Anhui Mental Health Center

Melatonin, a key regulator of circadian rhythms and sleep-wake cycles, is implicated in the pathophysiology of major depressive disorder (MDD). Emerging evidence supports its anti-inflammatory, cytoprotective, and neuroprotective roles, including promotion of neuroplasticity. This study aims to investigate alterations in serum melatonin, interleukin-6 (IL-6), and brain-derived neurotrophic factor (BDNF) levels in first-episode MDD patients, and explores their clinical correlations. A total of 74 first-episode patients diagnosed with MDD and 72 healthy controls were enrolled in this study. The severity of depressive symptoms was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24). All blood samples were collected in the morning, and serum levels of melatonin, IL-6, and BDNF were quantified via enzyme-linked immunosorbent assay (ELISA). Baseline serum concentrations of melatonin, IL-6, and BDNF were compared between the MDD group and the control group. Additionally, the discriminative ability of these biomarkers (melatonin, IL-6, and BDNF) in distinguishing MDD patients from healthy controls was evaluated using receiver operating characteristic (ROC) curve analysis. Pearson correlation analysis or Spearman's rank correlation analysis was performed to explore the relationships between serum melatonin levels and clinical disease severity, as well as with IL-6 and BDNF levels, in patients with MDD. Compared with the control group, the MDD group showed significantly higher serum levels of melatonin (Z = -3.861, P < 0.001) and IL-6 (Z = -4.240, P < 0.001), but significantly lower serum BDNF levels (t = 9.537, P < 0.001). Moreover, the combined panel of BDNF, IL-6, and melatonin achieved high accuracy in distinguishing MDD patients from healthy controls, with an area under the curve (AUC) of 0.905. Additionally, no significant correlations were found between serum melatonin levels and clinical disease severity (assessed by HAMD-24 scores), IL-6 levels, or BDNF levels in MDD patients (all P > 0.05). These findings suggest that dysregulation of melatonin, IL-6, and BDNF may contribute to the pathophysiology of first-episode MDD, with their combined measurement offering strong diagnostic potential.

ABSTRACT Behavioral and psychological symptoms of dementia (BPSD) are highly prevalent in Alzheimer's disease (AD), but the underlying mechanisms are unclear. This study aims to elucidate the neuropathological mechanisms underlying BPSD by investigating gray matter volume (GMV) and brain connectivity in AD patients with and without BPSD. Amnestic mild cognitive impairment (aMCI)/AD patients with BPSD ( n = 52) and without BPSD ( n = 45) were analyzed using voxel‐based morphometry to assess GMV and seed‐to‐voxel resting‐state functional connectivity to further analyze the disturbed brain regions and the large‐scale networks associated with BPSD. Significant reductions in GMV were identified in the left middle temporal gyrus, right angular gyrus, right supramarginal gyrus, right cerebellar Crus II, and left cerebellar Crus I in patients with BPSD. Aberrant functional connectivity was primarily observed between the right angular gyrus and the bilateral postcentral cortex, as well as between the right cerebellar Crus II and left cerebellar Lobule VIII. Our findings reveal that abnormal brain structure and connectivity exist not only in the cerebrum but also in the cerebellum, with structural and functional disruptions across multiple crucial brain regions and large‐scale networks. This suggests that these abnormalities might underpin the neuropathological mechanisms contributing to BPSD in AD.

ObjectiveTo evaluate the safety and effectiveness of lurasidone in Chinese patients with schizophrenia in a 12-week post-marketing study.MethodsThis 12-week, multicenter, prospective, open-label, single-arm study included schizophrenia patients from 36 sites in Mainland China, who initiated lurasidone between September 2019 and December 2023. Adverse events (AEs) and adverse drug reactions (ADRs) were primary safety endpoints. Other safety assessments included extrapyramidal symptoms (EPS) and weight gain. Effectiveness was evaluated using the Brief Psychiatric Rating Scale (BPRS) at baseline and week 12. Patients were also stratified to assess differences across ages.ResultsA total of 3170 patients were included in the Full Analysis Set (FAS) and 3178 in the Safety Set (SS). The mean daily lurasidone dose was 59.9±20.93 mg. ADRs occurred in 7.9% of patients, with incidences of 8.1%, 8.9%, 5.9%, and 2.3% in the <18, 18–45, 45–65, and >65-year age groups, respectively. EPS was the most common ADR (3.2%), typically emerging in weeks 3–4. Metabolic-related AEs occurred in 4.3% and 2.2% of patients with and without metabolic affecting agents. BPRS scores significantly improved at weeks 2/4, 6/8, and 12 compared with baseline (all P < 0.05): total score (−8.9 ± 9.62, −14.0 ± 12.16, −17.5 ± 13.61), anxiety-depression (−1.5 ± 2.37, −2.5 ± 2.88, −3.3 ± 3.31), anergia (−1.5 ± 2.34, −2.4 ± 2.83, −3.1 ± 3.11), thought disturbance (−2.4 ± 2.98, −3.8 ± 3.72, −4.7 ± 4.07), activation (−1.2 ± 1.97, −1.7 ± 2.38, −2.1 ± 2.63), and hostility-suspiciousness (−2.4 ± 2.81, −3.6 ± 3.44, −4.3 ± 3.76). In <18 years group, BPRS total score also significantly decreased from 46.1±15.10 at baseline to 26.3±9.72 at week 12.ConclusionThis real-world study demonstrated a favorable safety profile and significant clinical effectiveness of lurasidone in both adult and adolescent population with schizophrenia in China in real-world clinical settings, supporting its use across diverse patient populations.Trial RegistrationShanghai Clinical Research Center for Mental Health (SCRC-MH) NCT04432688. URL: www.smhc.org.cn/.

Autophagy has recently attracted increasing attention for its critical involvement in Alzheimer’s Disease (AD) pathogenesis. However, the role of microtubule-associated protein 1 light chain 3 A (LC3A) and B (LC3B), key autophagic-associated proteins in cognitive decline, AD risk, and underlying pathology remain unclear. This study included 568 participants from the Alzheimer’s Disease Neuroimaging Initiative with cerebrospinal fluid (CSF) measurements of LC3A and LC3B. We systematically assessed the associations of LC3A and LC3B with cognition, dementia risk, AD biomarkers, microglial activity, brain volume, and metabolism through both cross-sectional and longitudinal analyses. Additionally, subgroup analyses based on ATN classification were conducted to evaluate stage-specific effects, and Receiver operating characteristic (ROC) analyses were employed to explore the clinical predictive value of LC3A and LC3B. Finally, mediation models were used to investigate the potential mechanisms by which LC3A and LC3B affect AD pathology and cognition. Elevated CSF LC3A and LC3B levels were negatively associated with cognition, while positively associated with amyloid deposition, total tau (t-tau), phosphorylated tau (p-tau), progranulin (PGRN), and soluble Triggering Receptor Expressed on Myeloid Cells-2 (sTREM2) at baseline. Higher CSF LC3A and LC3B concentrations were linked to an increased risk of dementia, accelerated longitudinal cognitive decline, greater brain atrophy, faster accumulation of t-tau, and longitudinal associations with sTREM2. Individuals with TN+ exhibited significantly higher CSF LC3A and LC3B levels compared to those with TN−. Moreover, CSF LC3A alone could predict TN−/TN+ status with an area under the curve (AUC) of 0.903, which increased to 0.939 when combined with amyloid PET. Similarly, CSF LC3B alone yielded an AUC of 0.876, rising to 0.913 when combined with amyloid PET. Finally, amyloid deposition and microglial activation individually or jointly, mediated the effects of LC3A and LC3B on tau pathology and cognitive decline. CSF LC3A and LC3B, reflecting autophagic activation, are linked to elevated tau pathology and an increased risk of AD. They may jointly contribute to AD pathological progression alongside amyloid deposition and microglial activation. LC3A and LC3B as robust candidate biomarkers for monitoring pathological burden, stratifying disease risk, and informing clinical evaluation in AD.

Subclinical Hypothyroidism (SHypo), a condition that characteristically presents with normal Thyroid Hormone (TH) levels coexisting with elevated Thyroid-Stimulating Hormone (TSH) levels, has been associated with cognitive impairment. Nonetheless, its neurobiological effects remain somewhat unclear, forming the basis of this study. Herein, using a multimodal neuroimaging approach, we explored cortical thinning and disrupted Functional Connectivity (FC) in SHypo patients—particularly focusing on their relationship with cognitive deficits. Brain areas such as the Prefrontal Cortex (PFC) and Cingulate Gyrus (CG) exhibited substantial cortical thinning. Additionally, a marked decrease in FC was observed between the CG and regions associated with visuospatial processing. Furthermore, serum-free thyroxine (FT4) levels correlated negatively with Cortex Thickness (CT) of the right cuneus. Notably, FC also correlated positively with neuropsychological tests. Besides highlighting the potential for early neural alterations in SHypo, these findings also underscore the significance of early detection and intervention to mitigate cognitive decline.

Using the resting state functional network as a stable neural trait indicator, this study employed a panel cross-lag model to construct a dynamic, causal ‘brain-behavior-time’ framework. It identified key risk pathways from stable neural traits to addictive behaviors, mediated by psychological traits, thereby providing methodological support for targeted intervention. A recent study by He and colleagues has made a pivotal methodological contribution to addiction research by elucidating the dynamic, causal pathways through which stable neural traits, mediated by psychological vulnerabilities, predispose individuals to addictive behaviors [1]. The study's innovative integration of resting-state functional connectivity with panel cross-lagged modeling provides a breakthrough idea for addiction research and intervention, and its methodological value and practical significance are worth exploring. The study's strength lies in its rigorous methodology. It used a cross-sectional sample of 1142 college students for behavioral-brain analysis and a longitudinal follow-up cohort of 303 students for behavioral tracking. The functional connectivity strength between the limbic network (LN) and six other functional networks served as the core neural indicator. Resting-state functional networks, which reflect the brain's intrinsic activity patterns, demonstrate temporal stability [2] and function as biological markers. Specifically, the functional state of LN is directly associated with an individual's perception and response to reward and punishment signals [3, 4]. By correlating this stable neural characteristic with long-term psychological trait data—including reward sensitivity and punishment sensitivity—the study revealed significant findings that LN strength showed a positive correlation with reward sensitivity and a negative correlation with punishment sensitivity. Furthermore, the functional connectivity strength between LN and other networks also demonstrated relationships with reward and punishment sensitivity. These findings establish a foundational ‘brain-behavior’ link that exhibits considerable stability. The study further revealed the dynamic causal relationship between variables through a panel cross-lag model: reward sensitivity at baseline measurement (T1) positively predicted online game addiction tendencies at follow-up measurement (T2); the Iowa Gambling Task net score at T1 negatively predicted reward sensitivity at T2; and punishment sensitivity exerted an indirect effect on addictive tendencies by positively influencing reward sensitivity at T2. The punishment sensitivity also acts indirectly on addictive tendencies by positively affecting reward sensitivity in T2. These results break through the static limitations of cross-sectional studies [5] and provide strong evidence that ‘high reward sensitivity is a core risk factor for addiction’ with both chronological and causal orientation and upgrade the ‘brain-behavior’ model to a dynamic developmental model. He and colleagues integrated LN strength into the panel cross-lag model. Their analysis revealed that T1 LN strength not only directly shapes reward sensitivity at T1 and negatively influences punishment sensitivity at T1, but also predicts reward sensitivity at T2. This establishes the complete pathway from neural traits to psychological traits and finally to behavioral outcomes. This study provides a more coherent theoretical framework for understanding the underlying mechanisms of addiction development by defining how neural features predict behavior through psychological mediating factors. This research paradigm offers insights for clinical intervention and public health. Clinically, the ‘neural indicators → psychological traits → behavioral outcomes’ model can be used to predict individual addictive tendencies by evaluating the functional status of core brain regions like the LN [6], thereby enabling personalized intervention plans. In the field of public health, it facilitates the screening of at-risk adolescents based on characteristics such as abnormal LN function and high reward sensitivity, achieving early identification and prevention of addictive behaviors and promoting a shift from ‘passive treatment’ to ‘active early warning’. In summary, this study successfully translates fundamental discoveries into clinical and public health impacts through its innovative methodology. By mapping key risk pathways and enabling precisely targeted interventions, it establishes a new paradigm for neurobehavioral research. This approach not only provides a framework for understanding addiction mechanisms, but also paves the way for innovative strategies in preventing and treating a broad spectrum of mental health disorders, thereby offering substantial scientific and societal benefits. Zihan Lv: Writing—original draft. Xiaoming Li: Project administration; resources; supervision; validation; writing—review and editing. Xiaochu Zhang: Project administration; resources; supervision; validation; writing—review and editing. This work was supported by grants from National Key R&D Program of China (2024YFF0507600), the Chinese National Programs for Brain Science and Brain-like Intelligence Technology (2021ZD0202101), the National Natural Science Foundation of China (32571266, 32171080, 32400919 and 32200914), the Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform [ZSYS(2024)001], Natural Science Foundation of Anhui Province (2408085QC081), the Humanities and Social Science Fund of the Ministry of Education of China (24YJCZH014) and Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior. None.

BackgroundBipolar disorder is a severe mental disorder characterized by recurrent episodes of depression and mania, with a low diagnostic rate. This study aimed to use machine learning methods for risk stratification of lifetime suicide attempts in patients with bipolar disorder based on cross-sectional associations.MethodsThe discriminative performance of the models was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, F1-score, precision-recall (PR) curve, Average Precision (AP), and Brier score. LASSO logistic regression was used for variable selection, and SMOTE (synthetic minority over-sampling technique) was applied to handle class imbalance. A sensitivity analysis excluding patients with recent suicide attempts (within 6 months) was performed to reduce reverse causality bias. Thyroid function indicators were detected using chemiluminescent immunoassay with a Roche Cobas e601 analyzer; the reference range for normal TSH was 0.27-4.2 mIU/L, with values outside this range defined as abnormal.ResultsWe included 1,124 patients diagnosed with bipolar disorder in this study, with a lifetime suicide attempt rate of 31.32%.Among the three models tested, random forests exhibited superior performance metrics, attaining an accuracy (ACC) of 0.938, an AUC of 0.962, and an F1 score of 0.854 compared to gradient boosting (ACC: 0.920; AUC: 0.967; F1 score: 0.828) and the support vector machine (ACC: 0.893; AUC: 0.956; F1 score: 0.808). Suicidal ideation, education level, hopelessness score, the retardation symptoms severity score, and thyroid stimulating hormone levels were identified as the top five predictors. Sensitivity analysis excluding patients with recent suicide attempts (n=98) showed consistent predictor rank-order and maintained RF-AUC = 0.958.Subgroup analysis of euthyroid patients (n=887) preserved the predictor rank and RF-AUC ≥ 0.95.ConclusionsWe developed a robust clinical model for predicting (risk stratification of lifetime suicide attempts)suicide attempts in patients with bipolar disorder based on machine learning techniques. This model can assist psychiatric clinicians in understanding suicide risk among individuals diagnosed with bipolar disorder and in identifying those who may require early intervention or preventive measures. We identified a set of 20 clinical and biological markers significantly associated with lifetime suicide attempts in bipolar disorder patients. Importantly, due to the cross-sectional design, these associations cannot establish temporal precedence or support prospective risk prediction. A key limitation is that suicide attempts were assessed retrospectively without a defined time window between predictor measurement and outcome occurrence. Future prospective validation in multi-site, multinational cohorts is required to confirm the model’s clinical utility.