The efficacy of individual humanistic-experiential therapies for the treatment of depression: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: Conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating the efficacy of individual humanistic-experiential therapies (HEPs) for depression. METHOD: Database searches (Scopus, Medline, and PsycINFO) identified RCTs comparing any HEP intervention with a treatment-as-usual (TAU) control or active alternative intervention for the treatment of depression. Included studies were assessed using the Risk of Bias 2 tool and narratively synthesized. Post-treatment and follow-up effect sizes were aggregated using random-effects meta-analysis and moderators of treatment effect were explored (PROSPERO: CRD42021240485). RESULTS: Seventeen RCTs, synthesized across four meta-analyzes, indicated HEP depression outcomes were significantly better than TAU controls at post-treatment (g = 0.41, 95% CI [0.18, 0.65], n = 735), but not significantly different at follow-up (g = 0.14, 95% CI [-0.30, 0.58], n = 631). HEP depression outcomes were comparable to active treatments at post-treatment (g = -0.09, 95% CI [-0.26, 0.08], n = 2131), but significantly favored non-HEP alternative interventions at follow-up (g = -0.21, 95% CI [-0.35, -0.07], n = 1196). CONCLUSION: Relative to usual care, HEPs are effective in the short-term and comparable to non-HEP alternative interventions at post-treatment, but not at follow-up. However, imprecision, inconsistency, and risk of bias concerns were identified as limitations of the evidence included. Future large-scale trials of HEPs with equipoise between comparator conditions are required.

Early response as a prognostic indicator in person-centered experiential therapy for depression.

Currently, no reports exist on the phenomenon of early response in humanistic-experiential therapies. This study investigated the prognostic value of early response on posttreatment outcomes in person-centered experiential therapy (PCET) for depression within the English Improving Access to Psychological Therapies program. The design of the study was a retrospective observational cohort study. Routine clinical data were drawn from N = 3,321 patients with depression symptoms. The primary outcome was reliable and clinically significant improvement (RCSI) on the Patient Health Questionnaire-9 (PHQ-9) self-report depression measure at the end of treatment. Early response was operationalized as reliable improvement, defined as a PHQ-9 change score ≥ 6 from baseline to Session 4. Early response was examined as a predictor of RCSI using logistic regression controlling for baseline depression severity. In sensitivity analyses, therapist effects were controlled using multilevel modeling. A total of 38.7% of patients met the criterion for early response. Patients who experienced an early response to treatment were six times more likely to recover at the end of treatment compared to patients who did not have an early response. The early response effect was still evident after accounting for individual variability between therapists. However, a quarter of patients displayed a pattern of eventual response, reaching recovery at end of treatment despite not experiencing an initial improvement early in therapy. Early response to PCET is a reliable predictor of treatment outcome. Different response patterns evidenced in this study indicate that identifying subgroups of patients associated with early and eventual response could support clinical decision-making. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain.

Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8-18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.

Efficacy and safety of autologous haematopoietic stem cell transplantation versus alemtuzumab, ocrelizumab, ofatumumab or cladribine in relapsing remitting multiple sclerosis (StarMS): protocol for a randomised controlled trial.

INTRODUCTION: Autologous haematopoietic stem cell transplantation (aHSCT) is increasingly used as treatment for patients with active multiple sclerosis (MS), typically after failure of disease-modifying therapies (DMTs). A recent phase III trial, 'Multiple Sclerosis International Stem Cell Transplant, MIST', showed that aHSCT resulted in prolonged time to disability progression compared with DMTs in patients with relapsing remitting MS (RRMS). However, the MIST trial did not include many of the current high-efficacy DMTs (alemtuzumab, ocrelizumab, ofatumumab or cladribine) in use in the UK within the control arm, which are now offered to patients with rapidly evolving severe MS (RES-MS) who are treatment naïve. There remain, therefore, unanswered questions about the relative efficacy and safety of aHSCT over these high-efficacy DMTs in these patient groups. The StarMS trial (Autologous Stem Cell Transplantation versus Alemtuzumab, Ocrelizumab, Ofatumumab or Cladribine in Relapsing Remitting Multiple Sclerosis) will assess the efficacy, safety and long-term impact of aHSCT compared with high-efficacy DMTs in patients with highly active RRMS despite the use of standard DMTs or in patients with treatment naïve RES-MS. METHODS AND ANALYSIS: StarMS is a multicentre parallel-group rater-blinded randomised controlled trial with two arms. A total of 198 participants will be recruited from 19 regional neurology secondary care centres in the UK. Participants will be randomly allocated to the aHSCT arm or DMT arm in a 1:1 ratio. Participants will remain in the study for 2 years with follow-up visits at 3, 6, 9, 12, 18 and 24 months postrandomisation. The primary outcome is the proportion of patients who achieve 'no evidence of disease activity' during the 2-year postrandomisation follow-up period in an intention to treat analysis. Secondary outcomes include efficacy, safety, cost-effectiveness and immune reconstitution of aHSCT and the four high-efficacy DMTs. ETHICS AND DISSEMINATION: The study was approved by the Yorkshire and Humber-Leeds West Research Ethics Committee (20/YH/0061). Participants will provide written informed consent prior to any study specific procedures. The study results will be submitted to a peer-reviewed journal and abstracts will be submitted to relevant national and international conferences. TRIAL REGISTRATION NUMBER: ISRCTN88667898.

Explainable deep learning for insights in El Niño and river flows.

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing.

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

DeepEmSat: Deep Emulation for Satellite Data Mining.

The growing volume of Earth science data available from climate simulations and satellite remote sensing offers unprecedented opportunity for scientific insight, while also presenting computational challenges. One potential area of impact is atmospheric correction, where physics-based numerical models retrieve surface reflectance information from top of atmosphere observations, and are too computationally intensive to be run in real time. Machine learning methods have demonstrated potential as fast statistical models for expensive simulations and for extracting credible insights from complex datasets. Here, we develop DeepEmSat: a deep learning emulator approach for atmospheric correction, and offer comparison against physics-based models to support the hypothesis that deep learning can make a contribution to the efficient processing of satellite images.