Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group

Alterations in cortical thickness of frontoparietal regions in patients with social anxiety disorder

Although functional changes of the frontal and (para)limbic area for emotional hyper-reactivity and emotional dysregulation are well documented in social anxiety disorder (SAD), prior studies on structural changes have shown mixed results. This study aimed to identify differences in cortical thickness between SAD and healthy controls (CON). Thirty-five patients with SAD and forty-two matched CON underwent structural magnetic resonance imaging. A vertex-based whole brain and regional analyses were conducted for between-group comparison. The whole-brain analysis revealed increased cortical thickness in the left insula, left superior parietal lobule, left superior temporal gyrus, and left frontopolar cortex in patients with SAD compared to CON, as well as decreased thickness in the left superior/middle frontal gyrus and left fusiform gyrus in patients (after multiple-correction). The results from the ROI analysis did not align with these findings at the statistically significant level after multiple corrections. Changes in cortical thickness were not correlated with social anxiety symptoms. While consistent results were not obtained from different analysis methods, the results from the whole-brain analysis suggest that patients with SAD exhibit distinct neural deficits in areas involved in salience, attention, and socioemotional processing.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Grey matter structural alterations in anxiety disorders: a voxel-based meta-analysis

Anxiety disorders (ADs) are a group of prevalent and destructive mental illnesses, but the current understanding of their underlying neuropathology is still unclear. Employing voxel-based morphometry (VBM), previous studies have demonstrated several common brain regions showing grey matter volume (GMV) abnormalities. However, contradictory results have been reported among these studies. Considering that different subtypes of ADs exhibit common core symptoms despite different diagnostic criteria, and previous meta-analyses have found common core GMV-altered brain regions in ADs, the present research aimed to combine the results of individual studies to identify common GMV abnormalities in ADs. Therefore, we first performed a systematic search in PubMed, Embase, and Web of Science on studies investigating GMV differences between patients with ADs and healthy controls (HCs). Then, the anisotropic effect-size signed differential mapping (AES-SDM) was applied in this meta-analysis. A total of 24 studies (including 25 data sets) were included in the current study, and 906 patients with ADs and 1003 HCs were included. Compared with the HCs, the patients with ADs showed increased GMV in the left superior parietal gyrus, right angular gyrus, left precentral gyrus, and right lingual gyrus, and decreased GMV in the bilateral insula, bilateral thalamus, left caudate, and right putamen. In conclusion, the current study has identified some abnormal GMV brain regions that are related to the pathological mechanisms of anxiety disorders. These findings could contribute to a better understanding of the underlying neuropathology of ADs.

Emotion in action: When emotions meet motor circuits

The brain is a remarkably complex organ responsible for a wide range of functions, including the modulation of emotional states and movement. Neuronal circuits are believed to play a crucial role in integrating sensory, cognitive, and emotional information to ultimately guide motor behavior. Over the years, numerous studies employing diverse techniques such as electrophysiology, imaging, and optogenetics have revealed a complex network of neural circuits involved in the regulation of emotional or motor processes. Emotions can exert a substantial influence on motor performance, encompassing both everyday activities and pathological conditions. The aim of this review is to explore how emotional states can shape movements by connecting the neural circuits for emotional processing to motor neural circuits. We first provide a comprehensive overview of the impact of different emotional states on motor control in humans and rodents. In line with behavioral studies, we set out to identify emotion-related structures capable of modulating motor output, behaviorally and anatomically. Neuronal circuits involved in emotional processing are extensively connected to the motor system. These circuits can drive emotional behavior, essential for survival, but can also continuously shape ongoing movement. In summary, the investigation of the intricate relationship between emotion and movement offers valuable insights into human behavior, including opportunities to enhance performance, and holds promise for improving mental and physical health. This review integrates findings from multiple scientific approaches, including anatomical tracing, circuit-based dissection, and behavioral studies, conducted in both animal and human subjects. By incorporating these different methodologies, we aim to present a comprehensive overview of the current understanding of the emotional modulation of movement in both physiological and pathological conditions.

Brainwide Mendelian Randomization Study of Anxiety Disorders and Symptoms

BACKGROUND

To gain insights into the role of brain structure and function on anxiety (ANX), we conducted a genetically informed investigation leveraging information from ANX genome-wide association studies available from the UK Biobank (n = 380,379), the FinnGen Program (n = 290,361), and the Million Veteran Program (n = 175,163) together with UK Biobank genome-wide data (n = 33,224) related to 3935 brain imaging-derived phenotypes (IDPs).

METHODS

A genetic correlation analysis between ANX and brain IDPs was performed using linkage disequilibrium score regression. To investigate ANX-brain associations, a 2-sample Mendelian randomization was performed considering multiple methods and sensitivity analyses. A subsequent multivariable Mendelian randomization was conducted to distinguish between direct and indirect effects. Finally, a generalized linear model was used to explore the associations of brain IDPs with ANX symptoms.

RESULTS

After false discovery rate correction (q < .05), we identified 41 brain IDPs genetically correlated with ANX without heterogeneity among the datasets investigated (i.e., UK Biobank, FinnGen, and Million Veteran Program). Six of these IDPs showed genetically inferred causal effects on ANX. In the subsequent multivariable Mendelian randomization analysis, reduced area of the right posterior middle cingulate gyrus (β = -0.09, p = 8.01 × 10-4) and reduced gray matter volume of the right anterior superior temporal gyrus (β = -0.09, p = 1.55 × 10-3) had direct effects on ANX. In the ANX symptom-level analysis, the right posterior middle cingulate gyrus was negatively associated with "tense, sore, or aching muscles during the worst period of anxiety" (β = -0.13, p = 8.26 × 10-6).

CONCLUSIONS

This study identified genetically inferred effects that are generalizable across large cohorts, thereby contributing to our understanding of how changes in brain structure and function can lead to ANX.

Altered Putamen Activation for Social Comparison-Related Feedback in Social Anxiety Disorder: A Pilot Study

INTRODUCTION

Social anxiety disorder (SAD) is characterized by abnormal processing of performance-related social stimuli. Previous studies have shown altered emotional experiences and activations of different sub-regions of the striatum during processing of social stimuli in patients with SAD. However, whether and to what extent social comparisons affect behavioural and neural responses to feedback stimuli in patients with SAD is unknown.

MATERIALS AND METHODS

To address this issue, emotional ratings and functional magnetic resonance imaging (fMRI) responses were assessed while patients suffering from SAD and healthy controls (HC) were required to perform a choice task and received performance feedback (correct, incorrect, non-informative) that varied in relation to the performance of fictitious other participants (a few, half, or most of others had the same outcome).

RESULTS

Across all performance feedback conditions, fMRI analyses revealed reduced activations in bilateral putamen when feedback was assumed to be received by only a few compared to half of the other participants in patients with SAD. Nevertheless, analysis of rating data showed a similar modulation of valence and arousal ratings in patients with SAD and HC depending on social comparison-related feedback.

CONCLUSIONS

This suggests altered neural processing of performance feedback depending on social comparisons in patients with SAD.

Brain-Wide Mendelian Randomization Study of Anxiety Disorders and Symptoms

Background

To gain insights into the role of brain structure and function on anxiety (ANX), we conducted a genetically informed investigation leveraging information from ANX genome-wide association studies available from UK Biobank (UKB; N=380,379), FinnGen Program (N=290,361), and Million Veteran Program (MVP; N=199,611) together with UKB genome-wide data (N=33,224) related to 3,935 brain imaging-derived phenotypes (IDP).

Methods

A genetic correlation analysis between ANX and brain IDPs was performed using linkage disequilibrium score regression. To investigate ANX-brain associations, a two-sample Mendelian randomization (MR) was performed considering multiple methods and sensitivity analyses. A subsequent multivariable MR (MVMR) was executed to distinguish between direct and indirect effects. Finally, a generalized linear model was used to explore the associations of brain IDPs with ANX symptoms.

Results

After false discovery rate correction (FDR q<0.05), we identified 41 brain IDPs genetically correlated with ANX without heterogeneity among the datasets investigated (i.e., UKB, FinnGen, and MVP). Six of these IDPs showed genetically inferred causal effects on ANX. In the subsequent MVMR analysis, reduced area of the right posterior middle-cingulate gyrus (rpMCG; beta=-0.09, P= 8.01×10 -4 ) and reduced gray-matter volume of the right anterior superior temporal gyrus (raSTG; beta=-0.09, P=1.55×10 -3 ) had direct effects on ANX. In the ANX symptom-level analysis, rpMCG was negatively associated with "tense sore oraching muscles during the worst period of anxiety" (beta=-0.13, P=8.26×10 -6 ).

Conclusions

This study identified genetically inferred effects generalizable across large cohorts, contributing to understand how changes in brain structure and function can lead to ANX.

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.