Faculty Bibliography

BackgroundPosttraumatic stress disorder (PTSD) is common yet frequently underdiagnosed, in part due to barriers to systematic screening and the reliance on self-report instruments. Large language models (LLMs) have shown promise in extracting clinically relevant information from unstructured language, but their ability to infer item-level PTSD symptom severity from clinical interviews remains unclear.MethodsUsing the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WoZ), we analyzed 100 semi-structured clinical interview transcripts paired with item-level PTSD Checklist-Civilian Version (PCL-C) scores. Six LLMs (DeepSeek 3.1, Claude Sonnet 4, LLaMA 4 Scout, GPT-4o, GPT-5, and Gemini 2.5 Flash) used zero-shot prompting to predict all 17 PCL-C items. Performance was assessed for binary symptom endorsement (≥3 vs. < 3), 5-point Likert prediction, and DSM-IV symptom-cluster analyses using accuracy, F1 score, and Matthews correlation coefficient (MCC).ResultsFor binary prediction, Claude 4 achieved the highest mean accuracy (0.705; 95% CI, 0.681-0.728), followed by DeepSeek 3.1(0.699; 95% CI, 0.675-0.724) and Gemini 2.5 (0.698; 95% CI, 0.677-0.718). For Likert prediction, DeepSeek 3.1 performed best (accuracy = 0.438; 95% CI, 0.401-0.475), only modestly above the majority-class baseline (0.399; 95% CI, 0.355-0.443). Performance varied by symptom domain, with re-experiencing and hyperarousal symptoms generally predicted more accurately than avoidance/numbing symptoms. Across models, predicted item-level symptom patterns showed a meaningful alignment with observed PCL-C responses despite reduced accuracy in fine-grained severity estimation.ConclusionZero-shot LLMs' performance was insufficient for clinical application in predicting PTSD symptoms from semi-structured interview transcripts. While models showed some ability to capture overall symptom patterns, performance varied across domains and remained limited for fine-grained severity estimation. Given these constraints and the non-trauma-specific nature of the dataset, findings should be interpreted as preliminary, with only modest differences observed between models.Plain Language Summary TitleCan Artificial Intelligence Identify PTSD Symptoms from Conversations? A Study Using Clinical Interview TranscriptsPlain Language SummaryPost-traumatic stress disorder (PTSD) is a common mental health condition, but it is often missed in clinical settings. Screening usually relies on questionnaires that patients must complete themselves, which may not always happen due to time, stigma, or discomfort discussing trauma. Researchers are exploring whether artificial intelligence (AI) could help identify PTSD symptoms from conversations instead.In this study, we tested several advanced AI systems, known as large language models, to see if they could estimate PTSD symptoms based on written transcripts of clinical interviews. These interviews were not specifically designed to assess trauma, which makes the task more challenging but closer to real-world situations. We compared the AI predictions to participants' own questionnaire responses about their symptoms.We found that the AI models were somewhat able to recognize general patterns of PTSD symptoms, especially more visible ones like sleep problems or distressing dreams. However, they struggled with more internal or less obvious symptoms, such as avoidance or emotional numbness. Overall, their accuracy was moderate and not reliable enough for clinical use, particularly when trying to estimate how severe symptoms were.Importantly, differences between the AI models were small, and none performed well enough to replace existing screening methods. These findings suggest that while AI may have future potential as a supportive tool, it is not yet ready to be used for diagnosing or screening PTSD on its own.Further research using better data, improved methods, and real clinical settings is needed before this approach could be considered for practical use.

BACKGROUND:Psychedelic microdosing has gained increasing popularity for enhancing mood and cognition, yet its effects on psychological outcomes in healthy adults remain unclear. We aim to evaluate the efficacy and safety of psychedelic microdosing on psychological outcomes in healthy/non-clinical adult population. METHODS:This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251035294). We searched Embase, MEDLINE, and PsycINFO from inception to February 2026 for original studies in healthy adults using sub-hallucinogenic psychedelic doses on separate days. We included randomized studies, nonrandomized prospective studies, cross-sectional studies, and observational longitudinal designs and stratified meta-analyses by design. Random-effects models were used; safety in randomized controlled trials (RCTs) was pooled as risk differences (RDs). Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools appropriate for each study design. RESULTS:= 0%). Non-RCT within-arm estimates were imprecise and heterogeneous for depressive symptoms (SMCC -0.33; 95% CI -0.75, 0.08) and anxiety (SMCC -0.29; 95% CI-0.84, 0.26). Exploratory pooling across all designs suggested decreases in depressive symptoms and stress, while anxiety remained uncertain. Adverse event risks were similar between treatment and control. CONCLUSIONS:Microdosing does not show consistent immediate benefits for depressive, anxiety, or stress symptoms in healthy adults, and evidence from RCTs remains inconclusive. Although improvements were observed in some studies, these effects were not significantly different from placebo. Larger, well-designed RCTs are needed to clarify the efficacy and safety of psychedelic microdosing beyond placebo.

Traditional trauma-focused psychotherapies (TFPs) were developed based on an anxiety disorder model of posttraumatic stress disorder (PTSD). However, PTSD is a more complex disorder with heterogeneous onset, presentation, trajectory, and treatment responsivity. As half of treated patients do not respond to first-line treatments, innovative therapies are emerging to improve outcomes. This narrative review of therapist-delivered psychotherapies for PTSD focuses on interventions not yet endorsed by clinical guidelines. A systematic search of MEDLINE and American Psychological Association PsycINFO was conducted for English-language human clinical studies, guidelines, and reviews related to PTSD psychotherapy through June 29, 2024. Data were thematically analyzed, focusing on how emerging interventions modify or diverge from current guideline-recommended treatments. The review identified 4 key themes for improving trauma therapy: (1) optimizing existing TFPs, (2) adapting psychotherapies used for other conditions, (3) reimagining exposure therapies, and (4) new therapeutic modalities. New exposure treatments include those capitalizing on memory reconsolidation science, combination with pharmacotherapies, neuromodulation, or virtual reality technologies, and mind-body and somatic psychotherapies. Moral injury, identity, and spirituality-focused therapies aim to resolve intense internal conflicts, guilt and shame, and issues of meaning and purpose. Finally, multi-modal treatments like 3MDR and psychedelic-assisted psychotherapies have multiple synergistic mechanisms. Ongoing research will be crucial to validating emerging approaches and optimizing their combined potential. A PTSD staging model may provide a structured framework for rigorous empirical evaluation and clinical implementation. Future research should prioritize randomized controlled trials with diverse patient populations and long-term follow-up to ensure their safety, efficacy, and scalability.

The authors offer a model for curriculum for education and training in substance-assisted psychotherapy (SAP), that is, psychedelic, psycholytic, and entactogen/MDMA (3,4-methylenedioxymethamphetamine)-assisted psychotherapy, addressing both the detailed contents of training and the question of experiential training. All authors of this model have an abiding interest and extensive experience in both the theory and practical aspects of SAP and questions relating to training. The model curriculum has been written through an international consensus building process and represents a consensus statement about the topic. The model includes an enumeration of theoretical themes and topics, which we suggest for inclusion in an SAP curriculum. The practical part of the curriculum includes experiential training with the following components: (1) apprenticeship observation: learning from observing experienced therapists, (2) ongoing clinical supervision: conducting treatment under direct supervision of experienced SAP therapists, and (3) a proposal for the inclusion of self-experiences for the trainees. Other parts address the use of peer supervision and conventional supervision. The authors are aware of the abiding need for respect of intercultural differences. We are conscious that the proposed model is one largely adapted to western industrialized countries with established graduate level education and training procedures for psychotherapists. However, the model curriculum includes teachings about the use of related substances and treatment techniques in indigenous cultures and traditions. This curriculum model may be valuable to psychedelic researchers, those endeavoring to train therapists for research studies, and those preparing for the clinical work to follow, once SAP is conducted outside of research settings.

BACKGROUND:Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points. METHODS:We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367). RESULTS: = 0.05). No associations were observed for GrimAge residuals. CONCLUSIONS:Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

BACKGROUND:Epigenetic modifications, including DNA methylation (DNAm), can change in response to traumatic stress exposure, and may help to distinguish between individuals with and without PTSD. Here, we examine the DNAm patterns specific to immune cell types and inflammation in those with PTSD. METHODS:This study includes 3,277 participants from 11 cohorts participating in the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup. DNAm was assayed from blood with the MethylationEPIC BeadChip. A standardized QC pipeline was applied and used to impute cell composition. Within each cohort, we identified cell-type-specific DNAm patterns associated with PTSD, controlling for sex (if applicable), age, and ancestry. Meta-analyses were performed from summary statistics. RESULTS:PTSD cases had lower proportions of B cells and NK cells as well as higher proportions of neutrophils when compared to trauma-exposed controls. Overall, we identified 96 PTSD-associated CpGs across six types of immune cells. Most of these differences were identified in B cells, with 95 % exhibiting lower methylation levels in those with PTSD. Interestingly, the PTSD-associated CpGs annotated to a gene in B cells were enriched in a recent GWAS of PTSD (p < 0.0001). CONCLUSIONS:This study identifies novel PTSD-associated CpGs in individual immune cell types and supports the role of immune dysregulation and inflammation in PTSD.

Psilocybin, a serotonergic psychedelic, may have therapeutic benefits for Substance Use Disorders (SUDs), but its overall efficacy and safety remain uncertain. This systematic review assessed the safety and efficacy of psilocybin for SUDs through a systematic database search conducted via OVID on May 22, 2024, and summarized 26 ongoing clinical trials registered on clinicaltrials.gov. Among 16 published included studies, 7 (43.75%) focused on Alcohol Use Disorder (AUD), 5 (31.25%) on Tobacco Use Disorder (TUD), and the remainder on Cocaine Use Disorder (CUD) (1, 6.25%), Opioid Use Disorder (1, 6.25%), Nicotine Use Disorder (1, 6.25%), and multiple SUDs (1, 6.25%). Study designs included open-label trials (5, 31.25%), cross-sectional observational studies (6, 37.5%), qualitative analyses (2, 12.5%), one double-blind RCT (6.25%), one pilot fMRI study (6.25%), and one long-term follow-up (6.25%). Psilocybin-assisted psychotherapy (PAP) was used in 10 studies (62.5%), with doses ranging from microdosing to 20-40mg/70kg. PAP was associated with significant reductions in alcohol consumption, smoking cessation, and related psychological improvements. AUD studies reported fewer heavy drinking days, increased abstinence rates, and neuroimaging data indicating normalization of brain activity. TUD studies demonstrated high smoking abstinence rates, with mystical experiences predicting long-term outcomes. Findings for other SUDs were mixed, though psilocybin showed potential in reducing opioid dependence and nicotine use. Preliminary evidence supports psilocybin's efficacy and safety for AUD and TUD, particularly with psychotherapy, but larger clinical trials are needed to confirm these findings.