Precision Psychiatry

Terahertz biotechnology has been increasingly applied in various biomedical fields and has especially shown great potential for application in brain sciences. In this article, we review the development of terahertz biotechnology and its applications in the field of neuropsychiatry. Available evidence indicates promising prospects for the use of terahertz spectroscopy and terahertz imaging techniques in the diagnosis of amyloid disease, cerebrovascular disease, glioma, psychiatric disease, traumatic brain injury, and myelin deficit. In vitro and animal experiments have also demonstrated the potential therapeutic value of terahertz technology in some neuropsychiatric diseases. Although the precise underlying mechanism of the interactions between terahertz electromagnetic waves and the biosystem is not yet fully understood, the research progress in this field shows great potential for biomedical noninvasive diagnostic and therapeutic applications. However, the biosafety of terahertz radiation requires further exploration regarding its two-sided efficacy in practical applications. This review demonstrates that terahertz biotechnology has the potential to be a promising method in the field of neuropsychiatry based on its unique advantages.

The depression response trajectory after a course of repetitive transcranial magnetic stimulation(rTMS) remains understudied. We searched for blinded randomized controlled trials(RCTs) that examined conventional rTMS over left dorsolateral prefrontal cortex(DLPFC) for major depressive episodes(MDE). The effect size was calculated as the difference in depression improvement, between active and sham rTMS. We conducted a random-effects dose-response meta-analysis to model the response trajectory from the beginning of rTMS to the post-treatment follow-up phase. The area under curve (AUC) of the first 8-week response trajectory was calculated to compare antidepressant efficacy between different rTMS protocols. We included 40 RCTs(n = 2012). The best-fitting trajectory model exhibited a logarithmic curve(X=17.7, P < 0.001), showing a gradual ascent with tapering off around the 3-4th week mark and maintaining until week 16. The maximum effect size was 6.1(95 %CI: 1.25-10.96) at week 16. The subgroup analyses showed distinct trajectories across different rTMS protocols. Besides, the comparisons of AUC showed that conventional rTMS protocols with more pulse/session group or more total pulses were associated with greater efficacy than those with fewer pulse/session or fewer total pulses, respectively. A course of conventional left DLPFC rTMS could lead to both acute antidepressant effects and sustained after-effects, which were modeled by different rTMS protocols in MDE.

Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation.

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

To reduce child mental health disparities, it is imperative to improve the precision of targets and to expand our vision of social determinants of health as modifiable. Advancements in clinical research informatics and please state accurate measurement of child mental health service use and quality. Participatory action research promotes representation of underserved groups in informatics research and practice and may improve the effectiveness of interventions by informing research across all stages, including the identification of key variables, risk and protective factors, and data interpretation.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

Greater exposure to stressors over the life course is believed to promote striatum-dependent over hippocampus-dependent learning and memory processes under stressful conditions. However, little research in this context has actually assessed lifetime stressor exposure and, moreover, it remains unknown whether greater cumulative lifetime stressor exposure exerts comparable effects on striatum-dependent learning and hippocampus-dependent learning in non-stressful contexts. To investigate this issue, we used the Stress and Adversity Inventory for Adults (Adult STRAIN) and Multicued Search Task to investigate the relation between cumulative lifetime stressor exposure and striatum-dependent stimulus-response learning and hippocampus-dependent contextual learning under non-stressful conditions among healthcare professionals (N = 205; 157 females, 48 males; Age: M = 34.23, SD 9.3, range 20-59 years). Individuals with moderate, but not low, cumulative lifetime stressor exposure exhibited impaired learning for stimulus-response associations. In contrast, learning for context associations was unrelated to participants' lifetime stressor exposure profiles. These results thus provide first evidence that cumulative lifetime stressor exposure may have negative consequences on human striatum-dependent stimulus-response learning under non-stressful environmental conditions.

This is the second part of the Brazilian S20 mental health report. The mental health working group is dedicated to leveraging scientific insights to foster innovation and propose actionable recommendations for implementation in Brazil and participating countries. In addressing the heightened mental health challenges in a post-pandemic world, strategies should encompass several key elements. This second part of the S20 Brazilian Mental Health Report will delve into some of these elements, including: the impact of climate change on mental health, the influence of environmental factors on neurodevelopmental disorders, the intersection of serious mental illness and precision psychiatry, the co-occurrence of physical and mental disorders, advancements in biomarkers for mental disorders, the utilization of digital health in mental healthcare, the implementation of interventional psychiatry, and the design of innovative mental health systems integrating principles of innovation and human rights. Reassessing the treatment settings for psychiatric patients within general hospitals, where their mental health and physical needs are addressed should be prioritized in mental health policy. As the S20 countries prepare for the future, we need principles that stand to advance innovation, uphold human rights, and strive for the highest standards in mental health care.

Treatment-resistant depression (TRD) affects approximately 2.8 million people in the U.S. with estimated annual healthcare costs of $43.8 billion. Deep brain stimulation (DBS) is currently an investigational intervention for TRD. We used a decision-analytic model to compare cost-effectiveness of DBS to treatment-as-usual (TAU) for TRD. Because this therapy is not FDA approved or in common use, our goal was to establish an effectiveness threshold that trials would need to demonstrate for this therapy to be cost-effective. Remission and complication rates were determined from review of relevant studies. We used published utility scores to reflect quality of life after treatment. Medicare reimbursement rates and health economics data were used to approximate costs. We performed Monte Carlo (MC) simulations and probabilistic sensitivity analyses to estimate incremental cost-effectiveness ratios (ICER; USD/quality-adjusted life year [QALY]) at a 5-year time horizon. Cost-effectiveness was defined using willingness-to-pay (WTP) thresholds of $100,000/QALY and $50,000/QALY for moderate and definitive cost-effectiveness, respectively. We included 274 patients across 16 studies from 2009-2021 who underwent DBS for TRD and had ≥12 months follow-up in our model inputs. From a healthcare sector perspective, DBS using non-rechargeable devices (DBS-pc) would require 55% and 85% remission, while DBS using rechargeable devices (DBS-rc) would require 11% and 19% remission for moderate and definitive cost-effectiveness, respectively. From a societal perspective, DBS-pc would require 35% and 46% remission, while DBS-rc would require 8% and 10% remission for moderate and definitive cost-effectiveness, respectively. DBS-pc will unlikely be cost-effective at any time horizon without transformative improvements in battery longevity. If remission rates ≥8-19% are achieved, DBS-rc will likely be more cost-effective than TAU for TRD, with further increasing cost-effectiveness beyond 5 years.

Aging is a complex and natural process. The physiological decline related to aging is accompanied by a slowdown in cognitive processes, which begins shortly after individuals reach maturity. These changes have been sometimes interpreted as a compensatory sign and others as a fingerprint of deterioration.

Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

The use of evidence-based standardized outcome measures is increasingly recognized as key to guiding clinical decision-making in mental health. Implementation of these measures into clinical practice has been hampered by lack of clarity on what to measure and how to do this in a reliable and standardized way.

The brain undergoes extensive development during late childhood and early adolescence. Cortical thinning is a prominent feature of this development, and some researchers have suggested that differences in cortical thickness may be related to internalizing symptoms, which typically increase during the same period. However, research has yielded inconclusive results. We utilized a new method that estimates the combined effect of individual differences in vertex-wise cortical thickness on internalizing symptoms. This approach allows for many small effects to be distributed across the cortex and avoids the necessity of correcting for multiple tests. Using a sample of 8763 children aged 8.9 to 11.1 from the ABCD study, we decomposed the total variation in caregiver-reported internalizing symptoms into differences in cortical thickness, additive genetics, and shared family environmental factors and unique environmental factors. Our results indicated that individual differences in cortical thickness accounted for less than 0.5% of the variation in internalizing symptoms. In contrast, the analysis revealed a substantial effect of additive genetics and family environmental factors on the different components of internalizing symptoms, ranging from 06% to 48% and from 0% to 34%, respectively. Overall, while this study found a minimal association between cortical thickness and internalizing symptoms, additive genetics, and familial environmental factors appear to be of importance for describing differences in internalizing symptoms in late childhood. RESEARCH HIGHLIGHTS: We utilized a new method for modelling the total contribution of vertex-wise individual differences in cortical thickness to internalizing symptoms in late childhood. The total contribution of individual differences in cortical thickness accounted for <0.5% of the variance in internalizing symptoms. Additive genetics and shared family environmental variation accounted for 17% and 34% of the variance in internalizing symptoms, respectively. Our results suggest that cortical thickness is not an important indicator for internalizing symptoms in childhood, whereas genetic and environmental differences have a substantial impact.

Callous-unemotional (CU) traits are associated with interpersonal difficulties and risk for severe conduct problems (CP). The ability to communicate thoughts and feelings is critical to social success, with language a promising treatment target. However, no prior studies have examined objective linguistic correlates of childhood CU traits in early childhood, which could give insight into underlying risk mechanisms and novel target treatments.

The COVID-19 pandemic has been related to heightened anxiety in adolescents. The basolateral amygdala (BLA) and the nucleus accumbens (NAcc) have been implicated in response to stress and may contribute to anxiety. The role of threat- and reward-related circuitry in adolescent anxiety during the COVID-19 pandemic, however, is not clear. Ninety-nine adolescents underwent resting-state fMRI approximately one year before the pandemic. Following shelter-in-place orders, adolescents reported their perceived stress and, one month later, their anxiety. Generalized multivariate analyses identified BLA and NAcc seed-based whole-brain connectivity maps with perceived stress. We examined associations between seed-based connectivity in significant clusters and subsequent anxiety. Perceived stress was associated with bilateral BLA and NAcc connectivity across distributed clusters that included prefrontal, limbic, temporal, and cerebellar regions. Several NAcc connectivity clusters located in ventromedial prefrontal, parahippocampal, and temporal cortices were positively associated with anxiety; whereas NAcc connectivity with the inferior frontal gyrus was negatively associated. BLA connectivity was not associated with anxiety. These results underscore the integrative role of the NAcc in responding to acute stressors and its relation to anxiety in adolescents. Elucidating the involvement of subcortical-cortical circuitry in adolescents' capacity to respond adaptively to environmental challenges can inform treatment approaches for anxiety-related disorders.

The cautious optimism following recent anti-amyloid therapeutic trials for Alzheimer's disease (AD) provides a glimmer of hope after years of disappointment. Although these encouraging results represent discernible progress, they also highlight the need to enhance further the still modest clinical efficacy of current disease-modifying immunotherapies. Here, we highlight crucial milestones essential for advancing precision medicine in AD. These include reevaluating the choice of therapeutic targets by considering the key role of both central neuroinflammation and peripheral immunity in disease pathogenesis, refining patient stratification by further defining the inflammatory component within the forthcoming ATN(I) (amyloid, tau and neurodegeneration (and inflammation)) classification of AD biomarkers and defining more accurate clinical outcomes and prognostic biomarkers that better reflect disease heterogeneity. Next-generation immunotherapies will need to go beyond the current antibody-only approach by simultaneously targeting pathological proteins together with innate neuroinflammation and/or peripheral-central immune crosstalk. Such innovative immunomodulatory combination therapy approaches should be evaluated in appropriately redesigned clinical therapeutic trials, which must carefully integrate the neuroimmune component.

Although Coronavirus disease 2019 (COVID-19) is primarily a respiratory infectious disease, it has also been associated with a wide range of other clinical manifestations. It is widely accepted in the scientific community that many patients after recovery continue to experience COVID-19-related symptoms, including cognitive impairment. The aim of this systematic review was to investigate the cognitive profile of patients with long-COVID syndrome.

Recent microbiome research has incorporated a higher number of samples through more participants in a study, longitudinal studies, and metanalysis between studies. Physical limitations in a sequencing machine can result in samples spread across sequencing runs. Here we present the results of sequencing nearly 1000 16S rRNA gene sequences in fecal (stabilized and swab) and oral (swab) samples from multiple human microbiome studies and positive controls that were conducted with identical standard operating procedures. Sequencing was performed in the same center across 18 different runs. The simplified mock community showed limitations in accuracy, while precision (e.g., technical variation) was robust for the mock community and actual human positive control samples. Technical variation was the lowest for stabilized fecal samples, followed by fecal swab samples, and then oral swab samples. The order of technical variation stability was inverse of DNA concentrations (e.g., highest in stabilized fecal samples), highlighting the importance of DNA concentration in reproducibility and urging caution when analyzing low biomass samples. Coefficients of variation at the genus level also followed the same trend for lower variation with higher DNA concentrations. Technical variation across both sample types and the two human sampling locations was significantly less than the observed biological variation. Overall, this research providing comparisons between technical and biological variation, highlights the importance of using positive controls, and provides semi-quantified data to better understand variation introduced by sequencing runs. KEY POINTS: • Mock community and positive control accuracy were lower than precision. • Samples with lower DNA concentration had increased technical variation across sequencing runs. • Biological variation was significantly higher than technical variation due to sequencing runs.

We aimed to compare the ability of diffusion tensor imaging and multi-compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy-six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid-attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate-severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R-squared value of .57, significantly outperforming the R-squared value of .24 for FA (p-value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS-related impairments.

GNE Myopathy is a unique recessive neuromuscular disorder characterized by adult-onset, slowly progressive distal and proximal muscle weakness, caused by mutations in the GNE gene which is a key enzyme in the biosynthesis of sialic acid. To date, the precise pathophysiology of the disease is not well understood and no reliable animal model is available. Gne KO is embryonically lethal in mice.

To describe the pattern of the prevalence of mental health problems during the first year of the COVID-19 pandemic and examine the impact of containment measures on these trends.

Acute stressors tend to shift preferences toward comfort foods, yet they do not ubiquitously increase the amount of food consumed. Moreover, although many individuals eat more under stress, others eat less or show no change. Although the precise mechanisms explaining this variability in stress-related eating are unknown, they may be driven by individual differences in the rewarding effects of comfort eating, which are enhanced by greater lifetime stressor exposure. To investigate this possibility, we examined whether differences in lifetime stressor exposure predicted reductions in negative affect following snacking (i.e., negative reinforcement) and if this effect was specific to stress-related snacking or snacking in general. Participants were 26 women (23% non-White) between 20 and 45 years old (M = 31), with a mean body mass index of 26, who completed three laboratory visits. Participants completed an assessment of lifetime stressor exposure (i.e., STRAIN) on the first visit and, on two subsequent laboratory visits in counterbalanced order, were given snacks after an acute social stress task (i.e., TSST) or rest period. Greater lifetime stressor exposure was related to greater post-ingestive decreases in negative affect following the acute social stressor but not following the rest period. If stress-related eating is more comforting for women with greater lifetime stressors and contributes to a stronger stress-eating association, then this may inform obesity-related clinical treatments that target behaviors and cognitions related to reward-based learning.

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

In the nervous system, G protein-coupled receptors (GPCRs) control neuronal excitability, synaptic transmission, synaptic plasticity, and, ultimately, behavior through spatiotemporally precise initiation of a variety of signaling pathways. However, despite their critical importance, there is incomplete understanding of how these receptors are regulated to tune their signaling to specific neurophysiological contexts. A deeper mechanistic picture of neuromodulatory GPCR function is needed to fully decipher their biological roles and effectively harness them for the treatment of neurological and psychiatric disorders. In this review, we highlight recent progress in identifying novel modes of regulation of neuromodulatory GPCRs, including G protein- and receptor-targeting mechanisms, receptor-receptor crosstalk, and unique features that emerge in the context of chemical synapses. These emerging principles of neuromodulatory GPCR tuning raise critical questions to be tackled at the molecular, cellular, synaptic, and neural circuit levels in the future.

Early palliative care, palliative care integrated with oncology care early in the course of illness, has myriad benefits for patients and their caregivers, including improved quality of life, reduced physical and psychological symptom burden, enhanced prognostic awareness, and reduced health care utilization at the end of life. Although ASCO and others recommend early palliative care for all patients with advanced cancer, widespread implementation of early palliative care has not been realized because of barriers such as insufficient reimbursement and a palliative care workforce shortage. Investigators have recently tested several implementation strategies to overcome these barriers, including triggers for palliative care consultations, telehealth delivery, navigator-delivered interventions, and primary palliative care interventions. More research is needed to identify mechanisms to distribute palliative care optimally and equitably. Simultaneously, the transformation of the oncology treatment landscape has led to shifts in the supportive care needs of patients and caregivers, who may experience longer, uncertain trajectories of cancer. Now, palliative care also plays a clear role in the care of patients with hematologic malignancies and may be beneficial for patients undergoing phase I clinical trials and their caregivers. Further research and clinical guidance regarding how to balance the risks and benefits of opioid therapy and safely manage cancer-related pain across this wide range of settings are urgently needed. The strengths of early palliative care in supporting patients' and caregivers' coping and centering decisions on their goals and values remain valuable in the care of patients receiving cutting-edge personalized cancer care.

The recent acceleration of commercial, private, and multi-national spaceflight has created an unprecedented level of activity in low Earth orbit (LEO), concomitant with the highest-ever number of crewed missions entering space and preparations for exploration-class (>1 year) missions. Such rapid advancement into space from many new companies, countries, and space-related entities has enabled a"Second Space Age." This new era is also poised to leverage, for the first time, modern tools and methods of molecular biology and precision medicine, thus enabling precision aerospace medicine for the crews. The applications of these biomedical technologies and algorithms are diverse, encompassing multi-omic, single-cell, and spatial biology tools to investigate human and microbial responses to spaceflight. Additionally, they extend to the development of new imaging techniques, real-time cognitive assessments, physiological monitoring, and personalized risk profiles tailored for astronauts. Furthermore, these technologies enable advancements in pharmacogenomics (PGx), as well as the identification of novel spaceflight biomarkers and the development of corresponding countermeasures. In this review, we highlight some of the recent biomedical research from the National Aeronautics and Space Administration (NASA), Japan Aerospace Exploration Agency (JAXA), European Space Agency (ESA), and other space agencies, and also detail the commercial spaceflight sector's (e.g. SpaceX, Blue Origin, Axiom, Sierra Space) entrance into aerospace medicine and space biology, the first aerospace medicine biobank, and the myriad upcoming missions that will utilize these tools to ensure a permanent human presence beyond LEO, venturing out to other planets and moons.

Harmonized tools are essential for reliable data sharing and accurate identification of relevant factors in mental health research. The primary objective of this study was to create a harmonized questionnaire to collect demographic, clinical and behavioral data in diverse clinical trials in adult psychiatry.

Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype-guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6-months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient-Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ-8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug-drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow-up will be complete in April of 2024.

Immunity alterations have been observed in bipolar disorder (BD). However, whether serum positivity of antibodies to Toxoplasma gondii (T gondii), rubella, and cytomegalovirus (CMV) shared clinical relevance with BD, remains controversial. This study aimed to investigate this association.

Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.

Very low-field MR has emerged as a promising complementary device to high-field MRI scanners, offering several advantages. One of the key benefits is that very low-field scanners are generally more portable and affordable to purchase and maintain, making them an attractive option for medical facilities looking to reduce costs. Very low-field MRI systems also have lower RF power deposition, making them safer and less likely to cause tissue heating or other safety concerns. They are also simpler to maintain, as they do not require cooling agents such as liquid helium. However, these portable MR scanners are impacted by temperature, lower magnetic field strength, and inhomogeneity, resulting in images with lower signal-to-noise ratio (SNR) and higher geometric distortions. It is essential to investigate and tabulate the variations in these parameters to establish bounds so that subsequent in vivo studies and deployment of these portable systems can be well informed.

The etiopathogenesis of late-onset Alzheimer's disease (AD) is increasingly recognized as the result of the combination of the aging process, toxic proteins, brain dysmetabolism, and genetic risks. Although the role of mitochondrial dysfunction in the pathogenesis of AD has been well-appreciated, the interaction between mitochondrial function and genetic variability in promoting dementia is still poorly understood. In this study, by tissue-specific transcriptome-wide association study (TWAS) and further meta-analysis, we examined the genetic association between mitochondrial solute carrier family (SLC25) genes and AD in three independent cohorts and identified three AD-susceptibility genes, including SLC25A10, SLC25A17, and SLC25A22. Integrative analysis using neuroimaging data and hippocampal TWAS-predicted gene expression of the three susceptibility genes showed an inverse correlation of SLC25A22 with hippocampal atrophy rate in AD patients, which outweighed the impacts of sex, age, and apolipoprotein E4 (ApoE4). Furthermore, SLC25A22 downregulation demonstrated an association with AD onset, as compared with the other two transcriptome-wide significant genes. Pathway and network analysis related hippocampal SLC25A22 downregulation to defects in neuronal function and development, echoing the enrichment of SLC25A22 expression in human glutamatergic neurons. The most parsimonious interpretation of the results is that we have identified AD-susceptibility genes in the SLC25 family through the prediction of hippocampal gene expression. Moreover, our findings mechanistically yield insight into the mitochondrial cascade hypothesis of AD and pave the way for the future development of diagnostic tools for the early prevention of AD from a perspective of precision medicine by targeting the mitochondria-related genes.

Alzheimer's disease (AD) is the most common cause of dementia, and the gradual deterioration of brain function eventually leads to death. Almost all AD patients suffer from neuropsychiatric symptoms (NPS), the emergence of which correlates with dysfunctional serotonergic systems. Our aim is to generate hindbrain organoids containing serotonergic neurons using human induced Pluripotent Stem Cells (iPSCs). Work presented here is laying the groundwork for the application of hindbrain organoids to evaluate individual differences in disease progression, NPS development, and pharmacological treatment response. Human peripheral blood mononuclear cells (PBMCs) from healthy volunteers (n = 3), an AD patient without NPS (n = 1), and AD patients with NPS (n = 2) were reprogrammed into iPSCs and subsequently differentiated into hindbrain organoids. The presence of serotonergic neurons was confirmed by quantitative reverse transcription PCR, flow cytometry, immunocytochemistry, and detection of released serotonin (5-HT). We successfully reprogrammed PBMCs into 6 iPSC lines, and subsequently generated hindbrain organoids from 6 individuals to study inter-patient variability using a precision medicine approach. To assess patient-specific treatment effects, organoids were treated with different concentrations of escitalopram oxalate, commonly prescribed for NPS. Changes in 5-HT levels before and after treatment with escitalopram were dose-dependent and variable across patients. Organoids from different people responded differently to the application of escitalopram in vitro. We propose that this 3D platform might be effectively used for drug screening purposes to predict patients with NPS most likely to respond to treatment in vivo and to understand the heterogeneity of treatment responses.

A sport-related concussion (SRC) is a common injury that affects multiple clinical domains such as cognition, balance, and nonspecific neurobehavioral symptoms. Although multidimensional clinical assessments of concussion are widely accepted, there remain limited empirical data on the nature and clinical utility of distinct clinical profiles identified by multimodal assessments.

The present study was to investigate prevalence of suicidal ideation and its associations with biological and environmental factors in adolescents with different genotypes of rs12342 at adiponectin receptor 2 gene (ADIPOR2).

Automatic transdiagnostic risk calculators can improve detection of individuals at risk of psychosis. However, they rely on a single point in time assessment and can be refined with dynamic modelling techniques that account for changes in risk over time.

The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design.

Disrupted sleep and rest-activity pattern are common clinical features in depressed individuals. This meta-analysis compared sleep and circadian rest-activity rhythms in people with major depressive disorder (MDD) or depressive symptoms and healthy controls.

The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability.Electroencephalography (EEG) recorded during TMS registers fast neural dynamics-unfortunately, at the cost of anatomical precision. We employed analytic Common Spatial Patterns technique to derive excitability-related cortical activity from pre-TMS EEG signals while overcoming spatial specificity issues.High corticospinal excitability was predicted by alpha-band activity, localized adjacent to the stimulated left motor cortex, and suggesting a travelling wave-like phenomenon towards frontal regions. Low excitability was predicted by alpha-band activity localized in the medial parietal-occipital and frontal cortical regions.We established a data-driven approach for uncovering network-level neural activity that modulates TMS effects. It requires no prior anatomical assumptions, while being physiologically interpretable, and can be employed in both exploratory investigation and brain state-dependent stimulation.

NMDA receptors (NMDAR) may be crucial to working memory (WM). Computational models predict that they sustain neural firing and produce associative memory, which may underpin maintaining and binding information respectively. We test this in patients with antibodies to NMDAR (n=10, female) and compare them with healthy control participants (n=55, 20 male, 35 female). Patients were tested after recovery with a task that separates two aspects of WM: sustaining attention and feature binding. Participants had to remember two colored arrows. Then attention was directed to one of them. After a variable delay, they reported the direction of either the same arrow (congruent cue), or of the other arrow (incongruent cue). We asked how congruency affected recall precision and measured types of error. Patients had difficulty in both sustaining attention to an item over time and feature binding. Controls were less precise after longer delays and incongruent cues. In contrast, patients did not benefit from congruent cues at longer delays (Group x Congruency [long condition], p=0.041), indicating they could not sustain attention. Additionally, patients reported the wrong item (misbinding errors) more than controls after congruent cues (Group x Delay [congruent condition], main effect of group, p=<0.001). Our results suggest NMDARs are critical for both maintaining attention and feature binding. Computational theories suggest NMDA receptors (NMDARs) are critical for actively maintaining information, while other theories propose they allow us to associate or "bind" objects features together. This is the first causal test in humans of the role of NMDARs in actively maintaining attention in working memory and feature binding. We find patients have difficulty with both these processes in support of computational models. Notably, we demonstrate that patients with NMDA receptor-antibody encephalitis are an ideal model condition to study roles of receptors in human cognition. Secondly, few studies follow these patients long after treatment. Our findings demonstrate a specific long-term neuropsychological deficit, previously unreported to our knowledge, that highlights the need for greater focus on neurocognitive rehabilitation with these patients.

Accumulating evidence supports the idea that inflammation may contribute to the pathophysiology of major depressive disorder (MDD). Duloxetine, a serotonin-norepinephrine reuptake inhibitor, exhibits anti-inflammatory effects both in vitro and in vivo. In this study, we investigated the impact of duloxetine on changes in serum proinflammatory cytokine levels among individuals diagnosed with MDD.

The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices.

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

Investigation of the heterogeneity of the treatment effect (HTE) might guide the optimization of cognitive behavioral therapy for insomnia (CBT-I). This study examined HTE in CBT-I thereby analyzing if treatment setting, control group, different CBT-I components, and patient characteristics drive HTE. Randomized controlled trials investigating CBT-I were included. Bayesian random effect meta-regressions were specified to examine variances between the intervention and control groups regarding post-treatment symptom severity. Subgroup analyses analyzing treatment setting and control groups and covariate analysis analyzing treatment components and patient characteristics were specified. No significant HTE in CBT-I was found for the overall data set, settings and control groups. The covariate analyses yielded significant results for baseline severity and the treatment component relaxation therapy. Thus, this study identified potential causes for HTE in CBT-I for the first time, showing that it might be worthwhile to further examine possibilities for precision medicine in CBT-I.

Normal cells coordinate proliferation and differentiation by precise tuning of gene expression based on the dynamic shifts of the epigenome throughout the developmental timeline. Although non-mutational epigenetic reprogramming is an emerging hallmark of cancer, the epigenomic shifts that occur during the transition from normal to malignant cells remain elusive. Here, we capture the epigenomic changes that occur during tumorigenesis in a prototypic embryonal brain tumor, medulloblastoma. By comparing the epigenomes of the different stages of transforming cells in mice, we identify nuclear factor I family of transcription factors, known to be cell fate determinants in development, as oncogenic regulators in the epigenomes of precancerous and cancerous cells. Furthermore, genetic and pharmacological inhibition of NFIB validated a crucial role of this transcription factor by disrupting the cancer epigenome in medulloblastoma. Thus, this study exemplifies how epigenomic changes contribute to tumorigenesis via non-mutational mechanisms involving developmental transcription factors.

Schizophrenia (SZ) and bipolar disorders (BP) are chronic and severe neuropsychiatric diseases. These disorders are tightly related to immune deregulations. In the current study, we intended to replicate the previously reported involvement of the soluble HLA-E isoforms (sHLA-E) in the risk of developing the two conditions along with disease severity in a Tunisian population group.

As a vulnerable group, minors require special protection in studies. For this reason, researchers are often reluctant to initiate studies, and ethics committees are reluctant to authorize such studies. This often excludes minors from participating in clinical studies. This exclusion can lead to researchers and clinicians receiving only incomplete data or having to rely on adult-based findings in the treatment of minors. Using the example of the study "Computer-Assisted Risk Evaluation in the Early Detection of Psychotic Disorders" (CARE), which was conducted as an 'other clinical investigation' according to the Medical Device Regulation, we present a line of argumentation for the inclusion of minors which weighs the ethical principles of nonmaleficence (especially regarding possible stigmatization), beneficence, autonomy, and fairness. We show the necessity of including minors based on the development-specific differences in diagnostics and early intervention. Further, we present specific protective measures. This argumentation can also be transferred to other disorders with the onset in childhood and adolescence and thus help to avoid excluding minors from appropriate evidence-based care because of insufficient studies.

Burnout and secondary traumatic stress (STS), also referred to as compassion fatigue, are undeniable negative consequences experienced by healthcare professionals when working with patients. As frontline healthcare professionals are essential to communities, it is crucial to understand their mental health and how they cope with negative psychological responses. This study investigated the relationships between burnout, STS, compassion satisfaction, dispositional empathy, and stress management among Japanese healthcare professionals and students taking care of patients in clinical practice. The participants were 506 Japanese healthcare professionals and students (doctors, nurses, medical students, and nursing students) affiliated with Japanese Ministry of Defense Hospitals. The data were collected from March 2020 to May 2021. We assessed burnout, STS, and compassion satisfaction using the Professional Quality of Life Scale, dispositional empathy using the Interpersonal Reactivity Index, and coping with stress using the Coping Orientation to Problems Experienced Inventory (Brief-COPE). Exploratory factor analysis of the Brief-COPE yielded three factors: active coping; support-seeking; and indirect coping. Personal distress, a self-oriented emotional empathy index, was related to higher burnout and STS scores and lower compassion satisfaction. Empathic concern, an other-oriented emotional empathy index, was associated with lower burnout and higher compassion satisfaction. Active coping strategies were associated with lower burnout and higher compassion satisfaction, whereas indirect coping strategies were associated with higher burnout and STS scores. In a comparison of empathy in professional categories, nurses presented higher personal distress than nursing students, and medical doctors showed lower fantasy tendencies than medical students. These results imply the complex relationships between empathy, coping strategies, and psychological responses among healthcare professionals. Further longitudinal study is needed to explore these complex relationships and to develop more precise and effective psycho-educational interventions to prevent burnout and STS.

Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables.

Medical trainee well-being is often met with generalized solutions that overlook substantial individual variations in mental health predisposition and stress reactivity. Precision medicine leverages individual environmental, genetic, and lifestyle factors to tailor preventive and therapeutic interventions. In addition, an exclusive focus on clinical mental illness tends to disregard the importance of supporting the positive aspects of medical trainee well-being. We introduce a novel precision well-being framework for medical education that is built on a comprehensive and individualized view of mental health, combining measures from mental health and positive psychology in a unified, data-driven framework. Unsupervised machine learning techniques commonly used in precision medicine were applied to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 US medical students, clusters were formulated based on recognized metrics for depression, anxiety, and flourishing. The analysis identified three distinct clusters. Membership in the 'Healthy Flourishers' well-being phenotype was associated with no signs of anxiety or depression while simultaneously reporting high levels of flourishing. Students in the 'Getting By' cluster reported mild anxiety and depression and diminished flourishing. Membership in the 'At-Risk' cluster was associated with high anxiety and depression, languishing, and increased suicidality. Nearly half (49%) of the medical students surveyed were classified as 'Healthy Flourishers', whereas 36% were grouped into the 'Getting-By' cluster and 15% were identified as 'At-Risk'. Findings show that a substantial portion of medical students report diminished well-being during their studies, with a significant number struggling with mental health challenges. This novel precision well-being framework represents an integrated empirical model that classifies individual medical students into distinct and meaningful well-being phenotypes based on their holistic mental health. This approach has direct applicability to student support and can be used to evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.

Mobile health (mHealth) apps have the potential to enhance health care service delivery. However, concerns regarding patients' confidentiality, privacy, and security consistently affect the adoption of mHealth apps. Despite this, no review has comprehensively summarized the findings of studies on this subject matter.

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome ("one-step," "bias-adjusted three-step," "modal class assignment," "non-inclusive pseudo class draws," and "inclusive pseudo class draws") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.

Suicide is the second leading cause of death in U.S. adolescents and young adults, and generally associated with a psychiatric disorder. Suicidal behavior has a complex etiology and pathogenesis. Moderate heritability suggests genetic causes. Associations between childhood and recent life adversity indicate contributions from epigenetic factors. Genomic contributions to suicide pathogenesis remain largely unknown. This paper is based on a workshop held to design strategies to identify molecular drivers of suicide neurobiology that would be putative new treatment targets. The panel determined that, while bulk tissue studies provide comprehensive information, single-nucleus approaches identifying cell-type specific changes are needed. While single nuclei techniques lack information on cytoplasm, processes, spines, and synapses, spatial multiomic technologies on intact tissue detect cell alterations specific to brain tissue layers and subregions. Because suicide has genetic and environmental drivers, multiomic approaches combining cell-type specific epigenome, transcriptome, and proteome provide a more complete picture of pathogenesis. To determine the direction of effect of suicide risk gene variants on RNA and protein expression, and how these interact with epigenetic marks, single nuclei and spatial multiomics quantitative trait loci maps should be integrated with whole genome sequencing and genome-wide association databases. The workshop concluded with the recommendation for the formation of an international suicide biology consortium that will bring together brain banks and investigators with expertise in cutting-edge omics technologies to delineate the biology of suicide and identify novel potential treatment targets to be tested in cellular and animal models for drug and biomarkers discovery, to guide suicide prevention.

Depression accounts for a high proportion of neuropsychiatric disorders and is associated with abnormal states of neurons in specific brain regions. Microglia play a pivotal role in the inflammatory state during depression development; however, the exact mechanism underlying chronic mood states remains unknown. Thus, the present study aimed to determine whether microRNAs (miRNAs) alleviate stress-induced depression-like behavior in mice by regulating the expression levels of their target genes, explore the role of neuroinflammation induced by microglial activation in the pathogenesis and progression of depression, and determine whether the role of the miR-29a-5p/transmembrane protein 33 (TMEM33) axis.

Prescriptions for atypical antipsychotics in children and adolescents are increasing globally. However, a precise understanding of the clinical variables and evidence that prescribers consider before using these agents is lacking. While empirical literature on the long-term safety and efficacy of these medications is available, the literature concerning their use in these younger age groups is relatively sparse. In this study, we examined the current prescribing patterns of medical professionals employed by a public health service in Australia.

A wealth of neuromodulatory transmitters regulate synaptic circuits in the brain. Their mode of signaling, often called volume transmission, differs from classical synaptic transmission in important ways. In synaptic transmission, vesicles rapidly fuse in response to action potentials and release their transmitter content. The transmitters are then sensed by nearby receptors on select target cells with minimal delay. Signal transmission is restricted to synaptic contacts and typically occurs within ~1 ms. Volume transmission doesn't rely on synaptic contact sites and is the main mode of monoamines and neuropeptides, important neuromodulators in the brain. It is less precise than synaptic transmission, and the underlying molecular mechanisms and spatiotemporal scales are often not well understood. Here, we review literature on mechanisms of volume transmission and raise scientific questions that should be addressed in the years ahead. We define five domains by which volume transmission systems can differ from synaptic transmission and from one another. These domains are (1) innervation patterns and firing properties, (2) transmitter synthesis and loading into different types of vesicles, (3) architecture and distribution of release sites, (4) transmitter diffusion, degradation, and reuptake, and (5) receptor types and their positioning on target cells. We discuss these five domains for dopamine, a well-studied monoamine, and then compare the literature on dopamine with that on norepinephrine and serotonin. We include assessments of neuropeptide signaling and of central acetylcholine transmission. Through this review, we provide a molecular and cellular framework for volume transmission. This mechanistic knowledge is essential to define how neuromodulatory systems control behavior in health and disease and to understand how they are modulated by medical treatments and by drugs of abuse.

Depression, a prevalent and complex mental health condition, presents a significant global health burden. Depression is one of the most frequent mental disorders; deaths from it account for 14.3% of people worldwide. In recent years, the integration of complementary and alternative medicine, including traditional Chinese medicine (TCM), has gained attention as a potential avenue for addressing depression. This comprehensive review critically assesses the efficacy of TCM interventions in alleviating depressive symptoms. An in-depth look at different research studies, clinical trials, and meta-analyses is used in this review to look into how TCM practices like herbal formulations, acupuncture, and mind-body practices work. The review looks at the quality of the evidence, the rigor of the methods, and any possible flaws in the current studies. This gives us an idea of where TCM stands right now in terms of treating depression. This comprehensive review aims to assess the efficacy of TCM interventions in alleviating depressive symptoms. In order to learn more about their possible healing effects, the study also looks into how different types of TCM work, such as herbal formulas, acupuncture, and mind-body practices.

Studies have demonstrated that exercise can mitigate the intensity of menstrual pain in primary dysmenorrhea, but the most effective type of exercise remains unclear. The objective of this systematic review and network meta-analysis was to evaluate the effectiveness of different exercise regimens in reducing pain associated with primary dysmenorrhoea.

Psychological factors are amongst the most robust predictors of healthspan and longevity, yet are rarely incorporated into scientific and medical frameworks of aging. The prospect of characterizing and integrating the psychological influences of aging is therefore an unmet step for the advancement of geroscience. Psychogenic Aging research is an emerging branch of biogerontology that aims to address this gap by investigating the impact of psychological factors on human longevity. It is an interdisciplinary field that integrates complex psychological, neurological, and molecular relationships that can be best understood with precision medicine methodologies. This perspective argues that psychogenic aging should be considered an integral component of the Hallmarks of Aging framework, opening the doors for future biopsychosocial integration in longevity research. By providing a unique perspective on frequently overlooked aspects of organismal aging, psychogenic aging offers new insights and targets for anti-aging therapeutics on individual and societal levels that can significantly benefit the scientific and medical communities.

Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .

Applying deep brain stimulation (DBS) to several brain regions has been investigated in attempts to treat highly treatment-resistant depression, with variable results. Our initial pilot data suggested that the bed nucleus of the stria terminalis (BNST) could be a promising therapeutic target.

The prevalence of pediatric constipation ranges from 0.7 to 29.6% across different countries. Functional constipation accounts for 95% of pediatric constipation, and the efficacy of pharmacotherapy is limited, with a success rate of 60%. Several randomized controlled trials (RCTs) have shown the benefits of probiotic supplements in treating this condition. However, the reported strains of probiotics varied among the RCTs. We aimed to compare the efficacy and acceptability of different probiotic supplements for pediatric functional constipation. The current frequentist model-based network meta-analysis (NMA) included RCTs of probiotic supplements for functional constipation in children. The primary outcome was changes in bowel movement or stool frequency; acceptability outcome was all-cause discontinuation. Nine RCTs were included (N = 710; mean age = 5.5 years; 49.4% girls). Most probiotic products, used either alone or combined with laxatives, were associated with significantly better improvement in bowel movement or stool frequency than placebo/control. Protexin plus laxatives (standardized mean difference (SMD) = 1.87, 95% confidence interval (95% CI) = 0.85 to 2.90) were associated with the greatest improvement in bowel movement or stool frequency among all the investigated probiotic products. For the single probiotic interventions, only Lactobacillus casei rhamnosus Lcr35 was associated with significant efficacy compared to placebo/control treatments (SMD = 1.37, 95% CI: 0.32 to 2.43). All the investigated probiotic products had fecal incontinence and patient drop-out rates similar to those of placebo/control treatments.  Conclusion: The results of our NMA support the application of an advanced combination of probiotics and laxatives for pediatric functional constipation if there is no concurrent contraindication.  Registration: PROSPERO (CRD42022298724). What is Known: • Despite of the high prevalence of pediatric constipation, which ranges from 0.7% to 29.6%, the efficacy of pharmacotherapy is limited, with a success rate of 60%. Several randomized controlled trials (RCTs) have shown the benefits of probiotic supplements in treating this condition. However, the reported strains of probiotics varied among the RCTs. The widely heterogeneous strains of probiotics let the traditional meta-analysis, which pooled all different strains into one group, be nonsense and insignificant. What is New: • By conducting a comprehensive network meta-analysis, we aimed to compare the efficacy and acceptability of different strains of probiotic supplements for pediatric functional constipation. Network meta-analysis of nine randomized controlled trials revealed that the most probiotic products, used either alone or combined with laxatives, were associated with significantly better improvement in bowel movement or stool frequency than placebo/control. Protexin plus laxatives was associated with the greatest improvement in bowel movement or stool frequency among all the investigated probiotic products. For the single probiotic interventions, only Lactobacillus casei rhamnosus Lcr35 was associated with significant efficacy compared to placebo/control treatments. All the investigated probiotic products had fecal incontinence and patient drop-out rates similar to those of placebo/control treatments.

Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution with minimal redundancy. Finding an optimal measurement is challenging and has generally required intimate knowledge of the dynamics in the few cases where it has been done. We establish an equivalence between a perfect measurement and a variant of the information bottleneck. As a consequence, we can employ machine learning to optimize measurement processes that efficiently extract information from trajectory data. We obtain approximately optimal measurements for multiple chaotic maps and lay the necessary groundwork for efficient information extraction from general time series.

(1) Background: Precise diagnosis and early intervention are crucial for toddlers with autism spectrum disorder (ASD) to achieve a better prognosis. This study investigated the efficacy of the Clancy Behavior Scale (CBS) and Modified Checklist for Autism in Toddlers (M-CHAT) in detecting ASD among toddlers under 30 months of age. (2) Methods: A total of 215 toddlers (117 with ASD and 98 with development delays) aged between 18 and 29 months participated in this study. All the primary caregivers of these toddlers were recruited to complete the CBS and M-CHAT. (3) Results: The findings indicated that the accuracy of the CBS and M-CHAT was promising, and the short forms of these two instruments performed better than their full versions. The CBS:9 critical items presented a sensitivity of 0.75 and a specificity of 0.74, while the M-CHAT:14 brief items showed a sensitivity of 0.72 and a specificity of 0.85. (4) Conclusions: The diagnostic accuracy of high-risk ASD toddlers improved via the combination of CBS and M-CHAT, particularly when the information gathered from these two instruments were consistent. The findings may provide implications for enhancing the early detection of ASD.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by progressive cognitive deterioration, functional impairments, and neuropsychiatric symptoms. Valiltramiprosate is a tramiprosate prodrug being investigated as a novel treatment for AD.

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

Personality traits have been associated with eating disorders (EDs) and comorbidities. However, it is unclear which personality profiles are premorbid risk rather than diagnostic markers.

Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1.

Early detection and intervention of developmental disabilities (DDs) are critical to improving the long-term outcomes of afflicted children. In this study, our objective was to utilize facial landmark features from mobile application to distinguish between children with DDs and typically developing (TD) children.

Major depressive disorder (MDD) is associated with Alzheimer's disease (AD) but the precise mechanisms underlying this relationship are not understood. While it is well established that cerebrospinal fluid (CSF) soluble levels of triggering receptor expressed on myeloid cells 2 (sTREM2) increase during early stages of AD, how sTREM2 levels behave in subjects with MDD is not known. In a longitudinal study, we measured CSF sTREM2 levels in 27 elderly cognitively intact individuals with late-life major depression (LLMD) and in 19 healthy controls. We tested the hypothesis that, similarly to what happens in early stages of AD, CSF sTREM2 would be elevated in MDD. In addition, we compared the associations of CSF sTREM2, pro- and anti- inflammatory, and AD biomarkers in LLMD and control subjects. Surprisingly, we found that mean CSF sTREM2 levels were significantly reduced in LLMD compared to controls. This reduction was no longer significant at the 3-year follow-up visit when depression severity improved. In addition, we found that CSF sTREM2 was associated with AD biomarkers and proinflammatory cytokines in controls but not in LLMD. These findings suggest that impaired microglia phagocytic response to AD pathology may be a novel link between MDD and AD.

Physical frailty and genetic factors are both risk factors for increased dementia; nevertheless, the joint effect remains unclear. This study aimed to investigated the long-term relationship between physical frailty, genetic risk, and dementia incidence. A total of 274,194 participants from the UK Biobank were included. We applied Cox proportional hazards regression models to estimate the association between physical frailty and genetic and dementia risks. Among the participants (146,574 females [53.45%]; mean age, 57.24 years), 3,353 (1.22%) new-onset dementia events were recorded. Compared to non-frailty, the hazard ratio (HR) for dementia incidence in prefrailty and frailty was 1.396 (95% confidence interval [CI], 1.294-1.506, P < 0.001) and 2.304 (95% CI, 2.030-2.616, P < 0.001), respectively. Compared to non-frailty and low polygenic risk score (PRS), the HR for dementia risk was 3.908 (95% CI, 3.051-5.006, P < 0.001) for frailty and high PRS. Furthermore, among the participants, slow walking speed (HR, 1.817; 95% CI, 1.640-2.014, P < 0.001), low physical activity (HR, 1.719; 95% CI, 1.545-1.912, P < 0.001), exhaustion (HR, 1.670; 95% CI, 1.502-1.856, P < 0.001), low grip strength (HR, 1.606; 95% CI, 1.479-1.744, P < 0.001), and weight loss (HR, 1.464; 95% CI, 1.328-1.615, P < 0.001) were independently associated with dementia risk compared to non-frailty. Particularly, precise modulation for different dementia genetic risk populations can also be identified due to differences in dementia risk resulting from the constitutive pattern of frailty in different genetic risk populations. In conclusion, both physical frailty and high genetic risk are significantly associated with higher dementia risk. Early intervention to modify frailty is beneficial for achieving primary and precise prevention of dementia, especially in those at high genetic risk.

Psychiatric disorders often require pharmacological interventions to alleviate symptoms and improve quality of life. However, achieving an optimal therapeutic outcome is challenging due to several factors, including variability in the individual response, inter-individual differences in drug metabolism, and drug interactions in polytherapy. Therapeutic drug monitoring (TDM), by measuring drug concentrations in biological samples, represents a valuable tool to address these challenges, by tailoring medication regimens to each individual. This review analyzes the current landscape of TDM in psychiatric practice, highlighting its significance in optimizing drug dosages, minimizing adverse effects, and improving therapeutic efficacy. The metabolism of psychiatric medications (i.e., mood stabilizers, antipsychotics, antidepressants) often exhibits significant inter-patient variability. TDM can help address this variability by enhancing treatment personalization, facilitating early suboptimal- or toxic-level detection, and allowing for timely interventions to prevent treatment failure or adverse effects. Furthermore, this review briefly discusses technological advancements and analytical methods supporting the implementation of TDM in psychiatric settings. These innovations enable quick and cost-effective drug concentration measurements, fostering the widespread adoption of TDM as a routine practice in psychiatric care. In conclusion, the integration of TDM in psychiatry can improve treatment outcomes by individualizing medication regimens within the so-called precision medicine.

We examined the association between childhood adversity and fecundability (the per-cycle probability of conception), and the extent to which childhood social support modified this association. We used data from 6,318 female participants aged 21-45 years in Pregnancy Study Online (PRESTO), a North American prospective preconception cohort study (2013-2022). Participants completed a baseline questionnaire, bimonthly follow-up questionnaires (until pregnancy or a censoring event), and a supplemental questionnaire on experiences across the life course including adverse childhood experiences (ACE) and social support (using the modified Berkman-Syme Social Network Index [SNI]). We used proportional probabilities regression models to compute fecundability ratios (FR) and 95% confidence intervals (CI), adjusting for potential confounders and precision variables. Adjusted FRs for ACE scores 1-3 and ≥4 vs. 0 were 0.91 (95% CI: 0.85, 0.97) and 0.84 (95% CI: 0.77, 0.91), respectively. FRs for ACE scores ≥4 vs. 0 were 0.86 (95% CI: 0.78, 0.94) among participants reporting high childhood social support (SNI ≥4) and 0.78 (95% CI: 0.56, 1.07) among participants reporting low childhood social support (SNI <4). Our findings confirm results from two previous studies and indicate that high childhood social support slightly buffered the effects of childhood adversity on fecundability.

Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources.

Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein-protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene-miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein-Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, L-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein-Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

The rising prevalence of obesity in children calls for new strategies for the provision of effective care by a multidisciplinary team. Telemedicine has overall proven to be an effective tool for promoting a healthy lifestyle. The main objective of the current paper is to present the protocol of our ongoing CardioMetabolic Prevention (CAMP) study and compare its design with published studies on telemedicine in paediatric obesity. Additionally, we analysed the preliminary anthropometric and laboratory data to test the efficacy of our 12-week intensive program that combines in-person and telemedicine support. The program demonstrated a positive impact on body mass index (BMI) and its z-scores in 21 adolescents, and BMI in 18 participating parents. However, we found no effect on body composition, waist circumference, cardiometabolic parameters, or fitness evaluated via a 6-min walk test in adolescents. In conclusion, the combination of in-person and telemedicine intensive support over 35 h delivered by a multidisciplinary team can be beneficial not only for adolescents with obesity but also for their parents. The ongoing CAMP study serves as a platform for precision medicine in future decisions regarding anti-obesity medication in adolescents with obesity.

Since targeted treatment for gastrointestinal pain is elusive, identifying the mechanistic underpinning of this pain type is important. Facilitation of spinal neuronal responses underpins certain pain types, and the psychophysical temporal summation of pain (TSP) paradigm provides a proxy measure of spinal facilitatory processes. Our aim was to systematically review whether facilitated TSP is a feature of gastrointestinal pain in patients with, or pain-free people experiencing experimentally induced, gastrointestinal pain.

Transcranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed.

Neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD) constitute multifaceted behavioral manifestations that reflect processes of emotional regulation, thinking, and social behavior. They are as prevalent in AD as cognitive impairment and develop independently during the progression of neurodegeneration. As studying NPSs in AD is clinically challenging, most AD research to date has focused on cognitive decline. In this opinion article we summarize emerging literature on the prevalence, time course, and the underlying genetic, molecular, and pathological mechanisms related to NPSs in AD. Overall, we propose that NPSs constitute a cluster of core symptoms in AD, and understanding their neurobiology can lead to a more holistic approach to AD research, paving the way for more accurate diagnostic tests and personalized treatments embracing the goals of precision medicine.

Sepsis-associated encephalopathy (SAE) is a frequent complication of severe systemic infection resulting in delirium, premature death, and long-term cognitive impairment. We closely mimicked SAE in a murine peritoneal contamination and infection (PCI) model. We found long-lasting synaptic pathology in the hippocampus including defective long-term synaptic plasticity, reduction of mature neuronal dendritic spines, and severely affected excitatory neurotransmission. Genes related to synaptic signaling, including the gene for activity-regulated cytoskeleton-associated protein (Arc/Arg3.1) and members of the transcription-regulatory EGR gene family, were downregulated. At the protein level, ARC expression and mitogen-activated protein kinase signaling in the brain were affected. For targeted rescue we used adeno-associated virus-mediated overexpression of ARC in the hippocampus in vivo. This recovered defective synaptic plasticity and improved memory dysfunction. Using the enriched environment paradigm as a non-invasive rescue intervention, we found improvement of defective long-term potentiation, memory, and anxiety. The beneficial effects of an enriched environment were accompanied by an increase in brain-derived neurotrophic factor (BDNF) and ARC expression in the hippocampus, suggesting that activation of the BDNF-TrkB pathway leads to restoration of the PCI-induced reduction of ARC. Collectively, our findings identify synaptic pathomechanisms underlying SAE and provide a conceptual approach to target SAE-induced synaptic dysfunction with potential therapeutic applications to patients with SAE.

In this narrative, comprehensive, and updated review of the literature, we summarize evidence about the effectiveness of interventions aimed at reducing emotion dysregulation and improving emotion regulation in children, adolescents, and adults. After introducing emotion dysregulation and emotion regulation from a theoretical standpoint, we discuss the factors commonly associated with emotion regulation, including neurobiological and neuropsychological mechanisms, and the role of childhood adverse experiences and psycho-social factors in the onset of emotion dysregulation. We then present evidence about pharmacological and non-pharmacological interventions aiming at improving emotion dysregulation and promoting emotion regulation across the lifespan. Although our review was not intended as a traditional systematic review, and the search was only restricted to systematic reviews and meta-analyses, we highlighted important implications and provided recommendations for clinical practice and future research in this field.

Individuals with bipolar disorder (BD) have a greater suicide risk than the general population. In this study, we employed latent profile analysis (LPA) to explore whether Chinese individuals with different phases of BD differed at the levels of suicidal ideation. We recruited 517 patients. Depressive symptoms were measured using the 24-item Hamilton Depression Rating Scale (HAMD-24), and manic symptoms were evaluated using the Young Mania Rating Scale (YMRS). The extent of suicidal thoughts was determined through the Beck Scale for Suicide Ideation (BSSI). The scores of HAMD and YMRS were used to perform LPA. LPA categorized participants into three classes: one exhibiting severe depressive and mild manic symptomatology, another showing severe depressive and severe manic symptomatology, and the third one displaying severe depressive and intermediate manic symptomatology. Suicidal ideation levels were found to be remarkably elevated across all three classes. Additionally, the three classes showed no significant differences in terms of suicidal ideation. Our research confirms the link between depressive symptoms and suicide, independent of the manic symptoms. These findings carry meaning as they provide insight into the suicide risk profiles within different phases of BD.

Investigating the biophysiological substrates of psychiatric illnesses is of great interest to our understanding of disorders' etiology, the identification of reliable biomarkers, and potential new therapeutic avenues. Schizophrenia represents a consolidated model of γ alterations arising from the aberrant activity of parvalbumin-positive GABAergic interneurons, whose dysfunction is associated with perineuronal net impairment and neuroinflammation. This model of pathogenesis is supported by molecular, cellular, and functional evidence. Proof for alterations of γ oscillations and their underlying mechanisms has also been reported in bipolar disorder and represents an emerging topic for major depressive disorder. Although evidence from animal models needs to be further elucidated in humans, the pathophysiology of γ-band alteration represents a common denominator for different neuropsychiatric disorders. The purpose of this narrative review is to outline a framework of converging results in psychiatric conditions characterized by γ abnormality, from neurochemical dysfunction to alterations in brain rhythms.

This paper will focus on the works of one of Japan's representative psychiatrists, Yomishi Kasahara, particularly on his works in the 1970s in which he proposed the concept of student apathy, and will discuss how this work was carried over into a contemporary topic, the study of "." Kasahara's well-known paper "Clinical Classification of Depression" (Kasahara and Kimura, 1975) described the present state of patients with Type III as "they do not have a complete set of symptoms as in Type I, but sometimes show dependency, strong exaggeration, complication of other neurotic symptoms, little tendency of self-reproaching, and tendency of accusing others"; the two subtypes as Type III-1 "those that remain at the neurotic level" and Type III-2 "those that transiently drop to the psychotic level." We have summarized and introduced below the case presented in the paper with this Type III-1. From today's perspective, where the concept of "" exists, this case could be considered as a typical case of "," that is, a person with a tendency to avoid social roles and responsibilities and to immerse oneself in areas with no responsibilities, such as hobbies. "" was discovered in the late 1980s, but to be precise, it was just that the concept emerged. The same clinical condition had already been brilliantly found by Kasahara in the 1970s under the concept of "apathy syndrome," which was distinguished from depression.

Treatment-resistant depression (TRD) occurs when at least two different antidepressants, taken at the right dosage, for adequate period of time and with continuity, fail to give positive clinical effects. Esketamine, the S-enantiomer of ketamine, was recently approved for TRD treatment from U.S. Food and Drug Administration and European Medicine Agency. Despite proved clinical efficacy, many misconceptions by clinicians and patients accompany this medication. We aimed to review the most common "false myths" regarding TRD and esketemine, counterarguing with evidence-based facts.

Emerging data suggests liver disease may be initiated during development when there is high genome plasticity and the molecular pathways supporting liver function are being developed.

Alzheimer's disease psychosis (ADP) produces a significant burden for patients and their care partners, but at present there are no approved treatments for ADP. The lack of approved treatments may be due to the challenges of conducting clinical trials for this disease. This perspective article discusses distinct challenges and proposed solutions of conducting ADP trials involving seven key areas: (1) methods to reduce the variable and sometimes high rates of placebo response that occur for treatments of neuropsychiatric symptoms; (2) the use of combined or updated criteria that provide a precise, consensus definition of ADP; (3) the use of eligibility criteria to help recruit individuals representative of the larger ADP population and overcome the difficulty of recruiting patients with moderate-to-severe ADP; (4) consideration of multiple perspectives and implementation of technology to reduce the variability in the administration and scoring of neuropsychiatric symptom assessments; (5) the use of clinically appropriate, defined severity thresholds and responder cutoffs; (6) the use of statistical approaches that address absolute effect sizes and a three-tier approach to address the fluctuation of neuropsychiatric symptoms; and (7) the implementation of feasible diagnostic and target-engagement biomarkers as they become available. The goal of these proposed solutions is to improve the evaluation of potential ADP therapies, within the context of randomized, placebo-controlled trials with clinically meaningful endpoints and sustained treatment responses.

In recent years, several theoretical models have been suggested as complementary to the adaptative information processing model of eye movement desensitization and reprocessing therapy. A narrative review of such models was conducted to assess the contributions of each, as well as their convergences, contradictions, and potential complementarity. Seven theoretical models were identified. All focus on the effects of EMDR therapy as a comprehensive psychotherapy approach with its principles, procedures, and protocols. Several refer to concepts related to propositional or predictive processing theories. Overall, the contribution of these proposals does appear to bolster Shapiro's original AIP model, potentially offering additional depth and breadth to case conceptualization and treatment planning in clinical practice, as well as a more precise theoretical understanding. The current exploratory comparative analysis may serve as a preliminary baseline to guide research into the relative merit of suggested theoretical proposals to enhance current standards for the clinical practice and teaching of EMDR therapy.

The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: "COntainerized Statistical Genetics Analysis Pipelines," consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements.

How did mental healthcare utilization change during the COVID-19 pandemic period among individuals with pre-existing mental disorder? Understanding utilization patterns of these at-risk individuals and identifying those most likely to exhibit increased utilization could improve patient stratification and efficient delivery of mental health services. This study leveraged large-scale electronic health record (EHR) data to describe mental healthcare utilization patterns among individuals with pre-existing mental disorder before and during the COVID-19 pandemic and identify correlates of high mental healthcare utilization. Using EHR data from a large healthcare system in Massachusetts, we identified three "pre-existing mental disorder" groups (PMD) based on having a documented mental disorder diagnosis within the 6 months prior to the March 2020 lockdown, related to: (1) stress-related disorders (e.g., depression, anxiety) (N = 115,849), (2) serious mental illness (e.g., schizophrenia, bipolar disorders) (N = 11,530), or (3) compulsive behavior disorders (e.g., eating disorder, OCD) (N = 5,893). We also identified a "historical comparison" group (HC) for each PMD (N = 113,604, 11,758, and 5,387, respectively) from the previous year (2019). We assessed the monthly number of mental healthcare visits from March 13 to December 31 for PMDs in 2020 and HCs in 2019. Phenome-wide association analyses (PheWAS) were used to identify clinical correlates of high mental healthcare utilization. We found the overall number of mental healthcare visits per patient during the pandemic period in 2020 was 10-12% higher than in 2019. The majority of increased visits was driven by a subset of high mental healthcare utilizers (top decile). PheWAS results indicated that correlates of high utilization (prior mental disorders, chronic pain, insomnia, viral hepatitis C, etc.) were largely similar before and during the pandemic, though several conditions (e.g., back pain) were associated with high utilization only during the pandemic. Limitations included that we were not able to examine other risk factors previously shown to influence mental health during the pandemic (e.g., social support, discrimination) due to lack of social determinants of health information in EHR data. Mental healthcare utilization among patients with pre-existing mental disorder increased overall during the pandemic, likely due to expanded access to telemedicine. Given that clinical correlates of high mental healthcare utilization in a major hospital system were largely similar before and during the COVID-19 pandemic, resource stratification based on known risk factor profiles may aid hospitals in responding to heightened mental healthcare needs during a pandemic.

People with Parkinson's disease with motor fluctuations can be treated by continuous subcutaneous apomorphine infusion (CSAI) to reduce their symptoms. Nonetheless, factors are lacking to predict patients' quality-of-life amelioration after CSAI. This pilot study aimed to evaluate associations between personality dimensions and quality-of-life improvement after 6 months of CSAI. Thirty-nine people with Parkinson's disease awaiting CSAI were included. Linear regression models between 'Temperament and Character Inventory' personality dimensions at baseline and percentage of change in Parkinson's Disease Questionnaire-39 scores after 6 months of CSAI were realized ( = 35). The Temperament and Character Inventory was also compared between patients awaiting CSAI and patients awaiting deep brain stimulation of the sub-thalamic nucleus ( = 39 from the PREDI-STIM study). Higher reward dependence scores were associated with a better quality-of-life outcome after 6 months of CSAI, while self-directedness scores were associated with a better quality of life before CSAI (as opposed to harm avoidance, reward dependence and self-transcendence scores associated with a worse quality of life). Moreover, people with Parkinson's disease awaiting deep brain stimulation of the sub-thalamic nucleus had similar Temperament and Character Inventory dimensions compared to patients awaiting CSAI. People with Parkinson's disease with higher reward dependence scores at baseline had the best quality-of-life improvement after 6 months of CSAI. This finding could be used to better prepare and accompany people with Parkinson's disease during CSAI establishment. Moreover, this result could serve as an orientation factor to second-line treatments.

Anxiety and depression in children and adolescents warrant special attention as a public health concern given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10; N=11,875), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17; N=4,326) and IMAGEN (EUROPE, age of 14; N=1888). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school support index had the most significant and consistent impact across all three cohorts. In both meta, and mega-analysis, SNP rs79878474 in chr11p15 emerged as a particularly promising candidate associated with anxiety and depression, despite not reaching genomic significance. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine, and a trend of enrichment in the cerebellum. Our findings provide evidences of consistent environmental impact from early life stress and school support index on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels, which support the stress-depression connection via hypothalamic-pituitary-adrenal axis, along with the potential modulating role of potassium channels.

This study aims to examine the potential influence of RS4680 (), RS16965628 (), and RS1019385 () polymorphisms on the therapeutic response to repetitive transcranial magnetic stimulation (rTMS) and selective serotonin reuptake inhibitors (SSRIs) in individuals with obsessive-compulsive disorder (OCD).

Individuals with Alzheimer's disease (AD) commonly experience neuropsychiatric symptoms of psychosis (AD+P) and/or affective disturbance (depression, anxiety, and/or irritability, AD+A). This study's goal was to identify the genetic architecture of AD+P and AD+A, as well as their genetically correlated phenotypes.

There is evidence that adverse childhood experiences (ACEs) and negative life events (NLEs) are associated with major depression (MDD).