Predictive Psychiatry

Obsessive-compulsive disorder (OCD) is a complex psychiatric disorder. Genetic and broad environmental factors are common risk factors for OCD. The purpose of this study is to explore the molecular mechanism of OCD and to find new molecular targets for the diagnosis and management of OCD.

A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care.

National Guard soldiers experience unique reintegration challenges. In addition to managing the consequences of combat-related trauma, they also navigate multiple transitions between military and civilian life. Despite these obstacles, many soldiers report positive outcomes and personal growth due to deployment, a phenomenon most commonly referred to in the literature as posttraumatic growth (PTG). The current study explored PTG in National Guard soldiers using a multidimensional longitudinal approach, with the goal of validating reports of PTG in soldiers. Data were collected from National Guard soldiers at pre-deployment, reintegration, one year post-deployment and two years post-deployment. Informed by PTG theory, three PTG constructs were measured (perceived ability to handle stress, social support seeking, and purpose in life) at each of the four time points, with increases in these constructs indicating growth. Potential predictors of growth in these PTG constructs were also explored. Results from a repeated measure latent profile analysis indicated that PTG did occur in certain soldiers, and that higher optimism and less severe PTSD symptoms predict this growth. These findings emphasize the importance of making efforts to facilitate PTG in soldiers.

There is enormous potential to improve brain health and reduce the risk of cognitive decline and dementia based on modifiable risk factors. The Lifestyle for Brain Health (LIBRA) index was developed to quantify modifiable dementia risk or room for brain health improvement. The objective of the study was to investigate the utility of the LIBRA index in relation to cognitive functioning in a midlife to early late-life sample of New Zealanders.

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy in advanced Parkinson's disease (PD). Motor and non-motor outcomes, however, show considerable inter-individual variability. Preoperative morphometry-based metrics have recently received increasing attention to explain treatment effects. As evidence for the prediction of non-motor outcomes is limited, we sought to investigate the association between metrics of voxel-based morphometry and short-term non-motor outcomes following STN-DBS in this prospective open-label study. Forty-nine PD patients underwent structural MRI and a comprehensive clinical assessment at preoperative baseline and 6-month follow-up. Voxel-based morphometry was used to assess associations between cerebral volume and non-motor outcomes corrected for multiple comparisons using a permutation-based approach. We replicated existing results associating volume loss of the superior frontal cortex with subpar motor outcomes. Overall non-motor burden, however, was not significantly associated with morphometric features, limiting its use as a marker to inform patient selection and holistic preoperative counselling.

Stress affects brain serotonin (5HT) and dopamine (DA) function, and the effectiveness of 5HT and DA to regulate stress and emotional responses. However, our understanding of the long-term impact of early life adversity (ELA) on primate brain monoaminergic systems during adolescence is scarce and inconsistent. Filling this gap in the literature is critical, given that the emergence of psychopathology during adolescence has been related to deficits in these systems. Here, we use a translational nonhuman primate (NHP) model of ELA (infant maltreatment by the mother) to examine the long-term impact of ELA on adolescent 5HT, 5HT and D receptor systems. These receptor systems were chosen based on their involvement in stress/emotional control, as well as reward and reinforcement. Rates of maternal abuse, rejection, and infant's vocalizations were obtained during the first three postnatal months, and hair cortisol concentrations obtained at 6 months postnatal were examined as early predictors of binding potential (BP) values obtained during adolescence using positron emission tomography (PET) imaging. Maltreated animals demonstrated significantly lower 5HT receptor BP in prefrontal cortical areas as well as the amygdala and hippocampus, and lower 5HT receptor BP in striatal and prefrontal cortical areas. Maltreated animals also demonstrated significantly lower D BP in the amygdala. None of the behavioral and neuroendocrine measurements obtained early in life predicted any changes in BP data. Our findings suggest that early caregiving experiences regulate the development of brain 5HT and DA systems in primates, resulting in long-term effects evident during adolescence.

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer's disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean = 38, std = 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE = 6.63 years, std = 0.74 years) and GM volume (mean MAE = 6.95 years, std = 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE = 5.54 years, std = 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

To examine the unique contribution of alexithymia at 1 year after traumatic brain injury (TBI) to the prospective prediction of emotional and social health outcomes at 2 years post-injury.

: The study aims to provide a comprehensive neuropsychological analysis of psychotic spectrum disorders, including schizophrenia, bipolar disorder, and depression. It focuses on the critical aspects of cognitive impairments, diagnostic tools, intervention efficacy, and the roles of genetic and environmental factors in these disorders. The paper emphasizes the diagnostic significance of neuropsychological tests in identifying cognitive deficiencies and their predictive value in the early management of psychosis. : The study involved a systematic literature review following the PRISMA guidelines. The search was conducted in significant databases like Scopus, PsycINFO, PubMed, and Web of Science using keywords relevant to clinical neuropsychology and psychotic spectrum disorders. The inclusion criteria required articles to be in English, published between 2018 and 2023, and pertinent to clinical neuropsychology's application in these disorders. A total of 153 articles were identified, with 44 ultimately included for detailed analysis based on relevance and publication status after screening. The review highlights several key findings, including the diagnostic and prognostic significance of mismatch negativity, neuroprogressive trajectories, cortical thinning in familial high-risk individuals, and distinct illness trajectories within psychosis subgroups. The studies evaluated underline the role of neuropsychological tests in diagnosing psychiatric disorders and emphasize early detection and the effectiveness of intervention strategies based on cognitive and neurobiological markers. : The systematic review underscores the importance of investigating the neuropsychological components of psychotic spectrum disorders. It identifies significant cognitive impairments in attention, memory, and executive function, correlating with structural and functional brain abnormalities. The paper stresses the need for precise diagnoses and personalized treatment modalities, highlighting the complex interplay between genetic, environmental, and psychosocial factors. It calls for a deeper understanding of these neuropsychological processes to enhance diagnostic accuracy and therapeutic outcomes.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

Identifying biomarkers that predict substance use disorder (SUD) propensity may better strategize anti-addiction treatment. The melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH) critically mediates interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers.

The co-occurrence of substance use disorder with at least one other mental disorder is called dual pathology, which in turn is characterised by heterogeneous symptoms that are difficult to diagnose and have a poor response to treatment. For this reason, the identification and validation of biomarkers is necessary. Within this group, possible electroencephalographic biomarkers have been reported to be useful in diagnosis, treatment and follow-up, both in neuropsychiatric conditions and in substance use disorders. This article aims to review the existing literature on electroencephalographic biomarkers in dual pathology.

Anxiety disorders impacts about 4 % of aging individuals globally. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders.

As a major mental health disorder, symptoms of schizophrenia (SCZ) include delusions, reduced motivation, hallucinations, reduced motivation and a variety of cognitive disabilities. Many of these symptoms are now known to be associated with abnormal regulation of the immune system. Low blood levels of cytokines and chemokines have been suggested to be one of the underlying causes of SCZ. However, their biological roles at different stages of SCZ remain unclear. Our objective was to investigate expression patterns of cytokines and chemokines at different stages of onset and relapse in SCZ patients and to conduct an analysis of their relationship to disease progression. We also aimed to identify immune features associated with different disease trajectories in patients with SCZ. Gene set enrichment analysis (GSEA) was used to interrogate the GSE27383 dataset and identify key genes associated with inflammation. These results led us to recruit 36 healthy controls, 40 patients with first-episode psychosis (FEP), and 39 patients with SCZ relapse. Meso Scale Discovery technology was used to independently validate serum levels of 35 cytokines and chemokines. This was followed by a meta-analysis to gain a more comprehensive understanding of the role of interleukin-8 (IL-8/CXCL8) in SCZ. Analysis of the GSE27383 database revealed 3596 genes with distinct expression patterns. A significant portion of these genes were identified as inflammation-related and showed remarkable enrichment in three key pathways: IL17, cytokine-cytokine receptor, and AGE-RAGE signaling in diabetic complications. We observed co-expression of CXCL8 and IL16 within these three pathways. In a subsequent analysis of independently validated samples, a notable discrepancy was detected in the inflammatory status between individuals experiencing FEP and those in relapse. In particular, expression of CXCL8 demonstrated superior predictive capability in FEP and relapsed patients. Notably, results of the meta-analysis confirmed that Chinese and European populations were consistent with the overall results (Z = 4.60, P < 0.001; Z = 3.70, P < 0.001). However, in the American subgroup, there was no significant difference in CXCL8 levels between patients with SCZ compared to healthy controls (Z = 1.09, P = 0.277). Our findings suggest that the inflammatory response in patients with SCZ differs across the different stages, with CXCL8 emerging as a potential predictive factor. Collectively, our data suggest that CXCL8 has the potential to serve as a significant immunological signature of SCZ subtypes. Trial registration: The clinical registration number for this trial is ChiCTR2100045240 (Registration Date: 2021/04/09).

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder's multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.

Neuroinflammatory processes have been extensively implicated in the underlying neurobiology of numerous neuropsychiatric disorders. Elevated C-reactive protein (CRP), an indicator of nonspecific inflammation commonly utilized in clinical practice, has been associated with depression in adults. In adolescents, our group previously found CRP to be associated with altered neural reward function but not with mood and anxiety symptoms assessed cross-sectionally. We hypothesized that the distinct CRP findings in adolescent versus adult depression may be due to chronicity, with neuroinflammatory effects on psychiatric disorders gradually accumulating over time. Here, we conducted a longitudinal study to evaluate if CRP levels predicted future onset or progression of depression in adolescents. Participants were 53 adolescents (age = 14.74 ± 1.92 years, 35 female), 40 with psychiatric symptoms and 13 healthy controls. At baseline, participants completed semistructured diagnostic evaluations; dimensional assessments for anxiety, depression, anhedonia, and suicidality severity; and bloodwork to quantify CRP levels. Clinical assessments were repeated at longitudinal follow-up after ∼1.5 years. Spearman's correlation between CRP levels and follow-up symptom severity were controlled for body mass index, age, sex, and follow-up interval and considered significant at the two-tailed, Bonferroni-adjusted  < 0.05 level. After correction for multiple comparisons, no relationships were identified between baseline CRP levels and follow-up symptom severity. CRP levels were not significantly associated with future psychiatric symptoms in adolescents in this preliminary analysis. This may suggest that CRP is not a useful biomarker for adolescent depression and anxiety. However, future longitudinal studies with larger sample sizes and incorporating additional indicators of neuroinflammation are needed.

Schizophrenia is a severe, chronic neuropsychiatric disorder characterized by symptoms that profoundly impact behavior, cognition, perception, and emotions, leading to a reduced quality of life and physical impairment. Given the complexity of schizophrenia, there is a pressing need for clinical markers and tools to predict its course, enhance disease staging, facilitate early intervention, improve differential diagnosis, and tailor individualized treatment approaches. Previous studies focused on the relationship between neurological soft signs (NSS) and factors such as age, illness duration, and symptomatology, indicating NSS as state markers improving in parallel with psychotic symptom remission or predicting treatment resistance. However, there is a lack of consensus on NSS assessment tools, hindering routine clinical monitoring despite diagnostic and prognostic potential. The present longitudinal study involved 81 psychiatric inpatients diagnosed with schizophrenia. Patients were assessed at three time points: baseline, 1 month, and 6 months. The examination included the use of scales to evaluate psychotic and neurological symptoms, as well as the identification of adverse extrapyramidal reactions caused by neuroleptic treatment. The progression of NSS was correlated to both the symptomatology and the sociodemographic data of the patients. The main findings from the present investigation revealed a statistical correlation between NSS and psychopathological symptoms, especially with negative symptoms of schizophrenia. However, it is important to note that neuroleptic side effects only had a limited impact on NSS. Therefore, instead of being linked to extrapyramidal symptoms caused by neuroleptics, NSS appears to be more frequently related with symptoms of schizophrenia. Our findings provide further support for their strong association with the course of schizophrenia, independent of treatment side effects, thus emphasizing their potential as reliable assessment tools in both research and clinical settings.

Low desire in women is the most common sexual difficulty, and stress has been identified as a significant predictor of symptoms. We evaluated a mindfulness-based cognitive therapy (MBCT) group treatment versus a sex education comparison group treatment (STEP) on self-reported stress and on the physiological stress response measured via morning-to-evening cortisol slope in 148 women with a diagnosis of sexual interest/arousal disorder (SIAD). Perceived stress decreased following treatment in both groups, and significantly more after MBCT. The cortisol slope was steeper (indicative of better stress system regulation) from pre-treatment to 6-month follow-up, with no differences between the groups. As an exploratory analysis, we found that the reduction in perceived stress predicted increases in sexual desire and decreases in sex-related distress for participants after MBCT only. These findings suggest that group mindfulness targeting women with low sexual desire leads to improvements in self-reported and physiological stress, with improvements in self-reported stress partially accounting for improvements in sexual desire and distress.

The positive association of health with education level and socioeconomic status (SES) is well-established. Two theoretical frameworks have been delineated to understand main mechanisms leading to socioeconomic health inequalities: social causation and health selection but how these work in adolescence is poorly known. We studied if adolescent health and health behaviours predict higher education and higher SES in adulthood and if family background and school performance in adolescence explain these associations.

Sexual minority adolescents (SMA) have a disproportionately high prevalence of victimisation, self-harm, and depressed mood, relative to the general population. Yet, the contributing and mechanistic factors are unclear. We aim to explore the directional relationship between victimisation and self-harm and depressed mood, with poor sleep quality as a possible mediator. A secondary data analysis was conducted using a nationally representative birth cohort in the United Kingdom, where participants self-identified as sexual minority (N = 1922, aged 11-13, 67.1% female) and their parents completed questionnaires and interviews when the participants were aged 11, 14 and 17. Logistic and linear regression were used to test whether victimisation prospectively predicted self-harm and depressed mood with mediation analyses conducted to assess if sleep onset latency and nocturnal awakening mediated their relationships. After adjusting for demographic factors and baseline self-harm and depressed mood, victimisation at age 11 significantly predicted self-harm (OR = 1.40, p < .01) and depressed mood (B = 0.024, SE = 0.01, p < .05) at age 17. In the mediation analyses, frequent nocturnal awakening at age 14, but not sleep onset latency, significantly mediated the effect of victimisation at age 11 on self-harm (indirect effect B = 0.008, SE = 0.004, 95%CI = 0.001-0.017) and depressed mood (indirect effect B = 0.005, SE = 0.002 95%CI = 0.001-0.010) at age 17. Our findings supported that victimisation contributed to negative mental health among SMA. Poor sleep quality could be an indicator of maladjustment with victimisation, which further increased vulnerability to negative mental health. Victimisation and sleep quality could be important assessment targets in mental health campaign among sexual minority adolescents.

The objectives of this study were to (a) assess the associations between early behavioral problems and intergenerational income mobility (i.e., the degree to which income status is transmitted from one generation to the next), (b) verify whether these associations are moderated by child sex, and (c) explore indirect effects of early behavioral problems on income mobility via high school graduation.

The COVID-19 pandemic raised concerns regarding increased suicide-related behaviours. We compared characteristics and counts of Emergency Department (ED) presentations for self-harm, an important suicide-related outcome, during versus prior to the pandemic's first year. We included patients presenting with self-harm to the ED of two trauma centres in Toronto, Canada. Time series models compared intra-pandemic (March 2020-February 2021) presentation counts to predictions from pre-pandemic data. The self-harm proportion of ED presentations was compared between the intra-pandemic period and preceding three years. A retrospective chart review of eligible patients seen from March 2019-February 2021 compared pre- vs. intra-pandemic patient and injury characteristics. While monthly intra-pandemic self-harm counts were largely within expected ranges, the self-harm proportion of total presentations increased. Being widowed (OR=9.46; 95 %CI=1.10-81.08), employment/financial stressors (OR=1.65, 95 %CI=1.06-2.58), job loss (OR=3.83; 95 %CI=1.36-10.76), and chest-stabbing self-harm (OR=2.50; 95 %CI=1.16-5.39) were associated with intra-pandemic presentations. Intra-pandemic self-harm was also associated with Intensive Care Unit (ICU) admission (OR=2.18, 95 %CI=1.41-3.38). In summary, while the number of self-harm presentations to these trauma centres did not increase during the early pandemic, their proportion was increased. The association of intra-pandemic self-harm with variables indicating medically severe injury, economic stressors, and being widowed may inform future suicide and self-harm prevention strategies.

Social, familial, and physiological stressors may put maternal-infant bonding at risk. Therefore, it is plausible that the stressful conditions brought on by COVID-19 could influence maternal-infant bonding. This study aimed to elucidate the contribution of COVID-19-related experience to variance in maternal-infant bonding, beyond that of established risk factors and as moderated by social support.

Identifying individuals with intracranial injuries following mild traumatic brain injury (mTBI), i.e. complicated mTBI cases, is important for follow-up and prognostication. The main aims of our study were (1) to assess the temporal evolution of blood biomarkers of CNS injury and inflammation in individuals with complicated mTBI determined on computer tomography (CT) and magnetic resonance imaging (MRI); (2) to assess the corresponding discriminability of both single- and multi-biomarker panels, from acute to chronic phases after injury.

The precise roles of screen media activity (SMA) and sleep problems in relation to child/adolescent psychopathology remain ambiguous. We investigated temporal relationships among sleep problems, SMA, and psychopathology and potential involvement of thalamus-prefrontal-cortex (PFC)-brainstem structural covariation.

Posttraumatic stress disorder (PTSD) is a heterogeneous disorder, and symptom severity varies over time. Neurobiological factors that predict PTSD symptoms and their chronicity remain unclear. This study investigated whether the volume of the hippocampus and its subfields, particularly cornu ammonis (CA) 1, CA3, and dentate gyrus, are associated with current PTSD symptoms and whether they predict PTSD symptom changes over 2 years. We examined clinical and structural magnetic resonance imaging measures from 252 trauma-exposed post-9/11 veterans (159 with Time 1 PTSD diagnosis) during assessments approximately 2 years apart. Automated hippocampal subfield segmentation was performed with FreeSurfer Version 7.1, producing 19 bilateral subfields. PTSD symptoms were measured at each assessment using the Clinician-Administered PTSD Scale-IV (CAPS). All models included total intracranial volume as a covariate. First, similar to previous reports, we showed that smaller overall hippocampal volume was associated with greater PTSD symptom severity at Time 1. Notably, when examining regions of interest (CA1, CA3, dentate gyrus), we found that smaller Time 1 hippocampal volumes in the bilateral CA1-body and CA2/3-body predicted decreased PTSD symptom severity at Time 2. These findings were not accounted for by combat exposure or treatment history. Additionally, both Time 1 CA1-body and CA2/3-body volume showed unique associations with changes in avoidance/numbing, but not with changes in reexperiencing or hyperarousal symptoms. This supports a more complex and nuanced relationship between hippocampal structure and PTSD symptoms, where during the posttrauma years bigger may not always mean better, and suggests that the CA1-body and CA2/3-body are important factors in the maintenance of PTSD symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Verbal retrieval (VR) deficits often occur after traumatic brain injury (TBI), but the mechanisms remain unclear. We examined how event-related potentials (ERPs) during a Go-NoGo task were associated with VR deficits.

Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

Both endocannabinoid (EC) and endogenous opioid systems are involved in nociceptive processing and may work together synergistically based on preclinical models. This study evaluated the interactive effects of preoperative beta-endorphin concentrations (a key analgesic endogenous opioid) in cerebrospinal fluid (CSF) and ECs (CSF and plasma 2-arachidonoylglycerol [2-AG] and plasma anandamide [AEA]) on postoperative opioid use and pain intensity in a prospective cohort of n = 112 pregnant patients undergoing scheduled cesarean delivery. Maternal blood and CSF samples were collected preoperatively for beta-endorphin and EC assays. Patients completed measures of outpatient opioid use (number of tablets used and days of use) and average pain intensity at 2 weeks postoperatively. Results of general linear model analyses controlling for maternal age, BMI at time of delivery, and race revealed significant multiplicative interactions between EC and beta-endorphin concentrations on number of opioid tablets used (based on pill count), days of opioid use, and total milligram morphine equivalents used in the 2 week follow-up period. Elevated preoperative plasma and CSF 2-AG predicted reduced outpatient opioid analgesic use particularly for patients low in CSF beta-endorphin. Similar analyses for pain intensity at 2-week follow-up indicated a significant interaction (p<.02) characterized by higher preoperative beta-endorphin concentrations being associated with lower subsequent pain only for individuals with low preoperative plasma AEA concentrations. Further exploration of interactions between EC and endogenous opioid inhibitory systems as they influence responses to opioid analgesics in other clinical pain populations may help guide development of precision pain management approaches. PERSPECTIVE: In the postoperative setting of patients undergoing cesarean delivery, elevated endocannabinoids were linked to reduced outpatient opioid analgesic use in individuals who had low endogenous opioid concentrations in cerebrospinal fluid. Further exploration of interactions between these two inhibitory systems as they impact on responses to pain management interventions appears warranted.

Identifying mechanisms of childhood abuse-adulthood psychopathology relations could facilitate preventive efforts, but most prior studies used cross-sectional or two-wave designs and did not test the effects of childhood maternal and paternal abuse separately. Our 18-year three-wave study thus determined if Wave 2 daily stress reactivity and risk appraisal severity mediated Wave 1 retrospectively-reported childhood maternal and paternal abuse on Wave 3 generalized anxiety disorder (GAD), major depressive disorder (MDD), panic disorder (PD), alcohol (AUD), and substance use disorder (SUD) self-rated symptom severity. Longitudinal structural equation modeling was employed, adjusting for Wave 1 psychopathology severity. Higher childhood maternal and paternal abuse consistently predicted greater future daily stress reactivity and risk appraisal, and these mediators subsequently predicted increased GAD, MDD, and PD, but not AUD and SUD severity. Daily stress reactivity and risk appraisal consistently mediated the pathways between childhood maternal and paternal abuse predicting heightened adulthood GAD, MDD, and PD (Cohen's d = 0.333-0.888) but not AUD and SUD severity. Mediation effect sizes were stronger for childhood maternal (24.5-83.0 %) than paternal (19.5-56.0 %) abuse as the predictor. The latent interaction between Wave 1 childhood maternal and paternal abuse did not moderate the effect of Wave 1 maternal or paternal abuse on any Wave 3 adulthood psychopathology severity through Wave 2 daily stress reactivity and risk appraisal. Our research emphasizes the urgent requirement for continuous evaluation and intervention initiatives in trauma-informed care, both in inpatient and outpatient treatment settings.

With increases in cannabis use and potency, there is a need to improve our understanding of the impact of use on cognitive function. Previous research indicates long-term cannabis use may have a negative effect on executive function. Few studies have examined persistence of it in protracted abstinence, and there is limited evidence of predictors of worse cognitive function in current and former users. In this study, we aim to evaluate the associations between cannabis use status (current, former, and never use) and self-report cognition. Further, we investigate if cannabis use characteristics predict self-report cognitive function.

Hexafluoropropylene oxide trimer acid (HFPO-TA) has been found to cause hepatotoxicity, lipotoxicity, and cytotoxicity. However, the effects of HFPO-TA exposure on nervous system toxicity are still unclear. Here, six-week-old male C57BL/6J mice were treated with 2, 20, and 200 μg/L HFPO-TA for six weeks. The untargeted transcriptome analysis was employed to identify differentially expressed mRNAs in the tissue of mouse hippocampi. Then, the levels of neurotransmitters were detected by ELISA analysis in hippocampal and colonic tissues. Real-time quantitative PCR and western blotting analysis were performed to detect the expression of genes associated with modulation of serotonin (5-HT) metabolism and blood-brain barrier. HFPO-TA exposure reduced the mRNA and protein expression of several tight junction protein-coded genes, including Occludin, Claudin-1, and ZO-1, in mice hippocampi, indicating that the blood-brain barrier was disrupted. Moreover, HFPO-TA exposure elevated the expression of neuroinflammatory factors, including TNF-α, IL-6, IL-1β, TGF-α, and TGF-β. Analysis of hippocampal transcriptomics suggested that HFPO-TA exposure would impair 5-HT generation and metabolic pathways. In keeping with this prediction, our findings confirmed that the levels of several neurotransmitters, including tryptophan (TRP), 5-HT, 5-HTP, and 5-HIAA, were all impaired by HFPO-TA exposure in the serum, colon, and hippocampus, as was the colonic and hippocampal expression of TRP and 5-HT metabolism-related genes such as SERT, MAO-A, and IDO. These results suggest that HFPO-TA nervous system toxicity in mice may be partly modulated by the brain-gut axis and that HFPO-TA exposure may negatively impact human mental health.

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

Adherence to dietary guidelines is critical for optimizing health and weight outcomes after metabolic and bariatric surgery, yet many patients have difficulty. The purpose of this study was to identify the types and frequency of post-surgery non-adherent dietary behaviors and to determine pre-surgery predictors of adherence at 1-year post-surgery.

Depression in older adults with cognitive impairment increases progression to dementia. Microbiota is associated with current mood and cognition, but the extent to which it predicts future symptoms is unknown. In this work, we identified microbial features that reflect current and predict future cognitive and depressive symptoms. Clinical assessments and stool samples were collected from 268 participants with varying cognitive and depressive symptoms. Seventy participants underwent 2-year follow-up. Microbial community diversity, structure, and composition were assessed using high-resolution 16 S rRNA marker gene sequencing. We implemented linear regression to characterize the relationship between microbiome composition, current cognitive impairment, and depressive symptoms. We leveraged elastic net regression to discover features that reflect current or future cognitive function and depressive symptoms. Greater microbial community diversity associated with lower current cognition in the whole sample, and greater depression in participants not on antidepressants. Poor current cognitive function associated with lower relative abundance of Bifidobacterium, while greater GABA degradation associated with greater current depression severity. Future cognitive decline associated with lower cognitive function, lower relative abundance of Intestinibacter, lower glutamate degradation, and higher baseline histamine synthesis. Future increase in depressive symptoms associated with higher baseline depression and anxiety, lower cognitive function, diabetes, lower relative abundance of Bacteroidota, and lower glutamate degradation. Our results suggest cognitive dysfunction and depression are unique states with an overall biological effect detectable through gut microbiota. The microbiome may present a noninvasive readout and prognostic tool for cognitive and psychiatric states.

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

The surge in digital media consumption, coupled with the ensuing consequences of digital addiction, has witnessed a rapid increase, particularly after the initiation of the COVID-19 pandemic. Despite some studies exploring specific technological addictions, such as internet or social media addiction, in Bangladesh, there is a noticeable gap in research focusing on digital addiction in a broader context. Thus, this study aims to investigate digital addiction among students taking the university entrance test, examining its prevalence, contributing factors, and geographical distribution using GIS techniques.

The current study applied survival analysis to examine factors associated with nondisclosure of human immunodeficiency virus (HIV) serostatus among mothers living with HIV (MLH) who had participated in a cognitive-behavioral intervention to disclose their HIV status to their children.

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.

Inhibitory control plays an important role in children's cognitive and socioemotional development, including their psychopathology. It has been established that contextual factors such as socioeconomic status (SES) and parents' psychopathology are associated with children's inhibitory control. However, the relations between the neural correlates of inhibitory control and contextual factors have been rarely examined in longitudinal studies. In the present study, we used both event-related potential (ERP) components and time-frequency measures of inhibitory control to evaluate the neural pathways between contextual factors, including prenatal SES and maternal psychopathology, and children's behavioral and emotional problems in a large sample of children ( = 560; 51.75% females; = 7.13 years; Range = 4-11 years). Results showed that theta power, which was positively predicted by prenatal SES and was negatively related to children's externalizing problems, mediated the longitudinal and negative relation between them. ERP amplitudes and latencies did not mediate the longitudinal association between prenatal risk factors (i.e., prenatal SES and maternal psychopathology) and children's internalizing and externalizing problems. Our findings increase our understanding of the neural pathways linking early risk factors to children's psychopathology.

While it is known that vitamin D deficiency is associated with adverse bone outcomes, it remains unclear whether low vitamin D status may increase the risk of a wider range of health outcomes. We had the opportunity to explore the association between common genetic variants associated with both 25 hydroxyvitamin D (25OHD) and the vitamin D binding protein (DBP, encoded by the gene) with a comprehensive range of health disorders and laboratory tests in a large academic medical center. We used summary statistics for 25OHD and DBP to generate polygenic scores (PGS) for 66,482 participants with primarily European ancestry and 13,285 participants with primarily African ancestry from the Vanderbilt University Medical Center Biobank (BioVU). We examined the predictive properties of PGS, and two scores related to DBP concentration with respect to 1322 health-related phenotypes and 315 laboratory-measured phenotypes from electronic health records. In those with European ancestry: (a) the PGS and PGS scores, and individual SNPs rs4588 and rs7041 were associated with both 25OHD concentration and 1,25 dihydroxyvitamin D concentrations; (b) higher PGS was associated with decreased concentrations of triglycerides and cholesterol, and reduced risks of vitamin D deficiency, disorders of lipid metabolism, and diabetes. In general, the findings for the African ancestry group were consistent with findings from the European ancestry analyses. Our study confirms the utility of PGS and two key variants within the gene (rs4588 and rs7041) to predict the risk of vitamin D deficiency in clinical settings and highlights the shared biology between vitamin D-related genetic pathways a range of health outcomes.

Maintaining the good mental health of Taiwanese military personnel is crucial, especially in light of incidents such as the Taiwan Strait crisis. Suicide is a leading cause of death among military personnel and alexithymia is a significant risk factor for suicidal ideation. However, the mechanisms linking alexithymia and suicidal ideation in this psychologically burdened population remain poorly understood. In total, 863 voluntary army military personnel from Taiwanese reserve brigades and combined-arms brigades were enroled between May 2020 and February 2021. Structured questionnaires about alexithymia, perceived stress, depression, suicidal ideation, and background characteristics were used. Mediation analyses were conducted to examine the serial mediation roles of perceived stress and depression in the relationship between alexithymia and suicidal ideation. Significant positive correlations were observed between alexithymia, perceived stress, depression, and suicidal ideation in bivariate analyses. Serial mediation analyses revealed that alexithymia significantly predicted higher levels of perceived stress, subsequently leading to depressive symptoms, which were associated with suicidal ideation. Depression served as a significant mediator between alexithymia and suicidal ideation. The strongest mediating effect (71.4%) was observed in the pathway from alexithymia through perceived stress and depression to suicidal ideation. Limitations included the utilization of cross-sectional data and a reliance on retrospective self-report measures. Perceived stress and depression were identified as serial mediators in the association between alexithymia and suicidal ideation. Clinically, it is crucial to prioritise interventions that target emotional regulation skills and assess the presence of alexithymia to effectively reduce suicidal ideation in military personnel.

This study employs a descriptive phenomenological approach to investigate the challenges anesthesia nurses face in managing emergence delirium (ED), a common and complex postoperative complication in the post-anesthesia care unit. The role of nurses in managing ED is critical, yet research on their understanding and management strategies for ED is lacking.

The rare genetic variants 16p11.2 duplication and 16p11.2 deletion have opposing effects on brain structure and function, yet are associated with broadly similar clinical phenotypes that include autism, intellectual impairment, psychiatric illness, and motor difficulties. In recent years, studies have identified subtle distinctions between the phenotypic effects of 16p11.2 duplication and 16p11.2 deletion with respect to patterns of autism, intellectual impairment, and psychiatric illness. However, although divergent phenotypic findings in some motor domains have been reported, no study has yet made a comprehensive comparison of motor difficulties between 16p11.2 deletion and 16p11.2 duplication carriers to elucidate points of convergence and divergence. We sought to make such a comparison in a group of 133 16p11.2 deletion carriers, 122 duplication carriers, and 388 familial controls, hypothesizing that motor impairment would overall be greater in deletion than duplication carriers. In a series of regression models, we found that 16p11.2 deletion status tended to predict greater impairment along indices of gross motor function, but less impairment along indices of fine motor function. These findings point to a potential pattern of performance difficulties that could be investigated in future studies. Elucidating motor differences between 16p11.2 duplication and 16p11.2 deletion carriers may help in understanding the complex effect of 16p11.2 copy number variation and other rare genetic causes of autism.

Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Major depressive disorder (MDD) is a debilitating and prevalent mental disorder with a high disease burden. Despite a wide array of different treatment options, many patients do not respond to initial treatment attempts. Selection of the most appropriate treatment remains a significant clinical challenge in psychiatry, highlighting the need for the development of biomarkers with predictive utility. Recently, the epigenetic modification DNA methylation (DNAm) has emerged to be of great interest as a potential predictor of MDD treatment outcomes. Here, we review efforts to date that seek to identify DNAm signatures associated with treatment response in individuals with MDD. Searches were conducted in the databases PubMed, Scopus, and Web of Science with the concepts and keywords MDD, DNAm, antidepressants, psychotherapy, cognitive behavior therapy, electroconvulsive therapy, transcranial magnetic stimulation, and brain stimulation therapies. We identified 32 studies implicating DNAm patterns associated with MDD treatment outcomes. The majority of studies (N = 25) are focused on selected target genes exploring treatment outcomes in pharmacological treatments (N = 22) with a few studies assessing treatment response to electroconvulsive therapy (N = 3). Additionally, there are few genome-scale efforts (N = 7) to characterize DNAm patterns associated with treatment outcomes. There is a relative dearth of studies investigating DNAm patterns in relation to psychotherapy, electroconvulsive therapy, or transcranial magnetic stimulation; importantly, most existing studies have limited sample sizes. Given the heterogeneity in both methods and results of studies to date, there is a need for additional studies before existing findings can inform clinical decisions.

Neurological soft signs (NSSs), minor physical anomalies (MPAs), and oculomotor abnormalities were plausible biomarkers in bipolar disorder (BD). However, specific impairments in these markers in patients after the first episode mania (FEM), in comparison with first-degree relatives (high risk [HR]) of BD and healthy subjects (health control [HC]) are sparse.

We investigated whether defense mechanisms in patients with borderline personality disorder (BPD) predict treatment response of dialectical behavior therapy (DBT) and whether they moderate outcome in different treatment lengths.

Recent research in autism spectrum disorder (ASD) has suggested a higher prevalence of gender diversity in individuals diagnosed with ASD. Adolescence is a critical period for the consolidation of gender identity, yet the extent to which the experience of gender diversity is stable over adolescence and puberty in autistic youth is poorly understood. The aim of the study was to examine the consistency of gender diversity using the gender diversity screening questionnaire for self- and parent-report of youth (GDSQ-S, GDSQ-P) over a four-year longitudinal study of pubertal development in youth with ASD (N = 140, 36 assigned-female-at birth (AFAB)) and typical development (TD, N = 104, 58 assigned-male-at-birth [AMAB]) and their parents. The extent to which diagnosis (ASD vs. TD), assigned sex (AFAB vs. AMAB) and developmental level (age, puberty) predict GDSQ trajectory over time was explored. There was a significant diagnosis by sex-assigned-at-birth by age interaction for GDSQ-S Gender Diversity, p = 0.002, showing higher scores in autistic AFAB youth over adolescence, and TD AFAB showing initially lower, then increasing levels over adolescence. For GDSQ-P, Gender Incongruence was significantly different between the groups, p = 0.032, showing higher incongruence for autistic AFAB around age 10, decreasing between age 12-14 before increasing again, while TD AFAB evidence the inverse trend. AMAB trends were stable. The significant diagnostic, developmental and sex-based differences indicate AFAB youth experience greater gender diversity that evolves over development. Findings suggest gender identity formation is nuanced and may be influenced by pubertal progression, hormonal patterns, and psychosocial factors. Results underscore the need for enhanced understanding of the unique, dynamic profiles of females-assigned-at-birth.

The high incidence of potentially traumatic events (PTEs) in Indonesia warrants early identification of those with probable trauma-related disorders in order to tailor prevention and intervention for trauma-related symptoms.

Atypical patterns of social engagement and joint attention behaviors are diagnostic criteria for people with autism spectrum disorder. Experimental tasks using eye-tracking methodologies have, however, shown inconsistent results. The development of tasks with greater ecological validity and relevance for developmentally appropriate social milestones has been identified as important for the field.

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Prenatal glucocorticoids are one of the most widely proposed prenatal programming mechanisms, yet few studies exist that measure fetal cortisol via neonatal hair. Neonatal hair provides a window into the fetal experience and represents cortisol accumulation in the third trimester of pregnancy. In the current study, we test the links between two types of anxiety over the course of gestation (pregnancy-related anxiety and general anxiety) with neonatal hair cortisol.

To examine differences in sample characteristics and longitudinal sleep outcomes according to weighted blanket adherence.

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the US. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradients. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis. We observed high similarities of intrinsic neural timescales (INT) between patients with Alzheimer's Disease (AD) and people that will later progress to AD (called Converter), deviating from cognitively normal individuals. This indicates that pathological excitation/inhibition imbalance already started before the conversion to AD. We also revealed distinct pathophysiological changes in stable mild cognitive impairment (MCI) and AD/Converter. For the AD and Converter, neural information is stored for a longer time in lower brain hierarchical areas; while higher levels of the hierarchy seem to be preferentially impaired in stable MCI. These results suggest the potential for INT as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

Among plasma biomarkers for Alzheimer's disease (AD), pTau181 and pTau217 are the most promising. However, transition from research to routine clinical use will require confirmation of clinical performance in prospective cohorts and evaluation of cofounding factors.

Clinical manifestations of coronavirus disease 2019 (COVID-19) often persist after acute disease resolution. Underlying molecular mechanisms are unclear. The objective of this original article was to longitudinally measure plasma levels of markers of the innate immune response to investigate whether they associate with and predict post-COVID symptomatology.

Few studies have focused on functional impairment in depressed patients during symptomatic remission. The exact relationship between cognitive performance and functional outcomes of patients with Major depressive disorder (MDD) remains unclear.

It is well known the potential of severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection to induce post-acute sequelae, a condition called Long COVID. This syndrome includes several symptoms, but the central nervous system (CNS) main one is neurocognitive dysfunction. Recently it has been demonstrated the relevance of plasma levels of neurofilament light chain (pNfL), as a biomarker of early involvement of the CNS in COVID-19. The aim of this study was to investigate the relationship between pNfL in patients with post-acute neurocognitive symptoms and the potential of NfL as a prognostic biomarker in these cases. A group of 63 long COVID patients ranging from 18 to 59 years-old were evaluated, submitted to a neurocognitive battery assessment, and subdivided in different groups, according to results. Plasma samples were collected during the long COVID assessment and used for measurement of pNfL with the Single molecule array (SIMOA) assays. Levels of pNfL were significantly higher in long COVID patients with neurocognitive symptoms when compared to HC (p = 0.0031). Long COVID patients with cognitive impairment and fatigue symptoms presented higher pNfL levels when compared to long COVID patients without these symptoms, individually and combined (p = 0.0263, p = 0.0480, and 0.0142, respectively). Correlation analysis showed that levels of cognitive lost and exacerbation of fatigue in the neurocognitive evaluation had a significative correlation with higher pNfL levels (p = 0.0219 and 0.0255, respectively). Previous reports suggested that pNfL levels are related with higher risk of severity and predict lethality of COVID-19. Our findings demonstrate that SARS-CoV-2 infection seems to have a long-term impact on the brain, even in patients who presented mild acute disease. NfL measurements might be useful to identify CNS involvement in long COVID associated with neurocognitive symptoms and to identify who will need continuous monitoring and treatment support.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-rapid eye movement sleep (EE-SWAS), a sleep stage dominated by sleep spindles, brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, p≈0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (p≈0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 seconds after each thalamic spike (p<0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Few previous studies have investigated how different injury mechanisms leading to sport-related concussion (SRC) in soccer may affect outcomes.

Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.

Around 30% of people with schizophrenia are refractory to antipsychotic treatment (treatment-resistant schizophrenia; TRS). While abnormal structural neuroimaging findings, in particular volume and thickness reductions, are often observed in schizophrenia, it is anticipated that biomarkers of neuronal injury like neurofilament light chain protein (NfL) can improve our understanding of the pathological basis underlying schizophrenia. The current study aimed to determine whether people with TRS demonstrate different associations between plasma NfL levels and regional cortical thickness reductions compared with controls.

Middle-aged and older adults with physical disabilities exhibit more common and severe depressive symptoms than those without physical disabilities. Such symptoms can greatly affect the physical and mental health and life expectancy of middle-aged and older persons with disabilities.

Stigma is a public health concern. Stigmatizing attitudes toward persons with substance use disorders (SUDs) can adversely impact clinical care and outcomes. Beliefs about SUD, prior experience and familiarity to persons with SUD, and educational curricula drive attitudes among health-care workers. In 2019, nursing and nursing assistant students were recruited through an online survey platform. Participants completed an SUD knowledge test and a survey assessing education, beliefs, personal experience, and confidence in recognizing the signs and symptoms of SUD. One hundred and ten health-care students (nursing students,  = 67 and nursing assistant students,  = 43) completed the survey. Among nursing assistant students, endorsing a disease model of addiction ((2, 40) = 5.83,  < .001,  = .23), and personal familiarity with SUD ((2, 40) = 4.46,  < .001,  = .18), were significantly positively predictive of positive regard toward working with persons with SUD. For nursing students, endorsing a disease model of addiction, educational curricula involving persons with SUD, and personal familiarity were significantly positively predictive of positive regard toward working with persons with SUDs ((2, 61) = 11.52,  < .001, R = .36). Interventions to mitigate drug-related stigma among health-care students should center students with personal familiarity, promote the disease concept of addiction, and incorporate contact-based training.

The Clubhouse model of psychosocial rehabilitation supports individuals with mental health challenges using a person centered and recovery-oriented approach. Clubhouses around the world have been found to be effective in supporting their member's recovery. However, there is a lack of multi-site and longitudinal studies on the Clubhouse model. Therefore, the purpose of the present study was to longitudinally assess the psychosocial outcomes of Clubhouse members across six accredited Clubhouses in Canada. Due to the COVID-19 pandemic occurring midway through the study, a secondary aim was to assess the impact of the pandemic on the psychosocial outcomes of Clubhouse members. A total of 462 Clubhouse members consented to participate in the study. Members completed a questionnaire battery every 6 months over a 2-year period (five data points total). The last three data points were collected during the COVID-19 pandemic. Psychosocial outcomes included mental health symptoms, substance use, community integration, and satisfaction with life, and were analyzed using multilevel growth models. The results indicated that satisfaction with life and psychological integration increased over the study period, while mental health symptoms, substance use, and physical integration decreased. Examining Clubhouse participation, length of Clubhouse membership and frequency of Clubhouse use predicted higher life satisfaction, lower substance use, and fewer mental health symptoms over the study period. The results of the present study provide invaluable insight into the psychosocial impact of Clubhouses on Canadian Clubhouse members, particularly during COVID-19.

In addition to biological sex, the impact of gender on health outcomes is now well-recognized. Gender norms are changing rapidly, demanding contemporary gender assessment tools. This study sought to validate the recent US-based Stanford Gender-Related Variables for Health Research (SGVHR) scale in Canada. We also aimed to improve gender prediction by including socio-demographic information on education, income and occupations. We recruited 2445 Canadian online participants (~50% female; mean age: 49.3). Multigroup confirmatory factor analyses confirmed the SGVHR factor structure in our sample, indicating its generalizability beyond the USA. Regression analyses indicated that the SGVHR subscales were moderately predictive of self-reported gender. Incorporating socio-demographic factors Significantly enhanced gender prediction via the SGVHR. This study underscores the SGVHR's applicability in diverse Western populations and encourages the inclusion of easily accessible sociodemographic variables to approximate a gender metric. Future studies should test the health-relevance of such indicators along with the SGVHR.

One of the challenges in bipolar disorder (BD) lies in early detection of the illness and its recurrences, to improve prognosis. Sleep disturbances (SD) have been proposed as reliable predictive markers of conversion. While preliminary studies have explored the relationship between SD and the onset of mood episodes, the results remain heterogeneous and a few have specifically examined patients' perception of prodromal symptoms and their progression until the episode occurs. Identifying prodromes represents a crucial clinical challenge, as it enables early intervention, thereby reducing the severity of BD. Therefore, the objective of this study is to better characterize and evaluate the progressive nature of SD as prodromal symptoms of mood episodes, and patients' perception of it.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± SD age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (CU; n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5), as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test if baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and MCI conversion rates of CU and SCD participants. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological AD biomarker levels, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic-predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of MCI conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, both on the subject and group level, were excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for AD in applied settings. The implementation of atrophy subtype- and stage-specific end-points may increase the statistical power of pharmacological trials targeting early AD.

To evaluate the accuracy and precision of continuous overnight oxygen saturation (SpO) measurement by a commercial wrist device (WD) incorporating high-grade sensors, and investigate WD estimation of sleep-disordered breathing by quantifying overnight oxygen desaturation index (ODI) compared to polysomnography (PSG) ODI and apnea-hypopnea index (AHI) with and without sleep questionnaire data, to assess WD ability to detect obstructive sleep apnea (OSA) and determine its severity.

Atypical anticipation of social reward has been indicated to lie at the core of the social challenges faced by individuals with autism spectrum disorder (ASD). However, past research has yielded inconsistent results, often overlooking crucial characteristics of stimuli. Here, we investigated ASD reward processing using social and non-social tangible stimuli, carefully matched on several key dimensions.

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Recent research has highlighted the role of complement genes in shaping the microstructure of the brain during early development, and in contributing to common allele risk for Schizophrenia. We hypothesised that common risk variants for schizophrenia within complement genes will associate with structural changes in white matter microstructure within tracts innervating the frontal lobe. Results showed that risk alleles within the complement gene set, but also intergenic alleles, significantly predict axonal density in white matter tracts connecting frontal cortex with parietal, temporal and occipital cortices. Specifically, risk alleles within the Major Histocompatibility Complex region in chromosome 6 appeared to drive these associations. No significant associations were found for the orientation dispersion index. These results suggest that changes in axonal packing - but not in axonal coherence - determined by common risk alleles within the MHC genomic region - including variants related to the Complement system - appear as a potential neurobiological mechanism for schizophrenia.

Metacognitions about online gaming have been shown to be correlated with Internet Gaming Disorder (IGD). Knowledge of metacognitions about online gaming can help to understand IGD. The Metacognitions about Online Gaming Scale (MOGS) is a reliable and valid tool to measure specific metacognitions about online gaming in both adults and adolescents, which is lacking in China.

The prevalence of depression in medical students was greater than in the general population. Knowing of predictive factors for depression among medical students is useful. The objectives of this study included the assessment of personality traits as well as the association between the personality traits and the presence of symptoms of depression, and suicidal ideation among medical students covering several regions of Thailand.

Individuals with severe and treatment-resistant obsessive-compulsive disorder (trOCD) represent a small but severely disabled group of patients. Since trOCD cases eligible for deep brain stimulation (DBS) probably comprise the most severe end of the OCD spectrum, we hypothesize that they may be more likely to have a strong genetic contribution to their disorder. Therefore, while the worldwide population of DBS-treated cases may be small (~300), screening these individuals with modern genomic methods may accelerate gene discovery in OCD. As such, we have begun to collect DNA from trOCD cases who qualify for DBS, and here we report results from whole exome sequencing and microarray genotyping of our first five cases. All participants had previously received DBS in the bed nucleus of stria terminalis (BNST), with two patients responding to the surgery and one showing a partial response. Our analyses focused on gene-disruptive rare variants (GDRVs; rare, predicted-deleterious single-nucleotide variants or copy number variants overlapping protein-coding genes). Three of the five cases carried a GDRV, including a missense variant in the ion transporter domain of KCNB1, a deletion at 15q11.2, and a duplication at 15q26.1. The KCNB1 variant (hg19 chr20-47991077-C-T, NM_004975.3:c.1020G>A, p.Met340Ile) causes substitution of methionine for isoleucine in the trans-membrane region of neuronal potassium voltage-gated ion channel KV2.1. This KCNB1 substitution (Met340Ile) is located in a highly constrained region of the protein where other rare missense variants have previously been associated with neurodevelopmental disorders. The patient carrying the Met340Ile variant responded to DBS, which suggests that genetic factors could potentially be predictors of treatment response in DBS for OCD. In sum, we have established a protocol for recruiting and genomically characterizing trOCD cases. Preliminary results suggest that this will be an informative strategy for finding risk genes in OCD.

People with severe mental illness (SMI) face a higher risk of premature mortality due to physical morbidity compared to the general population. Establishing regular contact with a general practitioner (GP) can mitigate this risk, yet barriers to healthcare access persist. Population initiatives to overcome these barriers require efficient identification of those persons in need.

Rapidly improving acute respiratory distress syndrome (RIARDS) is an increasingly appreciated subgroup of ARDS in which hypoxemia improves within 24 h after initiation of mechanical ventilation. Detailed clinical and biological features of RIARDS have not been clearly defined, and it is unknown whether RIARDS is associated with the hypoinflammatory or hyperinflammatory phenotype of ARDS. The purpose of this study was to define the clinical and biological features of RIARDS and its association with inflammatory subphenotypes.

Facilities providing health- and social services for youth are commonly faced with the need for assessment and management of violent behavior. These providers often experience shortage of resources, compromising the feasibility of conducting comprehensive violence risk assessments. The Violence Risk Assessment Checklist for Youth aged 12-18 (V-RISK-Y) is a 12-item violence risk screening instrument developed to rapidly identify youth at high risk for violent behavior in situations requiring expedient evaluation of violence risk. The V-RISK-Y instrument was piloted in acute psychiatric units for youth, yielding positive results of predictive validity. The aim of the present study was to assess the interrater reliability of V-RISK-Y in child and adolescent psychiatric units and acute child protective services institutions.

Many individuals with serious mental illness are at high risk for hospitalization or death due to inadequate treatment of medical conditions or unhealthy behaviors. The authors describe demographic and clinical characteristics associated with increased risk in this population. Electronic data were obtained for individuals in treatment at a large Veterans' healthcare system who were at high risk according to a validated model. A random sample of these individuals was assessed in person. Multivariable regressions estimated the effect of numerous demographic, health, and clinical characteristics on risk. Emergency visits and hospitalizations were common. Greater risk was associated with being male, not married, and having more diagnoses. While risk varied by race, this effect was no longer significant after controlling for other factors. Health-related quality of life worsened with increasing risk. Routine data identify a large population of high-risk individuals who may benefit from outreach to provide healthcare services.

Manic depressive psychosis (MDP) or bipolar disorder, a prevalent psychiatric condition globally and in the Indian population, has been attributed to various pathological mechanisms. Hydrogen sulphide (HS), a member of the gasotransmitter family, may be linked to the development of bipolar disorder because it plays a crucial role in maintaining proper neuronal function in terms of excitability, plasticity, and homeostatic functions. There is very little data regarding the role of the gasotransmitter HS in MDP in terms of its association, diagnostic ability, and severity prediction, which led us to conduct this study among MDP patients in the Sub-Himalayan region of West Bengal.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

: Medical students represent the ideal target group for promoting mental health and mental wellbeing, being exposed to specific risk factors, such as the content of medical training, the exposure to sickness and death, and a stressful academic routine. Medical students report high levels of cynicism and emotional exhaustion, which represent two of the essential features of burnout syndrome. In this systematic review, studies assessing the levels of burnout among medical students through validated tools worldwide were analyzed. : A systematic review has been performed in order to identify studies: (1) focusing on samples of medical students; (2) evaluating burnout syndrome using validated tools; (3) providing prevalence data on burnout; and (4) written in English. : Out of the 5547 papers initially obtained, 64 were finally included in the analysis. The sample sizes ranged from 51 to 2682 participants. Almost all studies had a cross-sectional design; the Maslach Burnout Inventory and its related versions were the most frequently used assessment tools. The prevalence of burnout, which was stratified based on gender and academic stage, ranged from 5.6 to 88%. Burnout was mostly predicted by thoughts of stopping medical education, negative life events, lack of support, dissatisfaction, and poor motivation. : The prevalence of burnout syndrome in medical students is quite heterogeneous, reaching a peak of 88% in some countries. However, several predictors have been identified, including negative life events or poor motivation. These findings highlight the need to develop preventive interventions targeting the future generation of medical doctors, in order to improve their coping strategies and resilience styles.

Although the association between post-traumatic stress disorder (PTSD) and social support is well documented, few studies have tested the causal pathways explaining this association at several points in the acute post-trauma recovery period or examined whether the association varies for different sources of social support. To address these gaps, 151 community individuals (mean age = 37.20 years, 69.5% women) exposed to trauma within the previous 6 months were recruited to complete measures of PTSD and social support from intimate partners, friends, and relatives four times in 1 year. In line with recent recommendations for research on social support and PTSD symptoms, random intercept cross-lagged panel modeling (RI-CLPM) was used to examine dynamic changes between PTSD severity and social support over time. The pattern of RI-CLPM cross-lagged coefficients indicated that positive deviations from one's expected stable level of total social support (across all sources) sped up the recovery of PTSD symptoms at the end of the post-trauma year, and more severe PTSD symptoms than expected based on one's expected stable level of PTSD started eroding social support midway through the assessment year. When specific sources of social support were analyzed separately, the association between within-person increases in social support from friends at any given time point accelerated the recovery from PTSD across the entire year. Among participants with intimate partners ( = 53), intimate partner support did not predict PTSD symptoms, but more severe PTSD symptoms at any given time point predicted less support at the following time point. Results from this longitudinal study provide additional support for the bidirectional relationship between PTSD and social support over time and suggest that perceived social support from friends may be especially helpful during trauma recovery.

The proposed method focuses on speaker disentanglement in the context of depression detection from speech signals. Previous approaches require patient/speaker labels, encounter instability due to loss maximization, and introduce unnecessary parameters for adversarial domain prediction. In contrast, the proposed unsupervised approach reduces cosine similarity between latent spaces of depression and pre-trained speaker classification models. This method outperforms baseline models, matches or exceeds adversarial methods in performance, and does so without relying on speaker labels or introducing additional model parameters, leading to a reduction in model complexity. The higher the speaker de-identification score (), the better the depression detection system is in masking a patient's identity thereby enhancing the privacy attributes of depression detection systems. On the DAIC-WOZ dataset with ComparE16 features and an LSTM-only model, our method achieves an F1-Score of 0.776 and a score of 92.87%, outperforming its adversarial counterpart which has an F1Score of 0.762 and 68.37% , respectively. Furthermore, we demonstrate that speaker-disentanglement methods are complementary to text-based approaches, and a score-level fusion with a Word2vec-based depression detection model further enhances the overall performance to an F1-Score of 0.830.

To examine associations of service use (housing, mental health, substance use, education, and employment) with depression and substance use disorder (SUD) trajectories among young adults experiencing homelessness.

Delirium and depression are prevalent in aging. There is considerable clinical overlap, including shared symptoms and comorbid conditions, including Alzheimer's disease, functional decline, and mortality. Despite this, the long-term relationship between depression and delirium remains unclear. This study assessed the associations of depression symptom burden and its trajectory with delirium risk in a 12-year prospective study of older hospitalized individuals.

The COVID-19 pandemic caused multiple stressors that may lead to symptoms of adjustment disorder. We longitudinally examined relationships between risk and protective factors, pandemic-related stressors and symptoms of adjustment disorder during the COVID-19 pandemic, as well as whether these relationships differed by the time of assessment. The European Society for Traumatic Stress Studies (ESTSS) ADJUST Study included = 15,169 participants aged 18 years and above. Participants from 11 European countries were recruited and screened three times at 6-month intervals from June 2020 to January 2022. Associations between risk and protective factors (e.g. gender), stressors (e.g. fear of infection), and symptoms of adjustment disorder (AjD, ADNM-8) and their interaction with time of assessment were examined using mixed linear regression. The following predictors were significantly associated with higher AjD symptom levels: female or diverse gender; older age; pandemic-related news consumption >30 min a day; a current or previous mental health disorder; trauma exposure before or during the pandemic; a good, satisfactory or poor health status (vs. very good); burden related to governmental crisis management and communication; fear of infection; restricted social contact; work-related problems; restricted activity; and difficult housing conditions. The following predictors were associated with lower AjD levels: self-employment or retirement; working in healthcare; and face-to-face contact ≥ once a week with loved ones or friends. The effects of the following predictors on AjD symptoms differed by the time of assessment in the course of the pandemic: a current or previous mental disorder; burden related to governmental crisis management; income reduction; and a current trauma exposure. We identified risk factors and stressors predicting AjD symptom levels at different stages of the pandemic. For some predictors, the effects on mental health may change at different stages of a pandemic.

There is evidence that some childhood trauma increases the risk of the first onset of mental disorders and for the first time into adulthood. There are no studies that assessed whether exposure to war has this delayed long-term effect.

[This corrects the article DOI: 10.3389/fpsyt.2022.863225.].

Schizophrenia involves complex interactions between biological and environmental factors, including childhood trauma, cognitive impairments, and premorbid adjustment. Predicting its severity and progression remains challenging. Biomarkers like glial cell line-derived neurotrophic factor (GDNF) and miRNA-29a may bridge biological and environmental aspects. The goal was to explore the connections between miRNAs and neural proteins and cognitive functioning, childhood trauma, and premorbid adjustment in the first episode of psychosis (FEP).

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.

The importance of healthy aging is growing in China as it has the largest number of older adults in the world and is one of the fastest-aging countries. This study aimed to examine the predictive value of regular physical exercise in relation to the physical, emotional, and cognitive health among samples of adults aged ≥60 years in China during an 8-year period.

We investigated a potential sex difference in the relationship between alcohol consumption, brain age gap and cognitive function in older adults without cognitive impairment from the population-based Mayo Clinic Study of Aging.

Children and youth with neurological and/or neurodevelopmental conditions were at high risk for behavioral and mental health challenges during the COVID-19 pandemic. Positive and responsive parenting practices may be one way to prevent and manage potential difficulties in families. We aimed to identify whether positive parenting practices were associated with reduced behavioral concerns in children at neurological risk during the late stages and aftermath of the COVID-19 pandemic. In addition, we examined whether ongoing parental stress, anxiety, and depression impacted parenting practices during this time period. Families ( = 179) with children 4 to 15 years old ( = 7.11y,  = 2.02) diagnosed with neurological (84.3%), neurodevelopmental (54.8%) or comorbid neurological and/or neurodevelopmental conditions (21.2%) were contacted to complete online questionnaires regarding demographics, parent stress, child behavior, COVID-19 conditions, and parenting practices. Multivariable linear regression (MLR) analyses examined the association between positive parenting practices and parenting competency measures with child behavioral outcomes, controlling for relevant covariates, including COVID-19 related stress. MLR were also run to determine whether parental mental health impacted parenting practices. More positive parenting practices predicted fewer child problem behaviors and lower intensity of problem behaviors. Similarly, a higher sense of satisfaction with parenting competence also predicted fewer child problem behaviors and lower intensity of problem behaviors. In addition, higher reported parental depression, anxiety, and stress significantly predicted fewer reported positive parenting practices. Findings points to the promising application of positive parenting interventions to support vulnerable families, as well as the need for parental mental health intervention to support parenting practices.

In recent years, kynurenine metabolites generated by tryptophan catabolism have gained increasing attention in the context of brain diseases. The question of importance is whether there is a relationship between peripheral and central levels of these metabolites. Some of these compounds do not cross the blood-brain barrier; in particular, kynurenic acid, and most analyses of kynurenines from psychiatric patients have been performed using plasma samples. In the present study, we recruited 30 healthy volunteers with no history of psychiatric or neurological diagnosis, to analyze tryptophan, kynurenine, kynurenic acid, and quinolinic acid levels in CSF and plasma. In addition, kynurenic acid was analyzed in urine. The most important finding of this study is that CSF kynurenic acid levels do not correlate with those in plasma or urine. However, we found a correlation between plasma kynurenine and CSF kynurenic acid. Further, plasma kynurenine and plasma quinolinic acid were correlated. Our findings clarify the distribution of tryptophan and its metabolites in various body compartments and may serve as a guide for the analysis of these metabolites in humans. The most significant finding of the present study is that a prediction of brain kynurenic acid by of the analysis of the compound in plasma cannot be made.

Burnout syndrome usually begins with feelings of enthusiasm and idealized visualizations, and it is in contrast with subsequent disillusionment, disappointment, and symptoms which are related to chronic stress experienced later. This tendency to idealization is a parallel to the concept of "mental splitting" described by Kernberg with a pronounced "black and white" perceptual dichotomy between the early idealization and later disillusionment. This study intends examination of relationships between burnout syndrome, traumatic stress and Kernberg's concept of splitting.