Childhood abuse influences clinical features of major depressive disorder by modulating the functional network of the right amygdala subregions.

Childhood trauma and the amygdala play essential roles in major depressive disorder (MDD) mechanisms. However, the neurobiological mechanism among them remains unclear. Therefore, we explored the relationship among the amygdala subregion's abnormal functional connectivity (FC), clinical features, and childhood trauma in MDD. We obtained resting-state functional magnetic resonance imaging (fMRI) in 115 MDD patients and 91 well-matched healthy controls (HC). Amygdala subregions were defined according to the Human Brainnetome Atlas. The case vs. control difference in FCs was extracted. After controlling for age, sex, and education years, the mediations between the detected abnormal FCs and clinical features were analyzed, including the onset age of MDD and the Hamilton Depression Scale-24 (HAMD-24) reductive rate. Compared with HC subjects, we found, only the right amygdala subregions, namely the right medial amygdala (mAmyg.R) and the right lateral amygdala (lAmyg.R), showed a significant decrease in whole-brain FCs in MDD patients. Only childhood abuse experiences were significantly associated with amygdala subregion connectivity and clinical features in MDD patients. Additionally, The FCs between the mAmyg.R and extensive frontal, temporal, and subcortical regions mediated between the early life abuses and disease onset or treatment outcome. The findings indicate that the abnormal connectivity of the right amygdala subregions is involved in MDD's pathogenesis and clinical characteristics.

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l'efficacité précoce d'un antidépresseur chez des patients souffrant du trouble dépressif majeur d'après les caractéristiques multidimensionnelles de la méthylation de l'ADN du gène P11 et de la IRMf-rs.

OBJECTIVE: This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). METHODS: A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. RESULTS: The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. CONCLUSION: The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

Impact of Different Traditional Chinese Medicine Constitutions on the Clinical Outcomes of COVID-19 Patients Infected with SARS-CoV-2 Omicron Variant: A Retrospective Observational Study.

Purpose: Traditional Chinese Medicine (TCM) constitution and disease occurrence, development, and prognosis are interrelated. This study aimed to investigate the association between TCM constitution and the time to negative nucleic acid test results in patients with coronavirus disease 2019 (COVID-19) infected with the SARS-CoV-2 Omicron variant. Patients and Methods: We identified COVID-19 patients (≥18 years) infected with the SARS-CoV-2 Omicron variant and collected clinical data, including clinical symptoms, time to negative nucleic acid test results, and TCM constitution. Linear and logistic regression analyses explored the relationship between TCM constitution and the time to negative nucleic acid test results in patients with the COVID-19 Omicron variant. Results: We included 486 patients with COVID-19, with a mean age of 40.2 years; 321 (66.0%) men and 165 (34.0%) women. Balanced constitution accounted for 43.8%, and unbalanced constitution accounted for 56.2%. Chi-square test showed that different TCM constitutions had significant differences in the influence of clinical symptoms of COVID-19 patients (P < 0.01). After controlling for various factors, multiple linear regression analysis revealed that an unbalanced constitution was significantly positively correlated with time to negative nucleic acid test results (P < 0.05). After controlling for various factors, logistic regression analysis revealed that an unbalanced constitution was closely related to the 7-day nucleic acid test conversion rate (odds ratio (OR): 0.53, 95% confidence interval (CI): 0.36-0.80, P < 0.05). After dividing the unbalanced constitution into deficiency constitution and non-deficiency constitution, the non-deficiency constitution was closely associated with the 7-day nucleic acid test conversion rate (OR = 0.45, 95% CI: 0.28-0.74, P < 0.05). Further analysis revealed that damp-heat constitution in the non-deficiency constitution was associated with the 7-day nucleic acid test conversion rate (OR = 0.33, 95% CI: 0.18-0.60, P < 0.05). Conclusion: In patients with COVID-19, an unbalanced constitution is associated with a longer time to negative nucleic acid test results and lower 7-day nucleic acid test conversion rates.

Prognostic Value of Neutrophil-to-Lymphocyte Ratio and Vaccination for Negative Conversion Time of Nucleic Acid in Nonsevere COVID-19 Patients Infected by SARS-CoV-2 Omicron Variant.

SARS-CoV-2 Omicron variant is significantly different from all the previous variants and has rapidly replaced other variants as the dominant variant across the globe. An easily obtained, inexpensive, and rapid marker is needed to predict the negative conversion time (NCT) of nucleic acid in nonsevere COVID-19 patients infected by the Omicron variant. This retrospective study enrolled 226 patients infected by the Omicron variant between April 23, 2022, and May 16, 2022. The median age of the patients was 61 (interquartile range (IQR), 48-70) years, and 56.2% were male. 84 patients (37.2%) had at least one comorbidity, and 49 patients (21.7%) were classified into the moderate illness group. 145 patients (64.2%) received at least one dose of vaccine, in which 67 patients (29.6%) received a booster dose of vaccine. The median duration of NCT was 8 (IQR, 7-11) days. Univariate Cox analyses found that high NLR (>2.22), aged ≥65 years, vaccination, and moderate illness were significantly related to the NCT of nucleic acid. Multivariate Cox regression analysis showed that high NLR (NLR > 2.22, hazard ratio (HR):0.718, 95% CI: 0.534-0.964, p = 0.028) and vaccination (vaccinated ≥1 dose, HR: 1.536, 95% CI: 1.147-2.058, p = 0.004) were independently associated with NCT of nucleic acid. NLR is a rapid, simple, and useful prognostic factor for predicting NCT of nucleic acid in nonsevere COVID-19 patients with the Omicron variant. In addition, vaccination may also play a valuable role in predicting the NCT of nucleic acid.

Abnormal spontaneous activity of regions related to mood regulation mediates the effect of childhood emotional neglect on major depressive disorder.

This study investigated the mediating factors between childhood emotional neglect (EN) and major depressive disorder (MDD) and whether combining multi-indicator could help diagnose MDD. Regional homogeneity (ReHo) and clinical features were compared between 33 MDD patients and 36 healthy controls (HC). Mediation analysis was employed to explore whether social support or ReHo mediates the association between EN and MDD. The linear discriminant analysis model was constructed with EN, social support, and ReHo, and applied to distinguish MDD from HC in both primary and replication cohorts. We found that MDD patients experienced severer EN and poorer social support, and exhibited lower ReHo in the left middle occipital gyrus and bilateral postcentral gyrus, and higher ReHo in the right cerebellum crus1. EN could affect MDD directly and indirectly through ReHo in these discrepant brain regions and social support. Combining ReHo values of these four distinct brain regions, EN, and objective support could classify MDD patients from HC, and the 10-fold cross-validation accuracy within-study replication and in the independent cohort was 83.78 % ± 1.49 % and 82.72 % ± 2.22 %, respectively. These findings suggested that childhood EN, social support, and emotional-related regions' ReHo were associated with risks of MDD, providing new insights into the pathological mechanisms underlying MDD.

Non-inferiority of intermittent theta burst stimulation over the left V1 vs. classical target for depression: A randomized, double-blind trial.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) targeting the visual cortex (VC) has shown antidepressant effects for major depressive disorder (MDD) in sham-controlled trials, but comparisons with rTMS targeting the left dorsolateral prefrontal cortex (DLPFC) are lacking. We aimed to determine the non-inferiority of intermittent theta-burst stimulation (iTBS) over VC vs DLPFC for MDD. METHODS: Participants randomly received navigated iTBS over the left V1 or the left DLPFC twice daily for 14 days with a 3-month follow-up. The primary outcome was change in Hamilton Depression Rating Scale (HAMD-17) score from baseline to treatment end, with 2.5 points as the non-inferiority margin. Secondary outcomes included: improvement in Montgomery-Asberg Depression Rating Scale (MADRS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA); response and remission rates; suicidal ideation and adverse events. RESULTS: Of 75 randomized patients, 67 completed full treatment, including 52 first-episode patients and 15 relapsers. The primary outcome indicated the non-inferiority of VC (adjusted difference 1.14, lower 97.5 % CI -1.24; p = .002), confirmed by improvements in objective cognitive task and protein levels, as did most secondary outcomes. Reduced suicidal ideation after treatment, incidence of eye discomfort and pain score were lower in the VC group. CONCLUSIONS: Left VC iTBS has the potential to be non-inferior to DLPFC iTBS in most first-episode MDD in improving depressive symptoms and cognitive function, with less suicidal ideation and adverse events. LIMITATIONS: Given the limited sample size, the lack of a sham control and the use of antidepressants, the findings should be interpreted with caution.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis.

BACKGROUND: Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM. METHODS: 313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM. RESULTS: We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety. CONCLUSION: Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

MSTGC: Multi-Channel Spatio-Temporal Graph Convolution Network for Multi-Modal Brain Networks Fusion.

Multi-modal brain networks characterize the complex connectivities among different brain regions from structure and function aspects, which have been widely used in the analysis of brain diseases. Although many multi-modal brain network fusion methods have been proposed, most of them are unable to effectively extract the spatio-temporal topological characteristics of brain network while fusing different modalities. In this paper, we develop an adaptive multi-channel graph convolution network (GCN) fusion framework with graph contrast learning, which not only can effectively mine both the complementary and discriminative features of multi-modal brain networks, but also capture the dynamic characteristics and the topological structure of brain networks. Specifically, we first divide ROI-based series signals into multiple overlapping time windows, and construct the dynamic brain network representation based on these windows. Second, we adopt adaptive multi-channel GCN to extract the spatial features of the multi-modal brain networks with contrastive constraints, including multi-modal fusion InfoMax and inter-channel InfoMin. These two constraints are designed to extract the complementary information among modalities and specific information within a single modality. Moreover, two stacked long short-term memory units are utilized to capture the temporal information transferring across time windows. Finally, the extracted spatio-temporal features are fused, and multilayer perceptron (MLP) is used to realize multi-modal brain network prediction. The experiment on the epilepsy dataset shows that the proposed method outperforms several state-of-the-art methods in the diagnosis of brain diseases.

Connectivity based Real-Time fMRI Neurofeedback Training in Youth with a History of Major Depressive Disorder.

BACKGROUND: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes beyond the target region. Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task. METHOD: Ten remitted MDD subjects and twelve healthy controls (HC) performed an emotion regulation task in 4 runs of rtfMRI-nf training followed by one transfer run without neurofeedback conducted in a single session. The functional connectivity between amygdala and prefrontal cortex was presented as a feedback bar concurrent with the emotion regulation task. Participants' emotional state was measured by the Positive and Negative Affect Schedule (PANAS) prior to and following the rtfMRI-nf. Psychological assessments were used to determine subjects' history of depression. RESULTS: Participants with a history of MDD showed a trend of decreasing functional connectivity across the four rtfMRI-nf runs, and there was a marginally significant interaction between the MDD history and number of training runs. The HC group showed a significant increase of frontal cortex activation between the second and third neurofeedback runs. Comparing PANAS scores before and after connectivity-based rtfMRI-nf, we observed a significant decrease in negative PANAS score in the whole group overall, and a significant decrease in positive PANAS score in the MDD group alone.

Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients. METHODS: This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively. RESULTS: The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity. LIMITATIONS: Environmental factors and differences in locations of functional areas across individuals were not taken into account. CONCLUSIONS: This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.

Integrating functional neuroimaging and serum proteins improves the diagnosis of major depressive disorder.

BACKGROUND: The lack of effective objective diagnostic biomarkers for major depressive disorder (MDD) leads to high misdiagnosis. Compared with healthy controls (HC), abnormal brain functions and protein levels are often observed in MDD. However, it is unclear whether combining these changed multidimensional indicators could help improve the diagnosis of MDD. METHODS: Sixty-three MDD and eighty-one HC subjects underwent resting-state fMRI scans, among whom 37 MDD and 45 HC provided blood samples. Amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and serum levels of brain-derived neurotrophic factor (BDNF), cortisol, and multiple cytokines were measured and put into the linear discriminant analysis (LDA) to construct corresponding MDD diagnostic models. The area under the receiver operating characteristic curve (AUC) of 5-fold cross-validation was calculated to evaluate each model's performance. RESULTS: Compared with HC, MDD patients' spontaneous brain activity, serum BDNF, cortisol, interleukin (IL)-4, IL-6, and IL-10 levels changed significantly. The combinations of unidimensional multi-indicator had better diagnostic performance than a single one. The model consisted of multidimensional multi-indicator further exhibited conspicuously superior diagnostic efficiency than those constructed with unidimensional multi-indicator, and its AUC, sensitivity, specificity, and accuracy of 5-fold cross-validation were 0.99, 92.0 %, 100.0 %, and 96.3 %, respectively. LIMITATIONS: This cross-sectional study consists of relatively small samples and should be replicated in larger samples with follow-up data to optimize the diagnostic model. CONCLUSIONS: MDD patients' neuroimaging features and serum protein levels significantly changed. The model revealed by LDA could diagnose MDD with high accuracy, which may serve as an ideal diagnostic biomarker for MDD.

Predicting the diagnosis of various mental disorders in a mixed cohort using blood-based multi-protein model: a machine learning approach.

The lack of objective diagnostic methods for mental disorders challenges the reliability of diagnosis. The study aimed to develop an easily accessible and useable objective method for diagnosing major depressive disorder (MDD), schizophrenia (SZ), bipolar disorder (BPD), and panic disorder (PD) using serum multi-protein. Serum levels of brain-derived neurotrophic factor (BDNF), VGF (non-acronymic), bicaudal C homolog 1 (BICC1), C-reactive protein (CRP), and cortisol, which are generally recognized to be involved in different pathogenesis of various mental disorders, were measured in patients with MDD (n = 50), SZ (n = 50), BPD (n = 55), and PD along with 50 healthy controls (HC). Linear discriminant analysis (LDA) was employed to construct a multi-classification model to classify these mental disorders. Both leave-one-out cross-validation (LOOCV) and fivefold cross-validation were applied to validate the accuracy and stability of the LDA model. All five serum proteins were included in the LDA model, and it was found to display a high overall accuracy of 96.9% when classifying MDD, SZ, BPD, PD, and HC groups. Multi-classification accuracy of the LDA model for LOOCV and fivefold cross-validation (within-study replication) reached 96.9 and 96.5%, respectively, demonstrating the feasibility of the blood-based multi-protein LDA model for classifying common mental disorders in a mixed cohort. The results suggest that combining multiple proteins associated with different pathogeneses of mental disorders using LDA may be a novel and relatively objective method for classifying mental disorders. Clinicians should consider combining multiple serum proteins to diagnose mental disorders objectively.

Mental health symptoms and sleep quality of asymptomatic/mild SARS-CoV-2 infected individuals during the Omicron wave of the COVID-19 pandemic in Shanghai China.

OBJECTIVE: To investigate mental health symptoms (anxiety, depression, and sleep status) and their associated factors among people infected with the SARS-CoV-2 omicron variant during the quarantine period in Shanghai. METHODS: To investigate the mental health symptoms among participants with SARS-CoV-2 omicron infection, an anonymous online survey questionnaire was used. The survey panel included the 9-item Patient Health Questionnaire-9 (PHQ-9), 7-item Generalized Anxiety Disorder Scale (GAD-7), Pittsburgh Sleep Quality Index (PSQI), and 22-item Ruminative Responses Scale (RRS). Group comparisons and correlation analyses were employed to explore the epidemiological characteristics of patients and factors related to depression and anxiety symptoms. RESULTS: A total of 960 participants completed the survey. Of the total respondents, 583 participants (60.7%) were male, and the mean (SD) age was 34.33 (9.21) years (95% CI: 33.74-34.91). The prevalence of depressive and anxiety symptoms among the participants was 13.7% (n = 151, 95% CI: 11.6%-15.7%) and 8.6% (n = 90, 95% CI: 6.9%-10.3%), respectively. Age-stratified analysis showed that the prevalence of anxiety among the 36- to 45-year-old group (12.9%; n = 35, 8.9%-16.9%) was significantly higher than that of the 18- to 15-year-old group (7.4%; n = 42, 5.3%-9.6%, p = .011). Spearman's correlation analyses showed that rumination (assessed by the RRS) was significantly and positively correlated with depression (rho = .706, p < .001) and anxiety symptoms (rho = .758, p < .001). CONCLUSION: The results suggest that female and middle-aged populations manifest higher susceptibility to mental health distress during the current Omicron wave of the COVID-19 pandemic. Population-specific psychological crisis intervention is warranted to improve the quality of epidemic prevention methods and to promote the mental well-being of the public.

Functional whole-brain mechanisms underlying effects of tDCS on athletic performance of male rowing athletes revealed by resting-state fMRI.

Introduction: Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applied to modulate brain activity and enhance motor recovery. However, the neurobiological substrates underlying the effects of tDCS on brain function remain poorly understood. This study aimed to investigate the central mechanisms of tDCS on improving the athletic performance of male rowing athletes. Methods: Twelve right-handed male professional rowing athletes received tDCS over the left primary motor cortex while undergoing regular training. The resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired before and after tDCS. Measures of amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were calculated and compared between baseline and follow-up, as well as topological measures including global and local efficiency of functional brain networks constructed by graph theoretical analysis. Results: Male rowing athletes showed increased isokinetic muscle strength of the left knee and left shoulder after tDCS. Increased ALFF values were found in the right precentral gyrus of male rowing athletes after tDCS when compared with those before tDCS. In addition, male rowing athletes showed increased ReHo values in the left paracentral lobule following tDCS. Moreover, increased nodal global efficiency was identified in the left inferior frontal gyrus (opercular part) of male rowing athletes after tDCS. Conclusion: The findings suggested that simultaneous tDCS-induced excitation over the primary motor cortex might potentially improve the overall athletic performance in male rowing athletes through the right precentral gyrus and left paracentral lobule, as well as left inferior frontal gyrus.

Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder.

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

Clinical Efficacy of the Chinese Herbal Medicine Shumian Capsule for Insomnia: A Randomized, Double-Blind, Placebo-Controlled Trial.

Purpose: Shumian capsule (SMC) is a patent Chinese herbal medicine that can soothe the liver and relieves depression, quiet the spirit. Here, we aimed to investigate the efficacy of SMC for treating insomnia using both scales and polysomnography (PSG). Patients and Methods: A randomized, double-blind, placebo-controlled trial was performed. Twenty-six insomnia patients randomly received SMC (n = 11) or placebo (n = 15) for four weeks. Pittsburgh Sleep Quality Inventory (PSQI), Insomnia Severity Index (ISI), 9-items Patient Health Questionnaire (PHQ-9), 7-items Generalized Anxiety Disorder (GAD-7), 17-item Hamilton Depression Rating Scale (HAMD-17), and Hamilton Anxiety Rating Scale (HAMA) were applied at the baseline and the 2nd, 4th week after treatment. Treatment Emergent Symptom Scale was used to assess adverse reactions. We used PSG to record and analyze sleep features at baseline and after four weeks. Results: PSQI, ISI, PHQ-9, HAMD-17, and HAMA scores decreased significantly after SMC treatment. Also, the total sleep time, rapid-eye-movement (REM) sleep latency, stage 2 sleep, deep sleep, REM sleep, and sleep efficiency improved significantly after SMC treatment. In the placebo group, the only significant change was the decrease of PHQ-9 at week-2. Furthermore, both SMC and placebo reported no adverse events. Conclusion: SMC could safely improve sleep quality with depression and anxiety remission in insomnia patients.

Impaired robust interhemispheric function integration of depressive brain from REST-meta-MDD database in China.

BACKGROUND: Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations. METHODS: In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns. RESULTS: As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q < 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs. CONCLUSIONS: We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

The impact of hemodiafiltration on cognitive function in patients with end-stage renal disease.

Background: Patients with end-stage renal disease are more likely to suffer cognitive impairment. Cognitive impairment may lead to long-term severe adverse consequences. Purpose: To explore the impact of different blood purification therapy on cerebral blood flow and cognitive functions in end-stage renal disease. Materials and methods: This prospective study evaluated patients with end-stage renal disease undergoing blood purification from January to March 2021. Matched healthy controls were also included. Participants performed neurocognitive measurements, including a mini-mental state examination, logical memory test-20-minutes delayed, verbal fluency test, digit span test, clock drawing test, and stroop color and word test C. In addition, we tested plasma amyloid-ß protein levels, serum Fe and hemoglobin levels in blood samples. Cerebral blood flow was measured using pulsed pseudocontinuous arterial spin labeling. We analyzed and compared the correlation between cognitive function, biomarkers, and cerebral blood flow between patients and healthy subjects, as well as between patients with different treatments. Results: A total of 44 patients with end-stage renal disease (mean age, 57.39 years ± 8.63) and 46 healthy controls (mean age, 56.15 years ± 6.40) were recruited. Patients receive hemodialysis three times a week, and 27 of them have been replaced hemodialysis for hemodiafiltration twice a month. The cognitive function of patients was worse than healthy controls (P < 0.05). The patients showed higher plasma concentrations of amyloid-ß40, amyloid-ß42, Tau, and pTau181 than healthy controls (P < 0.05). The group receiving both hemodialysis and hemodiafiltration had higher cerebral blood flow signal values in the left caudate nucleus (chuster-level P < 0.05, voxel-level P < 0.001). They also exhibited better verbal fluency function than the hemodialysis-only group (P < 0.05). Conclusion: Patients with the end-stage renal disease showed widespread cognitive declines. Cerebral blood flow generally decreased in the cerebral cortex and increased in subcortical regions. The hemodiafiltration may protect verbal function by increasing cerebral blood flow in the left caudate.