HM patients using diffusion kurtosis imaging (DKI) and tractography in the present study.

Methods

Individual whole-brain and ROI-level WM networks were constructed using DKI tractography in 30 HM patients and 33 healthy controls. Graph theory analysis was then applied to explore the altered global and regional network topological properties. Pearson correlations between regional properties and disease duration in the HM group were also assessed.

Results

For global topology, although both groups showed a small-world network organization, HM patients exhibited significant decreased local efficiency and clustering coefficient compared with controls. For regional topology, HM patients and controls showed highly similar hub distributions, except for three additional hub regions in HM patients including left insula, anterior cingulate and paracingulate gyri (ACG), and median cingulate and paracingulate gyri (DCG). In addition, HM patients showed significantly altered nodal betweenness centrality (BC) mainly in the bilateral inferior occipital gyrus (IOG), left superior occipital gyrus (SOG), caudate nucleus, rolandic operculum and right putamen, pallidum, and gyrus rectus compared with controls. Intriguingly, the nodal BC of left IOG was negatively correlated with disease duration in HM patients.

Discussion

Our findings suggest that HM exhibited alterations in WM structural networks as indicated by decreased local specialization. This study may advance the current understanding of the pathophysiological mechanisms underlying HM.

Functional magnetic resonance imaging study on anxiety and depression disorders induced by chronic restraint stress in rats

Persistent and negative stress stimulation is one of the most important factors leading to anxiety and depression in individuals, and it can negatively affect the normal function and structure of brain-related regions. However, the maladaptive changes of brain neural networks in anxiety and depression induced by chronic stress have not been explored in detail. In this study, we analyzed the changes in global information transfer efficiency, stress related blood oxygen level dependent (BOLD)- and diffusion tensor imaging (DTI)- signals and functional connectivity (FC) in rat models based on resting-state functional magnetic resonance imaging (rs-fMRI). The results showed that compared to control group, rats treated with chronic restraint stress (CRS) for 5 weeks had reconstructed the small-world network properties. In addition, CRS group had increased coherence and activity in bilateral Striatum (ST_R & L), but decreased coherence and activity in unilateral (left) Frontal Association Cortex (FrA_L) and unilateral (left) Medial Entorhinal Cortex (MEC_L). DTI analysis and correlation analysis confirmed the disrupted integrity of MEC_L and ST_R & L and their correlation to anxiety- and depressive-liked behaviors. Functional connectivity further showed these regions of interest (ROI) had decreased positive correlations with several brain areas, respectively. Our study comprehensively revealed the adaptive changes of brain neural networks induced by chronic stress and emphasized the abnormal activity and functional connectivity of ST_R & L and MEC_L in the pathological condition.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

Introduction

The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks.

Methods

We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC).

Results

PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM).

Discussion

Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.

Topography of the Anxious Self: Abnormal Rest-Task Modulation in Social Anxiety Disorder

Social anxiety disorder (SAD) is characterized by social anxiety/fear, self-attention, and interoception. Functional magnetic resonance imaging studies demonstrate increased activity during symptom-sensitive tasks in regions of the default-mode network (DMN), amygdala (AMG), and salience network (SN). What is the source of this task-unspecific symptom-sensitive hyperactivity in DMN? We address this question by probing SAD resting state (rs) changes in DMN including their relation to other regions as possible source of task-unspecific hyperactivity in the same regions. Our findings show the following: (1) rs-hypoconnectivity within-DMN regions; (2) rs-hyperconnectivity between DMN and AMG/SN; (3) task-evoked hyperactivity in the abnormal rs-regions of DMN and AMG/SN during different symptom-sensitive tasks; (4) negative relationship of rest and task changes in especially anterior DMN regions as their rs-hypoconnectivity is accompanied by task-unspecific hyperactivity; (5) abnormal top-down/bottom-up modulation between anterior DMN regions and AMG during rest and task. Findings demonstrate that rs-hypoconnectivity among DMN regions is negatively related to task-unspecific hyperactivity in DMN and AMG/SN. We propose a model of "Topography of the Anxious Self" in SAD (TAS-SAD). Abnormal DMN-AMG/SN topography during rest, as trait feature of an "unstable social self", is abnormally aggravated during SAD-sensitive situations resulting in task-related hyperactivity in the same regions with an "anxious self" as state feature.

Altered single-subject gray matter structural networks in social anxiety disorder

Previous fMRI studies have reported more random brain functional graph configurations in social anxiety disorder (SAD). However, it is still unclear whether the same configurations would occur in gray matter (GM) graphs. Structural MRI was performed on 49 patients with SAD and on 51 age- and gender-matched healthy controls (HC). Single-subject GM networks were obtained based on the areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared, and the structure-function coupling was examined. These network measures were further correlated with the clinical characteristics in the SAD group. Compared with controls, the SAD patients demonstrated globally decreased clustering coefficient and characteristic path length. Altered topological properties were found in the fronto-limbic and sensory processing systems. Altered metrics were associated with the illness duration of SAD. Compared with the HC group, the SAD group exhibited significantly decreased structural-functional decoupling. Furthermore, structural-functional decoupling was negatively correlated with the symptom severity in SAD. These findings highlight less-optimized topological configuration of the brain structural networks in SAD, which may provide insights into the neural mechanisms underlying the excessive fear and avoidance of social interactions in SAD.

Candidate Biological Markers for Social Anxiety Disorder: A Systematic Review

Social anxiety disorder (SAD) is a common psychiatric condition associated with a high risk of psychiatric comorbidity and impaired social/occupational functioning when not promptly treated. The identification of biological markers may facilitate the diagnostic process, leading to an early and proper treatment. Our aim was to systematically review the available literature about potential biomarkers for SAD. A search in the main online repositories (PubMed, ISI Web of Knowledge, PsychInfo, etc.) was performed. Of the 662 records screened, 61 were included. Results concerning cortisol, neuropeptides and inflammatory/immunological/neurotrophic markers remain inconsistent. Preliminary evidence emerged about the role of chromosome 16 and the endomannosidase gene, as well as of epigenetic factors, in increasing vulnerability to SAD. Neuroimaging findings revealed an altered connectivity of different cerebral areas in SAD patients and amygdala activation under social threat. Some parameters such as salivary alpha amylase levels, changes in antioxidant defenses, increased gaze avoidance and QT dispersion seem to be associated with SAD and may represent promising biomarkers of this condition. However, the preliminary positive correlations have been poorly replicated. Further studies on larger samples and investigating the same biomarkers are needed to identify more specific biological markers for SAD.

Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor function in living humans in health and disease.

Resting-state neuroimaging in social anxiety disorder: a systematic review

There has been a growing interest in resting-state brain alterations in people with social anxiety disorder. However, the evidence has been mixed and contested and further understanding of the neurobiology of this disorder may aid in informing methods to increase diagnostic accuracy and treatment targets. With this systematic review, we aimed to synthesize the findings of the neuroimaging literature on resting-state functional activity and connectivity in social anxiety disorder, and to summarize associations between brain and social anxiety symptoms to further characterize the neurobiology of the disorder. We systematically searched seven databases for empirical research studies. Thirty-five studies met the inclusion criteria, with a total of 1611 participants (795 people with social anxiety disorder and 816 controls). Studies involving resting-state seed-based functional connectivity analyses were the most common. Individuals with social anxiety disorder (vs. controls) displayed both higher and lower connectivity between frontal-amygdala and frontal-parietal regions. Frontal regions were the most consistently implicated across other analysis methods, and most associated with social anxiety symptoms. Small sample sizes and variation in the types of analyses used across studies may have contributed to the inconsistencies in the findings of this review. This review provides novel insights into established neurobiological models of social anxiety disorder and provides an update on what is known about the neurobiology of this disorder in the absence of any overt tasks (i.e., resting state). The knowledge gained from this body of research enabled us to also provide recommendations for a more standardized imaging pre-processing approach to examine resting-state brain activity and connectivity that could help advance knowledge in this field. We believe this is warranted to take the next step toward clinical translation in social anxiety disorder that may lead to better treatment outcomes by informing the identification of neurobiological targets for treatment.

Alteration of resting-state functional connectivity network properties in patients with social anxiety disorder after virtual reality-based self-training

Social anxiety disorder (SAD) is a mental disorder characterized by excessive anxiety in social situations. This study aimed to examine the alteration of resting-state functional connectivity in SAD patients related to the virtual reality-based self-training (VRS) which enables exposure to social situations in a controlled environment. Fifty-two SAD patients were randomly assigned to the experimental group who received the VRS, or the control group who did not. Self-report questionnaires and resting-state functional magnetic resonance imaging (fMRI) were performed to assess clinical symptoms and analyze the resting-state network properties, respectively. Significant decrease in social anxiety and an increase in self-esteem was found in the experimental group. From the resting-state fMRI analysis, alteration of local network properties in the left dorsolateral prefrontal gyrus (-10.0%, p = 0.025), left inferior frontal gyrus (-32.3%, p = 0.044), left insula (-17.2%, p = 0.046), left Heschl's gyrus (-21.2%, p = 0.011), bilateral inferior temporal gyrus (right: +122.6%, p = 0.045; left:-46.7%, p = 0.015), and right calcarine sulcus (+17.0%, p = 0.010) were found in the experimental group. Average shortest path length (+8.3%, p = 0.008) and network efficiency (-7.6%, p = 0.011) are found to be altered from the global network property analysis. In addition, the experimental group displayed more positive and more negative changes in the correlation trend of average shortest path length (p = 0.004) and global network efficiency (p = 0.014) with the severity of social anxiety, respectively. These results suggest potential effectiveness of the VRS, which is possibly related to the change of aberrant processing and control of visual and auditory linguistic stimuli and the adaptive change in rumination pattern.

Global topology alteration of the brain functional network affects the 8-week antidepressant response in major depressive disorder

PURPOSE

Previous studies have indicated that the global topology of the brain functional network in patients with major depressive disorder (MDD) differs from that of those with normal controls (NCs). However, the relationship between an altered global topology and the response to antidepressants remains unclear. Here, we investigated whether differences in global topology affect the efficacy of antidepressants in MDD patients.

METHODS

108 MDD patients and 61 NCs were recruited. A magnetic resonance imaging (MRI) scan was performed at the baseline, and the Hamilton Depression Scale-24 (HAMD-24) was assessed at baseline and after 2 and 8 weeks of antidepressant treatment. Seven global topological parameters of the brain functional network were measured and compared between groups. A correlation analysis was performed to identify the relationships between global topological parameters and antidepressant efficacy.

RESULTS

The brain networks of MDD patients and NCs were both small-world networks. The clustering coefficient (C_p) and local efficiency (E_loc) were significantly smaller in MDD patients compared with those in NCs. The characteristic path length (L_p) were negatively correlated with the 8-week reductive rate of HAMD-24 in the MDD group.

CONCLUSION

The present research found that the brain functional network of MDD patients still had a small-world organization but with a lower C_p and E_loc than the NCs. In addition, the brain network global topology might have an impact on the antidepressant response and thus had the potential to become a treatment predictor of MDD.

Disrupted fronto-temporal function in panic disorder: a resting-state connectome study

Recent neuroimaging studies have identified alterations in activity and connectivity among many brain regions as potential biomarkers for panic disorder. However, the functional connectome of panic disorder is not well understood. Therefore, a graph-theoretical approach was applied in this study to construct functional networks of patients and healthy controls in order to discover topological changes in panic disorder. 31 patients and 33 age and sex matched healthy controls underwent resting-state functional magnetic resonance imaging. Brain networks for each participant were structured using nodes from the Anatomical Automatic Labeling template and edges from connectivity matrices. Then, topological organizations of networks were calculated. Network-based statistical analysis was conducted, and global and nodal properties were compared between patients and controls. Unlike controls, patients with panic disorder displayed a small-world network. Patients also revealed decreased nodal efficiency in right superior frontal gyrus (SFG), middle frontal gyrus (MFG), right superior temporal gyrus (STG), and left middle temporal gyrus (MTG). Decreased functional connectivity was found in panic disorder between right MTG and extensive temporal regions. Among these disrupted regions, the decreased nodal efficiency of SFG showed a positive correlation with clinical symptoms while nodal betweenness centrality in angular gyrus showed a negative correlation. Our results indicated decreased function of global and regional information transmission in panic disorder and emphasized the role of temporal regions in its pathology.

Viewing Ambiguous Social Interactions Increases Functional Connectivity between Frontal and Temporal Nodes of the Social Brain

Social behavior is coordinated by a network of brain regions, including those involved in the perception of social stimuli and those involved in complex functions, such as inferring perceptual and mental states and controlling social interactions. The properties and function of many of these regions in isolation are relatively well understood, but less is known about how these regions interact while processing dynamic social interactions. To investigate whether the functional connectivity between brain regions is modulated by social context, we collected fMRI data from male monkeys (Macaca mulatta) viewing videos of social interactions labeled as "affiliative," "aggressive," or "ambiguous." We show activation related to the perception of social interactions along both banks of the superior temporal sulcus, parietal cortex, medial and lateral frontal cortex, and the caudate nucleus. Within this network, we show that fronto-temporal functional connectivity is significantly modulated by social context. Crucially, we link the observation of specific behaviors to changes in functional connectivity within our network. Viewing aggressive behavior was associated with a limited increase in temporo-temporal and a weak increase in cingulate-temporal connectivity. By contrast, viewing interactions where the outcome was uncertain was associated with a pronounced increase in temporo-temporal, and cingulate-temporal functional connectivity. We hypothesize that this widespread network synchronization occurs when cingulate and temporal areas coordinate their activity when more difficult social inferences are being made.SIGNIFICANCE STATEMENT Processing social information from our environment requires the activation of several brain regions, which are concentrated within the frontal and temporal lobes. However, little is known about how these areas interact to facilitate the processing of different social interactions. Here we show that functional connectivity within and between the frontal and temporal lobes is modulated by social context. Specifically, we demonstrate that viewing social interactions where the outcome was unclear is associated with increased synchrony within and between the cingulate cortex and temporal cortices. These findings suggest that the coordination between the cingulate and temporal cortices is enhanced when more difficult social inferences are being made.

Altered structural balance of resting-state networks in autism

What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks' complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD), which has a surprising heterogeneity in manifestations with no clear-cut neuropathology. Accordingly, structural balance theory (SBT) affirms that in real-world signed networks, a link is remarkably influenced by each of its two nodes' interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matters in ASD, besides and beyond hypo (hyper) pair connectivity. To address this question, we explore triadic interactions based on SBT in the weighted signed resting-state functional magnetic resonance imaging networks of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing first-time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distributions of different triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks. Moreover, results reveal that the scale of change in the whole-brain networks' energy is more narrow in the ASD networks during development. Last but not least, we observe that energy of the salience network and the default mode network are lower in ASD, which may be a reflection of the difficulty in dynamic switching and flexible behaviors. Altogether, these results provide insight into the atypical structural balance of the ASD brain (sub) networks. It also highlights the potential value of SBT as a new perspective in functional connectivity studies, especially in the case of neurodevelopmental disorders.

Hemisphere-Specific Functional Remodeling and Its Relevance to Tumor Malignancy of Cerebral Glioma Based on Resting-State Functional Network Analysis

BACKGROUND

Functional remodeling may vary with tumor aggressiveness of glioma. Investigation of the functional remodeling is expected to provide scientific relevance of tumor characterization and disease management of glioma. In this study, we aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis.

SUBJECTS AND METHODS

One hundred and twenty-six right-handed subjects with histologically confirmed cerebral glioma were included with 80 tumors located in the left hemisphere (LH) and 46 tumors located in the right hemisphere (RH). Resting-state functional networks of the contralesional hemisphere were constructed using the human brainnetome atlas based on resting-state fMRI data. Functional connectivity and topological features of functional networks were quantified. The performance of functional features in predicting the glioma grade was evaluated using area under (AUC) the receiver operating characteristic curve (ROC). The dataset was divided into training and validation datasets. Features with high AUC values in malignancy classification in the training dataset were determined as predictive features. An MLR model was constructed based on predictive features and its classification performance was evaluated on the training and validation datasets with 10-fold cross validation.

RESULTS

Predictive functional features showed apparent hemispheric specifications. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas.

CONCLUSION

Functional remodeling of the contralesional hemisphere was hemisphere-specific and highly predictive of the malignant grade of glioma. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level.

Prefrontal Disinhibition in Social Fear: A Vital Action of Somatostatin Interneurons

Social fear and avoidance of social partners and social situations represent the core behavioral symptom of Social Anxiety Disorder (SAD), a prevalent psychiatric disorder worldwide. The pathological mechanism of SAD remains elusive and there are no specific and satisfactory therapeutic options currently available. With the development of appropriate animal models, growing studies start to unravel neuronal circuit mechanisms underlying social fear, and underscore a fundamental role of the prefrontal cortex (PFC). Prefrontal cortical functions are implemented by a finely wired microcircuit composed of excitatory principal neurons (PNs) and diverse subtypes of inhibitory interneurons (INs). Disinhibition, defined as a break in inhibition via interactions between IN subtypes that enhances the output of excitatory PNs, has recently been discovered to serve as an efficient strategy in cortical information processing. Here, we review the rodent animal models of social fear, the prefrontal IN diversity, and their circuits with a particular emphasis on a novel disinhibitory microcircuit mediated by somatostatin-expressing INs in gating social fear behavior. The INs subtype distinct and microcircuit-based mechanism advances our understanding of the etiology of social fear and sheds light on developing future treatment of neuropsychiatric disorders associated with social fear.

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.

Aberrant Gray Matter Networks in Non-comorbid Medication-Naive Patients With Major Depressive Disorder and Those With Social Anxiety Disorder

Major depressive disorder (MDD) and social anxiety disorder (SAD) are among the most prevalent and frequently co-occurring psychiatric disorders in adults and may have, at least in part, a common etiology. However, the unique and the shared neuroanatomical characteristics of the two disorders have not been fully identified. The aim of this study was to compare the topological organization of gray matter networks between non-comorbid medication-naive MDD patients and SAD patients. High-resolution T1-weighted images were acquired from 37 non-comorbid medication-naive MDD patients, 24 non-comorbid medication-naive SAD patients, and 41 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the three groups. The relationships between brain network measures and clinical characteristics were analyzed. Relative to healthy controls, both the MDD and the SAD patients showed global decreases in clustering coefficient, normalized clustering coefficient, and small-worldness and locally decreased nodal centralities and morphological connections in the left insular, lingual, and calcarine cortices. Compared with healthy controls, the SAD patients exhibited increased nodal centralities and morphological connections mainly involving the prefrontal cortex and the sensorimotor network. Furthermore, compared to the SAD patients, the MDD patients showed increased characteristic path length, reduced global efficiency, and decreased nodal centralities and morphological connections in the right middle occipital gyrus and the right postcentral gyrus. Our findings provide new evidence for shared and specific similarity-based gray matter network alterations in MDD and SAD and emphasize that the psychopathological changes in the right middle occipital gyrus and the right postcentral gyrus might be different between the two disorders.

Imaging the socially-anxious brain: recent advances and future prospects

Social anxiety disorder (SAD) is serious psychiatric condition with a genetic background. Insight into the neurobiological alterations underlying the disorder is essential to develop effective interventions that could relieve SAD-related suffering. In this expert review, we consider recent neuroimaging work on SAD. First, we focus on new results from magnetic resonance imaging studies dedicated to outlining biomarkers of SAD, including encouraging findings with respect to structural and functional brain alterations associated with the disorder. Furthermore, we highlight innovative studies in the field of neuroprediction and studies that established the effects of treatment on brain characteristics. Next, we describe novel work aimed to delineate endophenotypes of SAD, providing insight into the genetic susceptibility to develop the disorder. Finally, we outline outstanding questions and point out directions for future research.

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.

Whole-Brain Functional Networks for Phonological and Orthographic Processing in Chinese Good and Poor Readers

The neural basis of dyslexia in different languages remains unresolved, and it is unclear whether the phonological deficit as the core deficit of dyslexia is language-specific or universal. The current functional magnetic resonance imaging (fMRI) study using whole-brain data-driven network analyses investigated the neural mechanisms for phonological and orthographic processing in Chinese children with good and poor reading ability. Sixteen good readers and 16 poor readers were requested to make homophone judgments (phonological processing) and component judgments (visual-orthographic processing) of presented Chinese characters. Poor readers displayed worse performance than the good readers in phonological processing, but not in orthographic processing. Whole-brain activation analyses showed compensatory activations in the poor readers during phonological processing and automatic phonological production activation in the good readers during orthographic processing. Significant group differences in the topological properties of their brain networks were found only in orthographic processing. Analyses of nodal degree centrality and betweenness centrality revealed significant group differences in both phonological and orthographic processing. The present study supports the phonological core deficit hypothesis of reading difficulty in Chinese. It also suggests that Chinese good and poor readers might recruit different strategies and neural mechanisms for orthographic processing.

Abnormal spontaneous brain activity in patients with generalized anxiety disorder revealed by resting-state functional MRI

This study aimed to examine the spontaneous hemodynamic fluctuations and the dysfunctional brain regions in patients with generalized anxiety disorder (GAD) using resting-state functional MRI. Previous studies have demonstrated some neural networks that are different in this patient group compared with healthy controls. However, there is still a need for precise anatomical localization of the aberrantly operating networks. We used resting-state functional MRI to measure the hemodynamic fluctuations in 31 GAD patients and 31 control participants matched for sex, age, and education. On the basis of the hemodynamic fluctuations, we calculated regional homogeneity (ReHo) for our comparisons as this measure reflects coherent fluctuations in adjacently located brain regions. The Hamilton Anxiety Rating Scale was used to assess symptom severity of the GAD patients. There were no significant differences with respect to age, sex, handedness, and education. However, compared with controls, GAD patients showed higher Hamilton Anxiety Rating Scale scores (16.90±1.94, P<0.05). We identified decreased ReHo measures in the right inferior frontal gyrus and the left caudate nucleus in the GAD group compared with the healthy controls. In addition, we found increased ReHo measures within the left cingulate gyrus. This study further complements the network characteristics in anxiety patients and presents new and more accurate anatomical positioning about GAD patients.

Increased functional segregation of brain network associated with symptomatology and sustained attention in chronic post-traumatic stress disorder

BACKGROUND

Traditional regional or voxel-based analyses only focus on specific brain regions or connectivity rather than the whole brain's functional organization. Using resting state functional magnetic resonance imaging (rs-fMRI), we aimed to explore the altered topological metrics, clinical symptoms and cognitive function in chronic post-traumatic stress disorder (PTSD) in order to identify the brain network mechanisms underlying these clinical and cognitive symptoms.

METHODS

Forty patients with unmedicated chronic PTSD and forty-two matched trauma-exposed healthy controls (TEHCs) underwent rs-fMRI, and the topological organization of the whole-brain network was calculated using graph theory. The Rapid Visual Information Processing (RVP) task and Wechsler Memory Scale-IV (WMS-IV) were used to evaluate the subjects' sustained attention and memory capacity. All clinical and cognitive measures and topological parameters of the PTSD patients and TEHCs were compared, and the relationships between altered network metrics and symptom severity were explored.

RESULTS

Compared with the TEHCs, the patients showed increases in the normalized clustering coefficient, small-worldness, normalized local efficiency and efficiency-based small-worldness. The left middle occipital gyrus showed increases in nodal global efficiency and nodal degree that were negatively correlated with the severity of PTSD symptoms. The altered connections in PTSD only involved the default mode network (DMN) and the occipital network.

LIMITATIONS

Comorbid conditions were included, and current cross-sectional study cannot conclude on causality.

CONCLUSIONS

Patients with chronic PTSD showed increased functional brain network segregation, mainly in the occipital cortex, which could be a protective or compensatory mechanism to alleviate clinical symptoms.

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

Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

Resting regional brain metabolism in social anxiety disorder and the effect of moclobemide therapy

While there is mounting evidence of abnormal reactivity of several brain regions in social anxiety disorder, and disrupted functional connectivity between these regions at rest, relatively little is known regarding resting regional neural activity in these structures, or how such activity is affected by pharmacotherapy. Using 2-deoxy-2-(F-18)fluoro-D-glucose positron emission tomography, we compared resting regional brain metabolism between SAD and healthy control groups; and in SAD participants before and after moclobemide therapy. Voxel-based analyses were confined to a predefined search volume. A second, exploratory whole-brain analysis was conducted using a more liberal statistical threshold. Fifteen SAD participants and fifteen matched controls were included in the group comparison. A subgroup of SAD participants (n = 11) was included in the therapy effect comparison. No significant clusters were identified in the primary analysis. In the exploratory analysis, the SAD group exhibited increased metabolism in left fusiform gyrus and right temporal pole. After therapy, SAD participants exhibited reductions in regional metabolism in a medial dorsal prefrontal region and increases in right caudate, right insula and left postcentral gyrus. This study adds to the limited existing work on resting regional brain activity in SAD and the effects of therapy. The negative results of our primary analysis suggest that resting regional activity differences in the disorder, and moclobemide effects on regional metabolism, if present, are small. While the outcomes of our secondary analysis should be interpreted with caution, they may contribute to formulating future hypotheses or in pooled analyses.

Brain and Social Networks: Fundamental Building Blocks of Human Experience

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models.