Moral injury is a relatively new, but increasingly studied, construct in the field of mental health, particularly in relation to current and ex-serving military personnel. Moral injury refers to the enduring psychosocial, spiritual or ethical harms that can result from exposure to high-stakes events that strongly clash with one’s moral beliefs. There is a pressing need for further research to advance understanding of the nature of moral injury; its relationship to mental disorders such as posttraumatic stress disorder and depression; triggering events and underpinning mechanisms; and prevalence, prevention and treatment. In the meantime, military leaders have an immediate need for guidance on how moral injury should be addressed and, where possible, prevented. Such guidance should be theoretically sound, evidence-informed and ethically responsible. Further, the implementation of any practice change based on the guidance should contribute to the advancement of science through robust evaluation. This paper draws together current research on moral injury, best-practice approaches in the adjacent field of psychological resilience, and principles of effective implementation and evaluation. This research is combined with the military and veteran mental health expertise of the authors to provide guidance on the design, implementation and evaluation of moral injury interventions in the military. The paper discusses relevant training in military ethical practice, as well as the key roles leaders have in creating cohesive teams and having frank discussions about the moral and ethical challenges that military personnel face.

Background: Military members are at elevated risk of operational stress injuries, including posttraumatic stress disorder (PTSD) and moral injury. Although psychotherapy can reduce symptoms, some military members may experience treatment-resistant PTSD. Multimodular motion-assisted memory desensitization and reconsolidation (3MDR) has been introduced as a virtual reality (VR) intervention for military members with PTSD related to military service. The 3MDR intervention incorporates exposure therapy, psychotherapy, eye movement desensitization and reconsolidation, VR, supportive counselling, and treadmill walking. Objective: The objective of this study is to investigate whether 3MDR reduces PTSD symptoms among military members with combat-related treatment-resistant PTSD (TR-PTSD); examine the technology acceptance and usability of the Computer Assisted Rehabilitation ENvironment (CAREN) and 3MDR interventions by Canadian Armed Forces service members (CAF-SMs), veterans, 3MDR clinicians, and operators; and evaluate the impact on clinicians and operators of delivering 3MDR. Methods: This is a mixed-methods waitlist controlled crossover design randomized controlled trial. Participants include both CAF-SMs and veterans (N=40) aged 18-60 years with combat-related TR-PTSD (unsuccessful experience of at least 2 evidence-based trauma treatments). Participants will also include clinicians and operators (N=12) who have been trained in 3MDR and subsequently utilized this intervention with patients. CAF-SMs and veterans will receive 6 weekly 90-minute 3MDR sessions. Quantitative and qualitative data will be collected at baseline and at 1, 3, and 6 months postintervention. Quantitative data collection will include multiomic biomarkers (ie, blood and salivary proteomic and genomic profiles of neuroendocrine, immune-inflammatory mediators, and microRNA), eye tracking, electroencephalography, and physiological data. Data from outcome measures will capture self-reported symptoms of PTSD, moral injury, resilience, and technology acceptance and usability. Qualitative data will be collected from audiovisual recordings of 3MDR sessions and semistructured interviews. Data analysis will include univariate and multivariate approaches, and thematic analysis of treatment sessions and interviews. Machine learning analysis will be included to develop models for the prediction of diagnosis, symptom severity, and treatment outcomes. Results: This study commenced in April 2019 and is planned to conclude in April 2021. Study results will guide the further evolution and utilization of 3MDR for military members with TR-PTSD and will have utility in treating other trauma-affected populations. Conclusions: The goal of this study is to utilize qualitative and quantitative primary and secondary outcomes to provide evidence for the effectiveness and feasibility of 3MDR for treating CAF-SMs and veterans with combat-related TR-PTSD. The results will inform a full-scale clinical trial and stimulate development and adaptation of the protocol to mobile VR apps in supervised clinical settings. This study will add to knowledge of the clinical effectiveness of 3MDR, and provide the first comprehensive analysis of biomarkers, technology acceptance and usability, moral injury, resilience, and the experience of clinicians and operators delivering 3MDR. Trial Registration: ISRCTN Registry 11264368; http://www.isrctn.com/ISRCTN11264368. International Registered Report Identifier (IRRID): DERR1-10.2196/20620

Background: Psilocybin has shown promise in therapeutic applications for mental disorders. Understanding the pharmacokinetics of psilocybin and its active metabolite psilocin is crucial for optimizing its clinical use and minimizing adverse effects. Methods: This systematic review involved a comprehensive search across MEDLINE, APA PsycINFO, and Embase databases, from inception to December 2024, identifying original studies that investigated the pharmacokinetics of psilocybin. Results: Fourteen studies met the inclusion criteria: eight laboratory-based and six clinical studies. Laboratory studies used animal models or in vitro systems, while clinical studies included 112 healthy human participants. Psilocybin is rapidly dephosphorylated to psilocin, which is absorbed with Tmax values ranging from 1.8 to 4 h following oral administration. Cmax varied dose-dependently, from 8.2 ± 2.8 ng/mL (plasma) to 871 ng/mL (urine). One study reported psilocin bioavailability at 52.7 ± 20%. The volume of distribution was extensive, ranging from 277 ± 92 L to 1016 L, suggesting significant tissue distribution. Psilocin metabolism is primarily mediated by CYP2D6 and CYP3A4, with secondary contributions from monoamine oxidase A. It undergoes further hepatic biotransformation into 4-hydroxyindole-3-acetic acid and 4-hydroxytryptophol. Elimination half-life varied across studies, ranging from 1.5 to 4 h. Conclusions: Psilocybin pharmacokinetics demonstrate significant variability based on dosage, route, and species. CYP enzymes play a critical role in its metabolism, highlighting the potential for drug–drug interactions. These findings underscore the importance of further research to elucidate psilocybin’s pharmacokinetic profile, which is assessed in vivo by its active metabolite psilocin.

Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology – a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using ‘invisible’ neural indices of brain functioning to understand and differentiate these debilitating conditions.

Mental health diagnoses possess unique challenges that often lead to nuanced difficulties in managing an individual’s well-being and daily functioning. Self-report questionnaires are a common practice in clinical settings to help mitigate the challenges involved in mental health disorder screening. However, these questionnaires rely on an individual’s subjective response which can be influenced by various factors. Despite the advancements of Large Language Models (LLMs), quantifying self-reported experiences with natural language processing has resulted in imperfect accuracy. This project aims to demonstrate the effectiveness of zero-shot learning LLMs for screening and assessing item scales for depression using LLMs. The DAIC-WOZ is a publicly available mental health dataset that contains textual data from clinical interviews and self-report questionnaires with relevant mental health disorder labels. The RISEN prompt engineering framework was utilized to evaluate LLMs’ effectiveness in predicting depression symptoms based on individual PHQ-8 items. Various LLMs, including GPT models, Llama3_8B, Cohere, and Gemini were assessed based on performance. The GPT models, especially GPT-4o, were consistently better than other LLMs (Llama3_8B, Cohere, Gemini) across all eight items of the PHQ-8 scale in accuracy (M = 75.9%), and F1 score (0.74). GPT models were able to predict PHQ-8 items related to emotional and cognitive states. Llama 3_8B demonstrated superior detection of anhedonia-related symptoms and the Cohere LLM’s strength was identifying and predicting psychomotor activity symptoms. This study provides a novel outlook on the potential of LLMs for predicting self-reported questionnaire scores from textual interview data. The promising preliminary performance of the various models indicates there is potential that these models could effectively assist in the screening of depression. Further research is needed to establish a framework for which LLM can be used for specific mental health symptoms and other disorders. As well, analysis of additional datasets while fine-tuning models should be explored.