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. 2020 Apr 2;9:F1000 Faculty Rev-230. [Version 1] doi: 10.12688/f1000research.21214.1

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

Janna Marie Bas-Hoogendam 1,2,3,a, P Michiel Westenberg 1,2
PMCID: PMC7122428  PMID: 32269760

Abstract

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.

Keywords: social anxiety, MRI, biomarkers, endophenotypes

Introduction

Social anxiety disorder (SAD) is a serious psychiatric condition with a genetic background and typically evolves during late childhood and early adolescence 15. Patients are afraid of a negative evaluation by others and avoid social situations as much as possible, leading to significant adverse effects on important areas of functioning 69. SAD is characterized by a chronic course, has severe consequences for patients 1014 and high costs for society 15, and is often suboptimally treated 16, 17. In order to develop effective interventions, which could relieve individual suffering and reduce the serious societal consequences of SAD, insight into the neurobiological alterations underlying the disorder is essential 18. We are pleased to note that SAD is increasingly considered an interesting topic worthy of investigation 19. In this review, we highlight recent advances with respect to neuroimaging work on SAD while focusing on data from magnetic resonance imaging (MRI) studies. First, we review work on biomarkers for SAD, being any measurable indicator of disease; second, we summarize recent work on profiling SAD endophenotypes, which are heritable and measurable characteristics associated with a certain disorder 20. It is essential to distinguish biomarkers from endophenotypes, as they provide different types of information. For example, biomarker research is valuable for identifying treatment targets, whereas endophenotypes may be important for disentangling genetic underpinnings and identifying genetic mediators. Subsequently, we outline several outstanding questions and provide suggestions for future studies.

Biomarker research on social anxiety disorder

As recently outlined by Etkin 21, neuroimaging research in psychiatry often uses a case-control design, in which a selected group of patients, based mostly on meeting the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria for a specific disorder, is compared with a sample of healthy control participants. Such a design is especially suitable to identify biomarkers, as these are features associated with a particular condition. In 2014, Brühl et al. published an extensive overview of the neuroimaging biomarker literature on SAD 22. They summarized findings with respect to the structure and function of the brain in patients with SAD, reviewed data on brain connectivity in SAD, and performed a meta-analysis of functional MRI (fMRI) studies regarding brain responsiveness in SAD. Their overview resulted in a neurofunctional model of the socially anxious brain, characterized by hyperactivation of the fear circuit—consisting of the amygdala, insula, prefrontal cortex (PFC) and anterior cingulate cortex (ACC)—and hyper-responsive parietal and occipital regions, in reaction to SAD-relevant stimuli. Furthermore, Brühl et al. described SAD-related hypoconnectivity of these parieto-occipital brain areas, which are located in the medial part of the brain and are part of the default mode network and dorsal attention network; thereby, the authors suggest that these changes in network structure are reflective of the increased emotional arousal and enhanced focus on potentially threatening stimuli that characterize patients with SAD 22.

Since the publication of that review 22, new findings have added to our knowledge of the neurobiological basis of SAD. Several recent structural MRI studies have pointed to a role of the striatum in SAD and in related constructs such as “intolerance of uncertainty” 23; these studies, which concern (respectively) an international mega-analysis in the largest MRI sample of 174 patients with SAD and 213 healthy controls to date 24, a study in women with a varying range of social anxiety levels 25, and a correlational study in a sample of 61 healthy volunteers 26, 27, all implied increased striatal volume in (social) anxiety. Interestingly, a treatment study on SAD revealed that 8-weeks of paroxetine treatment led to significant reductions in gray matter in the bilateral caudate and putamen, both part of the striatum 28. However, another study reported decreased gray matter in the putamen in SAD 29. Furthermore, the increase in striatum volume was not replicated by a recent meta-analysis on brain structure in SAD 30; see also the commentary belonging to this work 31. These inconsistent findings underscore the need for replication studies on large datasets (for example, by pooling data from different research centers, which use preferably similar protocols for data acquisition and analysis) (compare the recommendation by 21). In recent years, interest in data sharing has grown, and initiatives such as Enhancing Neuro Imaging by Meta-Analysis (ENIGMA) 32 have increasingly received attention (see a summary of a decade of ENIGMA studies in 33); analyses on brain structure in SAD are currently being performed within the ENIGMA-Anxiety Working Group 34.

In addition to these structural MRI studies, recent fMRI studies explored the relationship between social anxiety and brain responses, aiming to identify functional biomarkers. Several research directions deserve to be highlighted. An intriguing line of research focuses on amygdala activation in social anxiety. This is not surprising, as the amygdala, a small structure located deep in the brain, plays a critical role in detecting and evaluating environmental cues that might be reflective of potential threat 35, 36, and amygdala hyperreactivity in SAD has been frequently reported 37. Interestingly, recent work aims to determine in more detail which functional alterations in amygdala responses are associated with social anxiety. We will mention several lines of research.

First of all, an influential article published around 20 years ago provided evidence that amygdala activation, in response to stimuli presented multiple times without a meaningful consequence, decreased over time (that is, higher amygdala activation was present for novel faces when compared with already presented faces, a process called habituation) 38; subsequently, a landmark article indicated that adult participants who were characterized at age 2 as “behavioral inhibited” (a temperamental trait associated with an increased risk for developing SAD later in life 3942) showed increased amygdala responses to novel faces 43, providing an initial explanation for their anxious feelings in social situations. Building upon these findings, work from 2011 showed that individuals with an extreme inhibited temperament were characterized by a sustained increase in amygdala activation to faces 44, and a follow-up article confirmed that the amygdala response failed to habituate in participants with an inhibited temperament 45. In 2016, Avery and Blackford demonstrated that habituation rate differed across the continuum of social fearfulness, and slower rates of habituation in participants were associated with higher levels of social fearfulness 46; furthermore, these differences were not limited to the amygdala but were present in multiple regions of the social brain, and connectivity analyses revealed that slower habituation was accompanied by increased connectivity between the amygdala and visual brain areas 46, 47. Although these findings need to be replicated in patients with SAD, this series of experiments increases our insight into the failed habituation response in socially anxious participants and provides a neurobiological basis for their experience of feeling uncomfortable in social settings.

Other lines of amygdala research are devoted to determining the specificity of SAD-related amygdala hyperactivation 48, the time course of amygdala activation 49, 50, and the specific roles of amygdala sub-regions during aversive processing 51. In addition, studies on task-related connectivity of the amygdala—for example, during emotion discrimination 52, related to performing an affective counting Stroop task with emotional faces 53, and during the perception of fearful faces 54 or disorder-related complex visual scenes 55—revealed that SAD is not characterized just by aberrant local amygdala activation; instead, the whole functional amygdala network displays alterations, and the direction of these changes depends on the specific task on hand.

Furthermore, the role of the bed nucleus of stria terminalis (BNST), as part of the extended amygdala network, has increasingly received attention in research on threat processing and in studies on (social) anxiety 5666 . The role of the BNST in threat and anxiety (in comparison with the function of the amygdala) is still a topic of debate; whereas some researchers attributed a specific and unique functional role to the BNST when compared with the amygdala 63, 67, others indicate that the BNST and amygdala have similar functional profiles 68. When we specifically consider BNST research in SAD, evidence for both viewpoints is provided. On the one hand, Blackford et al. (2019) demonstrated that social anxiety was associated with a BNST versus amygdala difference in response to unpredictable images 65. On the other hand, a recent study on the temporal profile of amygdala and BNST activation during the anticipation of temporally unpredictable aversive cues revealed similar activation patterns in the central amygdala and BNST: the investigators reported increased phasic activation in both the amygdala and BNST in patients with SAD compared with healthy controls, possibly reflective of hypervigilance in the SAD group; no group differences were present when sustained brain activation (in both amygdala and BNST) was considered 69. We argue that, in line with the comments of Gungor and Pare included in the article by Shackman and Fox 68, future research on fear and anxiety should not ignore the BNST but needs to acknowledge this structure as part of the integral anxiety network.

Next, we want to acknowledge recent studies on alterations in functional connectivity (FC) of the brain during rest 7075; cf. the review by MacNamara et al. 76. It should be mentioned that these studies often differ in their analytical approaches and investigate various networks of interest. A meta-analysis of their findings is beyond the scope of this expert review, but most studies report changes in the default mode network, a network involved in social referencing and the cognitive ability to understand other people’s mental state (theory of mind) 77. Interestingly, a study using multivariate pattern analysis demonstrated that patients with SAD could be reliably classified (versus healthy controls) on the basis of FC measures 78. Furthermore, recent studies using graph theory models provided insight into the topological organization of functional networks in SAD 7982.

A new line of biomarker research in SAD focuses on determining reliable biomarkers for treatment choice. (For a review on neuroprediction in anxiety disorders, we recommend 83; a review dedicated to SAD was recently provided by Klumpp and Fitzgerald 84.) For example, Frick et al. 85 investigated a sample of 48 patients with SAD and acquired fMRI scans before the start of a 9-week treatment with internet-based cognitive behavioral therapy (CBT), CBT plus the serotonin reuptake inhibitor escitalopram, or a placebo. The investigators explored how pre-treatment brain responses related to treatment outcome, and they demonstrated that pre-treatment brain activation in the dorsal ACC predicted the response to CBT 85, 86. Other recent examples of work on neuroprediction in SAD report that the outcome of CBT could be predicted from pre-treatment activation in the dorsolateral PFC 87, frontoparietal regions (including the dorsal ACC and insula) 88, and the rostral ACC 89. In addition, several research groups explored the use of brain connectomics as predictive biomarkers for treatment response to CBT. One group used the amygdala as a seed region and demonstrated that resting-state connectivity and integrity of a specific white matter tract (the right inferior longitudinal fasciculus) predicted clinical improvement in patients with SAD 90; another group recently showed that stronger inverse FC between the amygdala and the ventrolateral PFC, as measured during an implicit emotion regulation task, was related to better treatment response 91. Furthermore, activation in the ventromedial PFC during early extinction learning predicted the reduction of public speaking anxiety and social anxiety symptoms after exposure 92. Although these studies show the potential clinical relevance of neuroimaging in deciding on appropriate treatment options for patients with SAD, the results should be considered preliminary given the small sample sizes and heterogeneous findings (cf. 83, 84).

Related to research on neuroprediction are recent studies focusing on the effect of treatment on the brain. Using a longitudinal design and multiple MRI techniques, Steiger et al. demonstrated changes in several structural brain characteristics following group CBT 93. Another multimodal longitudinal study reported decreases in amygdala volume and amygdala activation levels after CBT and showed that the reduction in amygdala volume mediated the relationship between the diminished amygdala response and clinical improvement 94. Notably, in a one-year follow-up study on the same participants, the investigators still found reduced amygdala volume in CBT responders; this finding suggests that effective psychological interventions can induce long-lasting changes in human brain structure 95. However, given the small sample size (n = 13 patients with SAD) in the latter study, more longitudinal studies are essential to further substantiate this finding and to explore the long-term effects of treatment on brain function. Other studies demonstrated effects of treatment on FC between the amygdala and ventromedial PFC 96 and on network parameters of the precuneus 97.

Importantly, the majority of studies summarized above were performed in adult patients with SAD. However, given the early onset of the disorder, typically during late childhood and early adolescence 98, neuroimaging studies in socially anxious youth can provide valuable information about the neurobiology of the disorder, as the results of these studies are less likely to be confounded by the experience of recurrent SAD episodes. On the other hand, most neuroimaging studies on adolescents include comorbid anxiety disorders (for example, interesting work on attentional processing of social threat and responses to social evaluation 99104; a review of neuroimaging in pediatric anxiety is available here 105), which makes it difficult to establish specific SAD-related neurobiological alterations (see the work of McElroy et al. demonstrating strong associations between symptoms of depression and anxiety in childhood and early to mid-adolescence 106). Nevertheless, several studies provided insight into the neural substrates of SAD across developmental phases. Blair et al. (2011) 107, for example, revealed increased brain responses in the amygdala and rostral ACC in both adult and adolescent participants with SAD, suggesting that the neurobiological characteristics of adults with SAD are not the result of adaptive responses or developmental changes over time; rather, these alterations are stable characteristics related to the disorder. On the other hand, work of Britton et al. (2013) in anxious adolescents and adults distinguished shared and age-specific neurobiological correlates of fear conditioning 108, and Jarcho et al. reported on heightened striatal activity in adolescents with SAD but not in socially anxious adults 109. In addition, there is work on adolescents who are at risk for developing SAD, based on the fact that they are characterized as “behavioral inhibited”; such studies demonstrated, for example, increased amygdala response 110 and altered striatal activation 111, 112 in children and adolescents temperamentally at risk for anxiety. (An extensive review of literature on this topic is provided elsewhere 113; for a more complete overview of neuroimaging work in adolescent SAD, we recommend 1, 114.) Longitudinal studies (cf. 115), preferably following children into adulthood, are essential to shed light on this important topic 116.

Endophenotype research on social anxiety disorder

In addition to highlighting these biomarker studies, we want to mention recent work on SAD endophenotypes. Endophenotypes are measurable and heritable characteristics on the pathway from genotype to phenotype; as defined in literature on this topic 117, 118, endophenotypes are supposed to be (1) associated with a particular disorder, (2) stable trait characteristics, and (3) heritable. Furthermore, endophenotypes should co-segregate with the disorder of interest within families, and non-affected family members show altered levels of the endophenotype in comparison with the general population (fourth criterion). Thereby, endophenotypes are reflective of the genetic vulnerability to develop psychopathology and this important characteristic distinguishes endophenotypes from biomarkers. Biomarkers do not necessarily have a genetic basis; endophenotypes, on the other hand, are by definition heritable and supposed to be reflective of genetically based disease mechanisms 20. So, as stated by Lenzenweger, “all endophenotypes are biomarkers, but not all biomarkers are endophenotypes” 20. Given their genetic background, endophenotypes can be used to unravel the genetic vulnerability to psychopathology. In addition, endophenotypes provide insight into the pathways leading to complex psychiatric disorders 119, 120. Furthermore, endophenotypes can increase our understanding of the transdiagnostic characteristics of mental disorders 120, 121.

In the past decade, the endophenotype approach has been applied to psychiatric disorders such as depression 122, 123, obsessive–compulsive disorder 124126, and schizophrenia 127130, revealing alterations in brain structure and function in patients and their unaffected relatives. Thereby, these studies provide initial insight into the genetic vulnerability to these disorders, as they show that the changes are not just a manifestation of the disease state (as the alterations were present in unaffected family members as well) and are likely heritable because the characteristics were present in both patients and relatives (cf. 131). However, endophenotype research in SAD is new; nevertheless, given the results of family and twin studies showing genetic influences in the development of SAD 132135, such work is of importance in order to gain insight into the genetic susceptibility to SAD.

In the Leiden Family Lab study on Social Anxiety Disorder 136, we investigated candidate endophenotypes of SAD 137 by using a multiplex, multigenerational family design (MRI sample: eight families, n = 110). Within the sample, we explored evidence for two endophenotype criteria: the co-segregation of the endophenotypes with social anxiety, within families genetically enriched for SAD, and the heritability of the endophenotypes. This study revealed multiple promising neurobiological endophenotypes of SAD 138. To start, several structural brain characteristics, derived from cortical and subcortical brain regions, co-segregated with social anxiety within families and were at least moderately heritable 139; see accompanying commentary 140. Furthermore, we employed two fMRI paradigms to explore the potential of brain responses as SAD endophenotypes. Using the first paradigm, the Social Norm Processing Task (revised version 141; based on work by 142, 143), we found evidence for hyperreactivity of the medial PFC and frontal pole in response to unintentional social norm violations as a neurobiological endophenotype of social anxiety 144. Second, we investigated responses to neutral faces. Data revealed that impaired neural habituation in the hippocampus met the two endophenotype criteria of interest 145; in addition, amygdala engagement in response to conditioned faces with a social-evaluative meaning qualified as a neurobiological candidate endophenotype of social anxiety 146, 147. Although future studies are required to examine the stability of these candidate endophenotypes over time (endophenotype criterion 2) and to discover genetic variants underlying the abovementioned candidate endophenotypes, these promising findings offer a starting point for follow-up studies on the genetic susceptibility to SAD.

Outstanding questions and future research

The studies summarized above suggest that multiple brain regions are functionally or anatomically altered in patients with SAD (biomarkers) or involved in the genetic vulnerability to develop the disorder (endophenotypes). However, several important considerations remain and most of them are not specific for research on SAD but apply to the broader field of neuroimaging research in psychiatry 21. First of all, as recently outlined by Etkin in his thought-provoking review 21, meta-analyses of brain structure and function across psychiatry have shown that brain alterations are often non-specific 148, 149; that is, similar brain changes are apparent in distinct psychiatric disorders (based on DSM criteria); interestingly, recent studies implicate that the same non-specificity is present in the field of psychiatry genomics 150, 151. Therefore, it needs to be investigated whether a dimensional approach as described in the Research Domain Criteria (RDoC) framework 152, which focuses on symptom levels, could yield more reliable neurobiological biomarkers of specific clinical presentations.

A second important question concerns the issue of causality. Although studies with larger sample sizes could lead to more reliable results with respect to brain alterations related to psychopathology, their findings still concern associations and do not necessarily imply causal mechanisms 21, 153. For a more elaborate illustration of the risk of “just-so” stories (being “internally consistent explanations that have no basis in fact”), we recommend a recent viewpoint article 153. To increase our understanding of the functional implications of brain alterations in psychopathology, future research should combine neuroimaging with neurostimulation tools, which are able to intervene with normal brain functioning. Such tools (for example, non-invasive brain stimulation) enable causal relationships to be discerned and could reveal target points for interventions 154156.

This issue strongly relates to the final open questions that we wish to highlight, namely whether the SAD-related brain characteristics described above can be influenced in order to prevent the development of SAD or to alleviate its symptoms. It would be interesting to investigate whether a cutting-edge technique such as real-time fMRI-based neurofeedback 157160 could be successfully used in the prevention and treatment of SAD. In addition, a focus on individual level biomarkers, using new “precision MRI” approaches, offers promising prospects for optimizing diagnosis and treatment 161163. It is our hope that the insights from neuroimaging research will eventually lead to promising effective interventions that increase the quality of life of patients with SAD.

Abbreviations

ACC, anterior cingulate cortex; BNST, bed nucleus of stria terminalis; CBT, cognitive behavioral therapy; DSM, Diagnostic and Statistical Manual of Mental Disorders; ENIGMA, Enhancing Neuro Imaging by Meta-Analysis; FC, functional connectivity; fMRI, functional magnetic resonance imaging; MRI, magnetic resonance imaging; PFC, prefrontal cortex; SAD, social anxiety disorder

Editorial Note on the Review Process

F1000 Faculty Reviews are commissioned from members of the prestigious F1000 Faculty and are edited as a service to readers. In order to make these reviews as comprehensive and accessible as possible, the referees provide input before publication and only the final, revised version is published. The referees who approved the final version are listed with their names and affiliations but without their reports on earlier versions (any comments will already have been addressed in the published version).

The referees who approved this article are:

  • Su Lui, Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR, China

  • Jennifer Lau, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

[version 1; peer review: 2 approved]

References

  • 1. Haller SP, Cohen Kadosh K, Scerif G, et al. : Social anxiety disorder in adolescence: How developmental cognitive neuroscience findings may shape understanding and interventions for psychopathology. Dev Cogn Neurosci. 2015;13:11–20. 10.1016/j.dcn.2015.02.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Wittchen HU, Fehm L: Epidemiology and natural course of social fears and social phobia. Acta Psychiatr Scand Suppl. 2003;108(417):4–18. 10.1034/j.1600-0447.108.s417.1.x [DOI] [PubMed] [Google Scholar]
  • 3. Beesdo-Baum K, et al. : The natural course of social anxiety disorder among adolescents and young adults. Acta Psychiatr Scand. 2012;126(6):411–25. 10.1111/j.1600-0447.2012.01886.x [DOI] [PubMed] [Google Scholar]
  • 4. Miers AC, Blöte AW, de Rooij M, et al. : Trajectories of social anxiety during adolescence and relations with cognition, social competence, and temperament. J Abnorm Child Psychol. 2013;41(1):97–110. 10.1007/s10802-012-9651-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Miers AC, Blöte AW, Heyne DA, et al. : Developmental pathways of social avoidance across adolescence: The role of social anxiety and negative cognition. J Anxiety Disord. 2014;28(8):787–794. 10.1016/j.janxdis.2014.09.008 [DOI] [PubMed] [Google Scholar]
  • 6. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders. Fifth Edition (DSM-5).2013. Reference Source [Google Scholar]
  • 7. Leichsenring F, Leweke F: Social Anxiety Disorder. N Engl J Med. 2017;376(23):2255–2264. 10.1056/NEJMcp1614701 [DOI] [PubMed] [Google Scholar]
  • 8. Stein MB, Stein DJ: Social anxiety disorder. Lancet. 2008;371(9618):1115–25. 10.1016/S0140-6736(08)60488-2 [DOI] [PubMed] [Google Scholar]
  • 9. Clarke J, Fox J: The Impact of Social Anxiety on Occupational Participation in College Life. Occup Ther Ment Heal. 2017;33(1):31–46. 10.1080/0164212X.2016.1222323 [DOI] [Google Scholar]
  • 10. Mack S, Jacobi F, Beesdo-Baum K, et al. : Functional disability and quality of life decrements in mental disorders: Results from the Mental Health Module of the German Health Interview and Examination Survey for Adults (DEGS1-MH). Eur Psychiatry. 2015;30(6):793–800. 10.1016/j.eurpsy.2015.06.003 [DOI] [PubMed] [Google Scholar]
  • 11. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1545–1602. 10.1016/S0140-6736(16)31678-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Meier SM, Petersen L, Mattheisen M, et al. : Secondary depression in severe anxiety disorders: a population-based cohort study in Denmark. Lancet Psychiatry. 2015;2(6):515–23. 10.1016/S2215-0366(15)00092-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Meier SM, Mattheisen M, Mors O, et al. : Increased mortality among people with anxiety disorders: total population study. Br J Psychiatry. 2016;209(3):216–221. 10.1192/bjp.bp.115.171975 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Dryman MT, Gardner S, Weeks JW, et al. : Social anxiety disorder and quality of life: How fears of negative and positive evaluation relate to specific domains of life satisfaction. J Anxiety Disord. 2016;38:1–8. 10.1016/j.janxdis.2015.12.003 [DOI] [PubMed] [Google Scholar]
  • 15. Dams J, König HH, Bleibler F, et al. : Excess costs of social anxiety disorder in Germany. J Affect Disord. 2017;213:23–29. 10.1016/j.jad.2017.01.041 [DOI] [PubMed] [Google Scholar]
  • 16. Chapdelaine A, Carrier JD, Fournier L, et al. : Treatment adequacy for social anxiety disorder in primary care patients. PLoS One. 2018;13(11):e0206357. 10.1371/journal.pone.0206357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Alonso J, Liu Z, Evans-Lacko S, et al. : Treatment gap for anxiety disorders is global: Results of the World Mental Health Surveys in 21 countries. Depress Anxiety. 2018;35(3):195–208. 10.1002/da.22711 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 18. Beauchaine TP, Neuhaus E, Brenner SL, et al. : Ten good reasons to consider biological processes in prevention and intervention research. Dev Psychopathol. 2008;20(3):745–74. 10.1017/S0954579408000369 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Heimberg RG, Butler RM: Research on social anxiety disorder continues to escalate: A commentary on Asmundson and Asmundson's analysis of publication trends in the anxiety disorders. J Anxiety Disord. 2018;56:8–10. 10.1016/j.janxdis.2018.04.001 [DOI] [PubMed] [Google Scholar]
  • 20. Lenzenweger MF: Endophenotype, intermediate phenotype, biomarker: definitions, concept comparisons, clarifications. Depress Anxiety. 2013;30(3):185–9. 10.1002/da.22042 [DOI] [PubMed] [Google Scholar]
  • 21. Etkin A: A Reckoning and Research Agenda for Neuroimaging in Psychiatry. Am J Psychiatry. 2019;176(7):507–511. 10.1176/appi.ajp.2019.19050521 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
  • 22. Brühl AB, Delsignore A, Komossa K, et al. : Neuroimaging in social anxiety disorder—a meta-analytic review resulting in a new neurofunctional model. Neurosci Biobehav Rev. 2014;47:260–280. 10.1016/j.neubiorev.2014.08.003 [DOI] [PubMed] [Google Scholar]
  • 23. Allan NP, Cooper D, Oglesby ME, et al. : Lower-order anxiety sensitivity and intolerance of uncertainty dimensions operate as specific vulnerabilities for social anxiety and depression within a hierarchical model. J Anxiety Disord. 2018;53:91–99. 10.1016/j.janxdis.2017.08.002 [DOI] [PubMed] [Google Scholar]
  • 24. Bas-Hoogendam JM, van Steenbergen H, Nienke Pannekoek J, et al. : Voxel-based morphometry multi-center mega-analysis of brain structure in social anxiety disorder. NeuroImage Clin. 2017;16:678–688. 10.1016/j.nicl.2017.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Günther V, Ihme K, Kersting A, et al. : Volumetric Associations Between Amygdala, Nucleus Accumbens, and Socially Anxious Tendencies in Healthy Women. Neuroscience. 2018;374:25–32. 10.1016/j.neuroscience.2018.01.034 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
  • 26. Kim MJ, Shin J, Taylor JM, Shin J, Taylor JM, et al. : Intolerance of uncertainty predicts increased striatal volume. Emotion. 2017;17(6):895–899. 10.1037/emo0000331 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 27. Westenberg PM, Bas-Hoogendam JM: Faculty of 1000 evaluation for Intolerance of uncertainty predicts increased striatal volume. F1000 - Post-publication peer review of the biomedical literature. 2017:17(6):895–899. 10.3410/f.727715963.793533294 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Talati A, Pantazatos SP, Hirsch J: A pilot study of gray matter volume changes associated with paroxetine treatment and response in social anxiety disorder. Psychiatry Res. 2015;231(3):279–85. 10.1016/j.pscychresns.2015.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Zhao Y, Chen L, Zhang W, et al. : Gray Matter Abnormalities in Non-comorbid Medication-naive Patients with Major Depressive Disorder or Social Anxiety Disorder. EBioMedicine. 2017;228–235. 10.1016/j.ebiom.2017.06.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Wang X, Cheng B, Luo Q, et al. : Gray Matter Structural Alterations in Social Anxiety Disorder: A Voxel-Based Meta-Analysis. Front Psychiatry. 2018;9:449. 10.3389/fpsyt.2018.00449 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Bas-Hoogendam JM: Commentary: Gray Matter Structural Alterations in Social Anxiety Disorder: A Voxel-Based Meta-Analysis. Frontiers in Psychiatry. 2019;10:1. 10.3389/fpsyt.2019.00001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Bearden CE, Thompson PM: Emerging Global Initiatives in Neurogenetics: The Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) Consortium. Neuron. 2017;94(2):232–236. 10.1016/j.neuron.2017.03.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Thompson PM, Jahanshad N, Ching CRK, et al. : ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl Psychiatry. 2020;10:100 10.1038/s41398-020-0705-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Groenewold N, Bas-Hoogendam JM, Amod AR, et al. : F27. Subcortical Volumes in Social Anxiety Disorder: Preliminary Results From Enigma-Anxiety. Biol Psychiatry. 2018;83(9):S247–S248. 10.1016/j.biopsych.2018.02.640 [DOI] [Google Scholar]
  • 35. Hariri AR, Whalen PJ: The amygdala: inside and out. F1000 Biol Rep. 2011;3:2. 10.3410/B3-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Janak PH, Tye KM: From circuits to behaviour in the amygdala. Nature. 2015;517(7534):284–292. 10.1038/nature14188 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 37. Cremers HR, Roelofs K: Social anxiety disorder: a critical overview of neurocognitive research. Wiley Interdiscip Rev Cogn Sci. 2016;7(4):218–232. 10.1002/wcs.1390 [DOI] [PubMed] [Google Scholar]
  • 38. Schwartz CE, Wright CI, Shin LM, et al. : Differential amygdalar response to novel versus newly familiar neutral faces: a functional MRI probe developed for studying inhibited temperament. Biol Psychiatry. 2003;53(10):854–862. 10.1016/s0006-3223(02)01906-6 [DOI] [PubMed] [Google Scholar]
  • 39. Clauss JA, Blackford JU: Behavioral inhibition and risk for developing social anxiety disorder: a meta-analytic study. J Am Acad Child Adolesc Psychiatry. 2012;51(10):1066–1075. 10.1016/j.jaac.2012.08.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Blackford JU, Clauss JA, Benningfield MM: The Neurobiology of Behavioral Inhibition as a Developmental Mechanism.BT - Behavioral Inhibition: Integrating Theory, Research, and Clinical Perspectives. in (eds. Pérez-Edgar K & Fox NA) Springer International Publishing.2018;113–134. 10.1007/978-3-319-98077-5_6 [DOI] [Google Scholar]
  • 41. Buzzell GA, Troller-Renfree SV, Barker TV, et al. : A Neurobehavioral Mechanism Linking Behaviorally Inhibited Temperament and Later Adolescent Social Anxiety. J Am Acad Child Adolesc Psychiatry. 2017;56(12):1097–1105. 10.1016/j.jaac.2017.10.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Schwartz CE, Snidman N, Kagan J: Adolescent social anxiety as an outcome of inhibited temperament in childhood. J Am Acad Child Adolesc Psychiatry. 1999;38(8):1008–15. 10.1097/00004583-199908000-00017 [DOI] [PubMed] [Google Scholar]
  • 43. Schwartz CE, Wright CI, Shin LM, et al. : Inhibited and uninhibited infants "grown up": adult amygdalar response to novelty. Science. 2003;300(5627):1952–3. 10.1126/science.1083703 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
  • 44. Blackford JU, Avery SN, Cowan RL, et al. : Sustained amygdala response to both novel and newly familiar faces characterizes inhibited temperament. Soc Cogn Affect Neurosci. 2011;6(5):621–9. 10.1093/scan/nsq073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Blackford JU, Allen AH, Cowan RL, et al. : Amygdala and hippocampus fail to habituate to faces in individuals with an inhibited temperament. Soc Cogn Affect Neurosci. 2013;8(2):143–50. 10.1093/scan/nsr078 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Avery SN, Blackford JU: Slow to warm up: the role of habituation in social fear. Soc Cogn Affect Neurosci. 2016;11(11):1832–1840. 10.1093/scan/nsw095 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 47. Westenberg PM, Bas-Hoogendam JM: Faculty of 1000 evaluation for Slow to warm up: the role of habituation in social fear. F1000 - Post-publication peer Rev Biomed Lit. 2016;11(11):1832–1840. 10.3410/f.726593447.793539678 [DOI] [Google Scholar]
  • 48. Kraus J, Frick A, Fischer H, et al. : Amygdala reactivity and connectivity during social and non-social aversive stimulation in social anxiety disorder. Psychiatry Res Neuroimaging. 2018;280:56–61. 10.1016/j.pscychresns.2018.08.012 [DOI] [PubMed] [Google Scholar]
  • 49. Davies CD, Young K, Torre JB, et al. : Altered time course of amygdala activation during speech anticipation in social anxiety disorder. J Affect Disord. 2017;209:23–29. 10.1016/j.jad.2016.11.014 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 50. Westenberg PM, Bas-Hoogendam JM: Faculty of 1000 evaluation for Altered time course of amygdala activation during speech anticipation in social anxiety disorder. F1000 - Post-publication peer review of the biomedical literature. 2017;23–29. 10.3410/f.727005164.793528007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Michely J, Rigoli F, Rutledge RB, et al. : Distinct Processing of Aversive Experience in Amygdala Subregions. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;5(3):291–300. 10.1016/j.bpsc.2019.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Sladky R, Höflich A, Küblböck M, et al. : Disrupted effective connectivity between the amygdala and orbitofrontal cortex in social anxiety disorder during emotion discrimination revealed by dynamic causal modeling for FMRI. Cereb cortex. 2015;25(4):895–903. 10.1093/cercor/bht279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Minkova L, Sladky R, Kranz GS, et al. : Task-dependent modulation of amygdala connectivity in social anxiety disorder. Psychiatry Res Neuroimaging. 2017;262:39–46. 10.1016/j.pscychresns.2016.12.016 [DOI] [PubMed] [Google Scholar]
  • 54. Prater KE, Hosanagar A, Klumpp H, et al. : Aberrant amygdala-frontal cortex connectivity during perception of fearful faces and at rest in generalized social anxiety disorder. Depress Anxiety. 2013;30(3):234–41. 10.1002/da.22014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Heitmann CY, Feldker K, Neumeister P, et al. : Abnormal brain activation and connectivity to standardized disorder-related visual scenes in social anxiety disorder. Hum Brain Mapp. 2016;37(4):1559–72. 10.1002/hbm.23120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Lebow MA, Chen A: Overshadowed by the amygdala: the bed nucleus of the stria terminalis emerges as key to psychiatric disorders. Mol Psychiatry. 2016;21(4):450–63. 10.1038/mp.2016.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Tillman RM, Stockbridge MD, Nacewicz BM, et al. : Intrinsic functional connectivity of the central extended amygdala. Hum Brain Mapp. 2018;39(3):1291–1312. 10.1002/hbm.23917 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58. Goode TD, Maren S: Role of the bed nucleus of the stria terminalis in aversive learning and memory. Learn Mem. 2017;24(9):480–491. 10.1101/lm.044206.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59. Avery SN, Clauss JA, Winder DG, et al. : BNST neurocircuitry in humans. NeuroImage. 2014;91:311–23. 10.1016/j.neuroimage.2014.01.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60. Pedersen WS, TuganMuftuler L, Larson CL: Disentangling the effects of novelty, valence and trait anxiety in the bed nucleus of the stria terminalis, amygdala and hippocampus with high resolution 7T fMRI. NeuroImage. 2017;156:293–301. 10.1016/j.neuroimage.2017.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Herrmann MJ, Boehme S, Becker MP, et al. : Phasic and sustained brain responses in the amygdala and the bed nucleus of the stria terminalis during threat anticipation. Hum Brain Mapp. 2015;37(3):1091–102. 10.1002/hbm.23088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Brinkmann L, Buff C, Feldker K, et al. : Inter-individual differences in trait anxiety shape the functional connectivity between the bed nucleus of the stria terminalis and the amygdala during brief threat processing. NeuroImage. 2018;166:110–116. 10.1016/j.neuroimage.2017.10.054 [DOI] [PubMed] [Google Scholar]
  • 63. Avery SN, Clauss JA, Blackford JU: The Human BNST: Functional Role in Anxiety and Addiction. Neuropsychopharmacology. 2016;41(1):126–141. 10.1038/npp.2015.185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64. Fox AS, Oler JA, Tromp do PM, et al. : Extending the amygdala in theories of threat processing. Trends Neurosci. 2015;38(5):319–329. 10.1016/j.tins.2015.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65. Clauss JA, Avery SN, Benningfield MM, et al. : Social anxiety is associated with BNST response to unpredictability. Depress Anxiety. 2019;36(8):666–675. 10.1002/da.22891 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66. Ahrens S, Wu MV, Furlan A, et al. : A Central Extended Amygdala Circuit That Modulates Anxiety. J Neurosci. 2018;38(24):5567–5583. 10.1523/JNEUROSCI.0705-18.2018 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 67. Gungor NZ, Paré D: Functional Heterogeneity in the Bed Nucleus of the Stria Terminalis. J Neurosci. 2016;36(31):8038–8049. 10.1523/JNEUROSCI.0856-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Shackman AJ, Fox AS: Contributions of the Central Extended Amygdala to Fear and Anxiety. J Neurosci. 2016;36(31):8050–8063. 10.1523/JNEUROSCI.0982-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 69. Figel B, Brinkmann L, Buff C, et al. : Phasic amygdala and BNST activation during the anticipation of temporally unpredictable social observation in social anxiety disorder patients. NeuroImage Clin. 2019;22:101735. 10.1016/j.nicl.2019.101735 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 70. Yuan C, Zhu H, Ren Z, et al. : Precuneus-related regional and network functional deficits in social anxiety disorder: A resting-state functional MRI study. Compr Psychiatry. 2018;82:22–29. 10.1016/j.comppsych.2017.12.002 [DOI] [PubMed] [Google Scholar]
  • 71. Cui Q, Vanman EJ, Long Z, et al. : Social anxiety disorder exhibit impaired networks involved in self and theory of mind processing. Soc Cogn Affect Neurosci. 2017;12(8):1284–1295. 10.1093/scan/nsx050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72. Rabany L, Diefenbach GJ, Bragdon LB, et al. : Resting-State Functional Connectivity in Generalized Anxiety Disorder and Social Anxiety Disorder: Evidence for a Dimensional Approach. Brain Connect. 2017;7(5):289–298. 10.1089/brain.2017.0497 [DOI] [PubMed] [Google Scholar]
  • 73. Manning J, Reynolds G, Saygin ZM, et al. : Altered resting-state functional connectivity of the frontal-striatal reward system in social anxiety disorder. PLoS One. 2015;10(4):e0125286. 10.1371/journal.pone.0125286 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74. Ergül C, Ulasoglu-Yildiz C, Kurt E, et al. : Intrinsic functional connectivity in social anxiety disorder with and without comorbid attention deficit hyperactivity disorder. Brain Res. 2019;1722:146364. 10.1016/j.brainres.2019.146364 [DOI] [PubMed] [Google Scholar]
  • 75. Kim YK, Yoon HK: Common and distinct brain networks underlying panic and social anxiety disorders. Prog Neuro-Psychopharmacology Biol Psychiatry. 2018:80(Pt B):115–122. 10.1016/j.pnpbp.2017.06.017 [DOI] [PubMed] [Google Scholar]
  • 76. MacNamara A, DiGangi J, Phan KL: Aberrant Spontaneous and Task-Dependent Functional Connections in the Anxious Brain. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(3):278–287. 10.1016/j.bpsc.2015.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77. Buckner RL, DiNicola LM: The brain's default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci. 2019;20(10):593–608. 10.1038/s41583-019-0212-7 [DOI] [PubMed] [Google Scholar]
  • 78. Liu F, Guo W, Fouche JP, et al. : Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct. 2015;220(1):101–115. 10.1007/s00429-013-0641-4 [DOI] [PubMed] [Google Scholar]
  • 79. Yang X, Liu M, Meng Y, et al. : Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder. NeuroImage. 2019;190:213–223. 10.1016/j.neuroimage.2017.12.011 [DOI] [PubMed] [Google Scholar]
  • 80. Zhu H, Qiu C, Meng Y, et al. : Altered Topological Properties of Brain Networks in Social Anxiety Disorder: A Resting-state Functional MRI Study. Sci Rep. 2017;7:43089. 10.1038/srep43089 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81. Yun JY, Kim JC, Ku J, et al. : The left middle temporal gyrus in the middle of an impaired social-affective communication network in social anxiety disorder. J Affect Disord. 2017;214:53–59. 10.1016/j.jad.2017.01.043 [DOI] [PubMed] [Google Scholar]
  • 82. Liu F, Zhu C, Wang Y, et al. : Disrupted Cortical Hubs in Functional Brain Networks in Social Anxiety Disorder. Clin Neurophysiol. 2015;126(9):1711–1716. 10.1016/j.clinph.2014.11.014 [DOI] [PubMed] [Google Scholar]
  • 83. Lueken U, Zierhut KC, Hahn T, et al. : Neurobiological Markers Predicting Treatment Response in Anxiety Disorders: A Systematic Review and Implications for Clinical Application. Neurosci Biobehav Rev. 2016;66:143–162. 10.1016/j.neubiorev.2016.04.005 [DOI] [PubMed] [Google Scholar]
  • 84. Klumpp H, Fitzgerald JM: Neuroimaging Predictors and Mechanisms of Treatment Response in Social Anxiety Disorder: An Overview of the Amygdala. Curr Psychiatry Rep. 2018;20(10):89. 10.1007/s11920-018-0948-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85. Frick A, Engman J, Wahlstedt K, et al. : Anterior Cingulate Cortex Activity as a Candidate Biomarker for Treatment Selection in Social Anxiety Disorder. BJPsych Open. 2018;4(3):157–159. 10.1192/bjo.2018.15 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 86. Westenberg PM, Bas-Hoogendam JM: Faculty of 1000 evaluation for Anterior cingulate cortex activity as a candidate biomarker for treatment selection in social anxiety disorder. F1000 - Post-publication peer Rev Biomed Lit. 2018. 10.3410/f.733485082.793548054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87. Klumpp H, Roberts J, Kennedy AE, et al. : Emotion regulation related neural predictors of cognitive behavioral therapy response in social anxiety disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2017;75:106–112. | 10.1016/j.pnpbp.2017.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88. Klumpp H, Fitzgerald DA, Piejko K, et al. : Prefrontal control and predictors of cognitive behavioral therapy response in social anxiety disorder. Soc Cogn Affect Neurosci. 2016;11(4):630–640. 10.1093/scan/nsv146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89. Klumpp H, Fitzgerald JM, Kinney KL, et al. : Predicting cognitive behavioral therapy response in social anxiety disorder with anterior cingulate cortex and amygdala during emotion regulation. NeuroImage Clin. 2017;15:25–34. 10.1016/j.nicl.2017.04.006 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 90. Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A, et al. : Brain connectomics predict response to treatment in social anxiety disorder. Mol Psychiatry. 2016;21(5):680–685. 10.1038/mp.2015.109 [DOI] [PubMed] [Google Scholar]
  • 91. Young KS, LeBeau RT, Niles AN, et al. : Neural connectivity during affect labeling predicts treatment response to psychological therapies for social anxiety disorder. J Affect Disord. 2019;242:105–110. 10.1016/j.jad.2018.08.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92. Ball TM, Knapp SE, Paulus MP, et al. : Brain activation during fear extinction predicts exposure success. Depress Anxiety. 2017;34(3):257–266. 10.1002/da.22583 [DOI] [PubMed] [Google Scholar]
  • 93. Steiger VR, Brühl AB, Weidt S, et al. : Pattern of structural brain changes in social anxiety disorder after cognitive behavioral group therapy: a longitudinal multimodal MRI study. Mol Psychiatry. 2017;22(8):1164–1171. 10.1038/mp.2016.217 [DOI] [PubMed] [Google Scholar]
  • 94. Månsson KNT, Salami A, Frick A, et al. : Neuroplasticity in response to cognitive behavior therapy for social anxiety disorder. Transl Psychiatry. 2016;6(2):e727. 10.1038/tp.2015.218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95. Månsson KNT, Salami A, Carlbring P, et al. : Structural but not functional neuroplasticity one year after effective cognitive behaviour therapy for social anxiety disorder. Behav Brain Res. 2017;318:45–51. 10.1016/j.bbr.2016.11.018 [DOI] [PubMed] [Google Scholar]
  • 96. Young KS, Burklund LJ, Torre JB, et al. : Treatment for social anxiety disorder alters functional connectivity in emotion regulation neural circuitry. Psychiatry Res Neuroimaging. 2017;261:44–51. 10.1016/j.pscychresns.2017.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97. Yuan M, Zhu H, Qiu C, et al. : Altered regional and integrated resting-state brain activity in general social anxiety disorder patients before and after group cognitive behavior therapy. Psychiatry Res Neuroimaging. 2018;272:30–37. 10.1016/j.pscychresns.2017.12.004 [DOI] [PubMed] [Google Scholar]
  • 98. Westenberg PM, Gullone E, Bokhorst CL, et al. : Social evaluation fear in childhood and adolescence: Normative developmental course and continuity of individual differences. Br J Dev Psychol. 2007;25:471–483. 10.1348/026151006X173099 [DOI] [Google Scholar]
  • 99. Hamm LL, Jacobs RH, Johnson WM, et al. : Aberrant amygdala functional connectivity at rest in pediatric anxiety disorders. Biol Mood Anxiety Disord. 2014;4(1):15. 10.1186/s13587-014-0015-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100. Strawn JR, Hamm L, Fitzgerald DA, et al. : Neurostructural abnormalities in pediatric anxiety disorders. J Anxiety Disord. 2015;32:81–8. 10.1016/j.janxdis.2015.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101. Beesdo K, Lau JYF, Guyer AE, et al. : Common and distinct amygdala-function perturbations in depressed vs anxious adolescents. Arch Gen Psychiatry. 2009;66(3):275–85. 10.1001/archgenpsychiatry.2008.545 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102. Guyer AE, Lau JY, McClure-Tone EB, et al. : Amygdala and ventrolateral prefrontal cortex function during anticipated peer evaluation in pediatric social anxiety. Arch Gen Psychiatry. 2008;65(11):1303–12. 10.1001/archpsyc.65.11.1303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103. Williams LE, Oler JA, Fox AS, et al. : Fear of the unknown: uncertain anticipation reveals amygdala alterations in childhood anxiety disorders. Neuropsychopharmacology. 2015;40(6):1428–35. 10.1038/npp.2014.328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104. Guyer AE, Choate VR, Detloff A, et al. : Striatal functional alteration during incentive anticipation in pediatric anxiety disorders. Am J Psychiatry. 2012;169(2):205–212. 10.1176/appi.ajp.2011.11010006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105. Blackford JU, Pine DS: Neural substrates of childhood anxiety disorders: a review of neuroimaging findings. Child Adolesc Psychiatr Clin N Am. 2012;21(3):501–25. 10.1016/j.chc.2012.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106. McElroy E, Fearon P, Belsky J, et al. : Networks of Depression and Anxiety Symptoms Across Development. J Am Acad Child Adolesc Psychiatry. 2018;57(12):964–973. 10.1016/j.jaac.2018.05.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107. Blair KS, Geraci M, Korelitz K, et al. : The pathology of social phobia is independent of developmental changes in face processing. Am J Psychiatry. 2011;168(11):1202–9. 10.1176/appi.ajp.2011.10121740 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108. Britton JC, Grillon C, Lissek S, et al. : Response to learned threat: An FMRI study in adolescent and adult anxiety. Am J Psychiatry. 2013;170(10):1195–204. 10.1176/appi.ajp.2013.12050651 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109. Jarcho JM, Romer AL, Shechner T, et al. : Forgetting the best when predicting the worst: Preliminary observations on neural circuit function in adolescent social anxiety. Dev Cogn Neurosci. 2015;13:21–31. 10.1016/j.dcn.2015.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110. Pérez-Edgar K, Roberson-Nay R, Hardin MG, et al. : Attention alters neural responses to evocative faces in behaviorally inhibited adolescents. Neuroimage. 2007;35(4):1538–46. 10.1016/j.neuroimage.2007.02.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111. Bar-Haim Y, Fox NA, Benson B, et al. : Neural correlates of reward processing in adolescents with a history of inhibited temperament. Psychol Sci. 2009;20(8):1009–1018. 10.1111/j.1467-9280.2009.02401.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112. Guyer AE, Benson B, Choate VR, et al. : Lasting associations between early-childhood temperament and late-adolescent reward-circuitry response to peer feedback. Dev Psychopathol. 2014;26(1):229–43. 10.1017/S0954579413000941 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113. Liu P, Pérez-Edgar KE: Developmental Pathways from Early Behavioral Inhibition to Later Anxiety: An Integrative Review of Developmental Psychopathology Research and Translational Implications. Adolesc Res Rev. 2019;4:45–58. 10.1007/s40894-018-0092-5 [DOI] [Google Scholar]
  • 114. Jarcho JM, Leibenluft E, Walker OL, et al. : Neuroimaging studies of pediatric social anxiety: paradigms, pitfalls and a new direction for investigating the neural mechanisms. Biol Mood Anxiety Disord. 2013;3:14. 10.1186/2045-5380-3-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115. Battaglia M, Zanoni A, Taddei M, et al. : Cerebral responses to emotional expressions and the development of social anxiety disorder: a preliminary longitudinal study. Depress Anxiety. 2012;29(1):54–61. 10.1002/da.20896 [DOI] [PubMed] [Google Scholar]
  • 116. Haller SP, Mills KL, Hartwright CE, et al. : When change is the only constant: The promise of longitudinal neuroimaging in understanding social anxiety disorder. Dev Cogn Neurosci. 2018;33:73–82. 10.1016/j.dcn.2018.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117. Glahn DC, Thompson PM, Blangero J: Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Hum Brain Mapp. 2007;28(6):488–501. 10.1002/hbm.20401 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118. Gottesman II, Gould TD: The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160(4):636–45. 10.1176/appi.ajp.160.4.636 [DOI] [PubMed] [Google Scholar]
  • 119. Flint J, Timpson N, Munafò M: Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease. Trends Neurosci. 2014;37(12):733–41. 10.1016/j.tins.2014.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120. Miller GA, Rockstroh B: Endophenotypes in psychopathology research: where do we stand? Annu Rev Clin Psychol. 2013;9:177–213. 10.1146/annurev-clinpsy-050212-185540 [DOI] [PubMed] [Google Scholar]
  • 121. Beauchaine TP, Constantino JN: Redefining the endophenotype concept to accommodate transdiagnostic vulnerabilities and etiological complexity. Biomark Med. 2017;11(9):769–780. 10.2217/bmm-2017-0002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 122. Miskowiak KW, Larsen JE, Harmer CJ, et al. : Is negative self-referent bias an endophenotype for depression? An fMRI study of emotional self-referent words in twins at high vs. low risk of depression. J Affect Disord. 2018;226:267–273. 10.1016/j.jad.2017.10.013 [DOI] [PubMed] [Google Scholar]
  • 123. Goldstein BL, Klein DN: A review of selected candidate endophenotypes for depression. Clin Psychol. Rev. 2014;34(5):417–27. 10.1016/j.cpr.2014.06.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 124. Vaghi MM, Hampshire A, Fineberg NA, et al. : Hypoactivation and Dysconnectivity of a Frontostriatal Circuit During Goal-Directed Planning as an Endophenotype for Obsessive-Compulsive Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(8):655–663. 10.1016/j.bpsc.2017.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125. Bey K, Kaufmann C, Lennertz L, et al. : Impaired planning in patients with obsessive-compulsive disorder and unaffected first-degree relatives: Evidence for a cognitive endophenotype. J Anxiety Disord. 2018;57:24–30. 10.1016/j.janxdis.2018.05.009 [DOI] [PubMed] [Google Scholar]
  • 126. de Vries FE, de Wit SJ, Cath DC, et al. : Compensatory frontoparietal activity during working memory: an endophenotype of obsessive-compulsive disorder. Biol Psychiatry. 2014;76(11):878–887. 10.1016/j.biopsych.2013.11.021 [DOI] [PubMed] [Google Scholar]
  • 127. Glahn DC, Williams JT, McKay DR, et al. : Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry. 2015;77(1):75–83. 10.1016/j.biopsych.2014.06.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 128. Honea RA, Meyer-Lindenberg A, Hobbs KB, et al. : Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biol Psychiatry. 2008;63(5):465–74. 10.1016/j.biopsych.2007.05.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 129. McCarthy NS, Badcock JC, Clark ML, et al. : Assessment of Cognition and Personality as Potential Endophenotypes in the Western Australian Family Study of Schizophrenia. Schizophr Bull. 2018;44(4):908–921. 10.1093/schbul/sbx141 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130. Blakey R, Ranlund S, Zartaloudi E, et al. : Associations between psychosis endophenotypes across brain functional, structural, and cognitive domains. Psychol Med. 2018;48(8):1325–1340. 10.1017/S0033291717002860 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 131. Ursu S: Planning Ahead: The Future of Searching for Endophenotypes of Obsessive-Compulsive Disorder in the Era of Research Domain Criteria. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(8):638–639. 10.1016/j.bpsc.2017.09.011 [DOI] [PubMed] [Google Scholar]
  • 132. Isomura K, Boman M, Rück C, et al. : Population-based, multi-generational family clustering study of social anxiety disorder and avoidant personality disorder. Psychol Med. 2015;45(8):1581–9. 10.1017/S0033291714002116 [DOI] [PubMed] [Google Scholar]
  • 133. Merikangas KR, Lieb R, Wittchen HU, et al. : Family and high-risk studies of social anxiety disorder. Acta Psychiatr Scand Suppl. 2003; (417):28–37. 10.1034/j.1600-0447.108.s417.5.x [DOI] [PubMed] [Google Scholar]
  • 134. Scaini S, Belotti R, Ogliari A: Genetic and environmental contributions to social anxiety across different ages: a meta-analytic approach to twin data. J Anxiety Disord. 2014;28(7):650–6. 10.1016/j.janxdis.2014.07.002 [DOI] [PubMed] [Google Scholar]
  • 135. Bandelow B, Baldwin D, Abelli M, et al. : Biological markers for anxiety disorders, OCD and PTSD - a consensus statement. Part I: Neuroimaging and genetics. World J Biol Psychiatry. 2016;17(5):321–365. 10.1080/15622975.2016.1181783 [DOI] [PubMed] [Google Scholar]
  • 136. Bas-Hoogendam JM, Harrewijn A, Tissier RLM, et al. : The Leiden Family Lab study on Social Anxiety Disorder: a multiplex, multigenerational family study on neurocognitive endophenotypes. Int J Methods Psychiatr Res. 2018;27(2):e1616. 10.1002/mpr.1616 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137. Bas-Hoogendam JM, Blackford JU, Brühl AB, et al. : Neurobiological candidate endophenotypes of social anxiety disorder. Neurosci Biobehav Rev. 2016;71:362–378. 10.1016/j.neubiorev.2016.08.040 [DOI] [PubMed] [Google Scholar]
  • 138. Bas-Hoogendam JM: Extremely Shy & Genetically Close. Investigating neurobiological endophenotypes of Social Anxiety Disorder.Leiden University.2020. Reference Source [Google Scholar]
  • 139. Bas-Hoogendam JM, van Steenbergen H, Tissier RLM, et al. : Subcortical brain volumes, cortical thickness and cortical surface area in families genetically enriched for social anxiety disorder - A multiplex multigenerational neuroimaging study. EBioMedicine. 2018;36:410–428. 10.1016/j.ebiom.2018.08.048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 140. Frick A, Månsson KN: Brain changes in social anxiety disorder run in the family. EBioMedicine. 2018;36:5–6. 10.1016/j.ebiom.2018.09.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141. Bas-Hoogendam JM, van Steenbergen H, Kreuk T, et al. : How embarrassing! The behavioral and neural correlates of processing social norm violations. PLoS One. 2017;12(4):e0176326. 10.1371/journal.pone.0176326 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142. Berthoz S, Armony JL, Blair RJ: An fMRI study of intentional and unintentional (embarrassing) violations of social norms. Brain. 2002;125(Pt 8):1696–1708. 10.1093/brain/awf190 [DOI] [PubMed] [Google Scholar]
  • 143. Blair KS, Geraci M, Hollon N, et al. : Social norm processing in adult social phobia: atypically increased ventromedial frontal cortex responsiveness to unintentional (embarrassing) transgressions. Am J Psychiatry. 2010;167(12):1526–32. 10.1176/appi.ajp.2010.09121797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 144. Bas-Hoogendam JM, van Steenbergen H, Tissier RLM, et al. : Altered Neurobiological Processing of Unintentional Social Norm Violations: A Multiplex, Multigenerational Functional Magnetic Resonance Imaging Study on Social Anxiety Endophenotypes. Biol Psychiatry Cogn Neurosci Neuroimaging. In press. 2019; pii: S2451-9022(19)30071-0. 10.1016/j.bpsc.2019.03.003 [DOI] [PubMed] [Google Scholar]
  • 145. Bas-Hoogendam JM, van Steenbergen H, Blackford JU, et al. : Impaired neural habituation to neutral faces in families genetically enriched for social anxiety disorder. Depress Anxiety. 2019;36(12):1143–1153. 10.1002/da.22962 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146. Bas-Hoogendam JM, van Steenbergen H, van der Wee NJA, et al. : Amygdala hyperrreactivity to faces conditioned with a social-evaluative meaning: a multiplex, multigenerational fMRI study on social anxiety endophenotypes. NeuroImage: Clinical. 2020; in press. 10.1016/j.nicl.2020.102247 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147. Bas-Hoogendam JM, van Steenbergen H, van der Wee NJ, et al. : P.491 Social conditioning of neutral faces in families genetically enriched for social anxiety disorder. Eur Neuropsychopharmacol. 2019;29(Supplement 6):S345–S346. 10.1016/j.euroneuro.2019.09.498 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148. Goodkind M, Eickhoff SB, Oathes DJ, et al. : Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry. 2015;72(4):305–315. 10.1001/jamapsychiatry.2014.2206 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 149. Sha Z, Wager TD, Mechelli A, et al. : Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders. Biol Psychiatry. 2019;85(5):379–388. 10.1016/j.biopsych.2018.11.011 [DOI] [PubMed] [Google Scholar]
  • 150. Ohi K, Otowa T, Shimada M, et al. : Shared genetic etiology between anxiety disorders and psychiatric and related intermediate phenotypes. Psychol Med. 2020;50(4)692–704. 10.1017/S003329171900059X [DOI] [PubMed] [Google Scholar]
  • 151. Brainstorm Consortium, Anttila V, Bulik-Sullivan B, et al. : Analysis of shared heritability in common disorders of the brain. Science. 2018;360(6395): pii: eaap8757. 10.1126/science.aap8757 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
  • 152. Insel TR: The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. Am J Psychiatry. 2014;171(4):395–7. 10.1176/appi.ajp.2014.14020138 [DOI] [PubMed] [Google Scholar]
  • 153. Etkin A: Addressing the Causality Gap in Human Psychiatric Neuroscience. JAMA Psychiatry. 2018;75(1):3–4. 10.1001/jamapsychiatry.2017.3610 [DOI] [PubMed] [Google Scholar]
  • 154. Hoogendam JM, Ramakers GM, Di Lazzaro V: Physiology of repetitive transcranial magnetic stimulation of the human brain. Brain Stimul. 2010;3(2):95–118. 10.1016/j.brs.2009.10.005 [DOI] [PubMed] [Google Scholar]
  • 155. Hallett M: Transcranial magnetic stimulation and the human brain. Nature. 2000;406(6792):147–150. 10.1038/35018000 [DOI] [PubMed] [Google Scholar]
  • 156. Vicario CM, Salehinejad MA, Felmingham K, et al. : A systematic review on the therapeutic effectiveness of non-invasive brain stimulation for the treatment of anxiety disorders. Neurosci Biobehav Rev. 2019;96:219–231. 10.1016/j.neubiorev.2018.12.012 [DOI] [PubMed] [Google Scholar]
  • 157. Sitaram R, Ros T, Stoeckel L, et al. : Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci. 2017;18(2):86–100. 10.1038/nrn.2016.164 [DOI] [PubMed] [Google Scholar]
  • 158. Kadosh KC, Staunton G: A systematic review of the psychological factors that influence neurofeedback learning outcomes. Neuroimage. 2019;185:545–555. 10.1016/j.neuroimage.2018.10.021 [DOI] [PubMed] [Google Scholar]
  • 159. Brühl AB, Scherpiet S, Sulzer J, et al. : Real-time neurofeedback using functional MRI could improve down-regulation of amygdala activity during emotional stimulation: a proof-of-concept study. Brain Topogr. 2014;27(1):138–148. 10.1007/s10548-013-0331-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 160. Herwig U, Lutz J, Scherpiet S, et al. : Training emotion regulation through real-time fMRI neurofeedback of amygdala activity. Neuroimage. 2019;184:687–696. 10.1016/j.neuroimage.2018.09.068 [DOI] [PubMed] [Google Scholar]
  • 161. Gratton C, Kraus BT, Greene DJ, et al. : Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry. Biol Psychiatry. 2019; pii: S0006-3223(19)31829-3. 10.1016/j.biopsych.2019.10.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 162. Brennan BP, Wang D, Li M, et al. : Use of an Individual-Level Approach to Identify Cortical Connectivity Biomarkers in Obsessive-Compulsive Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(1):27–38. 10.1016/j.bpsc.2018.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 163. Quinlan EB, Banaschewski T, Barker GJ, et al. : Identifying biological markers for improved precision medicine in psychiatry. Mol Psychiatry. 2020;25(2):243–253. 10.1038/s41380-019-0555-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

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