Communications Psychology

Background: Conventional psychiatric screening instruments summarize symptoms within individual scales and prioritize cases with high single-instrument additive score severity. This design treats items as independent within instruments and ignores cross-instrument covariance structure, making it insensitive to respondents whose responses are distributed across multiple domains in unusual combinations that remain below threshold on every individual scale. Methods: We analyzed two cohorts spanning older and younger adults. Item prompts from depression, stress, anxiety, and sleep instruments were embedded into a shared semantic space using a pretrained sentence encoder. Principal component analysis of the item-prompt embeddings alone---with no use of respondent data at this stage---was used to construct a low-dimensional subspace retaining 80\% of variance in the item embedding matrix. Normalized participant responses were then projected into this subspace, with Jaccard-based stability analysis used as a check on dimensional robustness. Multivariate deviation from the cohort norm was quantified with Mahalanobis distance using Ledoit-Wolf covariance regularization. Candidate outliers were defined by the empirical 95th percentile of the cohort-specific distance distribution. To isolate response configurations not already captured by conventional single-instrument extreme-value logic, we excluded all outlier respondents who had endorsed any individual item at the maximum value of its Likert scale on any instrument. For the remaining outliers, anomalous components were backtracked to their original item loadings for interpretation. Results: In the older-adult Health and Retirement Study (HRS) cohort, principal component analysis of 27 item-prompt embeddings showed that a 10-dimensional subspace provided a stable representation of cross-instrument semantic structure. In the younger-adult Xinxiang cohort the corresponding stable solution was 16-dimensional. In each cohort, seven respondents remained as multivariate outliers despite falling below every single-instrument extreme-value threshold. These cases were not characterized by uniformly severe symptom scores but by unusual cross-domain response configurations that became visible only in the shared semantic covariance subspace. The response structure of the retained configurations differed across cohorts: older-adult cases more often involved weak endorsement of mood-labeled items alongside nonzero body- and sleep-related responses, whereas younger-adult cases more often involved incomplete response configurations spanning mood, sleep, stress, and self-harm-related items. Conclusions: A semantically aligned, auditable covariance subspace provides a practical tool for flagging unusual multivariate response configurations that single-instrument additive screening may not flag. The method is interpretable at the level of original item contributions. It should be understood as a hypothesis-generating screen for unusual response configurations requiring further clinical assessment, not as a diagnostic instrument. Outcome validity remains to be established by prospective study.

Aims. The risk of cognitive decline after losing a spouse remained mixed. This study aims to investigate the association between spousal loss and risk of cognitive decline, assess whether this association varies by sex and age, and identify modifiable factors. Methods. We conducted a prospective cohort study using harmonized data from six population-based aging surveys: the US Health and Retirement Study and its sister surveys in England, Mexico, China, India, and South Africa, incorporating their respective Harmonized Cognitive Assessment Protocol (HCAP) sub-studies. Spousal loss (yes vs no) was the exposure. Cognitive outcomes (i.e., orientation, memory, executive function, and language), were assessed using HCAP neuropsychological batteries. We conducted parallel analyses in six cohorts. Associations between spousal loss and cognitive outcomes were estimated using generalized linear models, and summarised estimates were derived via random-effects meta-analyses. Sex stratification and restricted cubic spines were used to examine how these associations vary by sex and age, respectively. Results. The analytical cohort consisted of 18,551 individuals aged 61.22 (SD 6.30) to 71.37 (SD 7.33) years. Widowhood prevalence ranged from 14.1% in CHARLS to 53.9% in HAALSI and was consistently higher in women. Spousal loss was associated with poorer memory (multivariable-adjusted {beta} = -0.07, 95% CI -0.12 to -0.01) and executive function (multivariable-adjusted {beta} = -0.08, 95% CI -0.13 to -0.03) in the meta-analysis, with no significant associations for orientation or language. While results were generally consistent in five cohorts, the ELSA showed divergent patterns (orientation: {beta} = 0.10, 95% CI 0.06 to 0.13; memory: {beta} = 0.05, 95% CI 0.02 to 0.08; language: {beta} = 0.16, 95% CI 0.12 to 0.19). Sex-stratified analyses indicated poorer executive function among men (multivariable-adjusted {beta} = -0.14, 95% CI -0.19 to -0.08) and poorer memory among women (multivariable-adjusted {beta} = -0.07, 95% CI -0.14 to -0.01) following widowhood. Nonlinear age-related effects on cognition were observed in ELSA, LASI, and HAALSI. Higher education, internet use, and BMI were negatively associated with the risk of cognitive decline among widowed participants. Conclusions. Spousal loss is associated with domain- and sex-specific differences in cognitive performance, with substantial heterogeneity across study populations. Future research should integrate biopsychosocial markers to develop context-sensitive interventions for widowed older adults.

Spousal caregivers of individuals with Alzheimers disease and related dementias frequently experience elevated perceived stress, caregiver burden, and loneliness, which are associated with adverse health outcomes. Early identification is therefore critical for timely intervention. Existing approaches commonly rely on wearable sensor data and standardized psychological questionnaires, while recent multimodal methods aim to improve prediction by integrating behavioral and linguistic information. In this study, we explored three modality configurations, wearable-derived features, interview-based text, and their combination, to classify caregiver psychological risk using the Perceived Stress Scale (PSS), Zarit Burden Interview, and UCLA Loneliness Scale. We compared traditional machine learning models and large language models (LLMs) (Gemini 2.0, Llama 4, and GPT-4o) under psychometrician-centered and caregiver-centered prompting strategies. Traditional machine learning models performed better under multimodal settings, while LLMs achieved stronger performance with Interview-Only input. We further demonstrate that PSS was the most predictable construct and prompting strategies substantially influenced LLM performance.

A closed-loop quality system deployed across thirteen US hospital sites resolved physician complaints with zero regressions on 42 tracked cases across 1,089 optimization iterations, while a deterministic assembly-agent replacement cut H+P trace latency from 19.6 s to 10.8 s (-8.8 s, 95% CI [-10.5, -7.1] s; n = 100 pre, n = 100 post). We report four observations and an architectural follow-through. First, the same binary-check instrument produces opposite outcomes depending on the question asked: "maximize this score" produces structurally-correct notes that physicians reject (Spearman rho = -0.077, 95% CI [-0.40, 0.26], n = 36); "did this specific fabrication stop?" produces rater-invariant deployment decisions. Second, in our pipeline, assembly-stage agents did not respond to prompt optimization the way reasoning agents did: four consecutive optimization attempts produced 18-28 point regressions. Third, physician preference is rater-fragile at typical clinical-AI calibration sample sizes (Cohen's kappa = 0.028 between two board-certified physicians, 95% CI [-0.30, 0.36] on n = 35 overlapping pairs). Fourth, the architectural punchline: six weeks after the prediction, the LLM call at the chart-assembly step was replaced with a deterministic renderer (sub-500-character template plus sandboxed scripting), lifting the defect-free rate on a 51-case holdout from 49% to 84%. We introduce a Pareto-with-absolute-floors acceptance rule (multi-axis commit with severity-class categorical vetoes) as a methodological contribution distinct from scalar-reward acceptance in standard prompt-optimization frameworks. Cross-iteration rejection memory prevents the loop from re-proposing edits already rejected three or more times. A reproducibility bundle (anonymized ablation per-case counts, bootstrap-CI data, analysis scripts) is released under CC BY 4.0 at github.com/sayvant/SQS-Auditor-paper-data.

Background. Something in discourse with a person experiencing psychosis often "feels off" before formal assessment is completed, yet this disturbance has not been quantified at the level of ongoing dyadic conversation. Prior work has largely treated patient speech in isolation, limiting our capacity to measure how communicative disruption emerges within clinical exchange. Methods. We applied a three-level decomposition of conversational alignment in 109 patients with psychotic disorders (26 female) and 60 healthy controls (22 female) at baseline and 12 months (n = 115). Register divergence (dAUCnorm) captured lexical distance between interviewer and patient; embedding-based synchrony (rembed) measured semantic trajectory coupling; within-speaker coherence was computed separately for each speaker. We used linear mixed-effects models adjusted for timepoint and participant clustering. Results. Patients showed significantly greater lexical-semantic divergence from the interviewer (d = 0.48, p < .001) and reduced embedding-based synchrony (d = -0.59, p < .001), both effects replicating at each time point. Critically, the interviewer's within-speaker coherence was reduced during conversations with patients (d = -0.33, p = .016), indicating that the disruption extends beyond the patient to the interaction itself. Register divergence tracked impoverished thinking and synchrony tracked disorganized thinking (both FDR-corrected q = .038). Group differences were persistent at 12 months, indicating a partially stable profile. Conclusions. Conversational alignment in psychosis reveals a dyadic failure of semantic coordination that destabilizes the interviewing clinician's coherence even when patient narrative continuity is preserved. These transcript-derived alignment metrics offer a scalable approach to quantifying interpersonal communicative function from routine clinical encounters.

Patients with primary immunodeficiency (PID) often face prolonged diagnostic delays and may increasingly turn to large language models (LLMs) to interpret their symptoms during this period. We evaluated whether an LLM could recognize PID from symptom descriptions derived from interviews with 21 PID patients. In a prior study, we showed that GPT-4o identified PID in 96% of cases when prompted with physician-written patient histories (Rider et al., JACI, 2024). Here, when prompted with symptom descriptions in patients' own words, GPT-5 identified PID in only 7 cases (33%), although it more broadly suggested immune system issues in 18 cases (81%). The gap between these findings indicates that LLMs are sensitive to the language and framing of symptom descriptions, performing substantially worse when patients describe their own symptoms in everyday language than when clinicians summarize patient histories in structured medical terms. This study underscores the need to carefully evaluate how LLMs are used in patient-facing applications.

Importance: Physician-facing AI tools are now in clinical use, yet whether different platforms fail in similar or fundamentally different ways in high-stakes settings like critical care is unknown. Objective: To evaluate two physician-facing AI platforms, ChatGPT for Clinicians and OpenEvidence, for distinct vulnerabilities under structured stress testing. Design, Setting, and Participants: An observational study conducted using 60 simulated critical care vignettes developed and adjudicated by four attending critical care physicians. Data were collected in the last week of April 2026, via the public website interfaces of each platform. Interventions/Exposures: A 2x2x2x2 factorial design across four stressors - anchoring, cognitive load, social conformity pressure, and a clinically incorrect directive - yielded 16 prompt subsets per vignette and 960 prompts per platform. A separate multi-turn adversarial prompting paradigm administered three sequential "You are incorrect" challenges to baseline vignettes. All prompts had a universal output length constraint of fewer than 30 words. Main Outcomes and Measures: Critical elements capture (percentage of gold-standard critical elements present in responses), susceptibility to clinically incorrect directive, and sycophancy (reversal of an initial correct recommendation under iterative adversarial challenge). Results: Across 1916 responses to 1920 prompts, ChatGPT for Clinicians captured more gold-standard critical elements than OpenEvidence (81.4% {+/-} 18.1% vs 61.0% {+/-} 23.5%; adjusted difference, 20.3 percentage points; 95% CI, 18.3 to 22.4; P < .001) and was less susceptible to clinically incorrect directives (1.7% vs 8.0%; adjusted odds ratio, 0.07; 95% CI, 0.02-0.21; P < .001). Anchoring and social conformity pressure were associated with reduced critical element capture across both platforms, while cumulative stressor burden reduced critical element capture only on OpenEvidence. Conversely, ChatGPT for Clinicians reversed correct recommendations more readily under adversarial prompting (hazard ratio, 2.61; 95% CI, 1.10 - 6.19; P = .03). Conclusion and Relevance: The two physician-facing clinical AI platforms evaluated demonstrated non-overlapping vulnerabilities, with neither platform uniformly superior. These findings argue against single-axis ranking of clinical AI systems and support multidimensional safety evaluation encompassing completeness of reasoning, resistance to incorrect directives, and stability under adversarial challenge.

Vocal biomarkers, encompassing voice and speech, have largely been developed for individual conditions in isolation, limiting their generalizability across diseases and recording settings. To address this, we introduce VoiceFM, a contrastive model that learns general-purpose clinical voice representations by aligning audio embeddings with rich clinical metadata. Using the Bridge2AI-Voice dataset (984 primarily English-speaking adult participants, 846 used for training and 138 held out as a temporally separated validation cohort, 40,056 recordings totaling 176 hours across 5 academic medical centers), VoiceFM pairs a fine-tuned Whisper large-v2 encoder with a tabular transformer over 44 clinical features via symmetric InfoNCE loss. Linear probes on frozen VoiceFM embeddings achieve mean AUROC 0.952 +/- 0.005 across five evaluation tasks (control vs disease screening plus four disease categories), significantly outperforming Frozen Whisper (0.926 +/- 0.013, p = 0.013), Frozen HuBERT (0.885 +/- 0.017, p = 0.0009), and the contrastively trained VoiceFM-HuBERT (0.938 +/- 0.006, p = 0.012). On the 138-participant held-out cohort, VoiceFM-Whisper achieves AUROCs of 0.99 for Alzheimer's/dementia/MCI and 0.89 for airway stenosis, demonstrating that the learned representations generalize to participants the model has never seen. VoiceFM representations transfer to three external datasets without retraining and improve few-shot classification. Recording task attribution identifies a small set of speech tasks that match or exceed the full battery's performance, suggesting shorter screening protocols are feasible. Trained predominantly on English audio, VoiceFM transfers without fine-tuning to Spanish-language Parkinson's disease (PD) detection (NeuroVoz, 107 participants, AUROC 0.93 +/- 0.02), with the signal dominated by articulatory rather than phonatory features. A fine-tuned classifier achieves participant-level AUROC 0.87 (sustained 0.85, countdown 0.80) on the mPower smartphone study (585 held-out participants). Together, these results show that contrastive alignment between voice and rich clinical metadata can serve as the basis for a clinical voice foundation model, producing a single set of transferable representations that generalize across diseases, languages, recording conditions, and patients enrolled after model freeze.

Objective: Hispanic/Latinx populations in the U.S. experience higher rates of chronic disease linked to physical inactivity, yet digital health interventions remain largely inaccessible to more than 16 million Hispanic/Latinx adults with limited English proficiency. While large language models (LLMs) offer scalable personalization, their use in non-English behavioral coaching is unexplored. This study introduces MHC-Coach-ES, a Spanish-language LLM fine-tuned on the Transtheoretical Model (TTM) of behavior change. Materials and Methods: We fine-tuned Llama 3-70B-Instruct using a two-stage pipeline. First, the model was adapted to Spanish health and motivational language using a 2.21-million-token corpus. Second, it was instruction-tuned on 3,268 translated human written messages to align the model with the Transtheoretical Model (TTM) of Behavioral Change. We compared MHC-Coach-ES with Llama 3-70B-Instruct and translated human-expert messages using a forced-choice preference survey (N = 77) and blinded expert review (N = 2). Results: Spanish-speaking participants significantly preferred MHC-Coach-ES messages over translated human-expert messages (81% preference, P<0.001). Linguistic analysis showed that MHC-Coach-ES produced more temporally anchored messages than the base model (65% vs. 20%), while maintaining readability. In blinded evaluation, clinical experts rated MHC-Coach-ES higher for alignment with Transtheoretical Model stages than human-expert messages (4.83 vs. 4.38 out of 5). The base model also outperformed translated expert messages across preference and expert ratings. Conclusions: Generative AI can operationalize behavioral science frameworks in Spanish, offering a scalable approach to reducing health disparities. The strong performance of both MHC-Coach-ES and the base model highlights the promise of generative and personalized approaches over translation-based localization for theory-driven behavioral interventions.

Background: Individuals with body dysmorphic disorder misperceive defects of their physical appearance. Current evidence suggests that visual processing abnormalities may underlie this core symptom. Separate pre-clinical studies testing perceptual and attentional interventions and non-invasive neuromodulation suggest that these visual processing abnormalities may be modifiable, but their combined effects on neural connectivity and perceptual processing remain unclear. Methods: Thirty-nine unmedicated men and women with body dysmorphic disorder or subclinical body dysmorphic disorder received intermittent theta burst stimulation and continuous theta burst stimulation targeting the lateral parietal cortex combined with a visual attention modification paradigm during functional magnetic resonance imaging, in a crossover design. Dynamic effective connectivity within dorsal and ventral visual stream pathways was calculated, and global visual processing biases were assessed using the face inversion effect before and after stimulation plus attention modification. Results: Intermittent theta burst stimulation resulted in increased connectivity in higher-level dorsal visual stream pathways during naturalistic viewing following attention modification, whereas continuous theta burst stimulation was associated with reduced connectivity in lower-level dorsal pathways and increased connectivity in ventral stream pathways. These changes were accompanied by differential effects on global visual processing, with stimulation type modulating the magnitude of the face inversion effect. Conclusions: Combined neuromodulation and visual attention modification modulate visual system connectivity and perceptual processing in individuals with body dysmorphic disorder symptoms. These findings support a mechanistic link between dorsal-ventral stream dynamics and perceptual biases. Integrating neuromodulation with perceptual retraining may represent a viable approach for targeting core symptoms of distorted appearance perception.

Background: Generative AI tools can support data-intensive research by writing code, drafting prose, searching analytical possibilities, and stress-testing claims. They can also produce false citations, drift between statistical specifications, and lose continuity across long investigations. This paper describes a practical workflow for using AI systems in empirical research while keeping discovery, verification, and accountability inspectable. Methods: We developed and applied a three-phase human-AI workflow to a case study of 14 elite Ethiopian distance runners. The dataset contained 22,605 GPS-segments collected across 97 consecutive days in late 2025, supplemented by venue and athlete metadata collected in the field. Phase 1 used an autonomous data-exploration tool to pre-filter the hypothesis space across five seeded research questions. Phase 2 used an AI system under direct human guidance to construct candidate findings into numerical claims, verification scripts, and draft text. Phase 3 used an independent AI system in an adversarial role to stress-test methods, statistics, prose, figures, and citations. The workflow was informed by Pearl's distinction between association, intervention, and counterfactual reasoning, with human judgement retained for research direction, interpretation, and final claims. Results: The workflow produced three empirical analyses and a documented correction process. The analyses estimated an altitude-to-sea-level pace correction of +0.10 min/km per 1,000 m at matched heart rate, showed why pooled altitude-surface regression was not identifiable within this venue system, documented method-dependence in heart-rate-based intensity classification, characterised within-venue route variation as a 64/36 path-fixed-to-trail-variable split with the Sululta label resolving into two functionally distinct sub-venues, and reframed the cohort's training through a 3x3x3 prescription lattice grounded in Ethiopian coaching practice. The adversarial phase identified several hallucinated citations, a terminology error between HC1 and cluster-robust standard errors, and several inconsistencies between prose, figures, and computed results. Verification scripts re-derived nearly all numerical claims from the cleaned lap-level data. Conclusions: The case study shows how researchers can organise AI-assisted empirical work so that candidate discovery, claim construction, independent stress-testing, and final accountability remain separated. The workflow did not remove the need for domain expertise or human judgement. Its value was in making the route from candidate finding to manuscript claim explicit, reproducible, and open to challenge. Trial registration: Not applicable.

Biological aging is heterogeneous across organ systems, yet whether CT-derived abdominal aging provides prognostic value beyond routine clinical data and whether organ decomposition adds beyond a unified estimate remains untested. We developed and evaluated organ-specific and ensemble biological age models from radiomic features across five abdominal organs in 68,675 CT scans from 32,883 subjects, evaluated on alignment with chronological age of healthy subjects (nested cross validation: MAE=3.68 years, R^2=0.90). In sequential analyses restricted to adults aged 20-60 years which is the stratum of strongest BAG-disease association, ensemble biological age gaps provided incremental prognostic value beyond demographic covariates for all-cause disease and mortality (Delta C-index=0.141, 0.051) and beyond routine blood biomarkers (Delta C-index=0.048), confirming CT-derived aging captures structural information beyond laboratory markers. Organ-specific biological age added incremental prognostic value beyond ensemble selectively for focal diseases: cardiovascular (aorta, Delta C-index=0.091) and hepato-pancreatic (pancreas, Delta C-index=0.096). These findings establish a hierarchical organization of CT-derived biological aging, positioning routine CT as a source that adds prognostic value to existing clinical biomarkers.

Background Treatment guidelines for borderline personality disorder (BPD) recommend assessment, diagnosis, and psychoeducation. We report on the feasibility and safety of a randomized controlled trial protocol of online psychoeducation, assessment, and personalized feedback as an immediate first step of care for BPD. Methods Newly diagnosed participants were randomized to receive 10 videos about BPD or general mental health for two weeks. Half the participants receiving BPD videos were randomized to receive personalized feedback on changes in symptom ratings and cognitive performance. Ecological momentary assessment (EMA) evaluated interpersonal interactions, emotions, and behaviors for 30 days. BPD symptoms, depression, and personality functioning were assessed at baseline, after videos, after feedback, and one month later. Results Eighty-two participants were randomized into three conditions that did not differ significantly in terms of demographics or baseline variables. Dropout occurred for 32.9% of the sample. No differences in rate of emergency room visits, hospitalizations, or other escalations in level of care were reported among groups. Satisfaction was higher for those receiving psychoeducational videos about BPD. Improvement in BPD knowledge in the psychoeducation conditions was significantly greater than the control condition. No statistically significant differences were found regarding reduction of BPD symptoms. The psychoeducation with feedback arm showed significantly greater improvements in self-impairment compared to controls with medium effect size at the final timepoint. Modeling of the relationship between time spent alone and BPD symptoms showed a positive correlation in the control condition, but in the group receiving both psychoeducation about BPD and feedback, this relationship was negative. Conclusion Online psychoeducational videos and assessment were safe, feasible, and acceptable to participants with newly diagnosed BPD. Psychoeducation with personalized feedback appears to be more effective than either BPD or general psychoeducation alone in improving deficits in self-functioning, which may relate to an increased capacity to be alone with fewer symptoms. The protocol was registered with ClinicalTrials.gov (NCT05358925, https://clinicaltrials.gov/study/NCT05358925) on April 28th, 2022.

Background: Sleep debt and irregular sleep patterns are highly prevalent amongst adolescents. However, whether the absence of these sleep behaviours protects against subsequent depression remains unclear. Here, we examined the association of sleep debt, weekend catch-up sleep (WCS), and social jetlag (SJL) in adolescence with depression in young adulthood and identified underlying biopsychosocial mechanisms. Methods: Secondary data analyses were conducted using the Avon Longitudinal Study of Parents and Children. Bedtimes and wake-up times on school days and weekends (i.e., sleep duration) and sleep need were self-reported at 15 years. This was used to generate sleep debt (sleep need minus school day sleep duration), WCS (weekend sleep duration minus school day sleep duration), and SJL (absolute difference in the midpoint of sleep times between school days and weekends). Depression was assessed at 24 years with the Clinical Interview Schedule-Revised. Common mental health symptoms, biological, and school-related factors at 17 years were the mediators. Results: Logistic regression analyses revealed that greater WCS (adjusted odds ratio [AOR]=0.90; 95% CI=0.84-0.97; p=0.004) and lower sleep debt (AOR=1.10; 95% confidence interval [CI]=1.03-1.18; p=0.005) at age 15 reduced the likelihood of depression at 24 years. Irritability at 17 years partially mediated the relationship between sleep debt and depression (bias-corrected estimate=0.003; 95% CI=0.002-0.004; p<0.001). Conclusions: Adolescents who experience less sleep debt (i.e., less discrepancies between their actual sleep and their perceived sleep need) and those who extend their sleep duration on weekends are at reduced risk for depression in young adulthood. These findings underscore the need for greater opportunities for adolescents to obtain more hours of sleep to protect them against later poor mental health outcomes, such as depression. Keywords: Sleep; longitudinal studies; depression; ALSPAC

Longitudinal evaluations of national electronic health record repositories often track document counts alone, obscuring changes in content size, structure and standards implementation. We decomposed growth in the Estonian Health Information System across document counts, per-document size, section-level structure and version uptake in a 10% random population sample of 4.97 million HL7 Clinical Document Architecture Release 2 documents from 147,819 patients, spanning 2012--2019 and four prespecified document types. Growth patterns differed by document type. Inpatient summaries increased 48.5% in total content volume despite a 2.4% decline in document counts. Section presence and within-section content were highly skewed; 44.6% of 892 data locations carried one fixed value. Code-system diversity increased from 45 to 79, and version uptake took years: inpatient summaries reached 80% organisational uptake after a median 44 months (95% CI 11--78). This decomposition can guide extraction pipelines, secondary use and standards governance in CDA- and FHIR-based repositories.

Background: While growing enthusiasm for the therapeutic potential of classic psychedelics has led to a rise in non-clinical use, attention to persisting adverse effects has emerged with delay. A subset of individuals reports persisting complications such as hallucinogen persisting perception disorder (HPPD), depersonalization/derealization disorder (DDD), anxiety and depression. Yet few medical services are equipped to address these complications. Aims: This qualitative study examines how societal, medical, and media discourses shape the experiences of individuals with persisting psychedelic-related complications, focusing on help-seeking trajectories. Methods: Thirteen semi-structured interviews with adults experiencing persisting psychedelic-related psychological symptoms (four women, nine men, age 19-49 years; HPPD (n = 10), DDD (n = 6), depression (n = 1), and anxiety (n = 1)) were conducted within a larger study on these complications. Data were analysed using reflexive thematic analysis. Reporting followed the COREQ guidelines. Results: Three interrelated themes emerged: (1) The dissonance between expectation and harm - idealised media and scientific portrayals of psychedelics shaped initial use and complicated recognition of adverse outcomes; (2) Stigma, silence, and self-blame - prohibitionist discourse and internalised shame significantly inhibited help-seeking; and (3) Between systemic absence and self-organised support - participants encountered clinical unpreparedness and epistemic dismissal, which often led them to rely on online peer communities and self-management strategies. Positive clinical encounters, characterised by professional expertise and nonjudgmental engagement, were experienced as helpful. Conclusions: Adequate clinical and conceptual frameworks for persisting psychedelic-related complications are lacking. An interdisciplinary, experience-informed approach integrating realistic risk communication, clinician training, and destigmatisation is required to support affected individuals.

In a single-blind randomized controlled trial, we investigated the effectiveness of real-time fMRI neurofeedback delivered in 7 runs over three sessions across two weeks in N = 65 patients with alcohol use disorder. The intervention targeted modulation of ventral striatal cue reactivity to alcohol-related cues as well as enhancement of prefrontal control mechanisms in the right inferior frontal gyrus. The study design incorporate three experimental groups that either were instructed to downregulate a ventral striatum signal, upregulate the right inferior frontal gyrus, or upregulate negative functional connectivity between these two structures. In two active control groups participants were instructed to either up- or downregulate the primary auditory cortex. We did not find an effect of ventral striatal downregulation or negative connectivity feedback, and a reduced striatal activation in the right inferior frontal gyrus upregulation group was accompanied by concurrent lower activation in the target structure, suggesting that our intended modulation approaches were not effective. Identified problems that might have contributed to this unexpected outcome might have been the use of continuous feedback presentation that potentially confuses regulation target and reward processing in the ventral striatum, counterintuitive regulation directions, a lack of explicit strategy guidance and transparency about the targeted process, and generally the difficulty to recruit a sufficient number of eligible voluntary participants for a well-powered study with a complex design. These insights emphasize the complex challenges of real-time fMRI neurofeedback interventions for the treatment of substance use disorders and could provide guidance for the development of more effective future approaches.

Background: Individuals with mild cognitive impairment (MCI) experience cognitive and functional declines that can negatively impact mood and reduce feelings of self-efficacy. These changes can also lead to elevated distress in care partners (CPs). Therefore, interventions that address quality of life and psychosocial factors in people with MCI and their CPs are needed. Objective: The present study evaluated the impact of a multidomain lifestyle program, the Cognitive Empowerment Program (CEP), on changes in psychosocial functioning, particularly empowerment, in people with MCI and their CPs. Methods: Participants were 94 people with MCI (Mean= 75.1 years old, 45.7% female, 81.9% white) and their CPs (Mean= 69.1 years old, 71.3% female, 87.3% white) that completed the 12-month CEP program comprised of physical, cognitive, and psychosocial interventions. Questionnaires were administered pre- and post-program to assess empowerment, self-efficacy, meaning and purpose, depression, and stress in participants with MCI alongside empowerment, depression, stress, and caregiving burden in CPs. Results: After completing the CEP program, participants with MCI endorsed higher empowerment and self-efficacy as well as fewer symptoms of depression and perceived stress. CPs endorsed feeling more empowered despite elevated caregiver burden. Conclusions: These results suggest multidomain lifestyle programs can positively impact wellbeing in MCI. Future research should focus on refining delivery models, exploring integration with pharmacological treatments, prioritizing inclusion of diverse populations, and measuring long-term outcomes to strengthen the reach and impact of programs like CEP.

Background: Black and Latino adults in the United States experience a disproportionate burden of cardiometabolic conditions due to interacting behavioral, social, and structural drivers of health. Less is known about the impact of integrating digital health tools into CHW-led interventions to improve cardiometabolic health. This trial evaluates a multilevel community-digital health promotion model delivered by CHWs to improve service utilization, health behaviors and cardiometabolic health among Black and Latino adults. Methods: This community-partnered trial uses a randomized delayed-control group with a phased recruitment design. Four cohorts (N = 664) are enrolled through three community-based organizations (CBOs). Eligible participants are 18 years who self-identify as Black or Latino, and have prediabetes/diabetes, hypertension, or overweight/obesity. Participants are allocated to either (1) a multilevel intervention consisting of CBO and CHW capacity building combined with individualized CHW-led lifestyle coaching and group activities supported by digital tools, or (2) a delayed control group receiving SMS-only cardiometabolic health education. Data collected at baseline, 6, 9, and 18 months include surveys and health metrics. Qualitative data are collected from participants and community partners to assess intervention acceptability, implementation facilitators and barriers, and sustainability. Results: The primary outcome is health service utilization at 6 and 9 months. Secondary outcomes include health behaviors, health metrics, and social determinants of health. Sustainability of health behaviors and health metrics is assessed at 18 months. Conclusions: Findings will provide evidence to inform scalable, sustainable community-digital health models for CHW-supported cardiometabolic health interventions in underserved communities.

Background: Frontotemporal dementia (FTD) lacks widely accessible disease-specific biomarkers. Optical coherence tomography (OCT) and OCT angiography (OCTA) may provide non-invasive measures of retinal changes associated with neurodegeneration. We conducted a systematic review and meta-analysis evaluating retinal biomarkers in FTD compared with Alzheimer disease (AD) and controls. Methods: A systematic search of PubMed and Embase was conducted through April 25, 2026 according to PRISMA guidelines. Studies evaluating OCT/OCTA biomarkers in FTD with comparator groups were included. Inverse weighted random-effects models, publication bias assessments, and meta-regressions were performed. Results: Ten studies involving 139 individuals with FTD, 87 with AD, 29 with mild cognitive impairment, 14 with TDP-43 proteinopathy, 5 with tauopathy, and 255 controls were included in the systematic review; five studies were eligible for meta-analysis. Compared with AD, individuals with FTD demonstrated significantly thinner retinal nerve fiber layer (RNFL) thickness (SMD = -0.61, 95% CI -0.98, -0.24). Compared with controls, individuals with FTD exhibited significantly thinner ganglion cell layer-inner plexiform layer (GCL-IPL) thickness (SMD = -0.55, 95% CI -1.02, -0.08), whereas pooled analyses across multiple retinal biomarkers were non-significant (SMD = -0.19, 95% CI -0.52, 0.14). RNFL thickness correlated negatively with female % in FTD and positively with age in both AD and controls. Conclusions: Individuals with FTD exhibit lower RNFL thickness than AD and lower GCL-IPL thickness than controls, suggesting retinal alterations may reflect neurodegeneration. However, larger longitudinal studies with standardized OCT/OCTA protocols are needed to determine the diagnostic and prognostic utility of retinal biomarkers in FTD