Anterior cingulate cortex activity as a candidate biomarker for treatment selection in social anxiety disorder

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.

Quantitative electroencephalographic biomarker of pharmacological treatment response in patients with anxiety disorder: a retrospective study

This study aimed to investigate the effectiveness of a quantitative electroencephalography (qEEG) biomarker in predicting the response to pharmacological treatment in patients with anxiety disorder. A total of 86 patients were diagnosed with anxiety disorder according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition, and subsequently treated with antidepressants. After 8-12 weeks, the participants were divided into treatment-resistant (TRS) and treatment-response (TRP) groups based on their Clinical Global Impressions-Severity (CGI-S) scores. We obtained the absolute-EEG measurements for 19-channels and analyzed qEEG findings according to the frequency range: delta, theta, alpha, and beta. The beta-wave was subdivided into low-beta, beta, and high-beta waves. The theta-beta ratio (TBR) was calculated, and an analysis of covariance was performed. Of the 86 patients with anxiety disorder, 56 patients (65%) were classified in the TRS group. The TRS and TRP groups did not differ in terms of age, sex, or medication-dosage. However, the baseline CGI-S was higher in the TRP group. After calibration by covariates, the TRP group showed higher beta-waves in T3 and T4, and a lower TBR, especially in T3 and T4, than the TRS group. These results indicate that patients with a lower TBR and higher beta and high-beta waves in T3 and T4 are more likely to respond to medication.

Serotonin and dopamine transporter availability in social anxiety disorder after combined treatment with escitalopram and cognitive-behavioral therapy

Selective serotonin reuptake inhibitors (SSRIs) and internet-based cognitive behavioral therapy (ICBT) are recommended treatments of social anxiety disorder (SAD), and often combined, but their effects on monoaminergic signaling are not well understood. In this multi-tracer positron emission tomography (PET) study, 24 patients with SAD were randomized to treatment with escitalopram+ICBT or placebo+ICBT under double-blind conditions. Before and after 9 weeks of treatment, patients were examined with positron emission tomography and the radioligands [¹¹C]DASB and [¹¹C]PE2I, probing the serotonin (SERT) and dopamine (DAT) transporter proteins respectively. Both treatment combinations resulted in significant improvement as measured by the Liebowitz Social Anxiety Scale (LSAS). At baseline, SERT-DAT co-expression was high and, in the putamen and thalamus, co-expression showed positive associations with symptom severity. SERT-DAT co-expression was also predictive of treatment success, but predictor-outcome associations differed in direction between the treatments. After treatment, average SERT occupancy in the SSRI + ICBT group was >80%, with positive associations between symptom improvement and occupancy in the nucleus accumbens, putamen and anterior cingulate cortex. Following placebo+ICBT, SERT binding increased in the raphe nuclei. DAT binding increased in both groups in limbic and striatal areas, but relations with symptom improvement differed, being negative for SSRI + ICBT and positive for placebo + ICBT. Thus, serotonin-dopamine transporter co-expression exerts influence on symptom severity and remission rate in the treatment of social anxiety disorder. However, the monoamine transporters are modulated in dissimilar ways when cognitive-behavioral treatment is given concomitantly with either SSRI-medication or pill placebo.

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Objective

Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET).

Methods

Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG-PET) data. MVPA analysis based on metabolic network features was employed to predict the pain relief after treatment in the pooled group and real acupuncture treatment, separately.

Results

Paired t-tests revealed significant alterations in pain intensity after real but not sham acupuncture treatment. Traditional mass-univariate correlations between brain metabolic and alterations in pain intensity were not significant. The MVPA results showed that the brain metabolic pattern in the DMN and SMN did predict the pain relief in the pooled group of patients with PDM (R2 = 0.25, p = 0.005). In addition, the metabolic pattern in the DMN could predict the pain relief after treatment in the real acupuncture treatment group (R2 = 0.40, p = 0.01).

Conclusion

This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

How a transdiagnostic approach can improve the treatment of emotional disorders: Insights from clinical psychology and neuroimaging

Multiple psychological treatments for emotional disorders have been developed and implemented, improving the quality of life of individuals. Nevertheless, relapse and poor response to psychotherapy are common. This article argues that a greater understanding of both the psychological and neurobiological mechanisms of change in psychotherapy is essential to improve treatment for emotional disorders. It aims to demonstrate how an understanding of these mechanisms provides a basis for (i) reconceptualizing some mental disorders, (ii) refining and establishing the evidence for existing therapeutic techniques and (iii) designing new techniques that precisely target the processes that maintain these disorders. Possible future directions for researchers and practitioners working at the intersection of neuropsychology and clinical psychology are discussed.

Neural effects of a short-term virtual reality self-training program to reduce social anxiety

BACKGROUND

Social anxiety disorder (SAD) is characterized by anxiety regarding social situations, avoidance of external social stimuli, and negative self-beliefs. Virtual reality self-training (VRS) at home may be a good interim modality for reducing social fears before formal treatment. This study aimed to find neurobiological evidence for the therapeutic effect of VRS.

METHODS

Fifty-two patients with SAD were randomly assigned to a VRS or waiting list (WL) group. The VRS group received an eight-session VRS program for 2 weeks, whereas the WL group received no intervention. Clinical assessments and functional magnetic resonance imaging scanning with the distress and speech evaluation tasks were repeatedly performed at baseline and after 3 weeks.

RESULTS

The post-VRS assessment showed significantly decreased anxiety and avoidance scores, distress index, and negative evaluation index for 'self', but no change in the negative evaluation index for 'other'. Patients showed significant responses to the distress task in various regions, including both sides of the prefrontal regions, occipital regions, insula, and thalamus, and to the speech evaluation task in the bilateral anterior cingulate cortex. Among these, significant neuronal changes after VRS were observed only in the right lingual gyrus and left thalamus.

CONCLUSIONS

VRS-induced improvements in the ability to pay attention to social stimuli without avoidance and even positively modulate emotional cues are based on functional changes in the visual cortices and thalamus. Based on these short-term neuronal changes, VRS can be a first intervention option for individuals with SAD who avoid society or are reluctant to receive formal treatment.

Neurocircuitry of Treatment in Anxiety Disorders

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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.

Treatment Response Biomarkers in Anxiety Disorders: From Neuroimaging to Neuronally-Derived Extracellular Vesicles and Beyond

Multiple and diverse psychotherapeutic or psychopharmacologic treatments effectively reduce symptoms for many patients with anxiety disorders, but the trajectory and magnitude of response vary considerably. This heterogeneity of treatment response has invigorated the search for biomarkers of treatment response in anxiety disorders, across the lifespan. In this review, we summarize evidence for biomarkers of treatment response in children, adolescents and adults with generalized, separation and social anxiety disorders as well as panic disorder. We then discuss the relationship between these biomarkers of treatment response and the pathophysiology of anxiety disorders. Finally, we provide context for treatment response biomarkers of the future, including neuronally-derived extracellular vesicles in anxiety disorders and discuss challenges that must be overcome prior to the debut of treatment response biomarkers in the clinic. A number of promising treatment response biomarkers have been identified, although there is an urgent need to replicate findings and to identify which biomarkers might guide clinicians in selecting from available treatments rather than just simply identifying patients who may be less likely to respond to a given intervention.

A Synthetic Literature Review on the Management of Emerging Treatment Resistance in First Episode Psychosis: Can We Move towards Precision Intervention and Individualised Care?

Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a ‘one-size fits all’ approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards ‘big data’ and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one’s illness may also be key to understanding the factors that drive illness course.

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.

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

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

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

BACKGROUND

Correct prediction of treatment response is a central goal of precision psychiatry. Here, we tested the predictive accuracy of a variety of pre-treatment patient characteristics, including clinical, demographic, molecular genetic, and neuroimaging markers, for treatment response in patients with social anxiety disorder (SAD).

METHODS

Forty-seven SAD patients (mean±SD age 33.9 ± 9.4 years, 24 women) were randomized and commenced 9 weeks' Internet-delivered cognitive behavior therapy (CBT) combined either with the selective serotonin reuptake inhibitor (SSRI) escitalopram (20 mg daily [10 mg first week], SSRI+CBT, n = 24) or placebo (placebo+CBT, n = 23). Treatment responders were defined from the Clinical Global Impression-Improvement scale (CGI-I ≤ 2). Before treatment, patients underwent functional magnetic resonance imaging and the Multi-Source Interference Task taxing cognitive interference. Support vector machines (SVMs) were trained to separate responders from nonresponders based on pre-treatment neural reactivity in the dorsal anterior cingulate cortex (dACC), amygdala, and occipital cortex, as well as molecular genetic, demographic, and clinical data. SVM models were tested using leave-one-subject-out cross-validation.

RESULTS

The best model separated treatment responders (n = 24) from nonresponders based on pre-treatment dACC reactivity (83% accuracy, P = 0.001). Responders had greater pre-treatment dACC reactivity than nonresponders especially in the SSRI+CBT group. No other variable was associated with clinical response or added predictive accuracy to the dACC SVM model.

LIMITATIONS

Small sample size, especially for genetic analyses. No replication or validation samples were available.

CONCLUSIONS

The findings demonstrate that treatment outcome predictions based on neural cingulate activity, at the individual level, outperform genetic, demographic, and clinical variables for medication-assisted Internet-delivered CBT, supporting the use of neuroimaging in precision psychiatry.

Novel pharmacological targets in drug development for the treatment of anxiety and anxiety-related disorders

Current medication for anxiety disorders is suboptimal in terms of efficiency and tolerability, highlighting the need for improved drug treatments. In this review an overview of drugs being studied in different phases of clinical trials for their potential in the treatment of fear-, anxiety- and trauma-related disorders is presented. One strategy followed in drug development is refining and improving compounds interacting with existing anxiolytic drug targets, such as serotonergic and prototypical GABAergic benzodiazepines. A more innovative approach involves the search for compounds with novel mechanisms of anxiolytic action using the growing knowledge base concerning the relevant neurocircuitries and neurobiological mechanisms underlying pathological fear and anxiety. The target systems evaluated in clinical trials include glutamate, endocannabinoid and neuropeptide systems, as well as ion channels and targets derived from phytochemicals. Examples of promising novel candidates currently in clinical development for generalised anxiety disorder, social anxiety disorder, panic disorder, obsessive compulsive disorder or post-traumatic stress disorder include ketamine, riluzole, xenon with one common pharmacological action of modulation of glutamatergic neurotransmission, as well as the neurosteroid aloradine. Finally, compounds such as D-cycloserine, MDMA, L-DOPA and cannabinoids have shown efficacy in enhancing fear-extinction learning in humans. They are thus investigated in clinical trials as an augmentative strategy for speeding up and enhancing the long-term effectiveness of exposure-based psychotherapy, which could render chronic anxiolytic drug treatment dispensable for many patients. These efforts are indicative of a rekindled interest and renewed optimism in the anxiety drug discovery field, after decades of relative stagnation.