Repetitive Transcranial Magnetic Stimulation Reversing Abnormal Brain Function in Mood Disorders with Early Life Stress: from preclinical models to clinical applications.

BACKGROUND: Early life stress (ELS) significantly increases the risk of mood disorders and affects the neurodevelopment of the primary cortex. HYPOTHESIS: Modulating the primary cortex through neural intervention can ameliorate the impact of ELS on brain development and consequently alleviate its effects on mood disorders. METHOD: We induced the chronic unpredictable mild stress (CUMS) model in adolescent rats, followed by applying repetitive transcranial magnetic stimulation (rTMS) to their primary cortex in early adulthood. To assess the applicability of primary cortex rTMS in humans, we recruited individuals aged 17-25 with mood disorders who had experienced ELS and performed primary cortex rTMS on them. Functional magnetic resonance imaging (fMRI) and depression-related behavioral and clinical symptoms were conducted in both rats and human subjects before and after the rTMS. RESULTS: In animals, fMRI analysis revealed increased activation in the primary cortex of CUMS rats and decrease subcortical activation. Following the intervention of primary cortex rTMS, the abnormal functional activity was reversed. Similarly, in mood disorders patients with ELS, increased activation in the primary cortex and decreased activation in the frontal cortex were observed. During rTMS intervention, similar neuroimaging improvements were noted, particularly decreased activation in the primary cortex. This suggests that targeted rTMS in the primary cortex can reverse the abnormal neuroimaging. CONCLUSION: This cross-species translational study has identified the primary cortex as a key region in mood disorders patients with ELS. Targeting the primary cortex with rTMS can correct abnormal functional activity while improving symptoms. Our study provides translational evidence for therapeutics targeting the ELS factor of mood disorders patients.

BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis.

In the medical field, datasets are mostly integrated across sites due to difficult data acquisition and insufficient data at a single site. The domain shift problem caused by the heterogeneous distribution among multi-site data makes autism spectrum disorder (ASD) hard to identify. Recently, domain adaptation has received considerable attention as a promising solution. However, domain adaptation on graph data like brain networks has not been fully studied. It faces two major challenges: (1) complex graph structure; and (2) multiple source domains. To overcome the issues, we propose an end-to-end structure-aware domain adaptation framework for brain network analysis (BrainDAS) using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed approach contains two stages: supervision-guided multi-site graph domain adaptation with dynamic kernel generation and graph classification with attention-based graph pooling. We evaluate our BrainDAS on a public dataset provided by Autism Brain Imaging Data Exchange (ABIDE) which includes 871 subjects from 17 different sites, surpassing state-of-the-art algorithms in several different evaluation settings. Furthermore, our promising results demonstrate the interpretability and generalization of the proposed method. Our code is available at https://github.com/songruoxian/BrainDAS.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

BACKGROUND: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.

BACKGROUND: Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device-based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings. OBJECTIVE: The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression. METHODS: A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression. RESULTS: The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64). CONCLUSIONS: This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study.

BACKGROUND: Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention. METHODS: In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants' symptoms, HRV, and GCBT outcomes. RESULTS: The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors. CONCLUSION: HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety. TRIAL REGISTRATION: The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

Designing Ultratough Single-Network Hydrogels with Centimeter-Scale Fractocohesive Lengths via Inelastic Crack Blunting.

Fractocohesive length, defined as the ratio of fracture toughness to work of fracture, measures the sensitivity of materials to fracture in the presence of flaws. The larger the fractocohesive length, the more flaw-tolerant and crack-resistant the hydrogel. For synthetic soft materials, the fractocohesive length is short, often on the scale of 1 mm. Here, highly flaw-insensitive (HFI) single-network hydrogels containing an entangled inhomogeneous polymer network of widely distributed chain lengths are designed. The HFI hydrogels demonstrate a centimeter-scale fractocohesive length of 2.21 cm, which is the highest ever recorded for synthetic hydrogels, and an unprecedented fracture toughness of ≈13 300 J m-2 . The uncommon flaw insensitivity results from the inelastic crack blunting inherent to the highly inhomogeneous network. When the HFI hydrogel is stretched, a large number of short chains break while coiled long chains can disentangle, unwind, and straighten, producing large inelastic deformation that substantially blunts the crack tip in a plastic manner, thereby deconcentrating crack-tip stresses and blocking crack extension. The flaw-insensitive design strategy is applicable to various hydrogels such as polyacrylamide and poly(N,N-dimethylacrylamide) hydrogels and enables the development of HFI soft composites.

State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study.

BACKGROUND: The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. METHODS: We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. RESULTS: We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. LIMITATIONS: Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. CONCLUSION: Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.

Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder.

Several lines of evidence support the involvement of transcriptomic and epigenetic mechanisms in the brain structural deficits of major depressive disorder (MDD) separately. However, research in these two areas has remained isolated. In this study, we proposed an integrative strategy that combined neuroimaging, brain-wide gene expression, and peripheral DNA methylation data to investigate the genetic basis of gray matter abnormalities in MDD. The MRI T1-weighted images and Illumina 850 K DNA methylation microarrays were obtained from 269 patients and 416 healthy controls, and brain-wide transcriptomic data were collected from Allen Human Brain Atlas. The between-group differences in gray matter volume (GMV) and differentially methylated CpG positions (DMPs) were examined. The genes with their expression patterns spatially related to GMV changes and genes with DMPs were overlapped and selected. Using principal component regression, the associations between DMPs in overlapped genes and GMV across individual patients were investigated, and the region-specific correlations between methylation status and gene expression were examined. We found significant associations between the decreased GMV and DMPs methylation status in the anterior cingulate cortex, inferior frontal cortex, and fusiform face cortex regions. These DMPs genes were primarily enriched in the neurodevelopmental and synaptic transmission process. There was a significant negative correlation between DNA methylation and gene expression in genes associated with GMV changes of the frontal cortex in MDD. Our findings suggest that GMV abnormalities in MDD may have a transcriptomic and epigenetic basis. This imaging-transcriptomic-epigenetic integrative analysis provides spatial and biological links between cortical morphological deficits and peripheral epigenetic signatures in MDD.

Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial.

Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual's affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. A 4-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (p = 0.001), insomnia (p = 0.013), and stress (p = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542.

Visual cortex repetitive transcranial magnetic stimulation (rTMS) reversing neurodevelopmental impairments in adolescents with major psychiatric disorders (MPDs): A cross-species translational study.

AIMS: Neurodevelopmental impairments are closely linked to the basis of adolescent major psychiatric disorders (MPDs). The visual cortex can regulate neuroplasticity throughout the brain during critical periods of neurodevelopment, which may provide a promising target for neuromodulation therapy. This cross-species translational study examined the effects of visual cortex repetitive transcranial magnetic stimulation (rTMS) on neurodevelopmental impairments in MPDs. METHODS: Visual cortex rTMS was performed in both adolescent methylazoxymethanol acetate (MAM) rats and patients with MPDs. Functional magnetic resonance imaging (fMRI) and brain tissue proteomic data in rats and fMRI and clinical symptom data in patients were analyzed. RESULTS: The regional homogeneity (ReHo) analysis of fMRI data revealed an increase in the frontal cortex and a decrease in the posterior cortex in the MAM rats, representing the abnormal neurodevelopmental pattern in MPDs. In regard to the effects of rTMS, similar neuroimaging changes, particularly reduced frontal ReHo, were found both in MAM rats and adolescent patients, suggesting that rTMS may reverse the abnormal neurodevelopmental pattern. Proteomic analysis revealed that rTMS modulated frontal synapse-associated proteins, which may be the underpinnings of rTMS efficacy. Furthermore, a positive relationship was observed between frontal ReHo and clinical symptoms after rTMS in patients. CONCLUSION: Visual cortex rTMS was proven to be an effective treatment for adolescent MPDs, and the underlying neural and molecular mechanisms were uncovered. Our study provides translational evidence for therapeutics targeting the neurodevelopmental factor in MPDs.

Melanin-Concentrating Hormone Is Associated With Delayed Neurocognitive Recovery in Older Adult Patients With Preoperative Sleep Disorders Undergoing Spinal Anesthesia.

BACKGROUND: Aging and preoperative sleep disorders are the main risk factors affecting postoperative cognitive outcomes. However, the pathogenesis of delayed neurocognitive recovery after surgery remains ambiguous, and there is still a lack of potential biomarkers for delayed neurocognitive recovery in older adult patients with preoperative sleep disorders. Our study aimed to explore the relationship between melanin-concentrating hormone (MCH) and delayed neurocognitive recovery early after surgery in older adult patients with preoperative sleep disorders. METHODS: In this monocentric prospective observational study, 156 older adult patients (aged 65 years or older) with preoperative sleep disorders undergoing elective total hip arthroplasty (THA) or total knee arthroplasty (TKA) were included at an academic medical center in Inner Mongolia, China, from October 2021 to November 2022, and all patients underwent spinal anesthesia. The Pittsburgh Sleep Quality Index (PSQI) was applied to assess the preoperative sleep quality of all patients, and preoperative sleep disorders were defined as a score of PSQI >5. We measured the levels of cerebrospinal fluid (CSF) MCH and plasma MCH of all patients. The primary outcome was delayed neurocognitive recovery early after surgery. All patients received cognitive function assessment through the Montreal Cognitive Assessment (MoCA) 1 day before and 7 days after surgery (postoperative day 7 [POD7]). Delayed neurocognitive recovery was defined as a score of POD7 MoCA <26. The potential confounders included variables with P < .2 in the univariate logistic analysis, as well as the important risk factors of delayed neurocognitive recovery reported in the literature. Multivariable logistic regression model based on the Enter method assessed the association of MCH and delayed neurocognitive recovery in older adult patients with preoperative sleep disorders. RESULTS: Fifty-nine (37.8%) older adult patients with preoperative sleep disorders experienced delayed neurocognitive recovery at POD7. Increase in CSF MCH levels (odds ratio [OR] for an increase of 1 pg/mL = 1.16, 95% confidence interval [CI], 1.09-1.23, P < .001) and decrease in plasma MCH levels (OR for an increase of 1 pg/mL = 0.92, 95% CI, 0.86-0.98, P = .003) were associated with delayed neurocognitive recovery, after adjusting for age, sex, education, baseline MoCA scores, American Society of Anesthesiologists (ASA) grade, and coronary heart disease (CHD). CONCLUSIONS: In older adult patients with preoperative sleep disorders, MCH is associated with the occurrence of delayed neurocognitive recovery after surgery. Preoperative testing of CSF MCH or plasma MCH may increase the likelihood of identifying the high-risk population for delayed neurocognitive recovery in older adult patients with preoperative sleep disorders.

A neuroimaging-based precision medicine framework for depression.

BACKGROUND: Symptom-based diagnostic criteria of depression leads to notorious heterogeneity and subjectivity. METHODS: The study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier was employed and 72 hospitalized, depressed youths at Center 2 received two-week precise rTMS. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation. RESULTS: Two neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression. CONCLUSIONS: A precision medicine framework for depression was developed based on objective neurobiomarkers and implemented with promising results, actualizing a subtyping-treatment-evaluation closed loop in depression. Future randomized controlled trials are warranted.

Aberrant Hippocampal Development in Early-onset Mental Disorders and Promising Interventions: Evidence from a Translational Study.

Early-onset mental disorders are associated with disrupted neurodevelopmental processes during adolescence. The methylazoxymethanol acetate (MAM) animal model, in which disruption in neurodevelopmental processes is induced, mimics the abnormal neurodevelopment associated with early-onset mental disorders from an etiological perspective. We conducted longitudinal structural magnetic resonance imaging (MRI) scans during childhood, adolescence, and adulthood in MAM rats to identify specific brain regions and critical windows for intervention. Then, the effect of repetitive transcranial magnetic stimulation (rTMS) intervention on the target brain region during the critical window was investigated. In addition, the efficacy of this intervention paradigm was tested in a group of adolescent patients with early-onset mental disorders (diagnosed with major depressive disorder or bipolar disorder) to evaluate its clinical translational potential. The results demonstrated that, compared to the control group, the MAM rats exhibited significantly lower striatal volume from childhood to adulthood (all P <0.001). In contrast, the volume of the hippocampus did not show significant differences during childhood (P >0.05) but was significantly lower than the control group from adolescence to adulthood (both P <0.001). Subsequently, rTMS was applied to the occipital cortex, which is anatomically connected to the hippocampus, in the MAM models during adolescence. The MAM-rTMS group showed a significant increase in hippocampal volume compared to the MAM-sham group (P <0.01), while the volume of the striatum remained unchanged (P >0.05). In the clinical trial, adolescents with early-onset mental disorders showed a significant increase in hippocampal volume after rTMS treatment compared to baseline (P <0.01), and these volumetric changes were associated with improvement in depressive symptoms (r = - 0.524, P = 0.018). These findings highlight the potential of targeting aberrant hippocampal development during adolescence as a viable intervention for early-onset mental disorders with neurodevelopmental etiology as well as the promise of rTMS as a therapeutic approach for mitigating aberrant neurodevelopmental processes and alleviating clinical symptoms.

Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects.

Adolescent psychotic mood disorder (MDP) is a specific phenotype that characterized by more severe symptoms and prognosis compared to nonpsychotic mood disorder (MDNP). But the underlying neural mechanisms remain unknown, and graph theory analysis can help to understand possible mechanisms of psychotic symptoms from the perspective of functional networks. A total of 177 adolescent patients with mood disorders were recruited, including 61 MDP and 116 MDNP. Functional networks were constructed, and topological properties were compared between the two groups at baseline and after treatment, and the association between properties changes and symptom improvement was explored. Compared to the MDNP group, the MDP group exhibited higher small-world properties (FDR q = 0.003) and normalized clustering coefficients (FDR q = 0.008) but demonstrated decreased nodal properties in the superior temporal gyrus (STG), Heschl's gyrus, and medial cingulate gyrus (all FDR q < 0.05). These properties were found to be correlated with the severity of psychotic symptoms. Topological properties also changed with improvement of psychotic symptoms after treatment, and changes in degree centrality of STG in the MDP was significantly positive correlated with improvement of psychotic symptoms (r = 0.377, P = 0.031). This study indicated that functional networks are more severely impaired in patients with psychotic symptoms. Topological properties, particularly those associated with the STG, hold promise as emerging metrics for assessing symptoms and treatment efficacy in patients with psychotic symptoms.

Do testosterone and sex hormone-binding globulin affect cancer risk? A Mendelian randomization and bioinformatics study.

Using Mendelian Randomization (MR) and large-scale Genome-Wide Association Study (GWAS) data, this study aimed to investigate the potential causative relationship between testosterone and sex hormone-binding globulin (SHBG) levels and the onset of several cancers, including pathway enrichment analyses of single nucleotide polymorphisms (SNPs) associated with cancer allowed for a comprehensive bioinformatics approach, which offered a deeper biological understanding of these relationships. The results indicated that increased testosterone levels in women were associated with a higher risk of breast and cervical cancers but a lower risk of ovarian cancer. Conversely, increased testosterone was linked to lower stomach cancer risk for men, whereas high SHBG levels were related to decreased risks of breast and prostate cancers. The corresponding genes of the identified SNPs, as revealed by pathway enrichment analysis, were involved in significant metabolic and proliferative pathways. These findings emphasize the need for further research into the biological mechanisms behind these associations, paving the way for potential targeted interventions in preventing and treating these cancers.

School-based Evaluation Advancing Response for Child Health (SEARCH): a mixed longitudinal cohort study from multifaceted perspectives in Jiangsu, China.

BACKGROUND: Schools play a crucial role in providing mental health services to children and adolescents. However, the vastness of the student population and mental health workforce shortage in China severely limit the capacity for adequate care access and delivery. OBJECTIVE: We propose a large, mixed longitudinal cohort study, 'School-based Evaluation Advancing Response for Child Health (SEARCH)', aimed at addressing the increasing demand from individuals seeking access to mental healthcare services. METHODS: SEARCH uses a digital platform and school-based protocol for comprehensive assessment of the mental well-being of Chinese students in grades 4-12 incorporating individual, caregiver and teacher input, including capture of facial and acoustic features and response times, as well as mental well-being assessments. FINDINGS: We completed first wave data collection from nearly 20 000 participants (students, caregivers and teachers) at 11 schools, grades 4-12, in 3 cities in Jiangsu province in Southeast China from September 2022 to February 2023. We intend to conduct follow-up assessments for grades 4 through 12 at the 11 school sites every 6 months for 5 years. CONCLUSIONS: SEARCH will provide important insight into the developmental trajectory of mental well-being in Chinese children and adolescents. The study protocol does not simply focus on student self-report and incorporates caregiver and teacher viewpoints as well. It also collects objective indicators that may facilitate development of screening tools. CLINICAL IMPLICATIONS: We believe future study findings will guide the development and implementation of school-based mental healthcare initiatives to improve the well-being of children and adolescents.

Towards a neuroimaging biomarker for predicting cognitive behavioural therapy outcomes in treatment-naive depression: Preliminary findings.

Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.

Total network controllability analysis discovers explainable drugs for Covid-19 treatment.

BACKGROUND: The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets. RESULTS: We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach's effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients. CONCLUSIONS: Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

Functional connectivity between the cerebellar vermis and cerebrum distinguishes early treatment response for major depressive episodes in adolescents.

BACKGROUND: The absence of biomarkers for predicting treatment response in adolescent mood disorder calls for further research. The vermis, a component of the cerebellum, is involved in mood disorder pathophysiology and relates to clinical symptoms and treatment outcomes. We investigated vermis functional connectivity (FC) as an early marker for treatment response identification. METHOD: One hundred thirty-two adolescents with mood disorders including major depressive disorder or bipolar disorder, were recruited, who were experiencing a major depressive episode. All adolescents underwent baseline and 2-week treatment resting-state MRI scans. Hamilton Rating Scale for Depression (HAMD) assessments were completed to assess the severity of symptoms. Patients were divided into treatment-responsive (≥50 % HAMD reduction, n = 75) and treatment-unresponsive subgroups (n = 57). Vermis FCs were compared between subgroups at baseline. And we compared the pre- and post-treatment FC differences within subgroups. RESULT: Higher vermis-left temporal lobe FC in treatment-responsive group compared to treatment-unresponsive group at baseline. The FC value showed positive prognosis for the efficacy, with the area under the curve (AUC) of 0.760 (95 % confidence interval: 0.678-0.843, p < 0.001), suggesting higher vermis-temporal FC is benefit to improve treatment-response. Furthermore, post-treatment analysis showed significant increases in the vermis-right frontal lobe FC values between in all patients, suggesting that vermis-frontal FCs were independent of treatment-outcome. LIMITATION: Sample size was relatively small, which may limit the generalizability of our results. CONCLUSION: Our study revealed that the FC between the vermis and the cortex is not only associated with symptom alleviation but also predictive of treatment outcomes.