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.

There are currently no reliable biomarkers to predict clinical response to pharmacological treatments of depressive disorders. Peripheral blood 5-hydroxytryptamine (5-HT; serotonin) has been suggested as a biomarker of antidepressant treatment response, but there has not been an attempt to systematically summarize and evaluate the scientific evidence of this hypothesis. In this systematic review we searched MEDLINE, Embase, PsycINFO, and the Cochrane Central Register of Controlled Trials. Twenty-six relevant studies investigating peripheral 5-HT as an antidepressant biomarker were identified. In all, we did not find robust support for an association between baseline 5-HT and treatment response. Several larger studies with lower risk of bias, however, showed that higher baseline 5-HT was associated with a greater antidepressant response to SSRIs, prompting future studies to investigate this hypothesis. Our results also confirm previous reports that SSRI treatment is associated with a decrease in peripheral 5-HT levels; however, we were not able to confirm that larger decreases of 5-HT are associated with better treatment outcome as results were inconclusive.

Environmental factors such as climate, urbanicity, and exposure to nature are becoming increasingly important influencers of mental health. Incorporating data gathered from real-life contexts holds promise to substantially enhance laboratory experiments by providing a more comprehensive understanding of everyday behaviors in natural environments. We provide an up-to-date review of current technological and methodological developments in mental health assessments, neuroimaging and environmental sensing.

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.

The expected increase of dementia prevalence in the coming decades will mainly be in low-income and middle-income countries and in people with low socioeconomic status in high-income countries. This study aims to reduce dementia risk factors in underserved populations at high-risk using a coach-supported mobile health (mHealth) intervention.

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.

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.

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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

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.

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.

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.

(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.

Hallucinations are a core feature of psychosis, and their severity during the acute phase of illness is associated with a range of poor outcomes. Various clinical and sociodemographic factors may predict hallucinations and other positive psychotic symptoms in first episode psychosis (FEP). Despite this, the precise factors associated with hallucinations at first presentation to an early intervention service have not been extensively researched. Through detailed interviews and chart reviews, we investigated sociodemographic and clinical predictors in 636 minimally-medicated patients who entered PEPP-Montréal, an early intervention service for FEP, between 2003 and 2018. Hallucinations were measured using the Scale for the Assessment of Positive Symptoms (SAPS), while negative symptoms were assessed using the Scale for the Assessment of Negative symptoms (SANS). Depressive symptoms were evaluated through the Calgary Depression Scale for Schizophrenia (CDSS), and anxiety symptoms via the Hamilton Rating Scale for Anxiety (HAS). A majority (n = 381, 59.9 %) of the sample presented with clinically significant hallucinations (SAPS global hallucinations score ≥ 3) at program entry. These patients had an earlier age at onset, fewer years of education, and a higher severity of delusions, depression and negative symptoms than those without clinical-level hallucinations. These results suggest that individuals with clinically significant hallucinations at admission tend to be younger and have a greater overall symptom burden. This makes it especially important to monitor hallucinations alongside delusions, depression and negative symptoms in order to identify who might benefit from targeted interventions. The implications of these findings for early intervention and person-centered care are discussed.

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

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.

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.

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.

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.

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.

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.

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.

FRILEUX, M., BOLTRI M. and al. Cognition and Gut microbiota in schizophrenia spectrum and mood disorders: a Systematic Review. NEUROSCI BIOBEHAV REV (1) 2024 Schizophrenia spectrum disorders and major mood disorders are associated with cognitive impairments. Recent studies suggest a link between gut microbiota composition and cognitive functioning. Here, we review the relationship between gut microbiota and cognition in these disorders. To do this, we conducted a systematic review, searching Cochrane Central Register of Controlled Trials, EBSCOhost, Embase, Pubmed, Scopus, and Web of Science. Studies were included if they investigated the relationship between gut microbiota composition and cognitive function through neuropsychological assessments in patients with bipolar, depressive, schizophrenia spectrum, and other psychotic disorders. Ten studies were identified. Findings underscore a link between gut dysbiosis and cognitive impairment. This relationship identified specific taxa (Haemophilus, Bacteroides, and Alistipes) as potential contributors to bolstered cognitive performance. Conversely, Candida albicans, Toxoplasma gondii, Streptococcus and Deinococcus were associated with diminished performance on cognitive assessments. Prebiotics and probiotics interventions were associated with cognitive enhancements, particularly executive functions. These results emphasize the role of gut microbiota in cognition, prompting further exploration of the underlying mechanisms paving the way toward precision psychiatry.

Most genetic variants associated with psychiatric disorders are located in noncoding regions of the genome. To investigate their functional implications, we integrate epigenetic data from the PsychENCODE Consortium and other published sources to construct a comprehensive atlas of candidate brain cis-regulatory elements. Using deep learning, we model these elements' sequence syntax and predict how binding sites for lineage-specific transcription factors contribute to cell type-specific gene regulation in various types of glia and neurons. The elements' evolutionary history suggests that new regulatory information in the brain emerges primarily via smaller sequence mutations within conserved mammalian elements rather than entirely new human- or primate-specific sequences. However, primate-specific candidate elements, particularly those active during fetal brain development and in excitatory neurons and astrocytes, are implicated in the heritability of brain-related human traits. Additionally, we introduce PsychSCREEN, a web-based platform offering interactive visualization of PsychENCODE-generated genetic and epigenetic data from diverse brain cell types in individuals with psychiatric disorders and healthy controls.

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.

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.

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.

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.

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit ( and ) and excitatory neurons ( and ). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Several genetic studies have been undertaken to elucidate the intricate interplay between genetics and drug responses in bipolar disorder (BD). However, there has been notably limited research on biomarkers specifically linked to valproate, with only a few studies investigating integrated proteomic and genomic factors in response to valproate treatment. Therefore, this study aimed to identify biological markers for the therapeutic response to valproate treatment in BD. Patients with BD in remission were assessed only at baseline, whereas those experiencing acute mood episodes were evaluated at three points (baseline, 8 ± 2 weeks, and 6 ± 1 months). The response to valproate treatment was measured using the Alda scale, with individuals scoring an Alda A score ≥ 5 categorized into the acute-valproate responder (acute-VPAR) group. We analyzed 158 peptides (92 proteins) from peripheral blood samples using multiple reaction monitoring mass spectrometry, and proteomic result-guided candidate gene association analyses, with 1,627 single nucleotide variants (SNVs), were performed using the Korean chip.

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.

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.

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.

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.

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.

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

The human brain is a complex system, whose activity exhibits flexible and continuous reorganization across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. Here, we investigate healthy participants taking the serotonergic psychedelic -dimethyltryptamine (DMT) with the Harmonic Decomposition of Spacetime (HADES) framework that can characterize how different harmonic modes defined in space are expressed over time. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the leading hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES' dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.

The prolonged usage of atypical antipsychotic drugs (AAPD) among individuals with schizophrenia often leads to metabolic side effects such as dyslipidemia. These effects not only limit one's selection of AAPD but also significantly reduce compliance and quality of life of patients. Recent studies suggest that bilirubin plays a crucial role in maintaining lipid homeostasis and may be a potential pre-treatment biomarker for individuals with dyslipidemia. The present study included 644 schizophrenia patients from two centers. Demographic and clinical characteristics were collected at baseline and 4 weeks after admission to investigate the correlation between metabolites, episodes, usage of AAPDs, and occurrence of dyslipidemia. Besides, we explored the combined predictive value of genotypes and baseline bilirubin for dyslipidemia by employing multiple PCR targeted capture techniques to sequence two pathways: bilirubin metabolism-related genes and lipid metabolism-related genes. Our results indicated that there existed a negative correlation between the changes in bilirubin levels and triglyceride (TG) levels in patients with schizophrenia. Among three types of bilirubin, direct bilirubin in the baseline (DBIL-bl) proved to be the most effective in predicting dyslipidemia in the ROC analysis (AUC = 0.627, p < 0.001). Furthermore, the odds ratio from multinomial logistic regression analysis showed that UGT1A1*6 was a protective factor for dyslipidemia (ß = -12.868, p < 0.001). The combination of baseline DBIL and UGT1A1*6 significantly improved the performance in predicting dyslipidemia (AUC = 0.939, p < 0.001). Schizophrenia patients with UGT1A1*6 mutation and a certain level of baseline bilirubin may be more resistant to dyslipidemia and have more selections for AAPD than other patients.

This commentary discusses the New Zealand Labour Party's announcement to remove tax on fresh and frozen fruits and vegetables. It aims to explore its potential impact on the psychological well-being of New Zealanders in the context of the growing global burden of mental illnesses in the current food environment.

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.

There is data paucity regarding users' awareness of privacy concerns and the resulting impact on the acceptance of mobile health (mHealth) apps, especially in the Saudi context. Such information is pertinent in addressing users' needs in the Kingdom of Saudi Arabia (KSA).

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.

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.

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 recent publications of the inter-areal connectomes for mouse, marmoset, and macaque cortex have allowed deeper comparisons across rodent vs. primate cortical organization. In general, these show that the mouse has very widespread, "all-to-all" inter-areal connectivity (i.e. a "highly dense" connectome in a graph theoretical framework), while primates have a more modular organization. In this review, we highlight the relevance of these differences to function, including the example of primary visual cortex (V1) which, in the mouse, is interconnected with all other areas, therefore including other primary sensory and frontal areas. We argue that this dense inter-areal connectivity benefits multimodal associations, at the cost of reduced functional segregation. Conversely, primates have expanded cortices with a modular connectivity structure, where V1 is almost exclusively interconnected with other visual cortices, themselves organized in relatively segregated streams, and hierarchically higher cortical areas such as prefrontal cortex provide top-down regulation for specifying precise information for working memory storage and manipulation. Increased complexity in cytoarchitecture, connectivity, dendritic spine density, and receptor expression additionally reveal a sharper hierarchical organization in primate cortex. Together, we argue that these primate specializations permit separable deconstruction and selective reconstruction of representations, which is essential to higher cognition.

Understanding the impact of CYP2D6 metabolism on paroxetine, a widely used antidepressant, is essential for precision dosing.

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.

Human brain organoid models have emerged as a promising tool for studying human brain development and function. These models preserve human genetics and recapitulate some aspects of human brain development, while facilitating manipulation in an in vitro setting. Despite their potential to transform biology and medicine, concerns persist about their fidelity. To fully harness their potential, it is imperative to establish reliable analytic methods, ensuring rigor and reproducibility. Here, we review current analytical platforms used to characterize human forebrain cortical organoids, highlight challenges, and propose recommendations for future studies to achieve greater precision and uniformity across laboratories.

Post-stroke depression (PSD) is one of the most common mental sequelae after a stroke and can damage the brain. Although PSD has garnered increasing attention in recent years, the precise mechanism remains unclear. Studies have indicated that the expression of DAPK1 is elevated in various neurodegenerative conditions, including depression, ischemic stroke, and Alzheimer's disease. However, the specific molecular mechanism of DAPK1-mediated cognitive dysfunction and neuronal apoptosis in PSD rats is unclear. In this study, we established a rat model of PSD, and then assessed depression-like behaviors and cognitive dysfunction in rats using behavioral tests. In addition, we detected neuronal apoptosis and analyzed the expression of DAPK1 protein and proteins related to the ERK/CREB/BDNF signaling pathway. The findings revealed that MCAO combined with CUMS can induce more severe depression-like behaviors and cognitive dysfunction in rats, while overexpression of DAPK1 may hinder the downstream ERK/CREB/BDNF signaling pathways, resulting in neuronal loss and exacerbation of brain tissue damage. In this study, we will focus on DAPK1 and explore its role in PSD.

The underlying brain mechanisms of ketamine in treating chronic suicidality and the characteristics of patients who will benefit from ketamine treatment remain unclear. To address these gaps, we investigated temporal variations of brain functional synchronisation in patients with suicidality treated with ketamine in a 6-week open-label oral ketamine trial. The trial's primary endpoint was the Beck Scale for Suicide Ideation (BSS). Patients who experienced greater than 50% improvement in BSS scores or had a BSS score less than 6 at the post-treatment and follow-up (10 weeks) visits were considered responders and persistent responders, respectively. The reoccurring and transient connectivity pattern (termed brain state) from 29 patients (45.6 years ± 14.5, 15 females) were investigated by dynamic functional connectivity analysis of resting-state functional MRI at the baseline, post-treatment, and follow-up. Post-treatment patients showed significantly more (FDR-Q = 0.03) transitions among whole brain states than at baseline. We also observed increased dwelling time (FDR-Q = 0.04) and frequency (FDR-Q = 0.04) of highly synchronised brain state at follow-up, which were significantly correlated with BSS scores (both FDR-Q = 0.008). At baseline, persistent responders had higher fractions (FDR-Q = 0.03, Cohen's d = 1.39) of a cognitive control network state with high connectivities than non-responders. These findings suggested that ketamine enhanced brain changes among different synchronisation patterns and enabled high synchronisation patterns in the long term, providing a possible biological pathway for its suicide-prevention effects. Moreover, differences in cognitive control states at baseline may be used for precise ketamine treatment planning.

This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and magnetoencephalographic (MEG) recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuo-spatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/non-match decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared to younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI. Several cognitive functions decline during aging, and this process is aggravated in MCI - a condition constituting a primary risk factor for developing dementia. One function susceptible to age-related cognitive decline is working memory: the ability to maintain information online for the flexible control of behavior, which entails persistent stimulus-selective neural activity in different regions of the cerebral cortex. We used computational modeling of behavioral and neural recordings to show that the stability of working memory contents is reduced in older human subjects and predicts overall cognitive decline in MCI patients. Our findings provide new mechanistic insight into cognitive aging and MCI and highlight working memory stability as an objective marker of the mechanisms underlying cognitive impairment.

Pseudobulbar affect (PBA) is a neurological condition characterized by recurrent, inappropriate, and involuntary outbursts of emotion, primarily crying and laughter, which are dissociated from the individual's emotional experience. The precise underlying cause of PBA remains unknown; however, existing evidence suggests the involvement of dopaminergic, serotonergic, and glutamatergic neurotransmission within the corticopontine-cerebellar pathways responsible for regulating the motor expression of emotions. Additionally, PBA has been observed to co-occur with other neurocognitive and psychiatric disorders. Therefore, it is crucial to consider the possibility of a PBA diagnosis in patients with underlying neurological damage and disorders.

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample.

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.

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

Noninvasive brain stimulation (NIBS) interventions have been shown to be efficacious in several mental disorders, but the optimal dose stimulation parameters for each disorder are unknown.

Recommendations state that multidisciplinary team expertise should be utilised for more accurate survival predictions. How the multidisciplinary team discusses prognoses during meetings and how they reference time, is yet to be explored.

Bipolar disorder (BD) is a complex heterogeneous illness, with 60% to 85% of its variance attributed to genetic factors. Adolescence marks the first peak period of risk for the onset of BD, with the initial (hypo)manic episode often preceded by childhood psychopathology, including anxiety and sleep disorders, as well as internalizing symptoms. Given the non-specific nature of childhood antecedents, combined with the prominence of depressive episodes in the early illness course, accurate diagnosis is often delayed by 8 to 10 years from onset. Yet, the early course of BD in youth is already associated with significant morbidity and mortality. Therefore, more accurate and timely diagnosis is a priority. One way forward could be to combine biomarkers with clinical variables to help validate diagnoses, improve individual risk prediction and treatment, and advance discovery research into pathogenesis.

The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on , , and genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.

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.

New National Institute for Health and Care Excellence (NICE) guidance endorses the prescription of statins in larger population groups for the prevention of cardiovascular and cerebrovascular morbidity and mortality, especially in people with severe mental illness. However, the evidence base for their safety and risk/benefit balance in depression is not established.

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Pharmacotherapy is one of the primary treatment modalities for depression. However, there is considerable variability in the individual response to antidepressant medications. Personalized medicine guided by pharmacogenomic testing may hold promise in addressing this issue.

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.

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.

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.

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.

Annotated bibliography of genetic addiction risk severity (GARS) publications, pro-dopamine regulation in nutraceuticals (KB220 nutraceutical variants), and policy documents. Further research is required to encourage the field to consider "Reward Deficiency Syndrome (RDS) Anti-addiction Modeling" which involves early risk identification by means of genetic assessment similar to GARS, followed by induction of dopamine homeostasis by means of genetically guided pro-dopamine regulation similar to KB220. These results suggest that genetically based treatments may be a missing piece in the treatment of substance use disorder (SUD).

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.

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.

Psychotherapists need effective tools to monitor changes in the patient's affective perception of the therapist and the therapeutic relationship during sessions to tailor therapeutic interventions and improve treatment outcomes. This study aims to evaluate the factor structure, reliability, and validity of the (SPARQ), a concise self-report measure designed for practical application in real-world psychotherapy settings.

The receptorial responsiveness method (RRM) enables the estimation of a change in concentration of an (even degradable) agonist, near its receptor, curve fitting to (at least) two concentration-effect (E/c) curves of a stable agonist. One curve should be generated before this change, and the other afterwards, in the same system. It follows that RRM yields a surrogate parameter ("c") as the concentration of the stable agonist being equieffective with the change in concentration of the other agonist. However, regression can be conducted several ways, which can affect the accuracy, precision and ease-of-use. This study utilized data of previous investigations. Known concentrations of stable agonists were estimated with RRM by performing individual (local) or global fitting, this latter with one or two model(s), using a logarithmic (logc) or a nonlogarithmic (c) parameter (the latter in a complex or in a simplified equation), with ordinary least-squares or robust regression, and with an "all-at-once" or "pairwise" fitting manner. We found that the simplified model containing logc was superior to all alternative models. The most complicated individual regression was the most accurate, followed closely by the moderately complicated two-model global regression and then by the easy-to-perform one-model global regression. The two-model global fitting was the most precise, followed by the individual fitting (closely) and by the one-model global fitting (from afar). Pairwise fitting (two E/c curves at once) improved the estimation. Thus, the two-model global fitting, performed pairwise, and the individual fitting are recommended for RRM, using the simplified model containing logc.

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.

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.

Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly precise prediction errors lead to an unstable model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for such an unstable model of the world and investigate the computational mechanisms underlying emerging paranoia. We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task designed to probe learning about others' changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility. There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between perceiving increased volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically. Our results suggest that FEP are characterised by a different computational mechanism - perceiving the environment as increasingly volatile - in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.

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

Depression constitutes one of our largest global health concerns and current treatment strategies lack convincing evidence of effectiveness in youth. We suggest that this is partly due to inherent limitations of the present diagnostic paradigm that may group fundamentally different conditions together without sufficient consideration of etiology, developmental aspects, or context. Alternatives that complement the diagnostic system are available yet understudied. The Power Threat and Meaning Framework (PTMF) is one option, developed for explanatory and practical purposes. While based on scientific evidence, empirical research on the framework itself is still lacking. This qualitative study was performed to explore the experiences of adolescents and young adults with depression from the perspective of the PTMF.

PRECISE-DYAD is an observational cohort study of mother-child dyads running in urban and rural communities in The Gambia and Kenya. The cohort is being followed for two years and includes uncomplicated pregnancies and those that suffered pregnancy hypertension, fetal growth restriction, preterm birth, and/or stillbirth.

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.

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.

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.