Precision Psychiatry

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.

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

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

The degree of physiological responses to individual antipsychotic drugs is unclear in children and adolescents. With network meta-analysis, we aimed to investigate the effects of various antipsychotic medications on physiological variables in children and adolescents with neuropsychiatric and neurodevelopmental conditions.

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 human stress response is characterized by increases in neuromodulators, including norepinephrine (NE) and cortisol. Both neuromodulators can enter the brain and affect neurofunctional responses. Two brain areas associated with stress are the amygdala and the hippocampus. The precise influence of NE and cortisol on the amygdala and hippocampal resting state functional connectivity (RSFC) is poorly understood.

Job burnout among anesthesiologists has been consistently high. This study evaluated the association of calcium and vitamin D supplementation with burnout among Chinese anesthesiologists.

Tourette syndrome (TS) is a developmental neuropsychiatric disorder that is characterized by tic movements. Deep brain stimulation (DBS) may be a treatment option for severe cases refractory to medical and behavioral therapies. In this study, we reviewed the surgical techniques used for DBS in patients with severe TS and its clinical outcomes and sought to determine the optimal surgical procedure and current issues based on our experience and the literature. A total of 14 patients, consisting of 13 men and 1 woman, who underwent centromedian thalamic DBS and were followed up for a mean duration of 2.3 ± 1.0 years, participated in this study. The mean Yale Global Tic Severity Scale severity score significantly improved from 41.4 ± 7.0 at baseline to 19.8 ± 11.4 at 6 months (P = 0.01) and 12.7 ± 6.2 at the last follow-up (P < 0.01). Moreover, the mean Yale Global Tic Severity Scale impairment score significantly improved from 47.1 ± 4.7 at baseline to 23.1 ± 11.1 at 6 months (P < 0.01) and 7.6 ± 2.9 at the last follow-up (P < 0.01). However, there were problems with continuous postoperative monitoring (three cases were lost to follow-up) and surgery-related adverse events, including one case each of lead misplacement and a delayed intracerebral hemorrhage due to severe self-injurious tics. This study aimed to highlight not only the clinical efficacy of DBS for TS but also its challenges. Clinicians should understand the three-dimensional brain anatomy so that they can perform precise surgical procedures, avoid adverse events, and achieve favorable outcomes of DBS for TS.

Lifeguards are the first responders to any type of aquatic incident, including rapid rescue situations such as boating and sporting accidents, animal bites/attacks, and cases involving massive bleeding. In their line of work, rescue boats such as Rescue Water Craft (RWC) are commonly utilized the aim of this study is to evaluate the time and technique of placing a tourniquet on the sled of an RWC navigating at full speed.

Fibromyalgia is associated with chronic widespread pain and disturbed sleep. Multidisciplinary, multimodal management often includes pharmacotherapy; however, current drugs used to treat fibromyalgia provide meaningful benefit to only 30-60% of treated individuals. Combining two or more different drugs is common in clinical practice with the expectation of better efficacy, tolerability or both; however, further research is needed to identify which combinations actually provide added benefit. Thus, we are planning a clinical trial to evaluate melatonin (MLT)-pregabalin (PGB) combination in participants with fibromyalgia.

To provide an overview of treatments in the pipeline for adults with attention-deficit/hyperactivity disorder (ADHD), we searched https://clinicaltrials.gov/and and https://www.clinicaltrialsregister.eu/ from 01/01/2010-10/18/2023 for ongoing or completed phase 2 or 3 randomised controlled trials (RCTs), assessing pharmacological or non-pharmacological interventions for adults with ADHD with no current regulatory approval. We found 90 eligible RCTs. Of these, 24 (27%) reported results with statistical analysis for primary efficacy endpoints. While several pharmacological and non-pharmacological interventions had evidence of superiority compared to the control condition from a single RCT, centanafadine (norepinephrine, dopamine, and serotonin re-uptake inhibitor) was the only treatment with evidence of efficacy on ADHD core symptoms (small effect size=0.28-0.40) replicated in at least one additional RCT, alongside reasonable tolerability. Overall, the body of ongoing RCTs in adults with ADHD is insufficient, without any intervention on the horizon to match the efficacy of stimulant treatment or atomoxetine and with better tolerability profile. Additional effective and well tolerated treatments for adults with ADHD require development and testing.

There is a growing literature exploring the placebo response within specific mental disorders, but no overarching quantitative synthesis of this research has analyzed evidence across mental disorders. We carried out an umbrella review of meta-analyses of randomized controlled trials (RCTs) of biological treatments (pharmacotherapy or neurostimulation) for mental disorders. We explored whether placebo effect size differs across distinct disorders, and the correlates of increased placebo effects. Based on a pre-registered protocol, we searched Medline, PsycInfo, EMBASE, and Web of Knowledge up to 23.10.2022 for systematic reviews and/or meta-analyses reporting placebo effect sizes in psychopharmacological or neurostimulation RCTs. Twenty meta-analyses, summarising 1,691 RCTs involving 261,730 patients, were included. Placebo effect size varied, and was large in alcohol use disorder (g = 0.90, 95% CI [0.70, 1.09]), depression (g = 1.10, 95% CI [1.06, 1.15]), restless legs syndrome (g = 1.41, 95% CI [1.25, 1.56]), and generalized anxiety disorder (d = 1.85, 95% CI [1.61, 2.09]). Placebo effect size was small-to-medium in obsessive-compulsive disorder (d = 0.32, 95% CI [0.22, 0.41]), primary insomnia (g = 0.35, 95% CI [0.28, 0.42]), and schizophrenia spectrum disorders (standardized mean change = 0.33, 95% CI [0.22, 0.44]). Correlates of larger placebo response in multiple mental disorders included later publication year (opposite finding for ADHD), younger age, more trial sites, larger sample size, increased baseline severity, and larger active treatment effect size. Most (18 of 20) meta-analyses were judged 'low' quality as per AMSTAR-2. Placebo effect sizes varied substantially across mental disorders. Future research should explore the sources of this variation. We identified important gaps in the literature, with no eligible systematic reviews/meta-analyses of placebo response in stress-related disorders, eating disorders, behavioural addictions, or bipolar mania.

Schizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision.

Polygenic risk scores (PRSs) enable early prediction of disease risk. Evaluating PRS performance for binary traits commonly relies on the area under the receiver operating characteristic curve (AUC). However, the widely used DeLong's method for comparative significance tests suffer from limitations, including computational time and the lack of a one-to-one mapping between test statistics based on AUC and . To overcome these limitations, we propose a novel approach that leverages the Delta method to derive the variance and covariance of AUC values, enabling a comprehensive and efficient comparative significance test. Our approach offers notable advantages over DeLong's method, including reduced computation time (up to 150-fold), making it suitable for large-scale analyses and ideal for integration into machine learning frameworks. Furthermore, our method allows for a direct one-to-one mapping between AUC and values for comparative significance tests, providing enhanced insights into the relationship between these measures and facilitating their interpretation. We validated our proposed approach through simulations and applied it to real data comparing PRSs for diabetes and coronary artery disease (CAD) prediction in a cohort of 28,880 European individuals. The PRSs were derived using genome-wide association study summary statistics from two distinct sources. Our approach enabled a comprehensive and informative comparison of the PRSs, shedding light on their respective predictive abilities for diabetes and CAD. This advancement contributes to the assessment of genetic risk factors and personalized disease prediction, supporting better healthcare decision-making.

A wealth of behavioral evidence indicate that sounds with increasing intensity (i.e. appear to be looming towards the listener) are processed with increased attentional and physiological resources compared to receding sounds. However, the neurophysiological mechanism responsible for such cognitive amplification remains elusive. Here, we show that the large differences seen between cortical responses to looming and receding sounds are in fact almost entirely explained away by nonlinear encoding at the level of the auditory periphery. We collected electroencephalography (EEG) data during an oddball paradigm to elicit mismatch negativity (MMN) and others Event Related Potentials (EPRs), in response to deviant stimuli with both dynamic (looming and receding) and constant level (flat) differences to the standard in the same participants. We then combined a computational model of the auditory periphery with generative EEG methods (temporal response functions, TRFs) to model the single-participant ERPs responses to flat deviants, and used them to predict the effect of the same mechanism on looming and receding stimuli. The flat model explained 45% variance of the looming response, and 33% of the receding response. This provide striking evidence that difference wave responses to looming and receding sounds result from the same cortical mechanism that generate responses to constant-level deviants: all such differences are the sole consequence of their particular physical morphology getting amplified and integrated by peripheral auditory mechanisms. Thus, not all effects seen cortically proceed from top-down modulations by high-level decision variables, but can rather be performed early and efficiently by feed-forward peripheral mechanisms that evolved precisely to sparing subsequent networks with the necessity to implement such mechanisms.

Previous work examining the extent to which individuals seek alcohol to enhance positive experiences (reward drinking) or relieve aversive states (relief drinking) has shown that reward/relief drinking predicts response to naltrexone and acamprosate treatment for alcohol use disorder. Yet, various measures of reward/relief drinking have been used in prior research, and the comparative psychometric properties of these measures are unknown. Evaluating and comparing the psychometric properties of these reward/relief drinking measures could identify measures with the most promise for translating precision medicine findings to clinical practice. In a community sample of 65 individuals with heavy/hazardous alcohol use on the Alcohol Use Disorder Identification Test, we showed good internal consistency reliability, test-retest reliability, and concurrent validity for theoretically aligned measures (e.g., reward drinking and reward responsiveness, relief drinking and depression/anxiety symptoms) of the reward and relief subscales across the six measures. We then used ecological momentary assessment to determine whether reward and relief drinking subscales predicted within-person associations between contextual factors of interest (e.g., negative affect, positive affect, distress intolerance, physical pain, hangover symptoms, social drinking situations, alcohol cues) and same-moment alcohol craving. All six measures demonstrated limited predictive validity for alcohol craving contexts in daily life as assessed via ecological momentary assessment. Despite these findings, reward and relief drinking measures show good reliability and concurrent validity and previously demonstrated clinical utility for predicting response to alcohol use disorder treatments, including naltrexone. Future research should aim to elucidate the mechanisms underlying the association between responses to reward/relief drinking measures and pharmacotherapy outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

Alzheimer's disease (AD) is a complex, progressive primary neurodegenerative disease. Since pivotal genetic studies in 1993, the ε4 allele of the apolipoprotein E gene (APOE ε4) has remained the strongest single genome-wide associated risk variant in AD. Scientific advances in APOE biology, AD pathophysiology and ApoE-targeted therapies have brought APOE to the forefront of research, with potential translation into routine AD clinical care. This contemporary Review will merge APOE research with the emerging AD clinical care pathway and discuss APOE genetic risk as a conduit to genomic-based precision medicine in AD, including ApoE's influence in the ATX(N) biomarker framework of AD. We summarize the evidence for APOE as an important modifier of AD clinical-biological trajectories. We then illustrate the utility of APOE testing and the future of ApoE-targeted therapies in the next-generation AD clinical-diagnostic pathway. With the emergence of new AD therapies, understanding how APOE modulates AD pathophysiology will become critical for personalized AD patient care.

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

Surgical registries do not have separate sex (the biological construct) and gender identity variables. We examined procedures specific to sexually dimorphic anatomy, such as ovaries, testes, and other reproductive organs, to identify "discrepancies" between recorded sex and the anatomy of a procedure. These "discrepancies" would represent a structural limitation of surgical registries, one that may unintentionally perpetuate health inequities.

Amyloid plaque deposition is recognized as the primary pathological hallmark of Alzheimer's disease(AD) that precedes other pathological events and cognitive symptoms. Plaque pathology represents itself with an immense polymorphic variety comprising plaques with different stages of amyloid fibrillization ranging from diffuse to fibrillar, mature plaques. The association of polymorphic Aβ plaque pathology with AD pathogenesis, clinical symptoms and disease progression remains unclear. Advanced chemical imaging tools, such as functional amyloid microscopy combined with MALDI mass spectrometry imaging (MSI), are now enhanced by deep learning algorithms. This integration allows for precise delineation of polymorphic plaque structures and detailed identification of their associated Aβ compositions. We here set out to make use of these tools to interrogate heterogenic plaque types and their associated biochemical architecture. Our findings reveal distinct Aβ signatures that differentiate diffuse plaques from fibrilized ones, with the latter showing substantially higher levels of Aβx-40. Notably, within the fibrilized category, we identified a distinct subtype known as coarse-grain plaques. Both in sAD and fAD brain tissue, coarse grain plaques contained more Aβx-40 and less Aβx-42 compared with cored plaques. The coarse grain plaques in both sAD and fAD also showed higher levels of neuritic content including paired helical filaments (PHF-1)/phosphorylated phospho Tau-immunopositive neurites. Finally, the Aβ peptide content in coarse grain plaques resembled that of vascular Aβ deposits (CAA) though with relatively higher levels of Aβ1-42 and pyroglutamated Aβx-40 and Aβx-42 species in coarse grain plaques. This is the first of its kind study on spatial biochemical characterization of different plaque morphotypes demonstrating the potential of the correlative imaging techniques used that further increase the understanding of heterogeneous AD pathology. Linking the biochemical characteristics of amyloid plaque polymorphisms with various AD etiologies and toxicity mechanisms is crucial. Understanding the connection between plaque structure and disease pathogenesis can enhance our insights. This knowledge is particularly valuable for developing and advancing novel, amyloid-targeting therapeutics.

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.

Kidney transplantation (KT) improves physical and psychological prognoses for patients with end-stage kidney disease (ESKD). However, few comparative studies have examined depression and suicide rates among patients with ESKD treated with dialysis versus KT.

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

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

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Impaired mitochondrial dynamics have been identified as a significant contributing factor to reduced neurogenesis under pathological conditions. However, the relationship among mitochondrial dynamics, neurogenesis, and spatial memory during normal development remains unclear. This study aims to elucidate the role of mitophagy in spatial memory mediated by neurogenesis during development.

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.

Suicide in youth and young adults is a serious public health problem. However, the biological mechanisms of suicidal ideation (SI) remain poorly understood. The primary goal of these analyses was to identify the connectome profile of suicidal ideation using resting state electroencephalography (EEG). We evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10-26 years, n = 111) with current or past diagnoses of either a depressive disorder or bipolar disorder who were enrolled in the Texas Resilience Against Depression Study (T-RAD). Neurocircuitry was analyzed using orthogonalized power envelope connectivity computed from resting state EEG. Suicidal ideation was assessed with the 3-item Suicidal Thoughts factor of the Concise Health Risk Tracking self-report scale. The statistical pipeline involved dimension reduction using principal component analysis, and the association of neuroimaging data with SI using regularized canonical correlation analysis. From the original 111 participants and the correlation matrix of 4950 EEG connectivity pairs in each band (alpha, beta, theta), dimension reduction generated 1305 EEG connectivity pairs in the theta band, 2337 EEG pairs in the alpha band, and 914 EEG connectivity pairs in the beta band. Overall, SI was consistently involved with dysfunction of the default mode network (DMN). This report provides preliminary evidence of DMN dysfunction associated with active suicidal ideation in adolescents. Using EEG using power envelopes to compute connectivity moves us closer to using neurocircuit dysfunction in the clinical setting to identify suicidal ideation.

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.

Observational studies are fraught with several biases including reverse causation and residual confounding. Overview of reviews of observational studies (ie, umbrella reviews) synthesise systematic reviews with or without meta-analyses of cross-sectional, case-control and cohort studies, and may also aid in the grading of the credibility of reported associations. The number of published umbrella reviews has been increasing. Recently, a reporting guideline for overviews of reviews of healthcare interventions (Preferred Reporting Items for Overviews of Reviews (PRIOR)) was published, but the field lacks reporting guidelines for umbrella reviews of observational studies. Our aim is to develop a reporting guideline for umbrella reviews on cross-sectional, case-control and cohort studies assessing epidemiological associations.

Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.

Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors.

Advancing our understanding of brain function and developing treatments for neurological diseases hinge on the ability to modulate neuronal groups in specific brain areas without invasive techniques. Here, we introduce Airy-beam holographic sonogenetics (AhSonogenetics) as an implant-free, cell type-specific, spatially precise, and flexible neuromodulation approach in freely moving mice. AhSonogenetics utilizes wearable ultrasound devices manufactured using 3D-printed Airy-beam holographic metasurfaces. These devices are designed to manipulate neurons genetically engineered to express ultrasound-sensitive ion channels, enabling precise modulation of specific neuronal populations. By dynamically steering the focus of Airy beams through ultrasound frequency tuning, AhSonogenetics is capable of modulating neuronal populations within specific subregions of the striatum. One notable feature of AhSonogenetics is its ability to flexibly stimulate either the left or right striatum in a single mouse. This flexibility is achieved by simply switching the acoustic metasurface in the wearable ultrasound device, eliminating the need for multiple implants or interventions. AhSonogentocs also integrates seamlessly with in vivo calcium recording via fiber photometry, showcasing its compatibility with optical modalities without cross talk. Moreover, AhSonogenetics can generate double foci for bilateral stimulation and alleviate motor deficits in Parkinson's disease mice. This advancement is significant since many neurological disorders, including Parkinson's disease, involve dysfunction in multiple brain regions. By enabling precise and flexible cell type-specific neuromodulation without invasive procedures, AhSonogenetics provides a powerful tool for investigating intact neural circuits and offers promising interventions for neurological disorders.

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

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

The autonomic nervous system plays a vital role in regulating physiological functions. Transcutaneous auricular vagus nerve stimulation (taVNS) is a method that provides insights into autonomic nerve modulation. This paper presents a research protocol investigating proof of mechanism for the impact of taVNS on autonomic functions and aims to both deepen theoretical understanding and pave the way for clinically relevant applications.

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

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

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

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

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

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.

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

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

Early identification of Alzheimer's disease (AD) risk is critical for improving treatment success. Cortical thickness is a macrostructural measure used to assess AD neurodegeneration. However, cortical microstructural changes appear to precede macrostructural atrophy and may improve early risk identification. Currently, whether cortical microstructural changes in aging are linked to vulnerability to AD pathophysiology remains unclear in non-clinical populations, who are precisely the target for early risk identification.

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

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Grape and grape derived products contain many bioactive phenolics which have a variety of impacts on health. Following oral ingestion, the phenolic compounds and their metabolites may be detectable in human urine. However, developing a reliable method for the analysis of phenolic compounds in urine is challenging. In this work, we developed and validated a new high-throughput, sensitive and reproducible analytical method for the simultaneous analysis of 31 grape phenolic compounds and metabolites using Oasis PRiME HLB cleanup for sample preparation combined with ultra-performance liquid chromatography with triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS). Using this new method, the accuracy achieved was 69.3 % ∼ 134.9 % (except for six compounds), and the recovery achieved was 52.4 % ∼ 134.7 % (except for two very polar compounds). For each of the 31 target analytes, the value of intra-day precision was less than 14.3 %. The value of inter-day precision was slightly higher than intra-day precision, with a range of 0.7 % ∼ 19.1 %. We report for the first time on the effect of gender and BMI on the accuracy and recovery of human urine samples, and results from analysis of variance (ANOVA), and principal component analysis (PCA) indicated there was no difference in the value of accuracy and recovery between different gender or BMI (>30) using our purposed cleanup and UHPLC-QqQ-MS/MS method. Overall, this newly developed method could serve as a powerful tool for analyzing grape phenolic compounds and metabolites in human urine samples.

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

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

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

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

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

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

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

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

Tissue barriers in a body, well known as tissue-to-tissue interfaces represented by endothelium of the blood vessels or epithelium of organs, are essential for maintaining physiological homeostasis by regulating molecular and cellular transports. It is crucial for predicting drug response to understand physiology of tissue barriers through which drugs are absorbed, distributed, metabolized and excreted. Since the FDA Modernization Act 2.0, which prompts the inception of alternative technologies for animal models, tissue barrier chips, one of the applications of organ-on-a-chip or microphysiological system (MPS), have only recently been utilized in the context of drug development. Recent advancements in stem cell technology have brightened the prospects for the application of tissue barrier chips in personalized medicine. In past decade, designing and engineering these microfluidic devices, and demonstrating the ability to reconstitute tissue functions were main focus of this field. However, the field is now advancing to the next level of challenges: validating their utility in drug evaluation and creating personalized models using patient-derived cells. In this review, we briefly introduce key design parameters to develop functional tissue barrier chip, explore the remarkable recent progress in the field of tissue barrier chips and discuss future perspectives on realizing personalized medicine through the utilization of tissue barrier chips.

Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data.

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

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

The 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.

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

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

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

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

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

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

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

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

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

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

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

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

We herein inferred the genetic diversity of CYP450 isoenzymes to predict the percentage of patients who need dose adjustment in drugs used in psychiatry. Data of 784 Greek patients receiving psychiatric care who were genotyped for CYP2D6, CYP2C19, CYP1A2, CYP3A5 and CYP2C9 isoenzymes were inferred to gene-drug pairs according to the US FDA, Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group annotations and published literature. Atypical metabolism was found for 36.8% of patients in CYP2D6, 49.2% in CYP2C19, 45% in CYP1A2, 16.7% in CYP3A5 and 41.8% in CYP2C9. Dosage adjustment need was estimated for 10.2% of venlafaxine, 10.0% of paroxetine, 6.4% of sertraline, 30.8% of citalopram, 52.1% of escitalopram, 18.2% of fluvoxamine, 54.1% of tricyclic antidepressants, 16.7% of zuclopenthixol, 10.6% of haloperidol and 13.3% of risperidone treated patients. Clinical psychiatric pharmacogenomic implementation holds promise to improve drug effectiveness and safety.

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.

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

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.

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.

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.

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.

The explosion of generative AI offers promise for neuroimaging biomarker development in psychiatry, but effective adoption of AI methods requires clarity with respect to specific applications and challenges. These center on dataset sizes required to robustly train AI models along with feature selection that capture neural signals relevant to symptom and treatment targets. Here we discuss areas where generative AI could improve quantification of robust and reproducible brain-to-symptom associations to inform precision psychiatry applications, especially in the context of drug discovery. Finally, this communication discusses some challenges that need solutions for generative AI models to advance neuroimaging biomarkers in psychiatry.

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

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

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.

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

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.

The risk of suicide can be decreased by accurately identifying high-risk suicide groups and implementing the right interventions. The aim of this study was to develop a nomogram for suicide attempts (SA) in patients with first-episode drug-naïve (FEDN) major depressive disorder (MDD).

Unexplained physical signs and symptoms represent a significant portion of patient presentations in acute care settings. Even in cases where a patient presents with a known medical condition, functional or somatic symptoms may complicate the diagnostic and treatment processes and prognostic outcome. One umbrella category for neurologically related somatic symptoms, functional neurological disorder (FND), presents as involuntary neurological symptoms incompatible with another medical condition. Symptoms may include weakness and/or paralysis, movement disorders, non-epileptic seizures, speech or visual impairment, swallowing difficulty, sensory disturbances, or cognitive symptoms (1). While FND presents as neuropsychiatric, providers commonly report feeling hesitant to diagnose these disorders. Inexperience or lack of appropriate education on relevant research regarding evidence-based practices or standard of practice (SOP) may result in over- or underperforming diagnostic workups and consultations, utilizing inappropriate medications, and failing to offer evidence-based psychological interventions. Being mindful of these challenges when treating patients presenting with functional symptoms in acute care settings can help to support and protect the patients and care team and appropriately control healthcare costs.

Studies suggest that both depression and disrupted sleep disturbance are linked to cardiovascular disease (CVD). However, the precise role of sleep disturbance in the connection between depression and CVD is poorly understood. Therefore, we sought to examine the associations among these factors and further explore the mediating role of sleep disturbance in the association between depression and CVD.

The identification of depression primarily relies on the clinical symptoms and psychiatric evaluation of the patient, in the absence of objective and quantifiable biomarkers within clinical settings. This study aimed to explore potential serum biomarkers associated with depression.

Recently, there have been a few reports of atypical post-coronavirus disease 2019 (COVID-19) myelopathy manifesting tract-specific lesions similar to those due to vitamin B deficiency. However, the precise characteristics of imaging or clinical course remain not well understood.

Structural imaging holds great potential for precise targeting and stimulation for deep brain stimulation (DBS). The anatomical information it provides may serve as potential biomarkers for predicting the efficacy of DBS in treatment-resistant depression (TRD).

Although kratom use has been part of life for centuries in Southeast Asia, the availability and use of kratom in the United States (US) increased substantially since the early 2000s when there was little information on kratom pharmacology, use patterns, and effects, all critical to guiding regulation and policy. Here we provide a synthesis of research with several hundred English-language papers published in the past 5 years drawing from basic research, epidemiological and surveillance data, and recent clinical research. This review of available literature aims to provide an integrated update regarding our current understanding of kratom's benefits, risks, pharmacology, and epidemiology, which may inform United States-based kratom regulation. Recent surveillance indicates there are likely several million past-year kratom consumers, though estimates vary widely. Even without precise prevalence data, kratom use is no longer a niche, with millions of United States adults using it for myriad reasons. Despite its botanical origins in the coffee tree family and its polypharmacy, kratom is popularly characterized as an opioid with presumed opioid-system-based risks for addiction or overdose. Neuropharmacology, toxicology, and epidemiology studies show that kratom is more accurately characterized as a substance with diverse and complex pharmacology. Taken together the work reviewed here provides a foundation for future scientific studies, as well as a guide for ongoing efforts to regulate kratom. This work also informs much-needed federal oversight, including by the United States Food and Drug Administration. We conclude with recommendations for kratom regulation and research priorities needed to address current policy and knowledge gaps around this increasingly used botanical product.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

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