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.

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.

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.

Disrupted intrinsic functional brain topology in patients with major depressive disorder.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

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.

Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder.

BACKGROUND: Alterations in gray matter (GM) have been recognized as playing an important role in the neurobiological mechanism underlying major depressive disorder (MDD) and antidepressant responses. However, little is known about white matter (WM) connectivity in MDD, leaving an incomplete understanding of the pathophysiology of the disorder. PURPOSE: To examine the functional connectivity (FC) of WM, GM, and WM-GM in MDD patients and explore the relationship between FC and antidepressant response. STUDY TYPE: Longitudinal study. SUBJECTS: In all, 129 MDD patients and 89 healthy controls (HC). FIELD STRENGTH/SEQUENCE: Whole-brain blood oxygen level-dependent (BOLD) single-shot echo planar imaging was acquired at 3.0T. ASSESSMENT: At baseline, all participants received Hamilton depression rating scale (HAMD) assessment and an fMRI scan. After 2- and 8-week antidepressant treatment, patients completed the HAMD again. The HAMD reductive rate of 2- and 8-weeks were calculated. STATISTICAL TESTS: The comparisons of age, education, HAMD scores, and FC values (false discovery rate correction) between patients and controls were calculated with a two-sample t-test. The chi-square test was employed to compare the differences of gender between these two groups. Correlations between FC and HAMD, as well as the reductive rate of HAMD, were analyzed with Pearson or Spearman correlation. Receiver operator curve analysis was performed to predict the antidepressant response. RESULTS: Compared to HC, MDD patients exhibited widespread decreases in FC of WM-GM. Furthermore, 28 GM regions and 11 WM bundles had lower connectivity in MDD patients. At baseline, four FC of WM-GM showed negative correlations with the HAMD scores. Six FC of WM-GM correlated with the 2-week reductive rate of HAMD. Moreover, FC in GM, WM, and WM-GM also exhibited significantly positive correlations with an 8-week reductive rate of HAMD. DATA CONCLUSION: The FC of WM-GM was decreased in MDD and may play a role in its pathophysiology and antidepressant responses. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Cognitive Deficit-Related Interhemispheric Asynchrony within the Medial Hub of the Default Mode Network Aids in Classifying the Hyperthyroid Patients.

Background and Purpose: Recent studies suggest that abnormal structure and function in the brain network were related to cognitive and emotional impairment in hyperthyroid patients (HPs). The association between altered voxel-mirrored homotopic connectivity (VMHC) and neuropsychological impairment in HPs remains unclear. This study is aimed at investigating the association between the disrupted functional coordination and psychological dysfunction in hyperthyroidism. Method: Thirty-three hyperthyroid patients and thirty-three matched healthy controls (HCs) were recruited, and they received resting-state functional magnetic resonance imaging (fMRI) scans and neuropsychological evaluation. The VMHC value was computed to reveal the functional coordination between homotopic regions in both groups. The neurobehavioral relevancy method was employed to explore the relationship between the altered VMHC and emotional, cognition measures. Further receiver operating characteristic (ROC) curve analysis was adopted to examine the power of changed regional VMHC in classifying the patients with hyperthyroidism. Results: Compared with the HCs, the HPs exhibited significantly declined VMHC values in the bilateral medial frontal gyrus (MeFG). The interhemispheric asynchrony in the MeFG was positively correlated with Z scores of episodic memory. The ROC analysis further determined that abnormal VMHC in the MeFG could efficiently distinguish the HPs from the HCs (area under the curve (AUC) = 0.808, P < 0.001). Conclusion: The altered interhemispheric coordination in the hub of the default mode network may implicated in the modulation of episodic memory in HPs patients and the distinct feature of the interhemispheric asynchrony may be treated as a potential target for the early recognition and intervention for the HPs with cognitive impairments. Clinical Trial Registration: This is a study of the neurological basis for dysfunction of mood and cognition in hyperthyroid patients: a resting-state fMRI study (registration number: ChiCTR-OOC-16008607).

Abnormal Functional Connectivity of Ventral Anterior Insula in Asthmatic Patients with Depression.

OBJECTIVE: To explore the underlying mechanism of depression in asthmatic patients, the ReHo in the insula and its FC was used to probe the differences between depressed asthmatic (DA) and nondepressed asthmatic (NDA) patients. METHODS: 18 DA patients, 24 NDA patients, and 60 healthy controls (HCs) received resting-state fMRI scan, severity of depression, and asthma control assessment. RESULTS: DA patients showed increased FC between the left ventral anterior insula (vAI) and the left middle temporal gyrus compared with both NDA and HC groups. In addition, compared with HCs, the DA and NDA patients both exhibited increased FC between the left vAI and the right anterior cingulate cortex (ACC), decreased FC between the left vAI and the bilateral parietal lobe, and increased FC between the right vAI and the left putamen and the right caudate, respectively. Furthermore, the increased FC between the left vAI and the right ACC could differentiate HCs from both DA and NDA patients, and the increased FC between the right vAI and both the left putamen and the right caudate could separate NDA patients from HCs. CONCLUSIONS: This study confirmed that abnormal vAI FC may be involved in the neuropathology of depression in asthma. The increased FC between the left vAI and the left MTG could distinguish DA from the NDA and HC groups.

Association between altered resting-state cortico-cerebellar functional connectivity networks and mood/cognition dysfunction in late-onset depression.

The objective of the study is to investigate the relationship between altered resting-state cortico-cerebellar functional connectivity (FC) and depression as well as cognitive impairment in late-onset depression (LOD). A total of 32 LOD and 39 well-matched normal controls (NCs) were recruited and underwent resting-state functional MRI (R-fMRI) scans. Seed-based correlation analysis was performed to explore the cortico-cerebellar FC. Hamilton Depression Rating Scale (HAMD) and mini-mental state examination (MMSE) were used to evaluate the depressive severity and cognitive impairment, respectively. A set of neuropsychological measurements was also applied to evaluate the detailed cognitions. Spearman correlations were applied to examine the depressive and cognitive association of these altered cortico-cerebellar networks. Compared with the NCs, LOD patients showed increased FC between the cerebellum and the right ventromedial frontal cortex (vmPFC), supplementary motor area (SMA), middle temporal gyrus (MTG), bilateral supramarginal gyrus (SMG), and anterior cingulated cortex (ACC). However, reduced cerebellar FC was observed in bilateral cerebellum, posterior cingulated cortex (PCC) and left dorsolateral prefrontal cortex (dlPFC). Moreover, the cerebellar FC with the vmPFC and ACC was positively correlated with HAMD score, whereas the cerebellar FC with the dlPFC and PCC was positively correlated with MMSE score in LOD patients. The cortico-cerebellar disconnections might underlie the pathogenesis of LOD. While depression mainly relates to the excessive cerebellar FC with the vmPFC and ACC, cognitive decline is primarily associated with the uncoupling of the cerebellar FC with the dlPFC and PCC.

Association analysis of the catechol-O-methyltransferase /methylenetetrahydrofolate reductase genes and cognition in late-onset depression.

AIMS: Increasing evidence suggests that the catechol-O-methyltransferase (COMT) gene might be associated with cognition in patients with mental disorders and healthy people. The metabolic pathways of COMT and methylenetetrahydrofolate reductase (MTHFR) are closely interconnected. In this study, we aimed to examine whether the COMT-MTHFR genotype interacted with cognitive function in late-onset depression (LOD) patients and COMT Val/Val homozygous individuals who also carried the MTHFR T allele and had poor neuropsychological test performance. METHODS: Ninety-seven unrelated LOD patients who met DSM-IV criteria for major depressive disorder were recruited for the study and 103 normal controls were recruited from the local community. All of these patients and 44 normal controls completed a series of neuropsychological tests. Patients and normal controls were genotyped for COMT (rs4680) and MTHFR (rs1801133) variants using polymerase chain reaction-restriction fragment length polymorphism. RESULTS: There were no significant differences in the frequencies of the single alleles and genotypes of two polymorphisms between LOD patients and normal controls. No main effects of COMT or MTHFR genotype on any neuropsychological test performance were observed. There was a significant interactive effect of COMT Val158Met and MTHFR C677T polymorphisms on the Symbol Digit Modalities Test independent of diagnosis (P < 0.05). After controlling for covariates, the subjects with COMT Met/ Met and MTHFR C/C genotype had better Symbol Digit Modalities Test performance. CONCLUSIONS: The results suggest no major effect of COMT or MTHFR on cognitive function alone. However, an interaction of COMT Val158Met and MTHFR C677T polymorphisms may be associated with cognitive function. Further studies in a large sample are needed to replicate the genetic role in the LOD patients.

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.

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.

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.

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.

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.

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.

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.

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.

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.