Dataset Information

Towards personalized, brain-based behavioral intervention for transdiagnostic anxiety: Transient neural responses to negative images predict outcomes following a targeted computer-based intervention.

ABSTRACT: OBJECTIVE:Clinical anxiety is prevalent, highly comorbid with other conditions, and associated with significant medical morbidity, disability, and public health burden. Excessive attentional deployment toward threat is a transdiagnostic dimension of anxiety seen at both initial and sustained stages of threat processing. However, group-level observations of these phenomena mask considerable within-group heterogeneity that has been linked to treatment outcomes, suggesting that a transdiagnostic, individual differences approach may capture critical, clinically relevant information. METHOD:Seventy clinically anxious individuals were randomized to receive 8 sessions of attention bias modification (ABM; n = 41 included in analysis), a computer-based mechanistic intervention that specifically targets initial stages of threat processing, or a sham control (n = 21). Participants completed a mixed block/event-related functional MRI task optimized to discriminate transient from sustained neural responses to threat. RESULTS:Larger transient responses across a wide range of cognitive-affective regions (e.g., ventrolateral prefrontal cortex, anterior cingulate cortex, amygdala) predicted better clinical outcomes following ABM, in both a priori anatomical regions and whole-brain analyses; sustained responses did not. A spatial pattern recognition algorithm using transient threat responses successfully discriminated the top quartile of ABM responders with 68% accuracy. CONCLUSIONS:Neural alterations occurring on the relatively transient timescale that is specifically targeted by ABM predict favorable clinical outcomes. Results inform how to expand on the initial promise of neurocognitive treatments like ABM by fine-tuning their clinical indications (e.g., through personalized mechanistic intervention relevant across diagnoses) and by increasing the range of mechanisms that can be targeted (e.g., through synergistic treatment combinations and/or novel neurocognitive training protocols designed to tackle identified predictors of nonresponse). (PsycINFO Database Record (c) 2018 APA, all rights reserved).

SUBMITTER: Price RB

PROVIDER: S-EPMC6287282 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Towards personalized, brain-based behavioral intervention for transdiagnostic anxiety: Transient neural responses to negative images predict outcomes following a targeted computer-based intervention.

Price Rebecca B RB Cummings Logan L Gilchrist Danielle D Graur Simona S Banihashemi Layla L Kuo Susan S SS Siegle Greg J GJ

Journal of consulting and clinical psychology 20181201 12

<h4>Objective</h4>Clinical anxiety is prevalent, highly comorbid with other conditions, and associated with significant medical morbidity, disability, and public health burden. Excessive attentional deployment toward threat is a transdiagnostic dimension of anxiety seen at both initial and sustained stages of threat processing. However, group-level observations of these phenomena mask considerable within-group heterogeneity that has been linked to treatment outcomes, suggesting that a transdiagn ...[more]

PMID: 30507228

Similar Datasets

Project description:Psychological interventions have a long history of successful treatment of patients suffering from mental health and certain medical conditions. At the same time, psychotherapy research has revealed key areas of growth for optimizing patient care. These include identifying novel treatment delivery methods that increase treatment adherence, developing new strategies to more effectively address the ever-growing population of patients with comorbid conditions, and elucidating the mechanisms by which effective treatments work in order to further refine their design. Acceptance and commitment therapy (ACT) is an empirically supported psychotherapy that offers promise for patients suffering from a wide range of mental and physical conditions, while addressing these gaps and challenges in the field. ACT rests on the fundamental premise that pain, grief, disappointment, illness, and anxiety are inevitable features of human life, with the therapeutic goal of helping individuals productively adapt to these types of challenges by developing greater psychological flexibility rather than engaging in counterproductive attempts to eliminate or suppress undesirable experiences. This is achieved through committed pursuit of valued life areas and directions, even in the face of the natural desire to escape or avoid painful and troubling experiences, emotions, and thoughts. ACT is transdiagnostic (applies to more than one condition), process-focused, and flexibly delivered. In a relatively short period of time, ACT has been effectively implemented across a broad range of therapeutic settings, including mental health, primary care, and specialty medical clinics. ACT has also been delivered in a variety of formats, including 1-day group workshops, online and smartphone applications, and telehealth. Focus on how best to package and deliver treatment to meet the unique needs of different patient populations helps to ensure treatment adherence and has fostered successful application of ACT for patients in everyday clinical settings.

Project description:BackgroundInternet interventions have been shown to be effective in treating anxiety disorders. Most interventions to date focus on single disorders and disregard potential comorbidities.ObjectiveThe aim of this mixed-methods study was to investigate feasibility, user experience, and effects of a newly developed individually tailored transdiagnostic guided internet intervention for anxiety disorders.MethodsThis study is an uncontrolled, within-group, baseline, postintervention pilot trial with an embedded qualitative and quantitative process and effect evaluation. In total, 49 adults with anxiety disorders (generalized anxiety disorder n=20, social phobia n=19, agoraphobia without panic n=12, panic with agoraphobia n=6, panic without agoraphobia n=4, subclinical depression n=41) received access to the 7-session intervention. We examined motivation and expectations, intervention use, user experience, impact, and modification requests. Qualitative data were assessed using semistructured interviews and analyzed by qualitative content analysis. Quantitative outcomes included symptom severity of anxiety and depression (Hamilton Anxiety Rating Scale [HAM-A], Quick Item Inventory of Depressive Symptomatology clinician rating [QIDS-C]), diagnostic status in clinical interviews (Mini International Neuropsychiatric Interview [MINI]), and web-based self-reports (Generalized Anxiety Disorder-7 [GAD-7], Center for Epidemiological Studies Depression Scale [CES-D], Beck Anxiety Inventory [BAI], Panic and Agoraphobia Scale [PAS], Social Phobia Scale [SPS], Patient Health Questionnaire-9 [PHQ-9]) at baseline and postassessment. Quantitative data was analyzed by comparing within-group means expressed as Cohen d.ResultsAnxiety symptom severity (HAM-A d=1.19) and depressive symptoms (QIDS-C d=0.42) improved significantly, and 54% (21/39) no longer were diagnosed as having any anxiety disorder. The main positive effects were the general improvement of disease burden and attentiveness to feelings and risk situations while the main negative effects experienced were lack of change in disease burden and symptom deterioration. The most prevalent reasons for participation were the advantages of online treatment, symptom burden, and openness toward online treatment. Helpful factors included support, psychoeducation and practicing strategies in daily life; the main hindering factors were too little individualization and being overwhelmed by the content and pace.ConclusionsThe intervention was found to be feasible and results show preliminary data indicating potential efficacy for improving anxiety and depression. The next step should be the evaluation within a randomized controlled trial. Concerning intervention development, it was found that future interventions should emphasize individualization even more in order to further improve the fit to individual characteristics, preferences, and needs.

Project description:BackgroundAnxiety and depressive disorders are prevalent in adolescents and young adults. However, most young people with mental health problems do not receive treatment. Computerized cognitive behavior therapy (cCBT) may provide an accessible alternative to face-to-face treatment, but the evidence base in young people is limited.ObjectiveThe objective was to perform an up-to-date comprehensive systematic review and meta-analysis of the effectiveness of cCBT in treating anxiety and depression in adolescents and young adults compared with active treatment and passive controls. We aimed to examine posttreatment and follow-up effects and explore the moderators of treatment effects.MethodsWe conducted systematic searches in the following six electronic databases: PubMed, EMBASE, PsycINFO, CINAHL, Web of Science, and Cochrane Central Register of Controlled Trials. We included randomized controlled trials comparing cCBT with any control group in adolescents or young adults (age 12-25 years) with anxiety or depressive symptoms. The quality of included studies was assessed using the Cochrane risk-of-bias tool for randomized trials, version 2.0. Overall quality of evidence for each outcome was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Posttreatment means and SDs were compared between intervention and control groups, and pooled effect sizes (Hedges g) were calculated. Random-effects meta-analyses were conducted using Comprehensive Meta-Analysis software. Subgroup analyses and meta-regression analyses were conducted to explore whether age, guidance level, and adherence rate were associated with treatment outcome.ResultsThe search identified 7670 papers, of which 24 studies met the inclusion criteria. Most included studies (22/24) had a high risk of bias owing to self-report measures and/or inappropriate handling of missing data. Compared with passive controls, cCBT yielded small to medium posttreatment pooled effect sizes regarding depressive symptoms (g=0.51, 95% CI 0.30-0.72, number needed to treat [NNT]=3.55) and anxiety symptoms (g=0.44, 95% CI 0.23-0.65, NNT=4.10). cCBT yielded effects similar to those of active treatment controls regarding anxiety symptoms (g=0.04, 95% CI -0.23 to 0.31). For depressive symptoms, the nonsignificant pooled effect size favored active treatment controls (g=-0.70, 95% CI -1.51 to 0.11, P=.09), but heterogeneity was very high (I2=90.63%). No moderators of treatment effects were identified. At long-term follow-up, cCBT yielded a small pooled effect size regarding depressive symptoms compared with passive controls (g=0.27, 95% CI 0.09-0.45, NNT=6.58). No other follow-up effects were found; however, power was limited owing to the small number of studies.ConclusionscCBT is beneficial for reducing posttreatment anxiety and depressive symptoms in adolescents and young adults compared with passive controls. Compared with active treatment controls, cCBT yielded similar effects regarding anxiety symptoms. Regarding depressive symptoms, however, the results remain unclear. More high-quality research involving active controls and long-term follow-up assessments is needed in this population.Trial registrationPROSPERO CRD42019119725; https://tinyurl.com/y5acfgd9.

Project description:BackgroundAnxiety and depression in children and adolescents are undertreated. Computer- and Internet-based cognitive behavioral treatments (cCBT) may be an attractive treatment alternative to regular face-to-face treatment.This meta-analysis aims to evaluate whether cCBT is effective for treating symptoms of anxiety and depression in youth.Methods and findingsWe conducted systematic searches in bibliographical databases (Pubmed, Cochrane controlled trial register, PsychInfo) up to December 4, 2013. Only randomized controlled trials in which a computer-, Internet- or mobile-based cognitive behavioral intervention targeting either depression, anxiety or both in children or adolescents up to the age of 25 were compared to a control condition were selected. We employed a random-effects pooling model in overall effect analyses and a mixed effect model for sub-group analyses. Searches resulted in identifying 13 randomized trials, including 796 children and adolescents that met inclusion criteria. Seven studies were directed at treating anxiety, four studies at depression, and two were of a transdiagnostic nature, targeting both anxiety and depression. The overall mean effect size (Hedges' g) of cCBT on symptoms of anxiety or depression at post-test was g=0.72 (95% CI:0.55-0.90, numbers needed to be treated (NNT)=2.56). Heterogeneity was low (I²=20.14%, 95% CI: 0-58%). The superiority of cCBT over controls was evident for interventions targeting anxiety (g=0.68; 95% CI: 0.45-0.92; p < .001; NNT=2.70) and for interventions targeting depression (g=0.76; 95% CI: 0.41-0.12; p < .001; NNT=2.44) as well as for transdiagnostic interventions (g=0.94; 95% CI: 0.23-2.66; p < .001; NNT=2.60).ConclusionsResults provide evidence for the efficacy of cCBT in the treatment of anxiety and depressive symptoms in youth. Hence, such interventions may be a promising treatment alternative when evidence based face-to-face treatment is not feasible. Future studies should examine long-term effects of treatments and should focus on obtaining patient-level data from existing studies, to perform an individual patient data meta-analysis.

Dataset Information

Towards personalized, brain-based behavioral intervention for transdiagnostic anxiety: Transient neural responses to negative images predict outcomes following a targeted computer-based intervention.

Publications

Towards personalized, brain-based behavioral intervention for transdiagnostic anxiety: Transient neural responses to negative images predict outcomes following a targeted computer-based intervention.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets