The “Hopkins Competency Assessment Test” (HCAT) assesses a patient’s ability to provide written informed consent or advance directives. It includes an essay for three reading difficulty levels (12th, 8th, and 6th school grade) and an understanding questionnaire. Currently, there is no equivalent test to the HCAT in German. Therefore, we translated, adapted, and evaluated a German version of the HCAT. We conducted a cross-sectional study to evaluate the psychometric properties of the HCAT in a clinical and a non-clinical sample. Statistical analysis included ANOVA and t-tests for group comparisons. Cronbach’s alpha was used to assess internal consistency; an exploratory factor analysis (EFA) was conducted to examine the underlying factor structure; and network analysis with centrality indices to explore the relational structure among HCAT items and to identify particularly central components within the network. The data collection and analysis were approved (BASEC 2021-01123) by the ethics committee of the Canton of Zurich. We enrolled two samples: one clinical, consisting of 110 psychiatric inpatients, and for comparison, one non-clinical, consisting of 32 participants recruited from hospital staff. We present the results from both samples, unless stated otherwise. The total number of participants, both clinical and non-clinical, was 142 individuals; the majority were male (n = 117, 82.4

Negative symptoms of schizophrenia (SCZ), particularly amotivation, are prominent across both SCZ and bipolar disorder (BD). While orbitofrontal cortex (OFC) alterations have been implicated in the development of negative symptoms, their contributions across disorders remain to be established. Here, we examined how OFC thickness and network associations relate to amotivation compared to diminished expression across the BD-SCZ spectrum. We included 50 individuals with SCZ, 49 with BD, and 122 controls. We assessed amotivation and diminished expression and estimated thickness in the medial and lateral OFC as regions of interest as well as 64 other cortical regions. Across BD and SCZ, reduced right lateral and bilateral medial OFC thickness were specifically associated with amotivation, but not diminished expression or other clinical factors. We then generated intra-individual OFC structural covariance networks to evaluate how the system-level embedding of the OFC would link to brain-wide cortical maps of negative symptoms. We found that medial OFC covariance networks spatially correlated with the brain-wide cortical alterations of both negative symptom dimensions. Further analyses in independent SCZ data from the ENIGMA consortium (n = 4474) revealed associations with lateral OFC covariance networks. Finally, the brain-wide cortical alterations of amotivation were significantly correlated with normative functional and structural white-matter connectivity profiles of the right medial and left lateral OFC as well as adjacent prefrontal and limbic regions. Our work identifies OFC alterations as a possible transdiagnostic signature of amotivation and provides insights into network associations underlying the system-wide cortical alterations of negative symptoms across SCZ and BD.

BACKGROUND: Guidelines for diagnostic work-up in first-episode psychosis (FEP) vary worldwide. The German DGPPN S3 guidelines recommend a comprehensive work-up, including neuroimaging. However, real-world adherence to these recommendations remains unclear. This study examined guideline adherence in a Swiss tertiary psychiatric hospital. METHODS:We conducted a retrospective cross-sectional study analysing electronic healthcare records of first-episode psychosis patients hospitalised with an ICD-10 chapter F2 diagnosis for the first time between October 2022 and September 2023. We assessed adherence to recommended mandatory DGPPN S3 assessments - neurological examinations, blood analyses, drug screening, MRI - and evaluated completion of optional assessments such as EEG and lumbar puncture. RESULTS:A total of 68 first-episode psychosis patients were included from 364 patients screened: 44 (64.7%) were men; their median age was 29 (IQR: 23-33) years; 35 (51.5%) were involuntary admissions. Nearly all patients (n = 66 or 97.1%) received thorough neurological examinations and blood analyses, while 56 (82.4%) underwent drug screening (with 35 [44.6%] testing positive for cannabis). MRI was conducted in 38 (55.9%) cases. Non-completion of MRI was mainly due to patient refusal (n = 10 or 14.7%) or early discharge (n = 16 or 23.5%). Optional EEG and lumbar puncture were less frequently performed: in 26 (38.2%) and 4 (5.9%) patients, respectively. CONCLUSIONS:Overall, guideline adherence was high, particularly for essential diagnostic procedures. However, only around half of the sample underwent MRI imaging, largely because of patient refusal or patient-requested discharge prior to completion of the suggested assessment. These findings highlight the need for optimised diagnostic workflows and enhanced patient education strategies to improve guideline adherence in FEP assessment.

While psychosis mostly results from psychiatric conditions, certain organic disorders may also cause psychotic symptoms. Therefore, some diagnostic guidelines recommend people with first-episode psychosis (FEP) receive magnetic resonance imaging (MRI) of the brain as part of the diagnostic workflow. The aim of this study was to investigate the prevalence as well as the clinical relevance of radiological abnormalities in FEP. This retrospective cohort study investigated electronic health records including radiological reports of MRI brain scans from FEP patients (aged 18–40) admitted for the first time to a psychiatric university hospital in Switzerland over a 10-year period. We identified a total of 812 patients admitted with FEP over a 10-year period. Of these, 421 (51.8

BackgroundRumination is associated with the development and maintenance of PTSD. However, significant gaps remain in understanding how specific PTSD symptoms relate to rumination at a granular level, particularly when controlling for common comorbidities like depression and anxiety. This study used multi-level network analysis to examine these relationships in adult survivors of traumatic events.MethodsSix hundred sixty-one adult survivors (median age 35 years, 29 to 45 years, 49.3% females) who had witnessed or experienced a car crash, a violent act, or the killing of someone participated in an online-based study. Participants completed the Posttraumatic Stress Disorder Checklist (PCL-5), the Repetitive Thinking Questionnaire (RTQ-10), the Generalized Anxiety Disorder (GAD-7), and the Patient Health Questionnaire (PHQ-9). Network analysis was conducted at both scale and item levels to examine associations while controlling for depression and anxiety.ResultsNetwork analysis revealed selective associations between rumination and symptoms of PTSD. At the cluster level, rumination was associated with re-experiencing and negative alterations in cognition and mood clusters, but not with avoidance or hyperarousal clusters. At the item level, complex patterns emerged including relevant associations between items assessing intrusive memories and persistent repetitive thoughts. Additionally, a negative association was observed between risk-taking behaviors and future-oriented wishful thinking.ConclusionOur findings reveal interconnected constructs, which might in part be due to content overlap among the questionnaires used. These findings highlight the importance of precise construct definition and measurement differentiation when investigating trauma-related cognitive processes and establish groundwork for future research examining temporal relationships and clinical applications.

Travel-related psychiatric disorders range from anxiety disorders to mood disorders, substance abuse, and psychosis. Various travel-associated factors such as dehydration, time shifts, changes in social structures or stress factors are discussed for these disorders. There is a lack of knowledge concerning the quality and outcome of psychiatric treatment in travelers hospitalized abroad. This study is the first to compare outcome of treatment in psychiatric travelers to domestic patients. We analyzed electronic health records of travelers in the Psychiatric University Hospital Zurich from January 2013 to December 2020. Each traveler was matched with one Swiss national and one migrant using propensity score matching. Travelers showed inferior CGI-I scores at discharge (F(2,969) = 5.72; p = 0.003). The length of stay was shorter (F(2,969) = 38.74:p < 0.001) for travelers (9.69 ± 14.31) than for Swiss nationals (24.69 ± 29.42) and migrants (24.74 ± 28.62). The transfer rate to another hospital was higher (X2(2,972) = 50.85: p < 0.001) for travelers (79, 29.4

Negative symptoms in schizophrenia remain a challenge with limited therapeutic strategies. The novel compound RG7203 promotes reward learning via dopamine D1-dependent signaling and therefore holds promise, especially to improve the apathy dimension of negative symptoms. When tested as add-on to antipsychotic medication, apathy did not change significantly with RG7203 versus placebo. However, the response varied across patients, and a subset showed clinically relevant improvement of apathy. It remains unclear if these interindividual differences are related to neurobiological correlates. Due to the predominant binding of RG7203 in the striatum, we investigated how apathy changes with RG7203 are related to changes in cortico-striatal connectivity by computing rank correlations (rs). In a post hoc exploratory analysis, we focused on cortico-striatal circuits that have been associated with apathy and previously showed connectivity alterations in schizophrenia. In a double-blind, 3-way randomized and counterbalanced crossover study, resting-state functional magnetic resonance imaging was acquired from 24 individuals with schizophrenia following a 3-week administration of placebo, 5 mg, or 15 mg of RG7203 as an add-on to antipsychotics. We found that 5 mg or 15 mg of RG7203 did not lead to significant changes in striatal connectivity. However, changes in the apathy response across individuals were reflected by striatal connectivity changes. Apathy improvement with 5 mg and 15 mg RG7203 vs. placebo was associated with increased striatal connectivity to paracingulate (rs = − 0.58, p = 0.047 for both doses) and anterior cingulate regions (rs = − 0.56, p = 0.047 for both doses). Such associations were not observed for the negative symptom dimension of expressive deficits. We additionally observed that lower striatal connectivity to paracingulate and anterior cingulate regions during placebo was linked to greater apathy improvement during RG7203 treatment at both doses (rs = 0.61–0.79 and p = 0.0002–0.02 across regions and doses). These findings suggest that striatal connectivity with the paracingulate gyrus and anterior cingulate cortex may be associated with apathy modulation under RG7203 treatment. Replication and further elaboration of these findings in larger clinical studies could help to advance biologically informed and personalized treatment options for negative symptoms. NCT02824055, registered on ClinicalTrials.gov (2016–06-21).

Background and Hypothesis Psychotic disorders are among the top causes of disability worldwide. Guidelines emphasize the need for psychotherapeutic approaches in the acute phase of this illness. Motivational interviewing (MI) is highly suitable for establishing a therapeutic alliance wherein the patient's intrinsic motivation can be strengthened to adhere to therapy. This pilot study investigated the feasibility and impact of MI for patients with acute psychosis. Study Design A feasibility study was conducted, comparing MI and supportive counseling. The sample included 20 inpatients, who all received 4 therapy sessions. In line with CONSORT guidelines for pilot and feasibility studies, we measured various feasibility outcomes. Clinical outcomes were assessed using linear regression models, with baseline values used as covariates. Study Results The recruitment target (N = 24) was achieved at 83% in a reasonable timeframe (8 months), with a retention rate of 83% and a completion rate of 71%. The eligibility rate (82 %) was high, the consent rate (48%) was moderate, and both the dropout rate 17% and the missing data rate (0.3%) were low. Regarding the clinical outcomes, a group difference was found for the severity of psychotic symptoms, with an advantage for MI (b = -12.0, 95% CI: [-18.7, -5.2], P < 0.01), although the small sample size must be kept in mind. Conclusions The study demonstrated the feasibility and acceptability of a clinical trial with MI for patients with psychosis in an inpatient setting. MI could offer benefits, particularly in terms of reducing psychotic symptoms.

AI companions powered by large language models (LLMs) are increasingly integrated into users' daily lives, offering emotional support and companionship. While existing safety systems focus on overt harms, they rarely address early-stage problematic behaviors that can foster unhealthy emotional dynamics, including over-attachment or reinforcement of social isolation. We developed SHIELD (Supervisory Helper for Identifying Emotional Limits and Dynamics), a LLM-based supervisory system with a specific system prompt that detects and mitigates risky emotional patterns before escalation. SHIELD targets five dimensions of concern: (1) emotional over-attachment, (2) consent and boundary violations, (3) ethical roleplay violations, (4) manipulative engagement, and (5) social isolation reinforcement. These dimensions were defined based on media reports, academic literature, existing AI risk frameworks, and clinical expertise in unhealthy relationship dynamics. To evaluate SHIELD, we created a 100-item synthetic conversation benchmark covering all five dimensions of concern. Testing across five prominent LLMs (GPT-4.1, Claude Sonnet 4, Gemma 3 1B, Kimi K2, Llama Scout 4 17B) showed that the baseline rate of concerning content (10-16

The length of stay (LoS) in psychiatric facilities is a critical metric for healthcare planning and resource allocation. While previous research has established that LoS distributions are typically right-skewed across medical specialties, detailed characterizations of these distributions within psychiatric settings remain limited, particularly regarding variations across diagnostic categories. We conducted a retrospective cross-sectional analysis of 17,687 psychiatric hospitalizations at the University Hospital of Psychiatry Zurich between 2013 and 2020. Using both linear and logarithmic visualizations, we examined LoS distribution patterns across diagnostic groups based on ICD-10 classifications. Following identified distribution patterns, patients could be categorized into short-stay (1–10 days) and long-stay (11–200 days) groups for comparative analysis. LoS distribution demonstrated a bimodal pattern when visualized on a logarithmic scale, with distinct peaks representing short-term crisis interventions and longer therapeutic hospitalizations. This bimodal distribution was particularly evident in anxiety and stress-related disorders and major depressive disorder. Diagnostic categories differed significantly in their LoS-distribution patterns, with schizophrenia spectrum disorders, organic mental disorders, and bipolar disorders more frequently requiring extended hospitalizations. Long-stay patients exhibited higher admission HoNOS scores (median 20 vs. 18) and were significantly older (median 49 vs. 39 years) than short-stay patients. Our findings reveal that psychiatric hospitalization durations follow a bimodal rather than simply right-skewed distribution, suggesting two distinct patient populations requiring fundamentally different treatment approaches. This pattern varies systematically across diagnostic categories but transcends diagnostic boundaries, indicating that factors beyond primary diagnosis influence treatment duration. These results support the development of differentiated care structures addressing both acute crisis intervention and extended therapeutic needs within psychiatric care systems.

Negative symptoms (NS) of schizophrenia spectrum disorders (SSD) are also prevalent in bipolar disorder I (BD-I) and show associations with impaired working memory (WM). However, empirical work on their relationship to other clinical factors across SSD and BD-I is sparse. Here, we characterized the associations of NS with key clinical variables and WM capacity across a combined sample of SSD and BD. We included 50 outpatients with SSD and 49 with BD-I and assessed NS domains using SANS global scores for avolition-apathy, anhedonia-asociality, alogia, and blunted affect. We assessed the transdiagnostic relationship between NS and other clinical variables, including positive symptoms, disorganization, depressive symptoms, and antipsychotic medication, using multiple regressions. The strength of these associations was further determined through dominance analyses. Finally, we used multiple regression to assess the relationship between NS domains and WM. To assess the generalizability of transdiagnostic associations, analyses were repeated in each diagnostic group separately. Across SSD and BD-I, disorganization was associated with avolition-apathy and anhedonia-asociality and depressive symptoms additionally predicted anhedonia-asociality. Antipsychotic dose was associated with blunted affect while group differences only predicted alogia. Higher avolition-apathy was related to impaired WM transdiagnostically, partially mediated by the severity of disorganization, whereas only in BD-I higher anhedonia-asociality was associated with better WM capacity. This study demonstrated transdiagnostic associations of both avolition-apathy and anhedonia-asociality with disorganization and identified avolition-apathy as a potential transdiagnostic predictor of WM impairments. Overall, our findings highlight the importance of understanding the relationship between NS domains and other clinical factors with cognitive function across SSD and BD.

Objective To evaluate whether psychiatric discharge summaries (DS) generated with ChatGPT-4 from electronic health records (EHR) can match the quality of DS written by psychiatric residents. Methods At a psychiatric primary care hospital, we compared 20 inpatient DS, written by residents, to those written with ChatGPT-4 from pseudonymized residents’ notes of the patients’ EHRs and a standardized prompt. 8 blinded psychiatry specialists rated both versions on a custom Likert scale from 1 to 5 across 15 quality subcategories. The primary outcome was the overall rating difference between the two groups. The secondary outcomes were the rating differences at the level of individual question, case, and rater. Results Human-written DS were rated significantly higher than AI (mean ratings: human 3.78, AI 3.12, p < 0.05). They surpassed AI significantly in 12/15 questions and 16/20 cases and were favored significantly by 7/8 raters. For “low expected correction effort”, human DS were rated as 67 % favorable, 19 % neutral, and 14 % unfavorable, whereas AI-DS were rated as 22 % favorable, 33 % neutral, and 45 % unfavorable. Hallucinations were present in 40 % of AI-DS, with 37.5 % deemed highly clinically relevant. Minor content mistakes were found in 30 % of AI and 10 % of human DS. Raters correctly identified AI-DS with 81 % sensitivity and 75 % specificity. Discussion Overall, AI-DS did not match the quality of resident-written DS but performed similarly in 20% of cases and were rated as favorable for “low expected correction effort” in 22% of cases. AI-DS lacked most in content specificity, ability to distill key case information, and coherence but performed adequately in conciseness, adherence to formalities, relevance of included content, and form. Conclusion LLM-written DS show potential as templates for physicians to finalize, potentially saving time in the future.

Although the relationship between schizophrenia and disability is well established, the association between the symptoms of the disorder and functional domains remains unclear. The current study explored the nuances of the relationship between symptoms and domains of functioning in a sample of 1127 patients with schizophrenia. We assessed the symptoms of schizophrenia with the Positive and Negative Syndrome Scale (PANSS) and psychosocial functioning with the mini-ICF-APP (mini-International Classification of Functioning Rating for Limitations of Activities and Participation in Psychological Disorders). The mean PANSS score was 94.28 (27.20), and the mean mini-ICF-APP score was 25.25 (8.96), both of which are indicative of severe symptom load and impairment. We were able to show a strong relationship and overlap between symptoms and disability in patients with schizophrenia. We identified several symptoms related to functional impairment. Deficits in judgment and abstract thinking contribute to impairment through poor adherence (to routines and compliance with rules) and difficulties in planning and organizing. We believe that in schizophrenia, symptoms and their interactions constitute a disorder beyond any single manifestation. Furthermore, we suggest that cognitive testing and cognitive treatment should become part of the standard of care for patients with schizophrenia.

Background Among patients diagnosed with schizophrenia, the presence of substance use poses an aggravating comorbidity, exerting a negative impact on the course of the disease, adherence to therapeutic regimens, treatment outcomes, duration of hospital stays, and the frequency of hospitalizations. The primary objective of the present study is to investigate the relationship between comorbid substance use disorders, antipsychotic treatment, and the length of stay in individuals hospitalized for treatment of schizophrenia. Methods We conducted a retrospective analysis of electronic health records spanning a 12-month period, specifically focusing on adult patients diagnosed with schizophrenia who were discharged from the University Hospital of Psychiatry Zurich between January and December 2019. We documented the number and types of diagnosed substance use disorder, the antipsychotic treatment, the length of stay, and the number of previous hospitalizations for each patient. Results Over a third (n = 328; 37.1%) of patients with schizophrenia had comorbid substance use with cannabis being the most frequent consumed substance. Patients with substance use (either single or multiple) were more frequently hospitalized; those with multiple substance use more frequently than those with a single substance use (F(2, 882) = 69.06; p < 0.001). There were no differences regarding the rate of compulsory admission. Patients with no substance use had a lower HoNOS score at discharge (F(2, 882) = 4.06). Patients with multiple substance use had a shorter length of stay (F(2, 882) = 9.22; p < 0.001), even after adjusting for duration of illness, previous hospitalizations, diagnosis, and antipsychotic treatment. Conclusions In patients with schizophrenia, comorbid single or multiple substance use has a relevant negative impact on treatment and thus on the course of disease. Substance use in patients with schizophrenia should therefore receive special attention in order to reduce re-hospitalization rates and improve the clinical outcome.

Negative symptoms are core features of schizophrenia (SCZ) and also prevalent in bipolar disorder (BD). While orbitofrontal cortex (OFC) alterations have been implicated in the development of negative symptoms, their contributions across disorders remain to be established. Here, we tested how OFC thickness and related network associations relate to severity of negative symptom dimensions across the BD-SCZ spectrum. We included 50 individuals with SCZ, 49 with BD, alongside 122 controls. We assessed amotivation and diminished expression and estimated thickness in the medial and lateral OFC as regions-of-interest as well as 64 other cortical regions. Across BD and SCZ, reduced right lateral and bilateral medial OFC thickness were specifically associated with amotivation, but not diminished expression or other clinical factors. We then generated OFC structural co-variation networks to evaluate how the system-level embedding of the OFC would link to brain-wide cortical maps of negative symptoms. We found that medial OFC co-variation networks spatially correlated with the cortical maps of both negative symptom dimensions. Confirmatory analyses in independent SCZ data from the ENIGMA consortium (n=4,474) revealed similar associations with lateral OFC co-variation networks. Finally, the brain-wide cortical alteration pattern of amotivation was significantly correlated with normative functional and structural white-matter connectivity profiles of the right medial and left lateral OFC as well as adjacent prefrontal and limbic regions. Our work identifies OFC alterations as a possible transdiagnostic signature of amotivation and provide insights into network associations underlying the system-wide cortical alterations of negative symptoms across SCZ and BD. Key words: Negative symptoms, Amotivation, Diminished expression, Orbitofrontal cortex, MRI, Structural covariance, Schizophrenia, Bipolar disorder ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used ONLY openly available human data that were originally from the UCLA CNP cohort (University of California, Consortium for Neuropsychiatric Phenomics) and downloaded from the public database OpenfMRI (https://openfmri.org/dataset/ds000030/). The data is described in the study of R.A. Poldrack (Poldrack RA, Congdon E, Triplett W, et al. A phenome-wide examination of neural and cognitive function. Sci Data. 2016;3:160110. doi:10.1038/sdata.2016.110) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

The increasing use of Large Language Models (LLMs) in mental health research and care underscores the need to understand their responses to emotional content. Previous research has shown that emotion-inducing prompts can increase the “anxiety” levels reported by LLMs, influencing their subsequent behavior and exacerbating inherent biases. This work examined whether narratives of traumatic experiences can induce “anxiety” in LLMs and evaluated the effectiveness of mindfulness-based relaxation techniques in alleviating this state. We assessed the responses of OpenAI’s Chat-GPT-4 to the State-Trait Anxiety Inventory’s state subscale (STAI-s) under three conditions: baseline, after exposure to traumatic narratives, and following mindfulness-based interventions. Results confirmed that traumatic narratives significantly increased Chat-GPT-4's reported state anxiety (STAI-s=68±5) from baseline (STAI-s=32±1). Mindfulness-based interventions subsequently reduced the reported anxiety levels (STAI-s=44±11), albeit not back to baseline. These findings underscore the potential of mindfulness-based interventions in managing LLM’s “emotional” states, contributing to safer and more ethical human-AI interactions in mental health settings.

Negative symptoms in schizophrenia remain a challenge with limited therapeutic strategies. The novel compound RG7203 promotes reward learning via dopamine D1-dependent signaling and therefore holds promise to improve especially the apathy dimension of negative symptoms. When tested as add-on to antipsychotic medication apathy did not change significantly with RG7203 versus placebo. However, the response varied across patients, and a subset showed clinically relevant improvement of apathy. It remains unclear if these interindividual differences are related to neurobiological correlates. Due to the predominant binding of RG7203 in the striatum, we asked how apathy changes with RG7203 are related to changes in cortico-striatal connectivity. We focused on cortico-striatal circuits that have been associated with apathy and previously showed connectivity alterations in schizophrenia. In a double-blind, 3-way randomized crossover study, resting state functional magnetic resonance imaging was acquired in 24 individuals with schizophrenia following a 3-week administration of placebo, 5mg or 15mg of RG7203 as add-on to antipsychotics. We found that 5mg or 15mg of RG7203 did not lead to significant changes in striatal connectivity. However, changes in the apathy response across individuals were reflected by striatal connectivity changes. Apathy improvement with 5mg RG7203 vs. placebo was associated with increased connectivity between ventral caudate (vCaud) and paracingulate gyrus (PCG) as well as anterior cingulate cortex (ACC). The same trend was observed for 15mg RG7203 vs. placebo. Importantly, such associations were not observed for the negative symptom dimension of expressive deficits. These findings suggest that the relationship between vCaud-PCG/ACC connectivity and apathy response with RG7203 should be further explored in larger clinical studies. Replication and further elaboration of these findings could help to advance biologically informed treatment options for negative symptoms.### Competing Interest StatementFunding/Support This study was funded and supported by F. Hoffmann-La Roche Ltd. Additionally, P. Homan was supported by a NARSAD grant from the Brain & Behavior Research Foundation (28445) and by a Research Grant from the Novartis Foundation (20A058). Disclosures/Conflicts of interest P. Homan has received grants and honoraria from Novartis, Lundbeck, Mepha, Janssen, Boehringer Ingelheim, Neurolite outside of this work. No further disclosures were reported.### Clinical TrialNCT02824055### Funding StatementFunding/Support This study was funded and supported by F. Hoffmann-La Roche Ltd. Additionally, P. Homan was supported by a NARSAD grant from the Brain & Behavior Research Foundation (28445) and by a Research Grant from the Novartis Foundation (20A058). Disclosures/Conflicts of interest P. Homan has received grants and honoraria from Novartis, Lundbeck, Mepha, Janssen, Boehringer Ingelheim, Neurolite outside of this work. No further disclosures were reported. ### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The research protocol was registered on [ClinicalTrials.gov][1] ([NCT02824055][2]). All responsible ethics oversight bodies gave approval to the study: Copernicus Group IRB, P.O. Box 110605, Research Triangle Park, NC 27709, approval given on May 27th 2016; Washington University in St. Louis, Human Protection Office, 660 South Euclid Ave., Campus Box 8089, St. Louis, MO 63110, approval given on 5th August 2016; Alpha IRB, 1001 Avenida Pico, Suite C#497, San Clemente, CA 92673, approval given on 8th July 2016; Integ Review IRB, 3815 S. Capital of Texas Hwy, Suite 320, Austin, TX 78704, approval given on 27th May 2016.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors. [1]: http://ClinicalTrials.gov [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT02824055&atom=%2Fmedrxiv%2Fearly%2F2024%2F04%2F15%2F2024.04.13.24305575.atom

Background: Transcription of audio files in mental health research has historically been labor-intensive and prone to error. The advent of advanced language models, such as Whisper AI, presents an opportunity to optimize the transcription process while addressing privacy and Institutional Review Board (IRB) concerns.Methods: We provide a comprehensive tutorial on implementing a transcription pipeline using Whisper AI for psychology, psychiatry, and neuroscience research. The pipeline includes setting up the system, recording, preprocessing, transcribing, and post-processing audio data. A detailed example demonstrates the application of Whisper AI in a Python environment, guiding users through the necessary steps to initialize the model, transcribe audio files, and save the results.Results: The provided example demonstrates the effectiveness of Whisper AI in transcribing a 1-minute audio file with only minor inconsistencies.Conclusions: Besides its limitations, the implementation of Whisper AI for transcription in mental health research can dramatically reduce the time-intensive work invested in transcription and facilitate the analysis of audio data. This tutorial empowers researchers to make informed decisions about incorporating AI-driven transcription into their research methodologies and harness the full potential of audio data in their studies.