Impact of perceived side-effects of psychotropic treatments on quality of life in patients with severe mental illness

BACKGROUND

Psychotropic medications are critical in managing severe mental illnesses (SMI) such as schizophrenia, major depressive disorder (MDD) and bipolar disorder. However, these treatments often lead to adverse side effects that can impair patients' quality of life (QoL) and affect treatment adherence.

OBJECTIVE

This study aims to investigate the specific side effects of psychotropic treatments that contribute to a decline in QoL among patients with SMI, independently of treatment adherence.

METHODS

We conducted a cross-sectional study with 1248 patients diagnosed with SMI, recruited from a university psychiatric unit in Marseille, France. QoL was assessed using the Schizophrenia Quality of Life Scale (SQoL-18), and side effects were measured using the UKU Side Effect Rating Scale. Treatment adherence was evaluated using the Medication Adherence Rating Scale (MARS). Statistical analyses included Pearson correlations and multiple linear regression models to identify predictors of QoL.

RESULTS

The study found that side effects, as identified by the UKU scores, could significantly predict a reduction in QoL across multiple domains, including multiple dimensions of QoL and the overall QoL index, independent of treatment adherence. Patients on antipsychotics and benzodiazepines reported higher levels of adverse side effects, which correlated with lower QoL scores. An increase in the number of psychotropic treatment classes was also associated with a significant decline in QoL (p < 0.001).

CONCLUSION

Managing psychic side effects and minimising polypharmacy are critical to improving QoL in patients with SMI. Clinicians should consider these factors when developing personalised treatment strategies to enhance patient outcomes.

Dissecting the shared molecular mechanisms underlying polycystic ovary syndrome and schizophrenia etiology: a translational integrative approach

Recent evidence suggests that individuals with polycystic ovary syndrome (PCOS) have an increased risk of developing mental health disorders and comorbidities linked to nervous system dysfunction. Interestingly, patients with schizophrenia (SCZ) often exhibit PCOS symptoms, indicating a possible connection between the two conditions. However, the underlying molecular links between these diseases remain poorly understood. We employed a comprehensive in-silico approach, utilizing publicly available datasets to investigate shared biomarkers candidates and key regulators involved in the development of PCOS and SCZ. We retrieved the datasets from the NCBI GEO database and differentially expressed genes (DEGs) were identified for each dataset. Common DEGs (cDEGs) were determined, and transcription factors (TFs) and miRNA targeting cDEGs were examined using the mirDIP portal and TRRUST database, respectively. We also assessed the TF-miRNA interactions by TransmiR database and constructed a regulatory network including TFs-microRNAs-cDEGs. Our analysis identified a total of 15 cDEGs that are regulated by 15 TFs and 8 mRNAs. Among our findings, we prioritized RELA as a potential TF regulator for both diseases, demonstrating synergistic interaction with four cDEGs (EGR1, CXCL8, IL1RN, IL1B) and seven microRNAs (hsa-miR-580, hsa-miR-5695, hsa-miR-936, hsa-miR-3675, hsa-miR-634, hsa-miR-603, hsa-miR-222) that target these genes. Our data highlights potential common biomarkers for PCOS and SCZ, presenting a novel regulatory network that elucidates the molecular mechanisms underlying both conditions. This emphasizes the importance of further research to explore new translational approaches, which may ultimately lead to improved diagnostic and therapeutic strategies for affected individuals.

Spatial and frequency domain-based feature fusion for accurate detection of schizophrenia using AI-driven approaches

Schizophrenia is a neuropsychiatric disorder that hampers brain functions and causes hallucinations, delusions, and bizarre behavior. The stigmatization associated with this disabling disorder drives the need to build diagnostic models with impeccable performances. Neuroimaging modality such as structural MRI is coupled with machine learning techniques to perform schizophrenia diagnosis with increased reliability. We investigate the structural aberrations present in the structural MR images using machine learning techniques. In this study, we propose a new hybrid approach using spatial and frequency domain-based features for the early automated detection of schizophrenia using machine learning techniques. The spatial or texture features are extracted using the local binary pattern method, and frequency-based features, including magnitude and phase, are extracted using the fast fourier transform feature extraction technique. Hybrid features, combining spatial and frequency-based features, are utilized for schizophrenia classification using support vector machine, random forest, and k-nearest neighbor with stratified 10-fold cross-validation. The support vector machine and random forest classifiers achieve encouraging detection performances on the hybrid feature set, with 86.5% and 85.1% accuracy, respectively. Among the three classifiers, k-nearest neighbor shows outstanding detection performance with an accuracy of 98.1%. The precision and recall achieved by the k-nearest neighbor classifier are 98.1% and 98.0% respectively, reflecting accurate detection of schizophrenia by the proposed model.

Dissociative experiences in individuals with subclinical psychosis and a history of developmental trauma: a qualitative study

Background: Among individuals with psychotic experiences, those with a history of developmental trauma face greater symptom severity and worse clinical outcomes compared to those without. Dissociation constitutes a prominent, characteristic of this group's clinical presentation, whose nuances and associated characteristics remain however understudied in psychosis research. We aimed to address this gap by conducting a qualitative study to investigate the phenomenology, context, and impact of dissociative experiences in individuals with subclinical psychosis and a history of developmental trauma.Methods: 25 UK-based participants with a history of developmental trauma and meeting criteria for subclinical psychosis, based on the CAPE-15, were recruited via social media. Participants attended semi-structured interviews online, which were transcribed verbatim and analysed with thematic analysis by two researchers.Results: Thematic analysis yielded the following themes: (1) Phenomenology of Dissociation; (2) Context of Dissociation; (3) Impact of Dissociation; (4) Dissociation and Psychotic-like Phenomena. Participants described experiences of detachment and compartmentalisation, which when experienced were confusing and often distressing. Dissociation was linked to affective experiences, their history of developmental trauma and psychotic-like experiences.Conclusion: This study elucidates the complex and varied nature of dissociative experiences in individuals with subclinical psychosis with a history of developmental trauma. These findings highlight the need for further research to understand the manifestation of dissociation in this population and the links with distress and other aspects of psychopathology. Importantly, it is essential to use this understanding to inform the development of interventions and improve clinical recognition and management.

Treatment stigma mediates relationships between morally injurious events and depression, PTSD and anxiety symptoms

Background: Morally injurious events (MIEs), encompassing personal transgressions, witnessing others commit transgressions, or experiencing betrayal by leaders, can conflict with one's moral/ethical principles, evoking outrage and profound mistrust. Although MIEs are associated with depression, PTSD, and anxiety, the mechanisms linking MIEs to psychiatric symptomatology remain unclear, especially among civilians in times of collective trauma.Objective: This study explored one potential mechanism: stigma toward mental-health treatment, which can deter help-seeking and exacerbate guilt, shame, and mistrust.Method: We focused on civilians (N = 1,052) exposed to MIEs in conflict zones in southern and northern Israel following the 7 October 2023 attack. Participants were recruited using an online platform and assessed for depression, PTSD, and anxiety symptoms. We hypothesised that stigma toward treatment would mediate relationships between MIE exposure levels and depression, PTSD, and anxiety symptoms.Results: Results showed high MIE exposure levels and symptomatology among civilians in conflict zones. Moreover, we found significant indirect effects of stigma toward treatment on all three symptom types.Conclusions: Our findings suggest that while MIEs directly link to symptoms, stigma toward treatment plays a significant role in understanding this link. These findings emphasise the importance of addressing stigma toward treatment for individuals experiencing MIEs and underscore the need for targeted interventions in conflict zones.

Childhood maltreatment and mental health: causal links to depression, anxiety, non-fatal self-harm, suicide attempts, and PTSD

Background: This study aims to elucidate the causal relationship between childhood maltreatment (CM) and subsequent mental health outcomes, including major depressive disorder (MDD), anxiety (ANX), post-traumatic stress disorder (PTSD), suicide attempts, and non-fatal self-harm. Utilising Mendelian Randomisation (MR) and genome-wide association studies (GWAS) data from individuals of European descent, this research applies a rigorous analytical methodology to large-scale datasets, overcoming the confounding variables inherent in previous observational studies.Methods: Genetic data were obtained from publicly available GWAS on individuals of European ancestry, focusing on Childhood Maltreatment (CM), Major Depressive Disorder (MDD), Anxiety (ANX), Post-Traumatic Stress Disorder (PTSD), Age at First Episode of Depression, Number of Depression Episodes, Non-fatal self-harm, and Suicide Attempts. Mendelian Randomisation (MR) analyses were conducted to investigate the causal impact of CM on these outcomes. Sensitivity analyses included IVW, MR Egger, WM, and MR-PRESSO. FDR corrections were applied to account for multiple testing. Results were presented as odds ratios (ORs) with confidence intervals (CIs).Results: Significant associations were identified between CM and the likelihood of developing MDD (IVW: OR = 2.28, 95% CI = 1.66-3.14, PFDR < .001), ANX (IVW: OR = 1.01, 95% CI = 1.00-1.02, PFDR =.032), and PTSD (IVW: OR = 2.29, 95% CI = 1.43-3.67, PFDR =.001). CM was also linked to increased non-fatal self-harm (IVW: OR = 1.06, 95% CI = 1.04-1.08, PFDR <.001), higher frequency of depressive episodes (IVW: β=0.31, 95% CI = 0.17-0.46, PFDR <.001), and earlier onset of depression (IVW: β=-0.17, 95% CI = -0.32 to - 0.02, PFDR =.033). No significant association was found between CM and suicide attempts (IVW: OR = 1.09, 95% CI = 0.81-1.45, PFDR =.573).Conclusion: This study provides robust evidence that CM is a significant causal factor for MDD, ANX, PTSD, and non-fatal self-harming behaviours. It is associated with a higher frequency of depressive episodes and earlier onset of depression. These findings highlight the need for early intervention and targeted prevention strategies to address the long-lasting psychological impacts of CM.

Commentary: Air pollution and neurodevelopmental disorders: a cause for concern in an urbanising world

The world's population is rapidly urbanising, especially in low- to middle-income countries. However, urban living is associated with an increased risk of neurodevelopmental disorders (NDD) like autism spectrum disorders (ASD) and schizophrenia. Exposure to urban air pollutants like particulate matter has been positively associated with both ASD and schizophrenia diagnosis rates. Mechanistic studies have shown that particulate matter exposure leads to brain inflammation and white matter pathologies consistent with these disorders. Children are especially susceptible to these effects due to their rapidly developing nervous systems. Despite this, few reviews on the subject recommend future steps to mitigate the teratogenic effects of particulate air pollution. This commentary both synthesises evidence and recommends research and policy goals to protect children, both present and future, from the neurodevelopmental consequences of particulate air pollution. These steps include further study of the relationship between air pollution and equitable resource distribution to address the coming global rises in NDD. Furthermore, capacity building in urbanising nations is essential to overcome barriers erected by resource extraction and pollution outsourcing by wealthy nations. With such an integrated approach, progress can be made in protecting the developing nervous systems of children and future generations in both affluent and resource-strained nations.

Association of antidepressant use with obesity risk in pregnant women: a retrospective cohort study

OBJECTIVE

This study evaluated the relationship between treatment for depression and anxiety in pregnancy and the risk of obesity.

DESIGN

Multivariate logistic regression analysis, nested case control study design.

SETTING

Births occurring 2008-2022 at an outer-suburban, lower socioeconomic maternity hospital in Australia. Population: 75,308 eligible births. Main outcome measures: BMI ≥ 30 (obesity), BMI ≥ 35 (severe obesity/obesity class II and III).

RESULTS

In this analysis, we found an adjusted OR of 1.56 of BMI ≥ 30, p < .000 with CI (1.35-1.80) in women taking antidepressants in pregnancy, OR 1.56 of BMI ≥ 35, p < .000, CI (1.31-1.84). Within this dataset, a cohort of 7559 women with a history or current diagnosis of depression or anxiety were found; adjusted OR of 1.40 of BMI ≥ 30, p < .000, CI (1.22-1.60) in women within this group taking antidepressants. Rates of severe obesity were similar in this cohort, BMI ≥ 35; OR 1.39, p < .000; CI 1.18-1.62).

CONCLUSIONS

These results suggest an increase in rates of obesity in women taking antidepressants in pregnancy not accounted for by other risks for obesity which might accrue from current mental ill-health or other cohort effects such as genetic vulnerability or lifestyle factors. This outcome is important in relation to the care of pregnant women with depression and anxiety. In view of current high rates of prescribing of antidepressants in pregnancy and the postpartum, this research may help inform clinician decision-making for women at risk of obesity in pregnancy.

EEG-based schizophrenia detection: integrating discrete wavelet transform and deep learning

Millions of people worldwide are afflicted with the psychological disease Schizophrenia (SZ). Symptoms of SZ include delusions, hallucinations, disoriented speech, and confused thinking. This disorder is manually diagnosed by a skilled medical practitioner. Nowadays, machine learning and deep learning techniques based on electroencephalogram (EEG) signals have been proposed to support medical practitioners. This paper proposes a deep learning system and a wavelet transform-based computer-aided detection method for detecting SZ disorder. The proposed technique aims to present a highly accurate EEG signal-based SZ detection technique. In this work, we first separate the EEG signal into sub-bands and extract the features for each sub-band using the Discrete Wavelet Transform (DWT). We have explored different mother wavelets and decomposition levels for the DWT setting; it is found that the Daubechies (db4) wavelet with 7-level decomposition performs the best for SZ detection. After obtaining the gathered features, the multilayer perceptron neural network (MLP) applies them to differentiate between SZ patients and healthy controls (HC). We validate our proposed automated SZ detection method using two publicly available datasets, Dataset-1 (DS1) with 81 records (32-HC and 49-SZ) and Dataset-2 (DS2) with 28 records (14-HC and 14-SZ), respectively. Compared with previous work, our proposed model surpasses the state-of-the-art technique for SZ detection. Our classification accuracy has increased, achieving an accuracy of 99.61% and 99.12% for DS1 and DS2. Our proposed method for identifying SZ using EEG signals is more reliable and accurate and is ready to support physicians in diagnosing SZ.

Bidirectional relationships among complex PTSD, dissociation, and psychotic symptoms in two samples

Background: Although trauma-related symptoms (e.g. complex PTSD and dissociative symptoms) and psychotic symptoms often co-occur, little is known about the complex relationships among these symptoms over time.Objective: This study examined the bidirectional relationships among complex PTSD symptoms, dissociative symptoms, and positive symptoms of psychosis.Methods: This study analyzed available longitudinal data from two convenience samples (Sample 1: N = 214, Chinese-speaking adults; Sample 2: N = 301, English-speaking adults). Participants in both samples completed validated measures of ICD-11 complex PTSD (that included measurement of 'classical' PTSD), dissociation, and positive symptoms of psychosis at baseline and follow-up, six months (Sample 1) or 12 months (Sample 2) apart. A cross-lagged panel model was used to examine the longitudinal relationship between trauma-related symptoms and psychotic symptoms in each sample.Results: In Sample 1, baseline dissociative symptoms significantly predicted positive symptoms of psychosis at follow-up. In Sample 2, no significant longitudinal relationships between trauma-related and psychotic symptoms were observed. In both samples, baseline disturbances in self-organization (DSO) symptoms predicted 'classical' PTSD symptoms at follow-up.Conclusion: This study made the first attempt to examine the longitudinal relationships among ICD-11 complex PTSD symptoms, dissociative symptoms, and psychotic symptoms. The inconsistent findings point to the importance of further research on the longitudinal relationships between trauma-related and psychotic symptoms. Moreover, our results indicate that addressing DSO symptoms may be important in the prevention and treatment of PTSD symptoms.

EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks

Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.

A penny for your thoughts: three perspectives on financial problems and their associated factors of people with psychotic disorders

PURPOSE

Financial problems are of influence on mental health, and vice versa. Indeed, finances are a key challenge for people with psychosis. To gain deeper insights into these challenges, a qualitative approach focusing on all perspectives within the therapeutic triad is needed. This study aims to investigate perspectives of people with psychosis, family members and mental healthcare professionals on people with psychosis' financial problems, and associated factors.

METHODS

Fourteen people with psychosis, 15 family members and 16 professionals were recruited using purposive sampling, and participated in semi-structured, one-on-one interviews. Data was analysed using iterative thematic data-analysis.

RESULTS

Interviews revealed five themes of financial problems: Covering expenses, Financial performance, Living conditions and housing, Personal conflicts and victimization, and Regulations and legislation. Five themes were identified as factors associated with financial problems: Psychotic symptoms, Indirect factors related to psychosis, Substance use and addiction, Financial upbringing and life events, and Societal contextual factors.

DISCUSSION

People in the therapeutic triad largely mentioned similar, wide-ranging, and often co-occurring (factors associated with) financial problems of people with psychosis, risking vicious cycles. Fostering awareness and collaborative efforts among stakeholders is essential to breaking these cycles of financial problems for individuals with psychosis.

Causal associations between circulating metabolites and chronic kidney disease: a Mendelian randomization study

BACKGROUND

Circulating metabolites have been associated with cross-sectional renal function in population-based research. Nevertheless, there is currently little proof to support the idea that metabolites either cause or prevent renal function. New treatment targets and ways to screen individuals with impaired renal function will be made possible via an in-depth analysis of the causal relationship between blood metabolites and renal function.

METHODS

We assessed the causal relationship between 452 serum metabolites and six renal phenotypes (CKD, rapid progression to CKD [CKDi25], rapid eGFR decline [CKD rapid3], dialysis, estimated glomerular filtration rate, and blood urea nitrogen) using univariate Mendelian randomization, primarily employing the inverse variance weighted method with robust sensitivity analyses. Heterogeneity and pleiotropy were examined via Cochrane's Q test and MR-Egger regression, and statistical significance was adjusted using Bonferroni correction. To assess potential adverse effects of metabolite modulation, we conducted a phenome-wide Mendelian randomization analysis, followed by multivariate Mendelian randomization to adjust for confounders.

RESULTS

We identified glycine and N-acetylornithine as potential causal mediators of CKD and renal dysfunction. Notably, lowering glycine levels may increase the risk of cholelithiasis and cholecystitis, while reducing N-acetylornithine could have unintended effects on tinnitus.

CONCLUSION

Glycine and N-acetylornithine represent promising therapeutic targets for CKD and renal function preservation, but their modulation requires careful risk-benefit assessment to avoid adverse effects.

Inhibitory gamma-aminobutyric acidergic neurons in the anterior cingulate cortex participate in the comorbidity of pain and emotion

Pain is often comorbid with emotional disorders such as anxiety and depression. Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairments in inhibitory gamma-aminobutyric acid neurotransmission. This review primarily aims to outline the main circuitry (including the input and output connectivity) of the anterior cingulate cortex and classification and functions of different gamma-aminobutyric acidergic neurons; it also describes the neurotransmitters/neuromodulators affecting these neurons, their intercommunication with other neurons, and their importance in mental comorbidities associated with chronic pain disorders. Improving understanding on their role in pain-related mental comorbidities may facilitate the development of more effective treatments for these conditions. However, the mechanisms that regulate gamma-aminobutyric acidergic systems remain elusive. It is also unclear as to whether the mechanisms are presynaptic or postsynaptic. Further exploration of the complexities of this system may reveal new pathways for research and drug development.

Impaired non-verbal auditory memory maintenance in schizophrenia: An ERP study

Individuals with schizophrenia (SZ) exhibit deficits in speech perception in noise, which are closely related to their abnormalities in auditory working memory (WM). Auditory WM, especially the non-verbal auditory WM, serves as a bridge between perception, action, and long-term memory, playing a crucial role in integrating sound sequences to facilitate auditory object perception and auditory scene analysis (ASA). Although considerable research has been conducted on auditory sensory memory and visual WM in schizophrenia, studies specifically addressing non-verbal auditory WM remain scarce. Therefore, this study recorded the behavioral performance and event related potentials of 36 SZ and 36 healthy controls (HC) during a modified non-musical tone-sequence delayed matching-to-sample task (DMTS). The results showed that, in the tone-sequence DMTS, SZ had not only lower accuracy but also slower reaction times compared to the HC. More importantly, during the retention period, the memory maintenance of SZ begins to decay rapidly from the mid-stage, manifested by a significantly reduction in the late sustained anterior negativity (SAN2). Meanwhile, the early sustained anterior negativity (SAN1) in patients showed a significant correlation with their general pathological symptoms. The pathological symptoms can be predicted by the SAN1 under load 4 condition. This study provides empirical evidence for the impairment of non-verbal auditory WM maintenance in schizophrenia, which is of significant importance for understanding the auditory dysfunction and ASA difficulties experienced by SZ.

The glare illusion in individuals with schizophrenia

Individuals with schizophrenia are known to display unique reactions to visual illusions, and prior research has indicated a potential link between their increased susceptibility to geometric illusions and specific symptom profiles. While various illusory experiences have been examined among individuals with schizophrenia, their responses to brightness-related illusions remain poorly understood. In this study, we investigated how individuals with schizophrenia perceive the glare illusion, in which the apparent brightness of the central region is increased. A total of 30 patients with schizophrenia and 34 control participants were recruited. During each trial, a glare or control image (standard stimulus) was presented alongside a control image (comparison stimulus) with one of seven luminance levels. In the glare condition, the standard stimulus was a glare image; in the control condition, two control images were presented, but only the luminance of the comparison stimulus varied. The participants were asked to judge which central region appeared brighter. The results revealed that individuals with schizophrenia exhibited greater susceptibility to the glare illusion than did the control participants. However, no significant associations were found between susceptibility to the glare illusion and scores assessing symptom severity. These findings suggest that differences in visual processing in patients with schizophrenia may increase their susceptibility to brightness illusions, although this phenomenon is independent of symptom characteristics. This information may provide a basis for exploring illusion susceptibility as a potential behavioral index for distinguishing between individuals with schizophrenia and control participants.

Domain-specific associations between social cognition and aggression in schizophrenia spectrum disorders

Aggression in schizophrenia spectrum disorders (SSD) is rare but elevated relative to the general population. Existing studies have not identified reliable personal predictors of aggression in SSD. In line with social information processing models suggesting that difficulties interpreting social cues and others' intentions may lead to aggression, we evaluated whether social cognitive domains or global social cognition could be modifiable risk factors in SSD. We examined aggression and social cognition in 59 participants with SSD and 43 healthy volunteers (HV). Self-reported aggression was measured via the Reactive-Proactive Aggression Questionnaire (RPAQ). Social cognition was assessed using five tasks measuring emotion processing, theory of mind, and social perception. Group differences were analyzed using Mann-Whitney-Wilcoxon tests. Multiple regressions examined effects of social cognition on aggression, controlling for demographic and clinical covariates. Supplemental mediation analyses tested whether impairments in emotion processing, theory of mind, or overall social cognition explained the relationship between SSD diagnosis and increased aggression. Reported aggression was higher in the SSD group, and social cognitive abilities were impaired across domains (p < .001). Better emotion processing (β = -0.35, p = .03) and theory of mind (β = -0.32, p = .03) predicted lower aggression in SSD, even when accounting for demographic and neurocognitive variables. Exploratory models adjusting for overall psychiatric symptom severity showed that theory of mind remained significant, while emotion processing attenuated. However, social cognition did not mediate the relationship between diagnosis and aggression. Future studies should examine other social processing factors, such as attributional bias.

A network analysis of depressive symptoms and cognitive performance in older adults with multimorbidity: A nationwide population-based study

BACKGROUND

Depression and cognitive impairment are prevalent mental health issues. Older adults in China exhibits a higher prevalence of multimorbidity, which is linked to an increased risk of depression and cognitive impairment. This study aims to investigate association between depressive symptoms and cognitive impairment in older adults with multimorbidity using network analysis, and to identify important bridge symptoms as potential intervention targets.

METHOD

The study included 5729 individuals aged 60 years and above with multimorbidity, drawn from the China Health and Retirement Longitudinal Survey (CHARLS) dataset. Depressive symptoms and cognitive performance were assessed utilizing the CESD-10 (10-item Center for Epidemiologic Studies Depression) and MMSE (Mini Mental State Examination) scales, respectively. We constructed a network structure of depressive symptoms and cognitive performance, and calculated index of strength and bridge strength for each symptom. Furthermore, a comparative analysis of the network structure across gender and age groups were conducted.

RESULTS

D3 (Felt depressed), C1 (Orientation), and D10 (Could not get going) were identified as the central symptoms of the depressive symptoms - cognitive performance network. C1 (Orientation), C5 (Linguistic skills), and D10 (Could not get going) were bridge symptoms connecting the two illnesses. Moreover, significant differences in edge weights were observed across gender and age groups.

CONCLUSIONS

The central symptoms and bridge symptoms in the network may represent the most effective intervention pathway for addressing cognitive impairment and depression in older adults with multimorbidity. Clinical interventions should properly focus on gender and age differences in symptom presentation.

Longitudinal trajectories of psychotic-like experiences influence adolescent suicidal ideation: The mediating role of depressive symptoms

BACKGROUND

Psychotic-like experiences (PLEs) are strongly associated with poor mental health among adolescents. This study aims to investigate distinct longitudinal trajectories of the PLEs and their relationship to suicidal ideation (SI), as well as to evaluate the mediating effect of depressive symptoms on this link.

METHODS

A total of 7529 Chinese adolescents were surveyed on PLEs online during April 21 to May 12, 2021 (Time 1, T1), December 17 to 26, 2021 (Time 2, T2), and May 17 to June 5, 2022 (Time 3, T3). Depressive symptoms and SI were assessed at T1 and T3.

RESULTS

The prevalence of frequent PLEs across the three surveys was 13.5 %, 13.2 %, and 11.0 %, respectively. Five PLEs trajectories were identified: resistance (74.4 % of the sample), recovery (9.2 %), delayed-dysfunction (6.6 %), relapsing/remitting (7.0 %), and chronic-dysfunction (2.8 %). Adolescents in recovery, delayed-dysfunction, relapsing/remitting, and chronic-dysfunction trajectories compared with those in the resistant group were all more likely to experience SI at T3 after adjusting for a wide range of covariates. Depressive symptoms partially mediated the effects of PLEs trajectories (relative to the resistance trajectory) on SI (95 % CIs: 0.07-0.13, 0.37-0.48, and 0.28-0.41 for relapsing/remitting, delayed-dysfunction, and chronic-dysfunction trajectories, respectively).

CONCLUSIONS

PLEs are common among adolescents and have heterogeneity of change patterns. The association between PLEs trajectories and SI is partially mediated by depressive symptoms. Timely assessment and targeted interventions for specific PLEs are necessary to reduce the incidence of mental disorders.

Attention bias variability as a cognitive marker of PTSD: A comparison of eye-tracking and reaction time methodologies

Identification of reliable behavioral markers for posttraumatic stress disorder (PTSD) can offer objective measures beyond self-reports to aid in diagnosis, treatment, and prevention. Attention bias variability (ABV), defined as fluctuations in attention toward or away from threat-related stimuli, has been associated with PTSD. Reaction time-based ABV (RT-ABV) has been extensively studied and shown to be linked with PTSD symptomatology. An eye-tracking-based ABV index (ET-ABV) was only recently introduced, and results show that it may be associated with trauma exposure rather than with PTSD symptoms. However, these two ABV types have yet to be studied within the same sample. The current study explored the association between trauma exposure, PTSD symptom severity, and ABV measures, applying both the classic RT-ABV and the new ET-ABV in the same sample. Participants with PTSD (n = 41), trauma-exposed healthy controls (TEHC; n = 71), and non-trauma-exposed healthy controls (HC; n = 38) completed an eye-tracking free-viewing matrix task and a response-based dot-probe task using the same threat-related stimuli (angry faces) across tasks. Results indicate that both RT-ABV and ET-ABV were significantly higher in participants with PTSD compared to participants in the TEHC and HC groups, with no significant difference between the latter groups. Additional analyses indicate that although moderately correlated, each of the ABV measures explains unique portions of the variance in PTSD severity. This study replicates and extends prior findings on the association between ABV and PTSD and highlights the potential of ET-ABV as a novel and reliable marker for PTSD severity.

Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging

BACKGROUND

Cortical morphological abnormalities in schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) have been identified in past research. However, their potential as objective biomarkers to differentiate these disorders remains uncertain. Machine learning models may offer a novel diagnostic tool.

METHODS

Structural MRI (sMRI) of 220 SCZ, 220 MDD, 220 BD, and 220 healthy controls were obtained using a 3T scanner. Volume, thickness, surface area, and mean curvature of 68 cerebral cortices were extracted using FreeSurfer. 272 features underwent 3 feature selection techniques to isolate important variables for model construction. These features were incorporated into 3 classifiers for classification. After model evaluation and hyperparameter tuning, the best-performing model was identified, along with the most significant brain measures.

RESULTS

The univariate feature selection-Naive Bayes model achieved the best performance, with an accuracy of 0.66, macro-average AUC of 0.86, and sensitivities and specificities ranging from 0.58-0.86 to 0.81-0.93, respectively. Key features included thickness of right isthmus-cingulate cortex, area of left inferior temporal gyrus, thickness of right superior temporal gyrus, mean curvature of right pars orbitalis, thickness of left transverse temporal cortex, volume of left caudal anterior-cingulate cortex, area of right banks superior temporal sulcus, and thickness of right temporal pole.

CONCLUSION

The machine learning model based on sMRI data shows promise for aiding in the differential diagnosis of SCZ, MDD, and BD. Cortical features from the cingulate and temporal lobes may highlight distinct biological mechanisms underlying each disorder.

Neuromodulating the rhythms of cognition

Rhythmic non-invasive brain stimulation (rh-NIBS) allows to modulate neural oscillations and study the functional role of these brain rhythms for cognition. We hope to draw attention to often neglected aspects of this field that limit the interpretations of the findings and their translational potential. We here review current rh-NIBS trends and propose to conceptually differentiate oscillatory synchronization, aimed at enhancing an intrinsic oscillatory amplitude, from frequency-shifting, designed to speed-up or slow-down a given oscillatory rhythm. At the same time, we offer a precise mechanistic account of these two rh-NIBS protocols that accounts for inter-individual differences in stimulation outcomes. Finally, we gap the bridge between entrainment, understood as an online manipulation of neural oscillations via rh-NIBS, versus plasticity, defined as the aftereffects of the TMS offline protocols. Specifically, we bring forward a promising possibility that the aftereffects of rh-NIBS protocols, preferably tuned to the dominant oscillatory frequency, might produce the desired outcome through a successful online oscillatory tuning, understood as a prerequisite for the generation of synaptic plasticity reflecting enduring aftereffects. This conceptual and mechanistic framework aims to provide a deeper theoretical understanding of recommended rh-NIBS best practices for noninvasively studying dynamic oscillation-cognition relationships in cognitive and clinical research.

A double-blind, placebo-controlled trial of exenatide for the treatment of olanzapine-related weight gain in obese and overweight adults

OBJECTIVE

To assess the safety and efficacy of exenatide in overweight or obese patients treated with olanzapine.

METHODS

Adults with stable major mood or psychotic disorders were randomized to double-blind exenatide or placebo for 16 weeks. Weight and body mass index (BMI) were monitored throughout the study. A secondary objective was to evaluate the tolerability of exenatide and its effects on mood and psychotic symptoms.

RESULTS

A significant difference in weight change was detected between the treatment groups. Participants in the exenatide group experienced on average a minor weight loss, while participants in the placebo group on average experienced weight gain (-0.5 kg [-0.6 %] vs. +2.6 kg [+2.8 %], both p < .01). The most common side effects in the exenatide group were gastrointestinal symptoms and headaches. There were no clinically meaningful differences between the groups in changes to mood or psychotic symptoms.

CONCLUSIONS

Exenatide is effective and well-tolerated for attenuating olanzapine-associated weight gain.

CLINICAL TRIAL REGISTRATION INFORMATION

Exenatide for the Treatment of Weight Gain Associated with Olanzapine in Obese Adults. NCT00845507.

The association of sex and age of treatment-resistant tendency to antidepressants: A cohort study of 325,615 patients with major depressive disorder

BACKGROUND

Treatment resistance to antidepressants can impose a significant burden on patients with major depressive disorder (MDD). This study aimed to evaluate the effects of age, sex, and psychiatric and physical comorbidities on the tendency toward treatment resistance to antidepressants (TRT).

METHODS

We utilized data from the Taiwan National Health Insurance Research Database. Patients diagnosed with MDD were included in the study. Physical comorbidities were assessed using the Charlson Comorbidity Index. TRT was defined as receiving antidepressant treatment at an adequate defined daily dose, followed by a subsequent switch to another antidepressant within one year after the initial diagnosis of depression. Logistic regression was used to estimate the odds ratios (ORs) of various potential factors associated with TRT.

RESULTS

A total of 325,615 patients with MDD were included in the study. After adjusting for key confounders, patients aged 20 to 29 years had the highest OR (1.60; 95 % confidence interval [CI]: 1.50-1.70) for TRT compared to the oldest age group (≥80 years). The ORs gradually decreased with increasing age. Males had a significantly lower OR (0.89; 95 % CI: 0.88-0.91) for TRT than females. TRT was also associated with the presence of physical and psychiatric comorbidities, except for autism spectrum disorder (ASD).

LIMITATIONS

As a naturalistic observational study, our findings are subject to potential confounding factors that cannot be fully controlled for, as would be possible in a formal randomized controlled trial.

CONCLUSIONS

Our study highlights the impact of age and sex on TRT. Clinicians should consider these risk factors when managing patients with MDD.

Classification schemes of altered states of consciousness

In recent years, there has been a renewed interest in the conceptual and empirical study of altered states of consciousness (ASCs), induced pharmacologically or otherwise, driven by their potential clinical applications. To draw attention to the rich history of research in this domain, we review prominent classification schemes that have been proposed to introduce systematicity into the scientific study of ASCs. The reviewed ASC classification schemes fall into three groups according to the criteria they use for categorization: (1) based on the nature, variety, and intensity of subjective experiences (state-based), including conceptual descriptions and psychometric assessments, (2) based on the technique of induction (method-based), and (3) descriptions of neurophysiological mechanisms of ASCs (neuro/physio-based). By comparing and extending existing classification schemes, we can enhance efforts to identify neural correlates of consciousness, particularly when examining mechanisms of ASC induction and the resulting subjective experiences. Furthermore, an overview of what defining ASC characteristics different authors have proposed can inform future research in the conceptualization and quantification of ASC subjective effects, including the identification of those that might be relevant in clinical research. This review concludes by clustering the concepts from the state-based schemes, which are suggested for classifying ASC experiences. The resulting clusters can inspire future approaches to formulate and quantify the core phenomenology of ASC experiences to assist in basic and clinical research.

Active information sampling in health and disease

Active information gathering is a fundamental cognitive process that enables organisms to navigate uncertainty and make adaptive decisions. Here we synthesise current knowledge on the behavioural, neural, and computational mechanisms underlying information sampling in healthy people and across several brain disorders. The role of cortical and subcortical regions spanning limbic, insular, fronto-parietal, and striatal systems is considered, along with the contributions of key neurotransmitters involving norepinephrine, dopamine, and serotonin. We also examine how various clinical conditions, including schizophrenia, obsessive-compulsive disorder, and Parkinson's disease have an impact on information gathering behaviours. To account for the findings, we outline a neuroeconomic perspective on how the brain may evaluate the costs and benefits of acquiring information to resolve uncertainty. This work highlights how active information gathering is a crucial brain process for adaptive behaviour in healthy individuals and how its breakdown is relevant to several psychiatric and neurological conditions. The findings have important implications for developing novel computational assays as well as targeted interventions in brain disorders.

Potential causal association between the oral microbiome and bipolar disorder

BACKGROUND

The oral microbiome can influence the growth, development, and regulation of the nervous system through various pathways; however, its relationship with bipolar disorder (BD) remains ambiguous. This study aims to investigate the causal relationship between the oral microbiome and BD through Mendelian randomization (MR) analysis.

METHODS

Data regarding single nucleotide polymorphisms (SNPs) in GWAS summary statistics for oral microbiota and BD were obtained from two studies: one reported the association between the oral microbiome and the human genome, encompassing both the dorsum of the tongue and saliva microbiomes. The other study investigated the association between BD and the human genome, categorizing BD into BD I and BD II for separate analyses. Thus, three data components were utilized: BD, BD I, and BD II. In this study, GWAS data for saliva and dorsum of the tongue microbiomes were analyzed separately for BD, BD I, and BD II. The inverse variance weighted (IVW) method was used to assess the causal relationship between the oral microbiome and BD. Analyses were conducted only when the number of instrumental variable SNPs was no less than two. The MR-Egger regression and IVW methods were employed to evaluate heterogeneity, whereas the MR-Egger intercept test was utilized to assess pleiotropy. For MR results exhibiting heterogeneity or pleiotropy, sensitivity analyses were performed using the weighted median, simple mode, weighted mode, MR-Egger test, and leave-one-out methods. Furthermore, funnel plots were employed to evaluate publication bias. Reverse MR analysis was also performed to investigate the potential bidirectional interactions between BD and the oral microbiota.

RESULTS

A causal relationship exists between the oral microbiome and BD. The effects of the microbiome from different regions of the oral cavity on BD are variable, with a more pronounced impact noted on BD I. This study identified two overlapping causal relationships shared between BD I and BD II, both exhibiting the same directional influence: ①Salivary s Prevotella aurantiaca SGB 2854 (Taxonomy ID: 596085, species); ② Tongue s Prevotella sp000467895 SGB 1817 (Taxonomy ID: 838, genus). Additionally, there are two overlapping bacteria with opposing directional effects: ① Salivary g Eggerthia (Taxonomy ID: 1279384, genus); ② Salivary s unclassified SGB 2636. Three differential bacteria that exclusively regulate one subtype were identified: ① Salivary s Lachnoanaerobaculum sp000296385 SGB 3537 (Taxonomy ID: 1164882, genus); ② Tongue s unclassified SGB 689; ③ Tongue s unclassified SGB 572. Among these, the genus g Eggerthia in saliva inhibits BD I while promoting BD II; conversely, salivary s unclassified SGB 2636 inhibits BD II while promoting BD I. The analysis of tongue s unclassified SGB 489 and s unclassified SGB 1215 demonstrated pleiotropy without causal significance. The reverse MR analysis identified that BD I may influence four microbial species, including f Leptotrichiaceae (Taxonomy ID: 1129771, family), f Streptococcaceae (Taxonomy ID: 1300, family), s unclassified SGB 1210, and s unclassified SGB 1950. There may be a bidirectional causal relationship between s unclassified SGB 1950 and BD I. Additionally, Reverse Mendel suggested that there was no significant causal relationship between BD and salivary and dorsal tongue microbes.

CONCLUSION

Our Mendelian randomization results indicate a causal relationship between the oral microbiome and the development of BD. However, the microbial profiles associated with the different subtypes, BD I and BD II, differ significantly; even within the same genus, the direction of influence on BD I and BD II varies, suggesting that the underlying mechanisms for the development of BD I and BD II may differ substantially.

The effects of selected phytochemicals on schizophrenia symptoms: A review

There are suggested treatment options for schizophrenia (SZ), including antipsychotic medications. Unfortunately, these drugs mostly ameliorate only the positive symptoms of SZ, and patients have less tendency for compliance due to the drug's side effects. Hence, there is a need for additional or adjunct therapeutic options. This review considers selected phytochemicals with anti-schizophrenic activity as an alternative therapy. We searched the scientific literature and reviewed the evidence from pre-clinical (animal) and clinical studies using some phytochemicals in SZ. The reviewed phytochemicals provided varying potential beneficial effects on SZ. Of particular interest, berberine may provide additional ameliorative advantages against the disorder. Although still nascent in scientific research, these studies suggest a potential adjunct therapeutic option against the pathophysiological pathways implicated in SZ. We recommend robust, carefully performed randomized controlled trials evaluating the role of these phytochemicals in SZ.

Neurocognitive effects of psilocybin: A systematic and comprehensive review of neuroimaging studies in humans

Psilocybin is a psychedelic serotonergic compound that is renowned for its potent psychoactive effects. Over the past 15 years, an increasing number of controlled clinical trials showed that it has a fast-acting and sustainable efficacy in treating various psychiatric disorders. Neuroimaging studies have been conducted with the objective of elucidating the neurobiological mechanisms underlying the subjective and therapeutic effects of psilocybin. However, the diversity of neuroimaging techniques, tasks, and analytical approaches makes it difficult to gain a comprehensive overview of psilocybin's effects on the brain. To address this gap in the literature, we conducted a systematic review in the Medline, Psychinfo and Cochrane databases between January 1, 1990, and May 9, 2025, following PRISMA recommendations. A total of 81 articles met the inclusion criteria. A variety of neuroimaging techniques were employed in small samples of healthy volunteers and patients with medical conditions. The studies investigated the effects of psilocybin on brain activity and connectivity, both at rest and during cognitive tasks. They revealed that psilocybin reproducibly impacted neuronal networks such as the default mode network. However, other findings were more inconsistent. Psilocybin effects on the brain were associated with acute alterations in self-experience, sensory and emotional processing, and sustained effects on mood, personality, and social functioning. In patients with depression, clinical outcomes correlated with brain changes. This review indicates that psilocybin induces acute and long-lasting functional brain changes. While these neuroimaging data require confirmation and further expansion, they shed light on the mechanisms of psilocybin's acute subjective and therapeutic effects in humans.

Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder of the central nervous system, primarily manifesting in childhood. It is characterized by atypical and repetitive behaviors. Conventional diagnostic methods mainly rely on questionnaire surveys and behavioral observations, which are prone to misdiagnosis due to their subjective nature. With advancements in medical imaging, MR imaging-based diagnostics have emerged as a more objective alternative. In this paper, we propose a Hierarchical Neural Network model for ASD identification, termed ASD-HNet, which hierarchically extracts features from functional brain networks based on resting-state functional magnetic resonance imaging (rs-fMRI) data. This hierarchical approach enhances the extraction of brain representations, improving diagnostic accuracy and aiding in the identification of brain regions associated with ASD. Specifically, features are extracted at three levels, i.e., the local region of interest (ROI) scale, the community scale, and the global representation scale. At the ROI scale, graph convolution is employed to transfer features between ROIs. At the community scale, functional gradients are introduced, and a K-Means clustering algorithm is applied to group ROIs with similar functional gradients into communities. Features from ROIs within the same community are then extracted to characterize the communities. At the global representation scale, we extract global features from the whole community-scale brain networks to represent the entire brain. We validate the effectiveness of the ASD-HNet model using the publicly available Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, ADHD-200,dataset and ABIDE-II dataset. Extensive experimental results demonstrate that ASD-HNet outperforms existing baseline methods. The code is available at https://github.com/LYQbyte/ASD-HNet.

Disruptions of the oxytocin system impair sociability and cognitive flexibility in a subchronic phencyclidine model of schizophrenia

Previous research suggests that the oxytocin (Oxt) system may play a role in the etiology of schizophrenia. To investigate, we used a subchronic phencyclidine (PCP) mouse model to test how disruption of Oxt or the Oxt receptor (Oxtr) affects schizophrenia-related behaviors. Specifically, we assessed how subchronic PCP impacted hyperlocomotion, sociability, and passive stress coping in male Oxt and Oxtr knockout (-/-) and wildtype (+/+) mice. Additionally, we evaluated immediate early gene activation in Oxtr -/- and +/+ mice to identify brain regions where the Oxt system might impact schizophrenia-associated behaviors. Lastly, we investigated cognitive flexibility in Oxtr -/- and +/+ mice. We found that subchronic PCP treatment decreased social interactions in Oxt -/- mice as compared to Oxt +/+ mice, with no genotypic differences in the Oxtr line of mice. Increased c-Fos expression was observed in Oxtr -/- mice relative to Oxtr +/+ controls in the medial amygdala and the paraventricular nucleus of the hypothalamus following a forced swim test. Finally, we found deficits in cognitive flexibility in Oxtr -/- mice treated with PCP, relative to Oxtr +/+ mice. These findings are consistent with the hypothesis that Oxt may buffer against some of the schizophrenia-associated symptoms induced by subchronic PCP treatment. Based on the data, we speculate that compensatory mechanisms may be able to accommodate the loss of the Oxt system, depending on the origin of the dysfunction and the behavioral endpoint in question. These findings also add support to data linking disruption of Oxt system signaling to schizophrenia.

Brain activation characteristics of schizophrenia and bipolar disorder-I patients during letter and category fluency tasks: An empirical study using functional near-infrared spectroscopy

BACKGROUND

Schizophrenia (SP) and bipolar disorder type I (BD-I) are severe psychiatric disorders with partially overlapping clinical symptoms, resulting in difficult differential diagnosis in many cases. Therefore, objective markers are needed to ensure diagnostic accuracy and timely treatment. One strategy with the potential to provide disease markers for diagnosis and reveal potential pathogenic mechanisms is combined cognitive testing and neuroimaging. In the current study, we compared cortical activation patterns among SP, BD-I, and healthy control (HC) groups during verbal fluency tasks using functional near-infrared spectroscopy (fNIRS) to identify distinguishing biomarkers.

METHODS

Sixty SP patients, 42 BD-I patients, and 44 age- and sex-matched HCs were examined by 52-channel fNIRS during the letter fluency task (LFT) and category fluency task (CFT).

RESULTS

Both patient groups exhibited performance deficits in the LFT and even larger deficits in the CFT compared to HCs. Patients with SP and HCs exhibited greater prefrontal cortex activation during the CFT than the LFT. Compared to HCs, SP patients demonstrated reduced prefrontal cortex activation during both the CFT and LFT, especially in bilateral dorsolateral and ventrolateral areas. In contrast, BD-I patients showed reduced cortical activation only during the CFT, mainly in right frontal lobe and bilateral superior temporal cortex.

LIMITATIONS

Cross-section study design, small sample sizes, and uncontrolled effects of medication.

CONCLUSIONS

Patients with SP or BD-I exhibited pronounced semantic deficits and frontal-temporal dysfunction during CFT performance compared to HCs. These findings provide several potential markers for distinguishing SP from BD-I as well as clues to the underlying network-level pathology.

Linking cellular-level phenomena to brain architecture: the case of spiking cerebellar controllers

Linking cellular-level phenomena to brain architecture and behavior is a holy grail for theoretical and computational neuroscience. Advances in neuroinformatics have recently allowed scientists to embed spiking neural networks of the cerebellum with realistic neuron models and multiple synaptic plasticity rules into sensorimotor controllers. By minimizing the distance (error) between the desired and the actual sensory state, and exploiting the sensory prediction, the cerebellar network acquires knowledge about the body-environment interaction and generates corrective signals. In doing so, the cerebellum implements a generalized computational algorithm, allowing it "to learn to predict the timing between correlated events" in a rich set of behavioral contexts. Plastic changes evolve trial by trial and are distributed over multiple synapses, regulating the timing of neuronal discharge and fine-tuning high-speed movements on the millisecond timescale. Thus, spiking cerebellar built-in controllers, among various computational approaches to studying cerebellar function, are helping to reveal the cellular-level substrates of network learning and signal coding, opening new frontiers for predictive computing and autonomous learning in robots.

Are cognitive behavioural therapy, cognitive therapy, and behavioural activation for depression effective in primary care? A systematic review and meta-analysis

Cognitive behavioural therapy (CBT) is a recommended first-line treatment for depression. Evidence mainly derives from studies in secondary care, though most treatment occurs in primary care. This review examined efficacy of CBT, cognitive therapy (CT), or behavioural activation (BA) for depression within primary care. Databases were searched for trials up to 23rd July 2024. Risk of bias was assessed using the Cochrane risk-of-bias tool, version 2.0.44 studies were included. CBT, CT, and BA significantly reduced depression symptoms compared to inactive controls (k = 40, g = 0.44, p < .001), but not active comparators (other therapies, medication or exercise) (k = 9, g = -0.06, p = .24). Heterogeneity was significant in studies comparing CBT, CT, or BA to inactive controls, but not in studies using active comparators. Most studies were rated at high risk of bias (36 studies, 81.8 %), predominantly due to use of patient-rated outcome measures in non-blinded studies, lack of ITT analyses, and lack of pre-registering protocols, all of which may result in inflated effect sizes. Although CBT, CT, or BA appears effective for depression in primary care against usual care or waiting list controls, when compared to active comparators no significant difference is seen, likely a result of variability in the quality of the included studies. Large studies of improved quality (including use of blinded observer-rated outcome measures and ITT analyses) may be required to justify guideline recommendations for CBT over other interventions for depression specifically in primary care.

Prenatal cannabinoid exposure and the developing brain: Evidence of lasting consequences in preclinical rodent models

Cannabis use by people who are pregnant is increasing. Understanding how prenatal cannabinoid exposure (PCE) affects infants and children is of high public health significance. Epidemiological studies have associated PCE with cognitive symptoms, including impaired learning, memory, attention, and executive control, and affective symptoms, including anxiety, emotional dysregulation, and social impairments, in children, adolescents, and young adults. PCE is also associated with neurobiological changes including decreased corticolimbic white matter and functional connectivity; however, the underlying mechanisms for these persisting effects remain unknown. Rodent models are essential for uncovering the effects of PCE on the developing brain. This review summarizes rodent studies focused on the cognitive and affective behavioral and neurobiological outcomes of PCE. Rodent studies have reported cognitive deficits, including impaired learning, memory, attention, and executive control, and affect-related impairments, including anxiety-like behavior, altered stress coping, social impairments, and anhedonia-like behavior, in adolescent and adult offspring. Studies have also demonstrated that PCE affects several underlying neurotransmitter systems, producing dopamine hyperactivity, glutamate and serotonin hypoactivity, and dysregulating GABA and opioid signaling. Evidence further suggests a marked difference in outcomes between males and females, with males being more susceptible to the enduring effects of PCE. However, studies that investigate female-specific outcomes or sex as a biological variable are scarce. Altogether, rodent studies provide corroborating evidence that PCE produces lasting cognitive and affective impairments underpinned by altered neurobiological mechanisms. Research is critically needed to improve our understanding of the risks associated with cannabis use during pregnancy and effects across the lifespan.

Sociodemographic and clinical predictors of depression in children and adolescents at clinical high-risk for psychosis: Results of a two-year follow-up study

Depressive disorders are a main cause of disability already in children and adolescents, in whom the clinical picture somewhat differs from adult-onset depression. Depression is also a frequent comorbidity in somatic and mental disorders and has been considered an actionable outcome for, for example, patients at clinical high-risk for psychoses (CHRP). Thus, we studied sociodemographic and clinical predictors of depression/dysthymia in an underage sample with focus on those considered at CHRP. Our baseline sample (N = 676) included CHR-P patients (n = 183), inpatients admitted for non-psychotic, non-affective disorders (n = 277), and community participants (n = 216) of age 8.0-17.9 years (43.8 % male). They were interviewed for mental disorders and symptoms with various instruments, including the Mini International Neuropsychiatric Interview for Children and Adolescents, which was also used to assess depression/dysthymia in the CHR-P group at one- and two-year-follow up (n = 117/73). Stepwise logistic regression analyses were used to first identify a cross-sectional baseline model in the complete sample that was then tested prospectively in CHR-P patients. The baseline model included nationality and 13 clinical variables, particularly mild depressive symptoms. Variables contributing significantly to the prediction of persistence or new occurrence of depression/dysthymia varied over time, indicating that depression/dysthymia in CHR-P minors may require different predictors depending on the follow-up time. Furthermore, the prospective accuracy of ruling out depression/dysthymia was superior to the accuracy of ruling it in. This lower positive likelihood ratio might be overcome in future by stepwise approaches that further stratify risk in those CHR-P minors identified as at increased risk of depression/dysthymia.

Dissociation as transdiagnostic mediator between trauma exposure and the post-traumatic stress disorder-psychosis symptom spectrum: A structural equation modelling meta-analysis

Trauma exposure is associated with dissociation, post-traumatic stress disorder (PTSD), and psychosis; however, less is known about whether dissociation is a transdiagnostic mediator between trauma exposure and the PTSD-psychosis symptom spectrum. Hence, we conducted a meta-analysis to elucidate the associations among trauma exposure, dissociation, PTSD symptoms, and psychotic symptoms. A total of 311 articles with 94,454 individuals were coded and a two-stage meta-analytical structural equation model was used to investigate the indirect effect of trauma exposure on PTSD symptoms and psychotic symptoms through dissociation. Subgroup analyses investigated the heterogeneity in indirect effects due to dissociation type (i.e., general dissociation versus traumatic dissociation), sample type, study design, study quality, and geographical differences. The indirect effect from trauma exposure to PTSD symptoms via dissociation was significant (β = 0.15, 95 % CI [0.13, 0.17]). The indirect effect from trauma exposure to psychotic symptoms via dissociation was significant (β = 0.14, 95 % CI [0.12, 0.17]). There was no statistical difference between both indirect effects. Subgroup analyses revealed larger indirect effects for traumatic dissociation, clinical samples, non-Western countries, and poor quality studies. The present meta-analytic results indicate that dissociation is a transdiagnostic mediator in the relationship between trauma exposure and the PTSD-psychosis symptom spectrum. Traumatic dissociation and clinical samples were associated with larger indirect effects. These findings inform clinical practice and future transdiagnostic research on PTSD and psychosis.

Long-term treatment with haloperidol modulates angiotensin I-converting enzyme (ACE) activity in transgenic animal model with construct validity for schizophrenia studies

Elevated angiotensin I-converting enzyme (ACE) activity has been correlated with worse cognitive performance in patients with first-episode psychosis (FEP) and chronic schizophrenia (SZ). In this study, we investigated ACE activity in drug-naïve transgenic rats overexpressing the full-length non-mutated human Disrupted-in-Schizophrenia 1 (tgDISC1) compared to wild-type (WT) controls, while we also assessed the effects of long-term treatment with typical antipsychotic haloperidol. Our findings indicated that untreated tgDISC1 rats show elevated serum ACE activity compared to WT animals, which is consistent with clinical observations in drug-naïve FEP patients. In contrast, baseline ACE activity in the brain of tgDISC1 was generally lower than in WT rats, with the exception of no difference in ACE activity observed in brain regions associated with learning, memory, and reward, such as the hippocampus and nucleus accumbens. Consistent with clinical observations in FEP patients following treatment with antipsychotics, 30-days of daily haloperidol-treatment significantly increased serum ACE activity in blood serum of both tgDISC1 and WT rats. However, ACE responses in brain were markedly different, as haloperidol treatment reduced ACE activity in most brain regions of both rat strains. These results support the existence of a central renin-angiotensin system (RAS) distinct from the peripheral RAS, suggesting that the treatment with a dopamine blocker exerts brain-specific effects on ACE activity, which was essentially opposite to that observed in the periphery. This region-specific alterations observed in cognition-related brain areas (notably with a relative stronger effect size in hippocampus and nucleus accumbens of tgDISC1 compared to WT rats) also suggest a critical interplay among dopamine homeostasis, ACE activity, and cognitive deficits in SZ. Understanding this interplay could help identifying novel biomarkers and/or therapeutic strategies for improving cognitive outcomes in SZ patients.

Motor dysfunction in late life depression: A mood or movement disorder?

AIMS

motor dysfunction, presenting as an altered quantity or quality of movements, is common in late life depression (LLD) but poorly understood. We characterized motor dysfunction with a multimodal clinical-experimental assessment and investigated associations with depressive symptoms, psychotropic medication and falls.

METHODS

75 participants ≥60 yr., 34 patients with LLD and 41 healthy controls, from the Leuven LLD study completed affective, cognitive and motor assessments including the Montgomery-Åsberg Depression Rating Scale (MADRS), Apathy Evaluation Scale, mini-mental state examination, neuropsychological battery, parkinsonism scale (MDS-UPDRS part III), ataxia scale (SARA), speech and gait analysis. A digital tablet drawing task tracked how fast patients initiated and executed movements with variable complexity and cueing. Past year fall history was collected. Multiple linear regression analyses evaluated associations of motor outcome with assessments scores, depending on diagnostic group, with age, exercise frequency, education duration as covariates. Movement initiation and execution were investigated with linear mixed models including the same covariates. Psychotropic medication effects and statistical predictors of falls were analyzed in patients.

RESULTS

patients (mean age 73.3 ± SD6.0 yr., MADRS 27.9 ± 11.1, 42 % fall incidence) performed worse than controls on all motor assessments which was overall not significantly associated with depression severity, apathy, cognitive dysfunction, antipsychotic or benzodiazepine use. Patients executed movements slower than controls, but movement initiation was similar if cueing was provided. Patients' SARA and gait outcome related to past year fall incidence.

CONCLUSION

motor dysfunction in LLD patients was not clearly related to core depressive symptoms or cognitive dysfunction. Cueing may facilitate movement initiation in LLD.

STUDY REGISTRATION

NCT03849417.

Liposome-loaded miR-34c-5p attenuates apoptosis and oxidative stress following hypoxia-ischemia brain damage in neonatal mice by targeting Arhgap26

Neonatal hypoxia-ischemia (HI) brain injury is considered a major cause of neonatal mortality and chronic disease morbidity worldwide. Despite its clinical importance, therapeutic options for HI injury remain limited. Here we demonstrated that miR-34c-5p expression peaks at postnatal day 10 in mice. Meanwhile, the miR-34c-5p levels in the lesioned cortex decreased following HI insult in neonatal mice. miR-34c-5p overexpression confers neuroprotective effects by attenuating brain injury and ROS production. These protective mechanisms were mediated through the inhibition of caspase 3 activation, suppression of microglial activation, and downregulation of pro-inflammatory cytokines in the injured cortex. In contrast, miR-34c-5p downregulation markedly aggravated the infarct area after HI injury. Additionally, miR-34c-5p overexpression improved short-term motor coordination and long-term neurological outcomes, including locomotor activity, learning, and memory functions, which were associated with upregulated synaptic protein expression. Importantly, we developed a non-invasive intranasal delivery system using liposome-encapsulated miR-34c-5p mimics, which significantly ameliorated brain injury at 3 days post-HI. Mechanistic studies revealed that miR-34c-5p directly targets the 3' untranslated region of GTPase activating protein 26 (Arhgap26). In conclusion, we identified a non-invasive method for successfully delivering miR-34c-5p to improve functional recovery after HI insult by targeting Arhgap26.

The relationship between self-reported adverse experiences and depressive symptoms among middle-aged and elderly individuals: A longitudinal study based on the CHARLS database

Background

Adverse experiences are critical determinants of late-life depressive symptomatology. Understanding how these experiences influence later-life health outcomes remains a research priority. This study examines the longitudinal associations between self-reported adverse childhood experiences (ACEs) and adverse adult experiences (AAEs) with depressive symptoms in older adults, as well as the underlying mechanisms.

Methods

A sample of 3941 adults aged ≥45 years from the China Health and Retirement Longitudinal Study (CHARLS) was analyzed. K-means for Longitudinal Data (KML), Logistic regression, and Bayesian Kernel Machine Regression (BKMR) models were employed to assess the effects of adverse experiences. Subgroup and mediation analyses were also performed.

Results

The high depressive symptomatology cluster (n = 1432) demonstrated significant associations with six ACEs: childhood hunger (OR = 1.23, 95%CI:1.03-1.47), dangerous growth environments (OR = 1.34, 95%CI:1.09-1.65), childhood loneliness (OR = 1.45, 95%CI:1.20-1.74), bullying (OR = 1.2, 95%CI:1.01-1.43), parental depression (OR = 1.80, 95%CI:1.50-2.16), and parental disability (OR = 1.44, 95%CI:1.03-2.02). Comprehensive effect estimation of ACEs indicated an 85.9% probability of a high depression score for those with all adverse experiences. AAEs like prolonged bed rest (OR = 1.39, 95%CI:1.08-1.79), and lifetime discrimination (OR = 1.37, 95%CI:1.12-1.66) independently predicted symptom severity. Effect modification analysis revealed stronger ACE impacts among individuals with higher cognitive reserve (OR = 3.32, 95%CI:2.34-4.70). Mediation analysis identified arthritis or rheumatism as a partial mediator of the ACE-depression pathway (natural indirect effect = 1.04, 95%CI:1.02-1.05).

Conclusions

Self-reported ACEs and AAEs demonstrate persistent associations with depressive symptoms in later life, mediated by chronic morbidity and moderated by cognitive reserve.

Validation of the Dutch Sensory Gating Inventory (D-SGI): Psychometric properties and a Confirmatory factor analysis

The Sensory Gating Inventory (SGI) is an established self-report questionnaire that is used to assess the capacity for filtering redundant or irrelevant environmental stimuli. Translation and cross-cultural validation of the SGI are necessary to make this tool available to Dutch speaking populations. This study, therefore, aimed to design and validate a Dutch Sensory Gating Inventory (D-SGI). To this end, a forward-backward translation was performed and 469 native Dutch speakers filled in the questionnaire. A confirmatory factor analysis assessed the psychometric properties of the D-SGI. Additionally, test-retest reliability was measured. Results confirmed satisfactory similarity between the original English SGI and the D-SGI in terms of psychometric properties for the factor structure. Internal consistency and discriminant validity were also satisfactory. Overall test-retest reliability was excellent (ICC = 0.91, p < 0.001, 95% CI [0.87-0.93]). These findings confirm that the D-SGI is a psychometrically sound self-report measure that allows assessing the phenomenological dimensions of sensory gating in Dutch. Moreover, the D-SGI is publicly available. This establishes the D-SGI as a new tool for the assessment of sensory gating dimensions in general- and clinical Dutch speaking populations.

Patient-Reported Outcome Measures (PROMs) for Recovery in Mental Health: A Scoping Review

Recovery is a holistic approach to mental health care which focusses on the ability of people with mental disorders to achieve their goals and live full, meaningful lives despite their limitations. Recovery assessment tools are instruments that make it possible to measure the progress of people with mental disorders on their path to recovery. To explore, map and describe patient-reported outcome measures (PROMs) found in the literature related to the recovery process in people with mental disorders (MDs). A scoping review was carried out of papers proposing personal recovery assessment tools. The search was performed in seven data bases: SCOPUS, CINAHL, Medline, Web of Science, Teseo, Opengrey and Dart-Europe. It included studies published between 1970 and 2023 with adult participants suffering from some form of MD. It excluded studies involving patients with intellectual disability and/or addictions. 82% of the instruments described in the 50 studies found used self-administered questionnaires. The most frequently referenced dimensions were hope, empowerment and wellbeing, with an average of 35 items per questionnaire. The most frequently assessed psychometric property was internal consistency, which was evaluated in 84% of the studies. Recovery is a complex concept which is not integrally measured in any of the existing scales. However, the tools identified in the review can help mental health professionals assess the recovery of MD patients and develop personalised treatment plans.

Canadian Undergraduate Perspectives on Medical Assistance in Dying for Mental Illness: Does Psychiatric Illness Type, Age, and Exposure to Information Influence Acceptance of MAiD?

Background and ObjectivesIn 2027, Canadians whose only medical condition is an untreatable mental illness and who otherwise meet all eligibility criteria will be able to request Medical Assistance in Dying (MAiD). This study investigates the attitudes of undergraduate students towards widening the scope of MAiD for physical illness for certain psychiatric conditions. We were interested in understanding if age, information, and type of mental illness influenced undergraduates' acceptance or rejection of MAiD for mental illness (MAiD-MI).Method413 undergraduate students participated in this study which examined the factors that correlate with the acceptance or rejection of MAiD-MI. Four scenarios were presented in which age (older or younger) and illness type (depression or schizophrenia) were manipulated. Demographic questions and measures assessing personality, religion, and attitudes towards euthanasia were administered. Questions assessing participants' general understanding of MAiD and their life experiences with death and suicide were also asked.ResultsMost of the participants accepted MAiD-MI for both depression and schizophrenia. As hypothesized, support for MAiD-MI was higher for patients with schizophrenia than for depression. Also as hypothesized, support was higher for older patients than for younger patients. Variables such as religion, personality and political affiliation were also associated with acceptance or rejection of MAiD-MI. Finally, consistent with our hypotheses, participants' understanding of MAiD and experiences with death and suicide was predictive of support for MAiD-MI.

High rates of suicidality and parasuicidal behavior in individuals at clinical high-risk for psychosis: Implications for suicide risk assessment and suicide prevention

BACKGROUND

Prior early psychosis studies have reported higher rates of suicidal ideation (SI) and parasuicidal behavior compared to healthy controls, but there is limited research examining rates and predictors of SI or protective factors in clinical high risk (CHR) youth. We investigated suicidality in CHR participants in the third sample of the North American Prodrome Longitudinal Study (NAPLS-3) and investigated associated demographic, life event, symptom, functional, treatment and outcome information.

METHODS

The sample included 710 CHR (mean age 18.2, 45.8 % female) and 96 healthy control (HC) participants (mean age 18.6, 50.0 % female). Past SI, plans, self-harm and attempts were assessed via the clinician-administered Structured Assessment of Violence Risk in Youth (SAVRY) scale and the Calgary Depression Scale for Schizophrenia (CDSS).

RESULTS

A significantly greater proportion of CHR participants compared to HC participants endorsed a history of SI or plan (30.7 % vs 0 %), a history of self-harm with no intent (25.6 % vs 4.2 %), serious suicide attempts (12 % vs 0 %) and suicide attempts designed to end in death (1 % vs 0 %). Within the CHR group, increasing levels of suicidality were significantly associated with worse symptoms; comorbid DSM diagnoses; decreased global and premorbid functioning; and stressful life events at baseline. Although parasuicidal behavior predicted future general symptoms including dysphoria and stress intolerance, it did not predict psychotic conversion at future timepoints.

CONCLUSION

Suicidality is prominent in CHR youth and an indicator of greater acuity, highlighting the importance of suicide risk assessments and suicide prevention interventions specifically targeted to CHR youth.

Phenomenological psychopathology meets machine learning: A multicentric retrospective study (Mu.St.A.R.D.) targeting the role of Aberrant Salience assessment in psychosis detection

BACKGROUND

The Aberrant Salience (AS) model conceptualizes psychosis onset as the altered attribution of salience to neutral stimuli. The Aberrant Salience Inventory (ASI), a psychometric tool, measures this phenomenon. This study utilized a multi-center, multi-country retrospective dataset to refine the ASI's screening capabilities using decision tree (DT) and multilayer perceptron (MLP) models. By integrating machine learning approaches with practical screening utility and phenomenological insights, this work advances understanding of psychosis development and its mechanisms.

METHODS

Data from 2981 individuals (537 people with a psychosis diagnosis, 2444 controls) were analyzed, including ASI item responses, age, and sex. Classification models - DTs and MLPs - were applied with hyperparameter tuning and cost adjustments for imbalanced data. Least Absolute Shrinkage and Selection Operator (LASSO) feature selection identified key ASI items for DT construction, and separate DTs were computed for demographic subgroups.

RESULTS

The psychosis group exhibited higher mean ASI scores and older average age than controls. The seven-item DT model achieved a balanced accuracy of 67.4 % and AUC of 0.72, with enhanced performance in older individuals and males. The MLP model outperformed the DT (balanced accuracy: 73.28 %, AUC: 0.74).

CONCLUSIONS

AS emerges as a critical paradigm in psychosis psychopathology, complementing other psychosis staging models. The seven-item DT demonstrated strong clinical utility, reflecting key psychopathological constructs like delusional mood and apophany. Future studies should validate findings in diverse populations and explore integration with neurobiological markers for refined psychosis prediction.

Compulsory admission as access to early intervention service for patients with first episode psychosis: What relevance for clinical outcomes? Further findings from the Pr-EP program

PURPOSE

Patients with psychosis have a higher risk of compulsory admission. However, knowledge about its prognostic relevance in First Episode Psychosis (FEP) is poor. The aims of this study were to calculate its baseline prevalence rate in FEP individuals treated within an "Early Intervention" (EI) service, and to longitudinally compare clinical outcomes between FEP patients with and without baseline compulsory admission across 2 years of follow-up.

METHODS

500 FEP youths completed the PANSS and the GAF. Chi-squared/Mann-Whitney tests, mixed-design ANOVA, logistic regression, and Kaplan-Meier survival analysis were used for inter-group comparisons.

RESULTS

53 (10.6 %) FEP participants were compulsory admitted at entry. They were likely to be unemployed and to have lower baseline GAF score and more severe positive symptoms. They showed significant "time x group" effects for improvements in GAF and PANSS uncooperativeness scores. No inter-group difference in terms of service disengagement and new compulsory admission was found.

CONCLUSION

A non-negligible portion (1/10) of FEP participants entered the EI program through compulsory admission. However, this did not negatively impact on longitudinal outcomes, suggesting the beneficial effect of EI intervention in longitudinally promoting functional recovery and treatment adherence also in FEP recruited though compulsory admission.

Increasing the Value of Health Intervention Trials: Qualitative Research in the Early Psychosis Intervention Network (EPINET)

Despite the substantial capacity of qualitative and mixed methods research to advance healthcare and interventions knowledge, most large-scale health intervention trials exclusively use quantitative methods. The authors argue that qualitative research can optimize investments in these studies. As researchers within the Early Psychosis Intervention Network (EPINET), the authors highlight examples of how qualitative research has enhanced this national initiative, organizing them with a Learning Health System (LHS) framework to demonstrate the ways qualitative research can increase value at each phase of a health trial. They emphasize the critical need for integrating qualitative research from the beginning of health trials, ensuring its influence in decision-making, creating infrastructure to support it, and promoting meaningful representation within research teams. By illustrating the advantages of qualitative research in EPINET, they advocate for sustained commitment to qualitative research in health trials to maximize value in client and provider experience, cost, and population health.

Association between RAR and the prevalence and prognosis of depression: A population-based study

BACKGROUND

Depression has become a significant public health issue. The red blood cell distribution width to albumin ratio (RAR) is regarded as a reliable marker of inflammation and nutritional status, yet the association between them remains unknown. Therefore, this study aims to evaluate the association between RAR and depression.

METHODS

28,737 participants were included in the National Health and Nutrition Examination Survey 2005-2018. The weighted multivariate logistic regression and Cox regression explored the relationship between RAR and the prevalence and mortality of depression. Restricted cubic spline (RCS) detected nonlinear relationships and threshold effects. The stratified analysis explored potential interaction variables.

RESULTS

In the cross-sectional study, 2519 participants (7.67 %) had depression. Weighted multivariate logistic regression showed RAR was independently associated depression (OR = 1.26, 95 % CI = 1.12, 1.42, P < 0.001). In the cohort study, there were 301 cases of all-cause mortality (9.94 %) in depression population, including 77 cases of cardiovascular mortality (2.66 %). Weighted multivariate Cox regression analysis presented that RAR was related to higher all-cause mortality (HR = 1.98, 95 % CI = 1.62, 2.42, P < 0.001) and cardiovascular mortality (HR = 1.73, 95 % CI = 1.19, 2.51, P = 0.004) in the depression population. Subgroup analysis showed that RAR was consistently related to the prevalence and mortality of depression across all subgroups (all P < 0.05). RCS revealed that the relationship between RAR and depression was nonlinear (P for nonlinear = 0.011), while the association between RAR and all-cause mortality was linear (P for nonlinear = 0.245).

CONCLUSIONS

RAR is significantly associated with the prevalence and prognosis of depression. Nevertheless, more investigation is required to elucidate the underlying processes.