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A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.

Prevailing factor models of psychosis are centered on schizophrenia-related disorders defined by the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases , restricting generalizability to other clinical presentations featuring psychosis, even though affective psychoses are more common. This study aims to bridge this gap by conducting exploratory and confirmatory factor analyses, utilizing clinical ratings collected from patients with either affective or nonaffective psychoses ( n = 1,042). Drawing from established clinical instruments, such as the Positive and Negative Syndrome Scale, Young Mania Rating Scale, and Montgomery-Åsberg Depression Rating Scale, a broad spectrum of core psychotic symptoms was considered for the model development. Among the candidate models considered, including correlated factors and multifactor models, a model with seven correlated factors encompassing positive symptoms, negative symptoms, depression, mania, disorganization, hostility, and anxiety was most interpretable with acceptable fit. The seven factors exhibited expected associations with external validators, were replicable through cross-validation, and were generalizable across affective and nonaffective psychoses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Trauma-related intrusive memories (TR-IMs), unwanted and vivid, are core symptoms of posttraumatic stress disorder (PTSD). Prior research links voluntary TR-IM suppression to inhibitory control of the right dorsolateral prefrontal cortex (dlPFC) over the hippocampus (HPC). However, the potential relevance of tonic resting-state inhibition has not been examined, nor has the functional differentiation of the anterior and posterior hippocampus (aHPC/HPC). This study examined relationships of TR-IM frequency and properties with resting-state negative coupling between the right dlPFC and right aHPC/pHPC in trauma-exposed individuals with PTSD symptoms., Methods: Participants (N=109; 88 female) completed two weeks of ecological momentary assessments capturing TR-IM frequency and properties (intrusiveness, emotional intensity, vividness, visual properties, and reliving). Using 3T resting-state magnetic resonance imaging, participant-specific 4-mm spheres were placed at the right dlPFC voxel most anticorrelated with the right aHPC and pHPC. Quasi-Poisson and linear mixed-effects models assessed relationships of TR-IM frequency and properties with right dlPFC-right aHPC and pHPC anticorrelation., Results: TR-IM emotional intensity was positively associated with right dlPFC-aHPC connectivity, while vividness and visual properties were linked to right dlPFC-pHPC connectivity. No significant associations were found between TR-IM frequency, intrusiveness, or reliving, and anticorrelation with either HPC subregion., Conclusions: This study provides novel insights into the neural correlates of TR-IMs, highlighting the relevance of intrinsic negative coupling between the right dlPFC and aHPC/pHPC to their emotional impact and perceptual properties. Further research on inhibitory mechanisms in this circuit could improve understanding of component processes of intrusive reexperiencing, a severe and treatment-refractory PTSD symptom.

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan time given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan time per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan time are broadly interchangeable up to 20-30 min of data. However, the returns of scan time diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed overhead costs associated with each participant (e.g., recruitment, non-imaging measures), prediction accuracy in many small-scale and some large-scale BWAS might benefit from longer scan time than typically assumed. These results generalize across phenotypic domains, scanners, acquisition protocols, racial groups, mental disorders, age groups, as well as resting-state and task-state functional connectivity. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations maximize sample size, at the expense of scan time, which can result in sub-optimal prediction accuracies and inefficient use of resources. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK., Competing Interests: Conflict of interest DB is shareholder and advisory board member of MindState Design Labs, USA.

Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we develop a generalizable method of genotype inference based on distant relatedness and deploy this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identify 9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn, the most common LQT5 variant. We uncover recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identifies 22 additional subjects sharing this haplotype, which we confirm to carry p.Asp76Asn. Referral and non-referral carriers have prolonged QT interval corrected for heart rate (QTc) compared to controls, and, among carriers, the QTc polygenic score is independently associated with QTc prolongation. Thus, our innovative analysis of shared chromosomal segments identifies undiagnosed cases of genetic disease and refines the understanding of LQT5 penetrance and phenotype., (© 2024. The Author(s).)

Background The coronavirus disease 2019 (COVID-19) pandemic reaffirmed health disparities in the United States (US) and highlighted the need for public health strategies to combat vaccine hesitancy, especially amongst vulnerable populations. The Green Family Foundation Neighborhood Health Education Learning Program (NeighborhoodHELP) at Florida International University (FIU) serves a predominantly uninsured population, making it a critical area of opportunity for addressing vaccine hesitancy. Motivational interviewing (MI), a technique that supports individuals in making autonomous health decisions, has shown promise in encouraging vaccine acceptance. Medical students at FIU's Herbert Wertheim College of Medicine (HWCOM) are involved in the longitudinal care of the individuals in NeighborhoodHELP and receive training in MI within their clinical skills curriculum, making them optimally positioned to conduct outreach to encourage COVID-19 vaccination. Project goals There were two primary goals of this project: first, to systematically track and improve COVID-19 vaccination rates among individuals in NeighborhoodHELP, and second, to equip future physicians with hands-on experience in MI. Methods The COVID-19 Vaccination Promotion Initiative recruited medical students previously trained in MI to conduct outreach to unvaccinated individuals within NeighborhoodHELP. Students engaged in discussions about the COVID-19 vaccine with NeighborhoodHELP members, assisted in scheduling vaccination appointments, and updated medical records. The student team regularly met with faculty advisors to discuss changes in vaccine and public health data and to discuss challenges and successes with outreach efforts. To incentivize participation and enhance vaccine uptake, $25 gift cards were offered to individuals who agreed to receive the vaccine following the outreach conversations. Results From June 2021 to January 2023, the team made an estimated 720-1516 phone calls to NeighborhoodHELP individuals. The team encountered a challenge of low answering rates, with 35% of individuals being unreachable despite multiple attempts. Among those reached, 20% expressed no interest in receiving the vaccine, while 50% were interested in receiving the vaccine or had already been vaccinated. Vaccination rates among NeighborhoodHELP adults rose from 15.2% to 44.3% during this time. Student experiences with MI were generally positive, with many noting success in engaging hesitant individuals. However, the team also encountered challenges, such as growing vaccine apathy within the community and difficulties in reaching patients via cold calls, which limited the overall impact of their outreach efforts. Conclusions By using MI techniques, medical students engaged with community members in meaningful conversations about the importance and safety of COVID-19 vaccination. However, the initiative fell short of the 50% vaccination target, facing challenges such as reliance on unsolicited phone calls and the complexities of incentivizing vaccinations through this outreach method.  Future initiatives could benefit from exploring alternative outreach methods, such as in-person engagement at community events or through partnerships with local organizations, to overcome the limitations of phone-based outreach. Additionally, investigating the relative efficacy of in-person versus telephone-based communication in promoting vaccination could provide valuable insights., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Florida International University Institutional Review Board (IRB) issued approval IRB-18-0039-CR03. The Florida International University IRB approved the dissemination of de-identified data sets, analyses, and case studies developed for NeighborhoodHELP program evaluation, program improvement, and required reporting to program funders. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Garza et al.)

Context: The integration of certified community health workers (CCHWs) with specialty chronic disease training into clinical care teams has demonstrated improvements in chronic disease quality of care, management, and outcomes., Program: Rhode Island Department of Health's Diabetes, Heart Disease, and Stroke Program expanded the roles of CCHWs employed by Community Health Teams for chronic disease with a focus on cardiovascular disease (CVD) and diabetes mellitus (DM) from 2020 to 2023. Rhode Island Department of Health's Diabetes, Heart Disease, and Stroke Program sought to determine whether patient health behaviors and clinical outcomes improved with specialty trained CCHW support., Implementation: Community Health Teams identified high-risk or rising-risk patients with hypertension, high cholesterol, and/or diabetes. During an infrastructure phase, patients were assigned a CCHW who had not received CVD/DM specialty training. During a performance phase, a separate cohort of patients was assigned a CVD/DM specialty-trained CCHW. In each phase, patients were seen by the CCHWs at least twice and completed baseline and follow-up health assessments. The trained CCHWs utilized the baseline assessment to offer health coaching specific to the patient's chronic disease-related needs., Evaluation: Improvements in blood pressure readings and cholesterol were observed at an individual level for CVD patients. However, a significant difference was not observed for hypertension or high cholesterol when comparing phases. Individual-level results indicated improved HbA1c values for DM patients; however, the differences in clinical values were not significant. Although there were no significant differences for clinical values between the phases, the proportion of patients who reported confidence in managing their condition(s) increased from baseline to follow-up for both phases., Discussion: It cannot be concluded that specialty-trained CCHWs have significant impact on patient behaviors and clinical outcomes. However, overall CCHW intervention did result in improved self-efficacy in patients to manage their chronic conditions. Further evaluation is needed to understand what factors led to improved patient confidence levels., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc.)

An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring., Competing Interests: Declaration of competing interest Authors have no competing interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Background: Heightened stress and insufficient sleep are common in the transition to college, often co-occur, and have both been linked to negative health outcomes. A challenge concerns disentangling whether perceived stress precedes or succeeds changes in sleep. These day-to-day associations may vary across individuals, but short study periods and group-level analyses in prior research may have obscured person-specific phenotypes., Objective: This study aims to obtain stable estimates of lead-lag associations between perceived stress and objective sleep duration in the individual, unbiased by the group, by developing an individual-level linear model that can leverage intensive longitudinal data while remaining parsimonious., Methods: In total, 55 college students (n=6, 11% second-year students and n=49, 89% first-year students) volunteered to provide daily self-reports of perceived stress via a smartphone app and wore an actigraphy wristband for the estimation of daily sleep duration continuously throughout the academic year (median usable daily observations per participant: 178, IQR 65.5). The individual-level linear model, developed in a Bayesian framework, included the predictor and outcome of interest and a covariate for the day of the week to account for weekly patterns. We validated the model on the cohort of second-year students (n=6, used as a pilot sample) by applying it to variables expected to correlate positively within individuals: objective sleep duration and self-reported sleep quality. The model was then applied to the fully independent target sample of first-year students (n=49) for the examination of bidirectional associations between daily stress levels and sleep duration., Results: Proof-of-concept analyses captured expected associations between objective sleep duration and subjective sleep quality in every pilot participant. Target analyses revealed negative associations between sleep duration and perceived stress in most of the participants (45/49, 92%), but their temporal association varied. Of the 49 participants, 19 (39%) showed a significant association (probability of direction>0.975): 8 (16%) showed elevated stress in the day associated with shorter sleep later that night, 5 (10%) showed shorter sleep associated with elevated stress the next day, and 6 (12%) showed both directions of association. Of note, when analyzed using a group-based multilevel model, individual estimates were systematically attenuated, and some even reversed sign., Conclusions: The dynamic interplay of stress and sleep in daily life is likely person specific. Paired with intensive longitudinal data, our individual-level linear model provides a precision framework for the estimation of stable real-world behavioral and psychological dynamics and may support the personalized prioritization of intervention targets for health and well-being., (©Constanza M Vidal Bustamante, Garth Coombs III, Habiballah Rahimi-Eichi, Patrick Mair, Jukka-Pekka Onnela, Justin T Baker, Randy L Buckner. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.04.2024.)

Introduction: This study investigated the acceptability and feasibility of digital phenotyping in a military sample with a history of traumatic brain injury and co-occurring psychological and cognitive symptoms. The first aim was to evaluate the acceptability of digital phenotyping by (1a) quantifying the proportion of participants willing to download the app and rates of dropout and app discontinuation and (1b) reviewing the stated reasons for both refusing and discontinuing use of the app. The second aim was to investigate technical feasibility by (2a) characterizing the amount and frequency of transferred data and (2b) documenting technical challenges. Exploratory aim 3 sought to leverage data on phone and keyboard interactions to predict if a participant (a) is depressed and (b) has depression that improves over the course of the study., Materials and Methods: A passive digital phenotyping app (Mindstrong Discovery) functioned in the background of the participants' smartphones and passively collected phone usage and typing kinematics data., Results: Fifteen out of 16 participants (93.8%) consented to install the app on their personal smartphone devices. Four participants (26.7%) discontinued the use of the app partway through the study, primarily because of keyboard usability and technical issues. Fourteen out of 15 participants (93.3%) had at least one data transfer, and the median number of days with data was 40 out of a possible 57 days. The exploratory machine learning models predicting depression status and improvement in depression performed better than chance., Conclusions: The findings of this pilot study suggest that digital phenotyping is acceptable and feasible in a military sample and provides support for future larger investigations of this technology., (© The Association of Military Surgeons of the United States 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR  < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

For some transgender people, hormone replacement therapy (HRT) is "an ontological necessity for a livable life" (Fondén, 2020, p. 29). Some trans people engage in do-it-yourself (DIY) HRT (aka "DIYers") because of care barriers, including medication costs, difficulty accessing healthcare providers, and mistrust in professionalized medical systems. Although DIY HRT is often framed as highly risky, we analyzed in-depth interviews with 36 U.S. DIYers to understand how they themselves perceived their goals, challenges, and risk mitigation using the Liberatory Harm Reduction and lay expertise frameworks. Participants emphasized experiences of transphobia within medical spaces. In contrast, participants characterized DIY HRT as a community-driven, accessible, and empowering practice. Through self-organized online forums and mutual aid, DIYers constructed adaptive health-promoting practices that challenge biomedical conceptualizations of risk and affirm trans agency., (Copyright © 2024. Published by Elsevier Ltd.)

The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients., Competing Interests: Conflict of interest: The authors have no conflict of interest to declare., (© The Royal Statistical Society 2024.)

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

This study aimed to investigate the nutritional and functional roles of β-mannanase on the intestinal health and growth of newly weaned pigs fed a typical or low-cost formulated feeds (LCF). Twenty-four newly weaned pigs at 6.2 kg ± 0.4 body weight (BW) were allotted to three dietary treatments based on a randomized complete block design with sex and initial BW as blocks. Three dietary treatments are as follows: Control, typical nursery feeds including animal protein supplements and enzyme-treated soybean meal; LCF with increased amounts of soybean meal, decreased amounts of animal protein supplements, and no enzyme-treated soybean meal; LCF+, low-cost formulated feed with β-mannanase at 100 g/t, providing 800 thermostable β-mannanase unit (TMU) per kg of feed. Pigs were fed based on a three-phase feeding program for a total of 37 d. On day 37 of feeding, all pigs were euthanized and the gastrointestinal tract was removed for sample collection to analyze intestinal health parameters, mucosa-associated microbiota, and gene expression of tight junction proteins. Pigs fed LCF increased (P < 0.05) the relative abundance of Proteobacteria and Helicobacter in the jejunal mucosa, tended to decrease (P = 0.097; P = 0.098) the concentration of malondialdehyde (MDA) and the expression of zona occluden 1 (ZO-1) gene in the jejunum, tended to decrease average daily gain (ADG; P = 0.084) and final BW (P = 0.090), and decreased (P < 0.05) average daily feed intake. Pigs fed LCF + tended to decrease (P = 0.088) digesta viscosity, decreased (P < 0.05) the relative abundance of Helicobacter, and increased (P < 0.05) Lactobacillus in the jejunal mucosa compared to LCF. Additionally, LCF + tended to increase final BW (P = 0.059) and ADG (P = 0.054), increased (P < 0.05) gain to feed ratio (G:F), and reduced (P < 0.05) fecal score compared to LCF. LCF with decreased amounts of animal protein supplements and increased amounts of soybean meal had negative effects on the composition of the mucosa-associated microbiota, intestinal integrity, and growth performance of nursery pigs. Beta-mannanase supplementation to LCF decreased digesta viscosity, increased the relative abundance of potentially health-benefitting microbiota such as Lactobacillus, and improved growth and fecal score, thus reflecting its efficacy in low-cost formulated feeds with increased amounts of soybean meal., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

This study aimed to investigate the effects of xylanase on growth performance and intestinal health of nursery pigs fed diets with reduced metabolizable energy (ME). One hundred ninety-two pigs at 8.7 kg ± 0.7 body weight (BW) after 7 d of weaning were allotted in a randomized complete block design with initial BW and sex as blocks. Eight dietary treatments consisted of 5 ME levels (3,400, 3,375, 3,350, 3,325, and 3,300 kcal ME/kg) below the NRC (2012) requirement and 4 levels of xylanase (0, 1,200, 2,400, and 3,600 XU/kg) to a diet with 3,300 kcal ME/kg. All pigs received their respective treatments for 35 d in 2 phases, pre-starter (14 d) and starter (21 d). On day 35, eight pigs in 3,400 kcal/kg (CON), 3,300 kcal/kg (LE), and 3,300 kcal/kg + 3,600 XU xylanase/kg (LEX) were euthanized to collect jejunal tissues and digesta for the evaluation of mucosa-associated microbiota, intestinal immune response, oxidative stress status, intestinal morphology, crypt cell proliferation, and digesta viscosity as well as ileal digesta to measure apparent ileal digestibility. Data were analyzed using the MIXED procedure on SAS 9.4. The LE increased (P < 0.05) jejunal digesta viscosity, tended to have decreased (P = 0.053) relative abundance of Prevotella, and tended to increase (P = 0.055) Lactobacillus. The LE also increased (P < 0.05) the concentration of protein carbonyl whereas malondialdehyde, villus height (VH), villus height to crypt depth ratio (VH:CD), apparent ileal digestibility (AID) of nutrients, and finally average daily feed intake were decreased (P < 0.05). The LE did not affect average daily gain (ADG). The LEX decreased (P < 0.05) digesta viscosity, increased (P < 0.05) the relative abundance of Prevotella, decreased (P < 0.05) Helicobacter, decreased (P < 0.05) the concentration of protein carbonyl, tended to increase (P = 0.065) VH, and decreased (P < 0.05) VH:CD and crypt cell proliferation. Moreover, LEX increased (P < 0.05) the AID of dry matter and gross energy and tended to increase (P = 0.099; P = 0.076) AID of crude protein, and ether extract. The LEX did not affect ADG but did tend to decrease (P = 0.070) fecal score during the starter phase. Overall, reducing ME negatively affected intestinal health parameters and nutrient digestibility without affecting growth. Supplementation of xylanase mitigated some of the negative effects observed by ME reduction on intestinal health and digestibility of nutrients without affecting growth., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Fertility problems, or the inability to conceive or carry a pregnancy to term for a period of over 12 months while engaging in unprotected sex, affects 12% of women and 9% of men of childbearing age. To answer calls for more research about individuals' fertility decision-making (DM) with their partners, we conducted in-depth, semi-structured interviews with 53 individuals who have experienced fertility decision-making with a romantic partner at some point in their lives. Our findings indicate at least three primary ways individuals and their partners navigated their decision-making communication in their infertility "journeys:" (1) the Driver-Navigator , (2) Driver-Passenger , and (3) Driver-Backseat Driver approaches. All decision-making communication approaches were viewed by individuals as collaborative (i.e. shared), but varied in degrees of "togetherness" (high, moderate, low) in how they communicated with each other about treatment decisions. Implications include helping couples and their clinicians to be aware of their DM approach(es) and offering alternative DM approaches based on understanding how and why certain approaches may (not) be effective in addressing goals, needs, and identities.

X-linked hypophosphatemia (XLH) is characterized by high serum fibroblast growth factor 23 (FGF23) levels, resulting in impaired 1,25-dihydroxyvitamin D3 (1,25D) production. Adults with XLH develop a painful mineralization of the tendon-bone attachment site (enthesis), called enthesopathy. Treatment of mice with XLH (Hyp) with 1,25D or an anti-FGF23 Ab, both of which increase 1,25D signaling, prevents enthesopathy. Therefore, we undertook studies to determine a role for impaired 1,25D action in enthesopathy development. Entheses from mice lacking vitamin D 1α-hydroxylase (Cyp27b1) (C-/-) had a similar enthesopathy to Hyp mice, whereas deletion of Fgf23 in Hyp mice prevented enthesopathy, and deletion of both Cyp27b1 and Fgf23 in mice resulted in enthesopathy, demonstrating that the impaired 1,25D action due to high FGF23 levels underlies XLH enthesopathy development. Like Hyp mice, enthesopathy in C-/- mice was observed by P14 and was prevented, but not reversed, with 1,25D therapy. Deletion of the vitamin D receptor in scleraxis-expressing cells resulted in enthesopathy, indicating that 1,25D acted directly on enthesis cells to regulate enthesopathy development. These results show that 1,25D signaling was necessary for normal postnatal enthesis maturation and played a role in XLH enthesopathy development. Optimizing 1,25D replacement in pediatric patients with XLH is necessary to prevent enthesopathy.

Background: Disorganization, presenting as impairment in thought, language and goal-directed behavior, is a core multidimensional syndrome of psychotic disorders. This study examined whether scalable computational measures of spoken language, and smartphone usage pattern, could serve as digital biomarkers of clinical disorganization symptoms., Methods: We examined in a longitudinal cohort of adults with a psychotic disorder, the associations between clinical measures of disorganization and computational measures of 1) spoken language derived from monthly, semi-structured, recorded clinical interviews; and 2) smartphone usage pattern derived via passive sensing technologies over the month prior to the interview. The language features included speech quantity, rate, fluency, and semantic regularity. The smartphone features included data missingness and phone usage during sleep time. The clinical measures consisted of the Positive and Negative Symptom Scale (PANSS) conceptual disorganization, difficulty in abstract thinking, and poor attention, items. Mixed linear regression analyses were used to estimate both fixed and random effects., Results: Greater severity of clinical symptoms of conceptual disorganization was associated with greater verbosity and more disfluent speech. Greater severity of conceptual disorganization was also associated with greater missingness of smartphone data, and greater smartphone usage during sleep time. While the observed associations were significant across the group, there was also significant variation between individuals., Conclusions: The findings suggest that digital measures of speech disfluency may serve as scalable markers of conceptual disorganization. The findings warrant further investigation into the use of recorded interviews and passive sensing technologies to assist in the characterization and tracking of psychotic illness., Competing Interests: Declaration of competing interest Justin T Baker has received consulting fees and equity from Mindstrong, Inc., as well as consultant fees from Verily Life Sciences, unrelated to the present work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question., Methods: Adolescent participants ( n = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2-3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage., Results: A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%)., Conclusions: To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief 'just-in-time' interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.

Objective: Cognitive-behavioral therapy (CBT) is considered a first-line treatment for obsessive-compulsive disorder (OCD) in pediatric and adult populations. Nevertheless, some patients show partial or null response. The identification of predictors of CBT response may improve clinical management of patients with OCD. Here, we aimed to identify structural magnetic resonance imaging (MRI) predictors of CBT response in 2 large series of children and adults with OCD from the worldwide ENIGMA-OCD consortium., Method: Data from 16 datasets from 13 international sites were included in the study. We assessed which variations in baseline cortical thickness, cortical surface area, and subcortical volume predicted response to CBT (percentage of baseline to post-treatment symptom reduction) in 2 samples totaling 168 children and adolescents (age range 5-17.5 years) and 318 adult patients (age range 18-63 years) with OCD. Mixed linear models with random intercept were used to account for potential cross-site differences in imaging values., Results: Significant results were observed exclusively in the pediatric sample. Right prefrontal cortex thickness was positively associated with the percentage of CBT response. In a post hoc analysis, we observed that the specific changes accounting for this relationship were a higher thickness of the frontal pole and the rostral middle frontal gyrus. We observed no significant effects of age, sex, or medication on our findings., Conclusion: Higher cortical thickness in specific right prefrontal cortex regions may be important for CBT response in children with OCD. Our findings suggest that the right prefrontal cortex plays a relevant role in the mechanisms of action of CBT in children., (Copyright © 2022 American Academy of Child and Adolescent Psychiatry. All rights reserved.)

Competing Interests: The authors declare no conflicts of interest.

Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented., (© 2023. Springer Nature America, Inc.)

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.)

The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors. More than two thousand MVC survivors were enrolled in the emergency department (ED) and completed a rotating battery of brief smartphone-based surveys over a 2-month period. Measurement models developed from survey item responses were used in latent growth curve/mixture modeling to characterize homogeneous symptom trajectories. Associations between individual trajectories and pre-trauma and peritraumatic characteristics and traditional outcomes were compared, along with associations within and between trajectories. APNS across all ten symptom domains were common in the first two months after trauma. Many risk factors and associations with high symptom burden trajectories were shared across domains. Both across and within traditional diagnostic boundaries, APNS trajectory intercepts, and slopes were substantially correlated. Across all domains, symptom severity in the immediate aftermath of trauma (trajectory intercepts) had the greatest influence on the outcome. An interactive data visualization tool was developed to allow readers to explore relationships of interest between individual characteristics, symptom trajectories, and traditional outcomes ( http://itr.med.unc.edu/aurora/parcoord/ ). Individuals presenting to the ED after MVC commonly experience a broad constellation of adverse posttraumatic symptoms. Many risk factors for diverse APNS are shared. Individuals diagnosed with a single traditional outcome should be screened for others. The utility of multidimensional categorizations that characterize individuals across traditional diagnostic domains should be explored., (© 2023. The Author(s).)

Introduction: Buprenorphine (BUP) is increasingly recognized and utilized as a valuable medication for the treatment of opioid use disorder. This article focuses on the problem of regulatory restrictions on access to buprenorphine products without naloxone (mono-product), involving patients in one geographic area, but which may represent a more general access problem in the United States., Design: In response to an audit by the Tennessee Board of Pharmacy, a pharmacy in northeast Tennessee designed a questionnaire to survey patient motivation for traveling long distances to fill their prescriptions for BUP, rather than buprenorphine/naloxone (BNx, combo-product), and to document their satisfaction with treatment with the mono-product., Results: Questionnaires were submitted by 194 patients, living in northeast Tennessee, southwest Virginia, and southeast Kentucky. Significant, intolerable, side effects were reported by all patients in the survey prescribed BNx, but because of legislative and regulatory restrictions in their respective states, they were unable to obtain BUP closer to home. Consequently, they were required to drive significant distances from their homes to fill their prescriptions, a median distance of 52 miles, and in some cases as far as 216 miles round trip. Intolerable reactions included severe headaches, nausea and vomiting, allergies, and severe dysphoria. All patients tolerated BUP and were clinically well maintained on the mono-product., Conclusion: Severe, intolerable reactions/side effects from the naloxone component of BNx are not uncommon, but legislative and regulatory restrictions on the mono-product prohibit providers and pharmacies in some states from prescribing and dispensing BUP. The participants in this qualitative study found it necessary to travel significant distances to obtain their prescribed medication, thereby potentially limiting access to this life-saving therapy.

Background: Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs., Methods: The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6-65) was divided into six successive 6-10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients., Results: Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6-13 and adults aged 50-65 with OCD taking SRIs (Cohen's d = -0.24 to -0.74). Volumes of putamen, pallidum (d = 0.18-0.40), and ventricles (d = 0.31-0.66) were greater in patients aged 20-29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = -0.27 to -1.31)., Conclusions: Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20-29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response., Competing Interests: Conflict of Interest Dr. Arnold reported holding the Alberta Innovates Translational Health Chair in Child and Youth Mental Health outside the submitted work. Prof. Mataix-Cols receives royalties for contributing articles to UpToDate, Wolters Kluwer Health and fees from Elsevier for editorial tasks (all unrelated to the submitted work). Dr. Narayanaswamy reported Government of India grants DST INSPIRE faculty grant IFA12-LSBM-26 and BT/06/IYBA/2012 outside the submitted work. Dr. Reddy reported Government of India grants SR/S0/HS/0016/2011 and BT/PR13334/Med/30/259/2009 outside the submitted work. Dr. Venkatasubramanian reported Wellcome-DBT India Alliance grant 500236/Z/11/Z outside the submitted work. Dr. Simpson reported Biohaven Research support for a clinical trial and royalties from UpToDate, Inc. and Cambridge University Press outside the submitted work. Dr. Soreni reported support from Lundbeck-IIT outside the submitted work. Dr. Walitza has received in the last 3 years royalties from Thieme Hogrefe, Kohlhammer, Springer, Beltz; Her work was supported in the last 3 years by the Swiss National Science Foundation (SNF), diff. EU FP7s, HSM Hochspezialisierte Medizin of the Kanton Zurich, Switzerland, Bfarm Germany, ZInEP, Hartmann Müller Stiftung, Olga Mayenfisch, Gertrud Thalmann Fonds (all unrelated to the submitted work). Dr. Thompson has received a research grant from Biogen, Inc., unrelated to the topic of this paper., (Copyright © 2022. Published by Elsevier B.V.)

Trauma-related pathological dissociation is characterized by disruptions in one's sense of self, perceptual, and affective experience. Dissociation and its trauma-related antecedents disproportionately impact women. However, despite the gender-related prevalence and high individual and societal costs, dissociation remains widely underappreciated in clinical practice. Moreover, dissociation lacks a synthesized neurobiological model across its subtypes. Leveraging the Triple Network Model of psychopathology, we sought to parse heterogeneity in dissociative experience by examining functional connectivity of three core neurocognitive networks as related to: (1) the dimensional dissociation subtypes of depersonalization/derealization and partially-dissociated intrusions; and, (2) the diagnostic category of dissociative identity disorder (DID). Participants were 91 women with and without: a history of childhood trauma, current posttraumatic stress disorder (PTSD), and varied levels of dissociation. Participants provided clinical data about dissociation, PTSD symptoms, childhood maltreatment history, and completed a resting-state functional magnetic resonance imaging scan. We used a novel statistical approach to assess both overlapping and unique contributions of dissociation subtypes. Covarying for age, childhood maltreatment and PTSD severity, we found dissociation was linked to hyperconnectivity within central executive (CEN), default (DN), and salience networks (SN), and decreased connectivity of CEN and SN with other areas. Moreover, we isolated unique connectivity markers associated with depersonalization/derealization in CEN and DN, to partially-dissociated intrusions in CEN, and to DID in CEN. This suggests dissociation subtypes have robust functional connectivity signatures that may serve as targets for PTSD/DID treatment engagement. Our findings underscore dissociation assessment as crucial in clinical care, in particular, to reduce gender-related health disparities., (© 2022. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT ), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP ), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2 (3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2 (3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2 (3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects., Competing Interests: Conflicts of Interest Dr. Abdallah has served as a consultant, speaker and/or on advisory boards for FSV7, Lundbeck, Psilocybin Labs, Genentech and Janssen, and editor of Chronic Stress for Sage Publications, Inc.; he has filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Davidson is the founder and president of, and serves on the board of directors for, the non-profit organization Healthy Minds Innovations, Inc. Dr. Jahanshad, Dr. Thompson and Dr. Ching received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the content of this manuscript. Dr. Krystal is a consultant for AbbVie, Inc., Amgen, Astellas Pharma Global Development, Inc., AstraZeneca Pharmaceuticals, Biomedisyn Corporation, Bristol-Myers Squibb, Eli Lilly and Company, Euthymics Bioscience, Inc., Neurovance, Inc., FORUM Pharmaceuticals, Janssen Research & Development, Lundbeck Research USA, Novartis Pharma AG, Otsuka America Pharmaceutical, Inc., Sage Therapeutics, Inc., Sunovion Pharmaceuticals, Inc., and Takeda Industries; is on the Scientific Advisory Board for Lohocla Research Corporation, Mnemosyne Pharmaceuticals, Inc., Naurex, Inc., and Pfizer; is a stockholder in Biohaven Pharmaceuticals; holds stock options in Mnemosyne Pharmaceuticals, Inc.; holds patents for Dopamine and Noradrenergic Reuptake Inhibitors in Treatment of Schizophrenia, US Patent No. 5,447,948 (issued September 5, 1995), and Glutamate Modulating Agents in the Treatment of Mental Disorders, U.S. Patent No. 8,778,979 (issued July 15, 2014); and filed a patent for Intranasal Administration of Ketamine to Treat Depression. U.S. Application No. 14/197,767 (filed on March 5, 2014); US application or Patent Cooperation Treaty international application No. 14/306,382 (filed on June 17, 2014); Filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Schmahl is a consultant for Boehringer Ingelheim International GmbH. Dr. Stein has received research grants and/or consultancy honoraria from Lundbeck and Sun. Dr. Lebois reports unpaid membership on the Scientific Committee for the International Society for the Study of Trauma and Dissociation (ISSTD) and spousal license payment for Vanderbilt IP from Acadia Pharmaceuticals unrelated to the topic of this manuscript. All other authors have no conflicts of interest to declare., (Copyright © 2022. Published by Elsevier Inc.)

Background: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA)., Methods: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2-85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis-Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2-148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks., Results: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks., Conclusions: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD., (Copyright © 2022 Society of Biological Psychiatry. All rights reserved.)

Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology., (© 2022. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Objectives: This study aimed to (1) determine the feasibility of collecting behavioral data from participants hospitalized with acute psychosis and (2) begin to evaluate the clinical information that can be computationally derived from such data., Methods: Behavioral data was collected across 99 sessions from 38 participants recruited from an inpatient psychiatric unit. Each session started with a semi-structured interview modeled on a typical "clinical rounds" encounter and included administration of the Positive and Negative Syndrome Scale (PANSS)., Analysis: We quantified aspects of participants' verbal behavior during the interview using lexical, coherence, and disfluency features. We then used two complementary approaches to explore our second objective. The first approach used predictive models to estimate participants' PANSS scores from their language features. Our second approach used inferential models to quantify the relationships between individual language features and symptom measures., Results: Our predictive models showed promise but lacked sufficient data to achieve clinically useful accuracy. Our inferential models identified statistically significant relationships between numerous language features and symptom domains., Conclusion: Our interview recording procedures were well-tolerated and produced adequate data for transcription and analysis. The results of our inferential modeling suggest that automatic measurements of expressive language contain signals highly relevant to the assessment of psychosis. These findings establish the potential of measuring language during a clinical interview in a naturalistic setting and generate specific hypotheses that can be tested in future studies. This, in turn, will lead to more accurate modeling and better understanding of the relationships between expressive language and psychosis., (Copyright © 2021. Published by Elsevier B.V.)

Objective: This study reports the feasibility, acceptability, and outcomes of a longitudinal, communication pilot intervention for patients with inherited cancer risk and their partners., Methods: Couples were recruited through social media and snowball sampling. At Time 1 and 2, 15 couples completed a structured discussion task about family building concerns and decisions, followed by an online post-discussion questionnaire and dyadic interview to provide feedback about the experience. Interview data were analyzed to assess outcomes using applied thematic analysis., Results: Participants reported the intervention created an opportunity for honest disclosure of family building goals and concerns. Participants also stated the structured nature of the discussion task was useful and did not cause additional stress. The intervention ultimately aided at-risk patients and their partners to realize their concordant concerns, discover/confront discordant concerns, and mutually agree upon next steps., Conclusions: This pilot intervention is feasible and acceptable. Furthermore, it offers a framework to facilitate effective communication about family building between patients with inherited cancer risk and their partners., Innovation: This intervention is the first conversational tool designed for at-risk patients and their partners., Competing Interests: The authors report no conflicts of interest., (© 2022 The Authors.)

Background: Psychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data-on location, movement, email and text communications, and social media-with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI)., Objective: The goal of the Ethics Checklist is to promote careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage and without consensus guidance, there are a range of reasonable choices researchers may make. However, the checklist is meant to make those ethical choices explicit, and to require researchers to give reasons for their decisions related to ELSI issues. The Ethics Checklist is primarily focused on procedural safeguards, such as consulting with experts outside the research group and documenting standard operating procedures for clearly actionable data (eg, expressed suicidality) within written research protocols., Methods: We explored the ELSI of digital health research in psychiatry, with a particular focus on what we label "deep phenotyping" psychiatric research, which combines the potential for virtually boundless data collection and increasingly sophisticated techniques to analyze those data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue., Results: Consistent with recent ELSI analyses, we find that existing ethical guidance and legal regulations are not sufficient for deep phenotyping research in psychiatry. At present, there are regulatory gaps, inconsistencies across research teams in ethics protocols, and a lack of consensus among institutional review boards on when and how deep phenotyping research should proceed. We thus developed a new instrument, an Ethics Checklist for Digital Health Research in Psychiatry ("the Ethics Checklist"). The Ethics Checklist is composed of 20 key questions, subdivided into 6 interrelated domains: (1) informed consent; (2) equity, diversity, and access; (3) privacy and partnerships; (4) regulation and law; (5) return of results; and (6) duty to warn and duty to report., Conclusions: Deep phenotyping research offers a vision for vastly more effective care for people with, or at risk for, psychiatric disease. The potential perils en route to realizing this vision are significant; however, and researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey., (©Francis X Shen, Benjamin C Silverman, Patrick Monette, Sara Kimble, Scott L Rauch, Justin T Baker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.02.2022.)

College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students' affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to study 49 first-year college students continuously throughout the academic year. Affect and sleep, academic, and social behavior showed substantial changes from school semesters to school breaks and from weekdays to weekends. Three student clusters were identified with behavioral and affective dissociations and varying levels of distress throughout the year. While academics were a common stressor for all, the cluster with highest distress stood out by frequent report of social stress. Moreover, the frequency of reporting social, but not academic, stress predicted subsequent clinical symptoms. Two years later, during the COVID-19 pandemic, the first-year cluster with highest distress again stood out by frequent social stress and elevated clinical symptoms. Focus on sustained interpersonal stress, relative to academic stress, might be especially helpful to identify students at heightened risk for psychopathology., (© 2022. The Author(s).)

Background: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD., Method: Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site., Results: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages., Discussion: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan., (© 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Background: Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep., Objective: This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest., Methods: The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each., Results: Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data., Conclusions: We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments., (©Habiballah Rahimi-Eichi, Garth Coombs III, Constanza M Vidal Bustamante, Jukka-Pekka Onnela, Justin T Baker, Randy L Buckner. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 06.10.2021.)

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care., Competing Interests: SH was employed by company T. J. Watson IBM Research Laboratory. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Barron, Baker, Budde, Bzdok, Eickhoff, Friston, Fox, Geha, Heisig, Holmes, Onnela, Powers, Silbersweig and Krystal.)