Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression.

BACKGROUND: Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance. METHODS: N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample). RESULTS: The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (ß = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (ß = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (ß = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (ß = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620). CONCLUSIONS: The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Elevated emotion network connectivity is associated with fluctuations in depression.

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

An observational treatment study of metacognition in anxious-depression.

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (ß=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (ß=0.31, SE = 0.08, p<0.001), but not among controls (ß=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention.

The Effectiveness of Low-Intensity Psychological Interventions for Comorbid Depression and Anxiety in Patients with Long-Term Conditions: A Real-World Naturalistic Observational Study in IAPT Integrated Care.

BACKGROUND: Low-intensity psychological interventions may be a cost-effective, accessible solution for treating depression and anxiety in patients with long-term conditions, but evidence from real-world service settings is lacking. This study examined the effectiveness of low-intensity psychological interventions provided in the Improving Access to Psychological Therapies programme in England for patients with and without long-term conditions. METHODS: A retrospective analysis was conducted on patients (total N = 21,051, long-term conditions n = 4024) enrolled in three low-intensity psychological interventions, i.e. Internet-delivered cognitive behavioural therapy (iCBT), guided self-help (GSH), and psychoeducational group therapy (PGT) within a Talking Therapies service from 2016 to 2020. Primary outcomes included pre-post-treatment changes in depression (Patient Health Questionnaire-9) and anxiety (Generalised Anxiety Disorder-7). RESULTS: Overall, both cohorts significantly improved on all outcomes post-treatment, with large effect sizes. Patients with long-term conditions experienced a greater reduction in depression while those without experienced a greater reduction in anxiety, but these differences were marginal (< 1 score difference on both measures). No difference between the cohorts was shown when comparing the differential effectiveness across interventions, but those engaging in iCBT showed greater reduction in depression and anxiety than those in GSH and PGT, while those in GSH improved more than PGT. CONCLUSIONS: Low-intensity psychological interventions, particularly iCBT, were effective in treating depression and anxiety in patients with long-term conditions in a real-world service setting. Our large-scale study supports the continued and increased implementation of low-intensity psychological interventions for this subpopulation via integrated care.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation.

BACKGROUND: Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field. METHODS: A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation. RESULTS: Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97). CONCLUSIONS: An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Assessing the Efficacy and Acceptability of a Web-Based Intervention for Resilience Among College Students: Pilot Randomized Controlled Trial.

BACKGROUND: College students are at elevated risk for developing mental health problems and face specific barriers around accessing evidence-based treatment. Web-based interventions that focus on mental health promotion and strengthening resilience represent one possible solution. Providing support to users has shown to reduce dropout in these interventions. Further research is needed to assess the efficacy and acceptability of these interventions and explore the viability of automating support. OBJECTIVE: This study investigated the feasibility of a new web-based resilience program based on positive psychology, provided with human or automated support, in a sample of college students. METHODS: A 3-armed closed pilot randomized controlled trial design was used. Participants were randomized to the intervention with human support (n=29), intervention with automated support (n=26), or waiting list (n=28) group. Primary outcomes were resilience and well-being, respectively measured by the Connor-Davidson Resilience Scale and Pemberton Happiness Index. Secondary outcomes included measures of depression and anxiety, self-esteem, and stress. Outcomes were self-assessed through online questionnaires. Intention-to-treat and per-protocol analyses were conducted. RESULTS: All participants demonstrated significant improvements in resilience and related outcomes, including an unexpected improvement in the waiting list group. Within- and between-group effect sizes ranged from small to moderate and within-group effects were typically larger for the human than automated support group. A total of 36 participants began the program and completed 46.46% of it on average. Participants were generally satisfied with the program and found it easy to use. CONCLUSIONS: Findings support the feasibility of the intervention. Preliminary evidence for the equal benefit of human and automated support needs to be supported by further research with a larger sample. Results of this study will inform the development of a full-scale trial, from which stronger conclusions may be drawn. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 11866034; http://www.isrctn.com/ISRCTN11866034. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.invent.2019.100254.