Treatment Response In Anxiety Disorders Research Articles

Neurocircuitry of treatment in anxiety disorders.

Background:Understanding how treatments change neurobiology is critical to developing predictors of treatment response. This is especially true for anxiety disorders—the most common psychiatric disorders across the lifespan. With this in mind, we examined neurofunctional predictors of treatment response and neurofunctional changes associated with treatment across anxiety disorders.Methods:PubMed/Medline was searched for prospective treatment studies that included parallel examinations of functional activation or connectivity (both task-based and resting state) in adults and youth with panic disorder and generalized, separation, and/or social anxiety disorders published before April 30, 2021. All studies examining baseline predictors or changes related to pharmacologic and psychotherapeutic treatment of DSM-TV and DSM-5 anxiety disorders were included. Demographic, clinical, and treatment data as well as neurofunctional outcomes were extracted and summarized.Results:Twenty-nine studies examined changes in functional activation and/or connectivity (56 treatment arms) related to treatment and twenty-three examined neurofunctional predictors of treatment response. Predictors of treatment response and treatment-related neurofunctional changes were frequently observed within amygdala-prefrontal circuits. However, immense heterogeneity and few replication studies preclude a cohesive neurofunctional treatment response model across anxiety disorders.Conclusions:The extant literature describing neurofunctional aspects of treatment response in anxiety disorders is best viewed as a partially constructed scaffold on which to build a clinically translatable set of robust neuroimaging biomarkers that can be used to guide treatment and to select from available treatment. The construction of this understanding will require harmonization of analytic and task approaches, larger samples, and replication of component studies.

Brain markers predicting response to cognitive\u2010behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015

Predictive brain markers promise a number of important scientific, clinical, and societal applications. Over 600 predictive brain markers have been described in published reports, but very few have been tested in independent replication attempts. Here, we conducted an independent replication of a previously published marker predicting treatment response to cognitive-behavioral therapy for social anxiety disorder from patterns of resting-state fMRI amygdala connectivity1. The replication attempt was conducted in an existing dataset similar to the dataset used in the original report, by a team of independent investigators in consultation with the original authors. The precise model described in the original report positively predicted treatment outcomes in the replication dataset, but with marginal statistical significance, permutation test p = 0.1. The effect size was substantially smaller in the replication dataset, with the model explaining 2% of the variance in treatment outcomes, as compared to 21% in the original report. Several lines of evidence, including the current replication attempt, suggest that features of amygdala function or structure may be able to predict treatment response in anxiety disorders. However, predictive models that explain a substantial amount of variance in independent datasets will be needed for scientific and clinical applications.

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.

Dose timing of d-cycloserine to augment cognitive behavioral therapy for social anxiety: Study design and rationale

The use of d-cycloserine (DCS) as a cognitive enhancer to augment exposure-based cognitive–behavioral therapy (CBT) represents a promising new translational research direction with the goal to accelerate and optimize treatment response for anxiety disorders. Some studies suggest that DCS may not only augment extinction learning but could also facilitate fear memory reconsolidation. Therefore, the effect of DCS may depend on fear levels reported at the end of exposure sessions. This paper presents the rationale and design for a randomized controlled trial examining the relative efficacy of tailoring DCS administration based on exposure success (i.e. end fear levels) during a 5-session group CBT protocol for social anxiety disorder (n=156). Specifically, tailored post-session DCS administration will be compared against untailored post-session DCS, untailored pre-session DCS, and pill placebo in terms of reduction in social anxiety symptoms and responder status. In addition, a subset of participants (n=96) will undergo a fear extinction retention experiment prior to the clinical trial in which they will be randomly assigned to receive either DCS or placebo prior to extinguishing a conditioned fear. The results from this experimental paradigm will clarify the mechanism of the effects of DCS on exposure procedures. This study aims to serve as the first step toward developing an algorithm for the personalized use of DCS during CBT for social anxiety disorder, with the ultimate goal of optimizing treatment outcome for anxiety disorders.

Stages of change and the treatment of OCD

The Stages of Change model conceptualizes the process of behavioral change. The University of Rhode Island Change Assessment (URICA) is a self-report measure divided into subscales that represent four of the five different stages (Precontemplation, Contemplation, Action, and Maintenance). Research has shown that higher Precontemplation scores predict reduced treatment response in anxiety disorders. The purpose of this investigation was to examine the role of the stages of change in treatment outcomes with individuals with OCD undergoing intensive residential treatment including exposure therapy and response prevention (EX/RP). We hypothesized that a patient׳s stage of change, initially and throughout treatment, would be associated with both treatment outcome and symptom severity. The URICA was administered to a sample of 424 patients admitted to a residential treatment program for OCD at admission and after 30 days of treatment. Y-BOCS was given at admission and discharge. Stage of change at admission did not predict outcome. However, Precontemplation at discharge was associated with shorter length of stay. Results and their implications will be presented along with a discussion of future directions.

Lack of influence of DAT1 and DRD2 gene variants on antidepressant response in generalized anxiety disorder.

Although antidepressant drugs are used as first-line intervention to treat patients with generalized anxiety disorder (GAD), only one-third of patients respond positively to treatment. In our study, we investigated whether functional genetic polymorphisms in the dopamine active transporter 1 (DAT1) and dopamine receptor D2 (DRD2) may play a role in antidepressant treatment response in GAD. We examined 156 patients diagnosed with GAD who received venlafaxine Extended-Release (XR) treatment as part of an 18-month relapse-prevention study to determine whether variation in these genes had an effect on treatment response after 6 months of open-label treatment. Genotypes were obtained for rs1076560 (DRD2), rs1800497 (DRD2), rs2550948 (DAT1), and a variable number tandem repeat in the 3' untranslated region of the DAT1 gene using standard methods. Results show that none of the tested variants were associated with treatment response to venlafaxine XR in GAD. Genotype and allele frequencies did not differ statistically significantly between responders and non-responders using either the Hamilton Anxiety or Clinical Global Impressions of Improvement Scale at 6 months. Although we detected no association in our sample, future studies using larger samples and more comprehensive gene coverage are needed to evaluate potential effects of dopaminergic variants on antidepressant treatment response in anxiety disorders.

Neuroimaging predictors of treatment response in anxiety disorders

Although several psychological and pharmacological treatment options are available for anxiety disorders, not all patients respond well to each option. Furthermore, given the relatively long duration of adequate treatment trials, finding a good treatment fit can take many months or longer. Thus, both clinicians and patients would benefit from the identification of objective pre-treatment measures that predict which patients will best respond to a given treatment. Recent studies have begun to use biological measures to help predict symptomatic change after treatment in anxiety disorders. In this review, we summarize studies that have used structural and functional neuroimaging measures to predict treatment response in obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), and social anxiety disorder (SAD). We note the limitations of the current studies and offer suggestions for future research. Although the literature is currently small, we conclude that pre-treatment neuroimaging measures do appear to predict treatment response in anxiety disorders, and future research will be needed to determine the relative predictive power of neuroimaging measures as compared to clinical and demographic measures.

The role of pharmacogenetics in the treatment of depression and anxiety disorders

Although effective treatment for mood and anxiety disorders have been available for more than 40 years, 30-50% of depressed patients and 25% of patients with anxiety disorder do not respond sufficiently to first-line treatment with antidepressants. Genetic factors are supposed to play a major role in both variation of treatment response and incidence of adverse effects to medication. So far, candidate genes of pharmacokinetic and pharmacodynamic pathways of antidepressants have been investigated, and associations between several candidate genes and response to antidepressants are reported. Two functional polymorphisms of the serotonin transporter gene, 5-HTTLPR and STin2 have been investigated in a large number of pharmacogenetic studies of depression; other candidate genes include serotonin receptor genes, brain-derived neurotrophic factor, P-glycoprotein (located in the blood-brain barrier), G-proteins, TPH1 and TPH2, MAOA, the noradrenaline transporter gene, FKBP5, or cytochrome P450 (CYP450) genes. CYP450 genes play a major role in the metabolism of a substantial part of psychotropics, including antidepressants, and the first estimates of dosage adjustments for antidepressants have been provided based on metabolizer status. Genome-wide association studies that use large numbers of single-nucleotide polymorphisms to screen the entire genome for alleles that influence a trait are now feasible, and the results of the first genome-wide association studies of antidepressant treatment outcome will soon be available. The current review not only updates pharmacogenetic research in depression but also focuses on antidepressant treatment response in anxiety disorders.

Preliminary support for gender differences in response to fluoxetine for generalized anxiety disorder

Women have a higher prevalence of generalized anxiety disorder (GAD) than do men, but few studies have assessed gender differences in response to pharmacotherapy. In this study we examined gender as a correlate of response to 6 weeks of open, prospective fluoxetine treatment in 23 men and 22 women with a primary diagnosis of GAD. There was no difference by gender in age or prevalence of mood and anxiety comorbidity; however, GAD onset occurred at a significantly younger age in women compared with men. Despite a lack of difference in baseline severity measures, women had a significantly poorer response to fluoxetine as measured by both the Hamilton Anxiety Rating Scale (HAM-A) and Clinician Global Impression-Severity Scale (CGI-S). In multivariate analyses, there was a significant interaction between age of onset and gender: men with younger age of onset and women with older age of onset exhibited poorer response on the HAM-A. These data, though limited in sample size and by the post hoc nature of our analyses, offer preliminary support that women with GAD, particularly those with a later age of onset, may have a poorer response to the selective serotonin reuptake inhibitor (SSRI) fluoxetine. Larger placebo-controlled trials are needed to more definitively examine gender and treatment response in anxiety disorders.

S.21.04 Early diagnosis and treatment response inanxiety disorders

Analysis of variance (ANOVA) is a type of general linear model suitable for factorial designs, in which one is interested in the main effects of, and interactions between, one or more factors. ANOVA assumes a spherical error covariance, and violations such as correlation induced by repeated measures from the same subjects, or inhomogeneous variance across conditions, require either post hoc corrections to the degrees of freedom, partitioning of the error into separate error terms, or explicit modeling of the error covariance.