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Large language models (LLMs) such as Open AI’s GPT-4 (which power ChatGPT) and Google’s Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.

Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their \"Optimal\" treatment versus those assigned to their \"Non-optimal\" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01). This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

Cognitive therapy and antidepressant medication are both effective treatments for depression. Derubeis and colleagues propose common and divergent neural changes that might underlie the antidepressant effects of both types of treatment and that could explain the enduring, relapse-preventing effects of cognitive therapy. An interview with Rob DeRubeis for Neuropod is available for download . Depression is one of the most prevalent and debilitating of the psychiatric disorders. Studies have shown that cognitive therapy is as efficacious as antidepressant medication at treating depression, and it seems to reduce the risk of relapse even after its discontinuation. Cognitive therapy and antidepressant medication probably engage some similar neural mechanisms, as well as mechanisms that are distinctive to each. A precise specification of these mechanisms might one day be used to guide treatment selection and improve outcomes.

Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). 134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.

Key Points Deficits in cognitive function — ranging from decreased attention and working memory to disrupted social cognition and language — are common in psychiatric disorders. They severely compromise quality of life, yet are currently poorly treated. Recent research has identified numerous interacting causes — genetic, epigenetic, developmental and environmental — that collectively disrupt the cerebral and cellular networks integrating and modulating cognition. Several pharmacotherapeutic strategies for the restoration of cognition are under investigation but most drugs have only been evaluated in rodents, and there is limited positive feedback from the clinic. The successful development of improved agents necessitates rigorous validation both in animals and in humans. In this regard, a broad palette of techniques, ranging from behavioural testing to brain imaging, is available for the exploration of innovative concepts and the characterization of new drugs. Despite the key importance of pharmacotherapy, the relevance of alternative strategies should not be neglected. The association of both approaches may emerge to be particularly effective for realizing the goal of enhanced cognitive performance and, accordingly, improved quality of life in patients suffering from psychiatric disorders. Studies of psychiatric disorders have traditionally focused on emotional symptoms, such as depression, anxiety and hallucinations, but poorly controlled cognitive deficits are also prominent and severely compromise quality of life. This article critically discusses our understanding of the nature and causes of cognitive impairment in psychiatric disorders, and reviews the opportunities and challenges in improving cognition in patients, including the development of more effective translational research approaches. Studies of psychiatric disorders have traditionally focused on emotional symptoms such as depression, anxiety and hallucinations. However, poorly controlled cognitive deficits are equally prominent and severely compromise quality of life, including social and professional integration. Consequently, intensive efforts are being made to characterize the cellular and cerebral circuits underpinning cognitive function, define the nature and causes of cognitive impairment in psychiatric disorders and identify more effective treatments. Successful development will depend on rigorous validation in animal models as well as in patients, including measures of real-world cognitive functioning. This article critically discusses these issues, highlighting the challenges and opportunities for improving cognition in individuals suffering from psychiatric disorders.

Controversy exists over antidepressant use in bipolar II depression. To compare the safety and effectiveness of antidepressantv.mood stabiliser monotherapy for bipolar type II major depressive episodes. Randomised, double-blind, parallel-group, 12-week comparison of venlafaxine (n= 65)v.lithium (n= 64) monotherapy in adult out-patients (trial registration numberNCT00602537). Primary outcome - venlafaxine produced a greater response rate (67.7%)v lithium (34.4%,P<0.001). Secondary outcomes - venlafaxine produced a greater remission rate (58.5%v 28.1%,P<0.001); greater decline in depression symptom scores over time (β = -5.32, s.e. = 1.16, χ(2)= 21.19,P<0.001); greater reduction in global severity scores over time (β = -1.05, s.e. = 0.22, w(2)= 22.33,P<0.001); and greater improvement in global change scores (β = -1.31, s.e. = 0.32, χ(2)= 16.95,P<0.001) relative to lithium. No statistically significant or clinically meaningful differences in hypomanic symptoms were observed between treatments. These findings suggest that short-term venlafaxine monotherapy may provide effective antidepressant treatment for bipolar II depression without a statistically significant increase in hypomanic symptoms relative to lithium.

ABSTRACT Background: Mental health problems cause significant distress and impairment in adolescents worldwide. One-fifth of the world's adolescents live in India, and much remains to be known about their mental health and wellbeing. Aim: In this preregistered study, we aimed to estimate the rates of depressive and anxiety symptoms, examine their relationship with indicators of wellbeing, and identify correlates of mental health among Indian adolescents. Methods: We administered self-report measures of depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), wellbeing (WEMWBS), and happiness (SHS) to 1,213 Indian adolescents (52.0% male; Mage = 14.11, SDage = 1.48). Results: Findings from the PHQ-9 (M = 8.08, SD = 5.01) and GAD-7 (M = 7.42, SD = 4.78) indicated high levels of depressive symptoms and anxiety symptoms. Thirty seven percent of the sample scored above the clinical cutoff for depressive symptoms, and 30.6% scored above the cutoff for anxiety symptoms. Although measures of mental health symptoms (PHQ-9 and GAD-7) were associated with measures of wellbeing and happiness (WEMWBS and SHS), these associations were only modest (Correlation < 0.45). Female students reported higher symptoms (and worse wellbeing) compared to male students, and older students reported higher symptoms (and worse wellbeing and happiness) compared to younger students. Conclusion: This study highlights the high prevalence of depressive symptoms and anxiety symptoms among Indian high school students. Symptom measures correlated only modestly with measures of wellbeing and happiness, suggesting that wellbeing and happiness reflect more than the absence of internalizing symptoms. Future research is needed to identify effective and appropriate ways to promote mental health and wellness among Indian students.

Difficulty making decisions is one of the symptoms of the depressive illness. Previous research suggests that depressed individuals may make decisions that differ from those made by the non-depressed, and that they use sub-optimal decision-making strategies. For this study we constructed an instrument that aims to measure a variety of decision-making styles as well as the respondent’s view of him or herself as a decision-maker (decisional self-esteem). These styles and estimates of decisional self-esteem were then related to depressive symptoms. Depressive symptomatology correlated negatively with perception of self as a decision-maker. Those with higher depression severity scores characterized themselves as being more anxious about decisions, and more likely to procrastinate. They also reported using fewer productive decision-making strategies, depending more on other people for help with decisions, and relying less on their own intuitions when making decisions. Further research is needed to determine the extent to which these decision-making styles are antecedents to depressive symptomatology or are instead products of, or aspects of, the phenomenology associated with depression.

One of the main debates in the study of psychotherapy is whether specific techniques are best indicated for different problems or whether “common factors” better account for the efficacy of psychotherapy. Evidence for the superiority of specific techniques is mixed and limited to a handful of diagnoses. By contrast, evidence for the importance of common factors is riddled with methodological weaknesses and may be of limited clinical utility. The stagnation in this debate may reflect that the research methods heretofore employed have reached a plateau in their ability to advance knowledge regarding psychotherapy processes. The articles of the special issue move beyond simple bivariate relationship and attempt to model the real-world complexity involved in the process of psychotherapy. It is argued that these types of investigations, which model the interactions of patient characteristics as well as multiple specific and “common factors,” are the best way to advance the state of knowledge regarding psychotherapy processes.

BackgroundMental health problems are the leading cause of disability among adolescents worldwide, yet access to treatment is limited. Brief digital interventions have been shown to improve youth mental health, but little is known about which digital interventions are most effective.AimsTo evaluate the effectiveness of two digital single-session interventions (Shamiri-Digital and Digital-CBT (cognitive-behavioural therapy)) among Kenyan adolescents.MethodsWe will perform a school-based comparative effectiveness randomised controlled trial. Approximately 926 Kenyan adolescents will be randomly assigned to one of three conditions: Shamiri-Digital (focused on gratitude, growth mindsets and values), Digital-CBT (focused on behavioural activation, cognitive restructuring and problem solving) or a study-skills control condition (focused on note-taking and essay writing skills). The primary outcomes include depressive symptoms (measured by the Patient Health Questionnaire-8), anxiety symptoms (Generalized Anxiety Disorder Screener-7) and subjective well-being (Short Warwick-Edinburgh Mental Well-being Scale). The secondary outcomes include acceptability, appropriateness, primary control and secondary control. Acceptability and appropriateness will be measured immediately post-intervention; other outcomes will be measured 2 weeks, 4 weeks and 12 weeks post-intervention.ResultsWe hypothesise that adolescents assigned to Shamiri-Digital and adolescents assigned to Digital-CBT will experience greater improvements (assessed via hierarchical linear models) than those assigned to the control group. We will also compare Shamiri-Digital with Digital-CBT, although we do not have a preplanned hypothesis.ConclusionsOur findings will help us evaluate two digital single-session interventions with different theoretical foundations. If effective, such interventions could be disseminated to reduce the public health burden of common mental health problems.Trial registration numberPACTR202011691886690.