Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder

Research hotspots and trends of neuroimaging in social anxiety: a CiteSpace bibliometric analysis based on Web of Science and Scopus database

Background

This study focused on the research hotspots and development trends of the neuroimaging of social anxiety (SA) in the past 25 years.

Methods

We selected 1,305 studies on SA neuroimaging from the Web of Science and Scopus from January 1998 to December 2023. CiteSpace was used to analyze the number of published articles visually, cited references, cooperation among authors and institutions, co-occurrence of keywords, clustering of keywords, burst of keywords, and time zone of co-occurring keywords.

Results

A total of 1,305 articles were included, and the annual number of articles published over nearly 25 years showed the overall trend is on the rise. The analysis of author and institutional collaboration reveals that most authors collaborate closely. Among them, the team led by Pine, Daniel S published 59 articles, making it the most central team. Harvard University is identified as the most central institution in this network. The research hotspots can be categorized into four areas: research techniques, cognitive processing research areas, core brain regions and brain networks, and the neural predictors of treatment outcomes in SA. The most recent burst keywords are "cognitive behavioral therapy," "systematic review," "machine learning," "major clinical study," "transcranial direct current stimulation," "depression," and "outcome assessment," which provided clues on research frontiers. Based on the burst map and keyword time zone map, it appears that exploring the activity of brain regions involved in cognitive processing, such as face processing and attentional bias, as well as the comorbidity of SA and depression, through brain imaging technology, using brain signals as predictors of treatment outcomes in SA.

Conclusion

This study conducted a comprehensive, objective, and visual analysis of publications, and revealed hot topics and trends concerning the study of the brain mechanism of SA from 1998 to 2023. This work might assist researchers in identifying new insights on potential collaborators and institutions, hot topics, and research directions.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Can Emotional Working Memory Training Improve Cognitive Behavioral Therapy Outcomes for Social Anxiety Disorder: A Pilot Study

Social anxiety disorder (SAD) models highlight maladaptive attention as a maintaining factor of SAD, potentially negatively impacting how individuals with SAD engage with cognitive behavioral therapy (CBT) content in a therapist's presence. Emotional working memory training (eWMT) has been shown to improve affective attentional control. This pilot study assessed the proposed methodology for a randomized controlled trial (RCT) to determine whether eWMT, by improving attentional control prior to internet-based CBT (iCBT), results in better CBT outcomes. The RCT would be considered feasible if the pilot study achieved rates ≥80% for eligible participants recruited, study measures completion, intervention completion, and participant retention. Results from 10 randomized participants showed rates ≥80% for recruitment of eligible participants and iCBT intervention completion. Completion of study measures, eWMT and Placebo training interventions, and participant retention were <80%. Results highlight the need to consider strategies to improve the methodology prior to the RCT.

Machine learning applied to functional magnetic resonance imaging in anxiety disorders

BACKGROUND

Brain functional abnormalities have been commonly reported in anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, and specific phobias. The role of functional abnormalities in the discrimination of these disorders can be tested with machine learning (ML) techniques. Here, we aim to provide a comprehensive overview of ML studies exploring the potential discriminating role of functional brain alterations identified by functional magnetic resonance imaging (fMRI) in anxiety disorders.

METHODS

We conducted a search on PubMed, Web of Science, and Scopus of ML investigations using fMRI as features in patients with anxiety disorders. A total of 12 studies (resting-state fMRI n = 5, task-based fMRI n = 6, resting-state and task-based fMRI n=1) met our inclusion criteria.

RESULTS

Overall, the studies showed that, regardless of the classifiers, alterations in functional connectivity and aberrant neural activation involving the amygdala, anterior cingulate cortex, hippocampus, insula, orbitofrontal cortex, temporal pole, cerebellum, default mode network, dorsal attention network, sensory network, and affective network were able to discriminate patients with anxiety from controls, with accuracies spanning from 36 % to 94 %.

LIMITATIONS

The small sample size, different ML approaches and heterogeneity in the selection of regions included in the multivariate pattern analyses limit the conclusions of the present review.

CONCLUSIONS

ML methods using fMRI as features can distinguish patients with anxiety disorders from healthy controls, indicating that these techniques could be used as a helpful tool for the diagnosis and the development of more targeted treatments for these disorders.

Triple network model of brain connectivity changes related to adverse mood effects in an oral contraceptive placebo-controlled trial

Combined oral contraceptives (COC) are among the most commonly used contraceptive methods worldwide, and mood side effects are the major reason for discontinuation of treatment. We here investigate the directed connectivity patterns associated with the mood side effects of an androgenic COC in a double-blind randomized, placebo-controlled trial in women with a history of affective COC side effects (n = 34). We used spectral dynamic causal modeling on a triple network model consisting of the default mode network (DMN), salience network (SN) and executive control network (ECN). Within this framework, we assessed the treatment-related changes in directed connectivity associated with adverse mood side effects. Overall, during COC use, we found a pattern of enhanced connectivity within the DMN and decreased connectivity within the ECN. The dorsal anterior cingulate cortex (SN) mediates an increased recruitment of the DMN by the ECN during treatment. Mood lability was the most prominent COC-induced symptom and also arose as the side effect most consistently related to connectivity changes. Connections that were related to increased mood lability showed increased connectivity during COC treatment, while connections that were related to decreased mood lability showed decreased connectivity during COC treatment. Among these, the connections with the highest effect size could also predict the participants' treatment group above chance.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Dorsal anterior cingulate cortex activity during cognitive challenge in social anxiety disorder

BACKGROUND

Social anxiety disorder (SAD) is associated with aberrant emotional information processing while little is known about non-emotional cognitive processing biases. The dorsal anterior cingulate cortex (dACC) has been implicated in SAD neuropathology and is activated both by emotional and non-affective cognitive challenges like the Multisource Interference Task (MSIT).

METHODS

Here, we used fMRI to compare dACC activity and test performance during MSIT in 69 SAD patients and 38 healthy controls. In addition to patient-control comparisons, we examined whether neural activity in the dACC correlated with social anxiety, trait anxiety or depression levels.

RESULTS

The MSIT activated the dACC as expected but with no differences in task performance or neural reactivity between SAD patients and controls. There were no significant correlations between dACC activity and social or trait anxiety symptom severity. In patients, there was a significant negative correlation between dACC activity and depressive symptoms.

CONCLUSIONS

In absence of affective challenge, we found no disorder-related cognitive profile in SAD patients since neither MSIT task performance nor dACC neural activity deviated in patients relative to controls.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Anterior cingulate cortex activation during attentional control as a transdiagnostic marker of psychotherapy response: a randomized clinical trial

Anterior cingulate cortex (ACC) response during attentional control in the context of task-irrelevant emotional faces is a promising biomarker of cognitive behavioral therapy (CBT) outcome in patients with social anxiety disorder (SAD). However, it is unclear whether this biomarker extends to major depressive disorder (MDD) and is specific to CBT outcome. In the current study, 72 unmedicated patients with SAD (n = 39) or MDD (n = 33) completed a validated emotional interference paradigm during functional magnetic resonance imaging before treatment. Participants viewed letter strings superimposed on task-irrelevant threat and neutral faces under low perceptual load (high interference) and high perceptual load (low interference). Biomarkers comprised anatomy-based rostral ACC (rACC) and dorsal ACC (dACC) response to task-irrelevant threat (>neutral) faces under low and high perceptual load. Patients were randomly assigned to 12 weeks of CBT or supportive therapy (ST) (ClinicalTrials.gov identifier: NCT03175068). Clinician-administered measures of social anxiety and depression severity were obtained at baseline and every 2 weeks throughout treatment (7 assessments total) by an assessor blinded to the treatment arm. A composite symptom severity score was submitted to latent growth curve models. Results showed more baseline rACC activity to task-irrelevant threat>neutral faces under low, but not high, perceptual load predicted steeper trajectories of symptom improvement throughout CBT or ST. Post-hoc analyses indicated this effect was driven by subgenual ACC (sgACC) activation. Findings indicate ACC activity during attentional control may be a transdiagnostic neural predictor of general psychotherapy outcome.

Genetics of social anxiety disorder: a systematic review

Social anxiety disorder (SAD) is a common psychiatric disorder, often associated with avoidant temperament. Research studies have implicated a strong genetic architecture of SAD. We have conducted a systematic review on the genetics of SAD and yielded 66 articles. In general, prior research studies have focused on the serotonin transporter, oxytocin receptor, brain-derived neurotrophic factor and catechol-O-methyltransferase genes. Mixed and inconsistent results have been reported. Additional approaches and phenotypes have also been investigated, including pharmacogenetics of treatment response, imaging genetics and gene-environment interactions. Future directions warrant further international collaborative efforts, deep-phenotyping of clinical characteristics including consistent and reliable measurement-based symptom severity, and larger sample sizes to ensure sufficient power for stratification due to the heterogeneity of this chronic and often debilitating condition.

Neurocircuitry of Treatment in Anxiety Disorders

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Moment-to-Moment Brain Signal Variability Reliably Predicts Psychiatric Treatment Outcome

BACKGROUND

Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes.

METHODS

A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes.

RESULTS

Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total r_{ACTUAL,PREDICTED} = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long.

CONCLUSIONS

Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

Acute Neurofunctional Effects of Escitalopram in Pediatric Anxiety: A Double-Blind, Placebo-Controlled Trial

OBJECTIVE

Amygdala-ventrolateral prefrontal cortex (VLPFC) circuitry is disrupted in pediatric anxiety disorders, yet how selective serotonin reuptake inhibitors (SSRIs) affect this circuitry is unknown. We examined the impact of the SSRI escitalopram on functional connectivity (FC) within this circuit, and whether early FC changes predicted treatment response in adolescents with generalized anxiety disorder (GAD).

METHOD

Resting-state functional magnetic resonance (MR) images were acquired before and after 2 weeks of treatment in 41 adolescents with GAD (12-17 years of age) who received double-blind escitalopram or placebo for 8 weeks. Change in amygdala-based whole-brain FC and anxiety severity were analyzed.

RESULTS

Controlling for age, sex, and pretreatment anxiety, escitalopram increased amygdala-VLPFC connectivity compared to placebo (F = 17.79, p = .002 FWE-corrected). This early FC change predicted 76.7% of the variability in improvement trajectory in patients who received escitalopram (p < .001) but not placebo (p = .169); the predictive power of early amygdala-VLPFC FC change significantly differed between placebo and escitalopram (p = .013). Furthermore, this FC change predicted improvement better than baseline FC or clinical/demographic characteristics. Exploratory analyses of amygdala subfields' FC revealed connectivity of left basolateral amygdala (BLA) -VLPFC (F = 19.64, p < .001 FWE-corrected) and superficial amygdala-posterior cingulate cortex (F = 22.92, p = .001 FWE-corrected) were also increased by escitalopram, but only BLA-VLPFC FC predicted improvement in anxiety over 8 weeks of treatment.

CONCLUSION

In adolescents with GAD, escitalopram increased amygdala-prefrontal connectivity within the first 2 weeks of treatment, and the magnitude of this change predicted subsequent clinical improvement. Early normalization of amygdala-VLPFC circuitry might represent a useful tool for identifying future treatment responders as well as a promising biomarker for drug development.

CLINICAL TRIAL REGISTRATION INFORMATION

Neurofunctional Predictors of Escitalopram Treatment Response in Adolescents With Anxiety; https://www.clinicaltrials.gov/; NCT02818751.

Clinical predictors of treatment response towards exposure therapy in virtuo in spider phobia: A machine learning and external cross-validation approach

While being highly effective on average, exposure-based treatments are not equally effective in all patients. The a priori identification of patients with a poor prognosis may enable the application of more personalized psychotherapeutic interventions. We aimed at identifying sociodemographic and clinical pre-treatment predictors for treatment response in spider phobia (SP). N = 174 patients with SP underwent a highly standardized virtual reality exposure therapy (VRET) at two independent sites. Analyses on group-level were used to test the efficacy. We applied a state-of-the-art machine learning protocol (Random Forests) to evaluate the predictive utility of clinical and sociodemographic predictors for a priori identification of individual treatment response assessed directly after treatment and at 6-month follow-up. The reliability and generalizability of predictive models was tested via external cross-validation. Our study shows that one session of VRET is highly effective on a group-level and is among the first to reveal long-term stability of this treatment effect. Individual short-term symptom reductions could be predicted above chance, but accuracies dropped to non-significance in our between-site prediction and for predictions of long-term outcomes. With performance metrics hardly exceeding chance level and the lack of generalizability in the employed between-site replication approach, our study suggests limited clinical utility of clinical and sociodemographic predictors. Predictive models including multimodal predictors may be more promising.

Neural processing of emotional facial stimuli in specific phobia: An fMRI study

BACKGROUND

Patients with specific phobia (SP) show altered brain activation when confronted with phobia-specific stimuli. It is unclear whether this pathogenic activation pattern generalizes to other emotional stimuli. This study addresses this question by employing a well-powered sample while implementing an established paradigm using nonspecific aversive facial stimuli.

METHODS

N = 111 patients with SP, spider subtype, and N = 111 healthy controls (HCs) performed a supraliminal emotional face-matching paradigm contrasting aversive faces versus shapes in a 3-T magnetic resonance imaging scanner. We performed region of interest (ROI) analyses for the amygdala, the insula, and the anterior cingulate cortex using univariate as well as machine-learning-based multivariate statistics based on this data. Additionally, we investigated functional connectivity by means of psychophysiological interaction (PPI).

RESULTS

Although the presentation of emotional faces showed significant activation in all three ROIs across both groups, no group differences emerged in all ROIs. Across both groups and in the HC > SP contrast, PPI analyses showed significant task-related connectivity of brain areas typically linked to higher-order emotion processing with the amygdala. The machine learning approach based on whole-brain activity patterns could significantly differentiate the groups with 73% balanced accuracy.

CONCLUSIONS

Patients suffering from SP are characterized by differences in the connectivity of the amygdala and areas typically linked to emotional processing in response to aversive facial stimuli (inferior parietal cortex, fusiform gyrus, middle cingulate, postcentral cortex, and insula). This might implicate a subtle difference in the processing of nonspecific emotional stimuli and warrants more research furthering our understanding of neurofunctional alteration in patients with SP.

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.

Enriching CBT by Neuroscience: Novel Avenues to Achieve Personalized Treatments

Although cognitive behavioral therapy (CBT) is an established and efficient treatment for a variety of common mental disorders, a considerable number of patients do not respond to treatment or relapse after successful CBT. Recent findings and approaches from neuroscience could pave the way for clinical developments to enhance the outcome of CBT. Herein, we will present how neuroscience can offer novel perspectives to better understand (a) the biological underpinnings of CBT, (b) how we can enrich CBT with neuroscience-informed techniques (augmentation of CBT), and (c) why some patients may respond better to CBT than others (predictors of therapy outcomes), thus paving the way for more personalized and effective treatments. We will introduce some key topics and describe a selection of findings from CBT-related research using tools from neuroscience, with the hope that this will provide clinicians and clinical researchers with a brief and comprehensible overview of the field.

Parsing differences in amygdala volume among individuals with and without social and generalized anxiety disorders across the lifespan

Structural differences in the amygdala (AMG) are implicated in anxiety and observed among individuals with generalized (GAD) and social anxiety (SAD) disorders. Findings have been mixed, perhaps because studies rarely examine differences between GAD and SAD, test comorbidity, or examine age-related differences. We tested AMG volume differences among a sample of adults and youth with/without SAD and GAD. Participants (N = 242; ages 7-60 years) completed an MRI scan, diagnostic interviews, and anxiety symptom measures. Groups were formed from diagnostic interviews: 1) Typically developing (TD; n = 91); 2) GAD (n = 53); 3) SAD (n = 35); and 4) comorbid SAD/GAD (n = 63). We used analysis of covariance with a bonferroni correction to examine group differences in AMG volume. The SAD and comorbid SAD/GAD groups exhibited increased bilateral AMG volume compared to the TD group. GAD and TD groups did not differ from each other in AMG size. The SAD, but not the comorbid SAD/GAD group, displayed greater right AMG size relative to the GAD group. SAD and comorbid SAD/GAD groups did not differ from the GAD group in left AMG volume. SAD and SAD/GAD groups did not exhibit different bilateral AMG size. Linear regression analyses demonstrated that greater social anxiety but not generalized anxiety symptom severity was associated with enlarged AMG volume. Age was not associated with AMG volume and nor did age moderate any group or symptom effects. Future longitudinal studies should examine whether larger AMG volume is a unique biomarker for SAD across the lifespan.