Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis

Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment response in those receiving CBTp (n = 22) could be predicted by pre-therapy neural responses to facial affect that was either threat-related (ambiguous ‘neutral’ faces perceived as threatening in psychosis, in addition to angry and fearful faces) or prosocial (happy faces). The models predicted improvement in psychotic (r = 0.63, p = 0.003) and affective (r = 0.31, p = 0.05) symptoms following CBTp, but not in the treatment-as-usual group (n = 16). Psychotic symptom improvement was predicted by neural responses to threat-related affect across sensorimotor and frontal-limbic regions, whereas affective symptom improvement was predicted by neural responses to fearful faces only as well as prosocial affect across sensorimotor and frontal regions. These findings suggest that CBTp most likely improves psychotic and affective symptoms in those endorsing more threatening appraisals and mood-congruent processing biases, respectively, which are explored and reframed as part of the therapy. This study improves our understanding of the neurobiology of treatment response and provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients.Wellcome Trust (Senior Research Fellowship in Basic Biomedical Science to V.

Adaptive Training for Aggression De-escalation

Item does not contain fulltextThe ability to de-escalate confrontations with aggressive individuals is a useful skill, in particular within professions in public domains. Nevertheless, offering appropriate training that enables students to develop such skills is a nontrivial matter. As a complementary approach to real-world training, the STRESS project proposes a simulation-based environment for training of aggression de-escalation. The main focus of the current paper is to make this system adaptive to the performance of the trainee. To realize this, first a number of learning goals have been identified. Based on these, several levels of difficulty were established, as well as a mechanism to switch up and down between these levels based on the user’s score. A preliminary evaluation demonstrated that the system successfully adapts its difficulty level to the performance of the user, and that users are generally positive about the adaptation mechanism.First International Symposium, ALIA 2014, Bangor, UK, November 5-6, 2014. Revised Selected Paper

Homologous Recombination Repair Deficiency and Implications for Tumor Immunogenicity

Homologous recombination repair deficiency (HRD) can be observed in virtually all cancer types. Although HRD sensitizes tumors to DNA-damaging chemotherapy and poly(ADP-ribose) polymerase (PARP) inhibitors, all patients ultimately develop resistance to these therapies. Therefore, it is necessary to identify therapeutic regimens with a more durable efficacy. HRD tumors have been suggested to be more immunogenic and, therefore, more susceptible to treatment with checkpoint inhibitors. In this review, we describe how HRD might mechanistically affect antitumor immunity and summarize the available translational evidence for an association between HRD and antitumor immunity across multiple tumor types. In addition, we give an overview of all available clinical data on the efficacy of checkpoint inhibitors in HRD tumors and describe the evidence for using treatment strategies that combine checkpoint inhibitors with PARP inhibitors

Diversity in news recommendation:Manifesto from Dagstuhl Perspectives Workshop 19482

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and recommender systems to consume information challenging the traditional concept of news diversity. In addition, the very concept of diversity, which differs between disciplines, will need to be re-evaluated requiring an interdisciplinary investigation, which requires a new level of mutual cooperation between computer scientists, social scientists, and legal scholars. Based on the outcome of a interdisciplinary workshop, we have the following recommendations, directed at researchers, funders, legislators, regulators, and the media industry: - Conduct interdisciplinary research on news recommenders and diversity. - Create a safe harbor for academic research with industry data. - Strengthen the role of public values in news recommenders. - Create a meaningful governance framework for news recommenders. - Fund a joint lab to spearhead the needed interdisciplinary research, boost practical innovation, develop reference solutions, and transfer insights into practice

Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

Abstract No consensus strategies exist for prognosticating metastatic castration-resistant prostate cancer (mCRPC). Circulating tumor DNA fraction (ctDNA%) is increasingly reported by commercial and laboratory tests but its utility for risk stratification is unclear. Here, we intersect ctDNA%, treatment outcomes, and clinical characteristics across 738 plasma samples from 491 male mCRPC patients from two randomized multicentre phase II trials and a prospective province-wide blood biobanking program. ctDNA% correlates with serum and radiographic metrics of disease burden and is highest in patients with liver metastases. ctDNA% strongly predicts overall survival, progression-free survival, and treatment response independent of therapeutic context and outperformed established prognostic clinical factors. Recognizing that ctDNA-based biomarker genotyping is limited by low ctDNA% in some patients, we leverage the relationship between clinical prognostic factors and ctDNA% to develop a clinically-interpretable machine-learning tool that predicts whether a patient has sufficient ctDNA% for informative ctDNA genotyping (available online: https://www.ctDNA.org ). Our results affirm ctDNA% as an actionable tool for patient risk stratification and provide a practical framework for optimized biomarker testing