Health economics study of paliperidone palmitate in the treatment of schizophrenia: a 12-month cohort study.

BACKGROUND: To analyze the economic benefits of paliperidone palmitate in the treatment of schizophrenia. METHODS: We collected 546 patients who met the diagnostic criteria for schizophrenia according to the 《International Statistical Classification of Diseases and Related Health Problems,10th》(ICD-10). We gathered general population data such as gender, age, marital status, and education level, then initiated treatment with paliperidone palmitate. Then Follow-up evaluations were conducted at 1, 3, 6, 9, and 12 months after the start of treatment to assess clinical efficacy, adverse reactions, and injection doses. We also collected information on the economic burden before and after 12 months of treatment, as well as the number of outpatient visits and hospitalizations in the past year to analyze economic benefits. RESULTS: The baseline patients totaled 546, with 239 still receiving treatment with paliperidone palmitate 12 months later. After 12 months of treatment, the number of outpatient visits per year increased compared to before (4 (2,10) vs. 12 (4,12), Z=-5.949, P < 0.001), while the number of hospitalizations decreased (1 (1,3) vs. 1 (1,2), Z = 5.625, P < 0.001). The inpatient costs in the direct medical expenses of patients after 12 months of treatment decreased compared to before (5000(2000,12000) vs. 3000 (1000,8050), P < 0.05), while there was no significant change in outpatient expenses and direct non-medical expenses (transportation, accommodation, meal, and family accompanying expenses, etc.) (P > 0.05); the indirect costs of patients after 12 months of treatment (lost productivity costs for patients and families, economic costs due to destructive behavior, costs of seeking non-medical assistance) decreased compared to before (300(150,600) vs. 150(100,200), P < 0.05). CONCLUSION: Palmatine palmitate reduces the number of hospitalizations for patients, as well as their direct and indirect economic burdens, and has good economic benefits.

Synthesis of phenol formaldehyde resin (PFR) based fluorescence resonance energy transfer (FRET) composites for double channel detection of latent fingerprints.

A reversible two-channel fluorescent nanocomposite with fluorescence resonance energy transfer (FRET) effect was designed for the development, analysis, and characterization of latent fingerprints (LFPs). For the construction of the FRET probe, a core of mesoporous silicas (MSNs) were used to encapsulate the organic dye rhodamine 6G (RhD-6) as an acceptor, while green-emitting monodisperse phenolic resin nanoparticles (PFR NPs) were selected as a donor. The up-conversion material (UC) of NaYF4:Yb,Er was synthesized using a simple hydrothermal method, and the MSNs-RhD-6/PFR (PRM) was electrostatically adsorbed onto the UC nanoparticles using a layer-by-layer method to obtain MSNs-RhD-6/PFR-UC (PMU). Compared to ordinary single-channel materials, PMU can be excited by different light sources (365 nm UV/980 nm laser) and its fluorescence can be reversibly switched between yellow and green, demonstrating excellent light reversibility. The PMU composites were successfully used to visualize and detect LFPs on various substrate surfaces using a simple powder coating method. Due to the existing FRET effect and dual-channel characteristics, this composite material displays excellent contrast, outperforming commercially available products for wider applicability. Even on complex backgrounds and after aging or washing treatments, it still clearly recognizes fingerprints in first-, second-, and third-level details, showing its great potential in latent fingerprint detection.

Fabrication of Nitrogen Based Magnetic Conjugated Microporous Polymer for Efficient Extraction of Neonicotinoids in Water Samples.

Facile and sensitive methods for detecting neonicotinoids (NEOs) in aquatic environments are crucial because they are found in extremely low concentrations in complex matrices. Herein, nitrogen-based magnetic conjugated microporous polymers (Fe3O4@N-CMP) with quaternary ammonium groups were synthesized for efficient magnetic solid-phase extraction (MSPE) of NEOs from tap water, rainwater, and lake water. Fe3O4@N-CMP possessed a suitable specific surface area, extended π-conjugated system, and numerous cationic groups. These properties endow Fe3O4@N-CMP with superior extraction efficiency toward NEOs. The excellent adsorption capacity of Fe3O4@N-CMP toward NEOs was attributed to its π-π stacking, Lewis acid-base, and electrostatic interactions. The proposed MSPE-HPLC-DAD approach based on Fe3O4@N-CMP exhibited a wide linear range (0.1-200 µg/L), low detection limits (0.3-0.5 µg/L), satisfactory precision, and acceptable reproducibility under optimal conditions. In addition, the established method was effectively utilized for the analysis of NEOs in tap water, rainwater, and lake water. Excellent recoveries of NEOs at three spiked levels were in the range of 70.4 to 122.7%, with RSDs less than 10%. This study provides a reliable pretreatment method for monitoring NEOs in environmental water samples.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Alkaline sphingomyelinase deficiency impairs intestinal mucosal barrier integrity and reduces antioxidant capacity in dextran sulfate sodium-induced colitis.

BACKGROUND: Ulcerative colitis is a chronic inflammatory disease of the colon with an unknown etiology. Alkaline sphingomyelinase (alk-SMase) is specifically expressed by intestinal epithelial cells, and has been reported to play an anti-inflammatory role. However, the underlying mechanism is still unclear. AIM: To explore the mechanism of alk-SMase anti-inflammatory effects on intestinal barrier function and oxidative stress in dextran sulfate sodium (DSS)-induced colitis. METHODS: Mice were administered 3% DSS drinking water, and disease activity index was determined to evaluate the status of colitis. Intestinal permeability was evaluated by gavage administration of fluorescein isothiocyanate dextran, and bacterial translocation was evaluated by measuring serum lipopolysaccharide. Intestinal epithelial cell ultrastructure was observed by electron microscopy. Western blotting and quantitative real-time reverse transcription-polymerase chain reaction were used to detect the expression of intestinal barrier proteins and mRNA, respectively. Serum oxidant and antioxidant marker levels were analyzed using commercial kits to assess oxidative stress levels. RESULTS: Compared to wild-type (WT) mice, inflammation and intestinal permeability in alk-SMase knockout (KO) mice were more severe beginning 4 d after DSS induction. The mRNA and protein levels of intestinal barrier proteins, including zonula occludens-1, occludin, claudin-3, claudin-5, claudin-8, mucin 2, and secretory immunoglobulin A, were significantly reduced on 4 d after DSS treatment. Ultrastructural observations revealed progressive damage to the tight junctions of intestinal epithelial cells. Furthermore, by day 4, mitochondria appeared swollen and degenerated. Additionally, compared to WT mice, serum malondialdehyde levels in KO mice were higher, and the antioxidant capacity was significantly lower. The expression of the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) in the colonic mucosal tissue of KO mice was significantly decreased after DSS treatment. mRNA levels of Nrf2-regulated downstream antioxidant enzymes were also decreased. Finally, colitis in KO mice could be effectively relieved by the injection of tertiary butylhydroquinone, which is an Nrf2 activator. CONCLUSION: Alk-SMase regulates the stability of the intestinal mucosal barrier and enhances antioxidant activity through the Nrf2 signaling pathway.

Reduced gray matter volume of the hippocampal tail in melancholic depression: evidence from an MRI study.

BACKGROUND: Melancholic depression (MD) is one of the most prevalent and severe subtypes of major depressive disorder (MDD). Previous studies have revealed inconsistent results regarding alterations in grey matter volume (GMV) of the hippocampus and amygdala of MD patients, possibly due to overlooking the complexity of their internal structure. The hippocampus and amygdala consist of multiple and functionally distinct subregions, and these subregions may play different roles in MD. This study aims to investigate the volumetric alterations of each subregion of the hippocampus and amygdala in patients with MD and non-melancholic depression (NMD). METHODS: A total of 146 drug-naïve, first-episode MDD patients (72 with MD and 74 with NMD) and 81 gender-, age-, and education-matched healthy controls (HCs) were included in the study. All participants underwent magnetic resonance imaging (MRI) scans. The subregional segmentation of hippocampus and amygdala was performed using the FreeSurfer 6.0 software. The multivariate analysis of covariance (MANCOVA) was used to detect GMV differences of the hippocampal and amygdala subregions between three groups. Partial correlation analysis was conducted to explore the relationship between hippocampus or amygdala subfields and clinical characteristics in the MD group. Age, gender, years of education and intracranial volume (ICV) were included as covariates in both MANCOVA and partial correlation analyses. RESULTS: Patients with MD exhibited a significantly lower GMV of the right hippocampal tail compared to HCs, which was uncorrelated with clinical characteristics of MD. No significant differences were observed among the three groups in overall and subregional GMV of amygdala. CONCLUSIONS: Our findings suggest that specific hippocampal subregions in MD patients are more susceptible to volumetric alterations than the entire hippocampus. The reduced right hippocampal tail may underlie the unique neuropathology of MD. Future longitudinal studies are required to better investigate the associations between reduced right hippocampal tail and the onset and progression of MD.

scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics.

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Subcortical neural mechanisms of childhood trauma impacts on personality traits.

This study aims to explore the relationships between childhood trauma (CT), personality traits, and subcortical structures. 171 healthy individuals completed the Childhood Trauma Questionnaire (CTQ), the Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO-FFI), and underwent 3D T1-weighted MRI scans. Linear regression analyses indicated the complex relationship between CT, personality traits, and subcortical gray matter volume (GMV). Mediation analyses revealed that the right hippocampal GMV partially mediated the effects of CT on neuroticism. These findings suggest that CT affects the development of the Big Five personality traits, and alterations in subcortical structures are closely related to this process. Altered GMV in the right hippocampus may be a key neural mechanism for CT-induced neuroticism.

Hub genes, a diagnostic model, and immune infiltration based on ferroptosis-linked genes in schizophrenia.

Background: Schizophrenia (SCZ) is a prevalent and serious mental disorder, and the exact pathophysiology of this condition is not fully understood. In previous studies, it has been proven that ferroprotein levels are high in SCZ. It has also been shown that this inflammatory response may modify fibromodulin. Accumulating evidence indicates a strong link between metabolism and ferroptosis. Therefore, the present study aims to identify ferroptosis-linked hub genes to further investigate the role that ferroptosis plays in the development of SCZ. Material and methods: From the GEO database, four microarray data sets on SCZ (GSE53987, GSE38481, GSE18312, and GSE38484) and ferroptosis-linked genes were extracted. Using the prefrontal cortex expression matrix of SCZ patients and healthy individuals as the control group from GSE53987, weighted gene co-expression network analysis (WGCNA) was performed to discover SCZ-linked module genes. From the feed, genes associated with ferroptosis were retrieved. The intersection of the module and ferroptosis-linked genes was done to obtain the hub genes. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were conducted. The SCZ diagnostic model was established using logistic regression, and the GSE38481, GSE18312, and GSE38484 data sets were used to validate the model. Finally, hub genes linked to immune infiltration were examined. Results: A total of 13 SCZ module genes and 7 hub genes linked to ferroptosis were obtained: DECR1, GJA1, EFN2L2, PSAT1, SLC7A11, SOX2, and YAP1. The GO/KEGG/GSEA study indicated that these hub genes were predominantly enriched in mitochondria and lipid metabolism, oxidative stress, immunological inflammation, ferroptosis, Hippo signaling pathway, AMP-activated protein kinase pathway, and other associated biological processes. The diagnostic model created using these hub genes was further confirmed using the data sets of three blood samples from patients with SCZ. The immune infiltration data showed that immune cell dysfunction enhanced ferroptosis and triggered SCZ. Conclusion: In this study, seven critical genes that are strongly associated with ferroptosis in patients with SCZ were discovered, a valid clinical diagnostic model was built, and a novel therapeutic target for the treatment of SCZ was identified by the investigation of immune infiltration.

Family-based genetic analysis in schizophrenia by whole-exome sequence to identify rare pathogenic variants.

Schizophrenia (SCZ) is influenced by a combination of genetic and environmental factors. Although several studies have been conducted to identify the causative loci and genes, few of these loci or genes can be repeated due to the high phenotypic and genetic heterogeneity of disease, and their mechanisms are not fully understood. There may be some "missing heritability" that has not yet been found. In order to investigate the deleterious heritable mutations, whole-exome sequencing (WES) in pedigrees with SCZ was used in the current work. Two unrelated pedigrees with SCZ were recruited to perform WES. Genetic analysis was next performed to find potential variants in accordance with the prioritized strategy. Followed by genetic analysis to detect candidate variants according to the prioritized strategy. Next, a series of algorithms was used to predict the pathogenicity of variants. Sanger sequencing was finally conducted to verify the co-segregation. Recessive mutations in six genes (TFEB, SNAI2, TFAP2B, PRKDC, ST18 in Pedigree 1 and PKHD1L1 in Pedigree 2) that co-segregated with SCZ in two families were discovered through genetic analysis by WES. Sanger sequencing verified that all of the mutations in the affected siblings were homozygous. These results corroborated the hypothesis that SCZ exhibits strong heterogeneity and complex inheritance patterns. The newly discovered homozygous variations deepen our understanding of the mutation spectrum and offer more proof for the involvement of TFEB, SNAI2, TFAP2B, PRKDC, ST18, and PKHD1L1 in the development of SCZ.

Interaction of 5-HTTLPR and SLE disease status on resting-state brain function.

BACKGROUND: Neuropsychiatric involvement in systemic lupus erythematosus (SLE) is a common clinical manifestation. In SLE patients, cerebral function is a more sensitive predictor of central nervous system damage, and abnormalities in cerebral function may be apparent before substantial neuropsychiatric symptoms occur. The 5-hydroxynyptamine(5-HT) system has the ability to interact with the majority of the neurochemical systems in the central nervous system (CNS), influencing brain function. Serotonin transporter gene-linked polymorphic region (5-HTTLPR) is an essential element of the 5-HT system gene polymorphism and is directly related to the control of 5-hydroxytryptamine transporter (5-HTT)gene expression. The relationship between 5-HTTLPR and functional brain measurements in SLE patients requires more investigation because it is one of the most attractive imaging genetics targets for shedding light on the pathophysiology of neuropsychiatric lupus. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) images were collected from 51 SLE patients without obvious neuropsychiatric manifestations and 44 healthy volunteers. Regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) were selected as indicators for evaluating brain function. In accordance with the Anatomical Automatic Labeling template, the gray matter was divided into 116 regions. The mean ReHo value, mean ALFF value, and mean fALFF value of each brain region were extracted. 5-HTTLPR genotypes of all research objects were tested by polymerase chain reaction and agarose gel electrophoresis. Two-way analysis of covariance was used to investigate whether there is an interaction effect between SLE disease status and 5-HTTLPR genotype on resting-state brain function. RESULTS: In SLE patients with S/S homozygosity, there were notably lower mean ReHo, mean ALFF, and mean fALFF values observed in the right parietal, inferior angular gyrus, and the right paracentral lobule compared to healthy controls. However, this distinction was not evident among carriers of the L allele. Within the S/S genotype, SLE patients exhibited decreased mean ReHo in the left posterior cingulate gyrus, reduced mean fALFF in the left caudate nucleus, and diminished mean ALFF in the left temporal pole: superior temporal gyrus, in contrast to the HC group. Conversely, no such differences were discerned among carriers of the L allele. Notably, among L allele carriers, SLE patients displayed a higher mean ReHo value in the right hippocampus compared to the HC group, while demonstrating a lower mean ALFF value in the left medial and paracingulate gyrus in contrast to the HC group. Conversely, these differences were not apparent among S/S homozygotes. CONCLUSIONS: Brain function in the right parietal and inferior angular gyrus and the right paracentral lobule is affected by the interaction effect of SLE disease status and 5-HTTLPR genotype.

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network.

Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated ( [Formula: see text]) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks ( [Formula: see text] or [Formula: see text]). These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.

Higher Blood-brain barrier permeability in patients with major depressive disorder identified by DCE-MRI imaging.

BACKGROUND: Studies from animal models and clinical trials of blood and cerebrospinal fluid have proposed that blood-brain barrier (BBB) dysfunction in depression (MDD). But there are no In vivo proves focused on BBB dysfunction in MDD patients. The present study aimed to identify whether there was abnormal BBB permeability, as well as the association with clinical status in MDD patients using dynamic contrast-enhanced magnetic resonance (DCE-MRI) imaging. METHODS: Patients with MDD and healthy adults were recruited and underwent DCE-MRI and structural MRI scans. The mean volume transfer constant (Ktrans) values were calculated for a quantitative assessment of BBB leakage. For each subject, the mean Ktrans values were calculated for the whole gray matter, white matter, and 90 brain regions of the anatomical automatic labeling template (AAL). The differences in Ktrans values between patients and controls and between treated and untreated patients were compared. RESULTS: 23 MDD patients (12 males and 11 females, mean age 28.09 years) and 18 healthy controls (HC, 8 males and 10 females, mean age 30.67 years) were recruited in the study. We found that the Ktrans values in the olfactory, caudate, and thalamus were higher in MDD patients compared to healthy controls (p<0.05). The Ktrans values in the orbital lobe, anterior cingulate gyrus, putamen, and thalamus in treated patients were lower than the patients never treated. There were positive correlations between HAMD total score with Ktrans values in whole brain WM, hippocampus and thalamus. The total HAMA score was positively correlated with the Ktrans of hippocampus. CONCLUSION: These findings supported a link between blood-brain barrier leakage and depression and symptom severity. The results also suggested a role for non-invasive DCE-MRI in detecting blood-brain barrier dysfunction in depression patients.

Potential mechanisms of non-suicidal self-injury (NSSI) in major depressive disorder: a systematic review.

Background: Non-suicidal self-injury (NSSI) is a frequent and prominent phenomenon in major depressive disorder (MDD). Even though its prevalence and risk factors are relatively well understood, the potential mechanisms of NSSI in MDD remain elusive. Aims: To review present evidence related to the potential mechanisms of NSSI in MDD. Methods: According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, articles for this systematic review were searched on Medline (through PubMed), Embase (through Elsevier), PsycINFO (through OVID) and Web of Science databases for English articles, as well as China National Knowledge Infrastructure (CNKI), SinoMed, Wanfang Data, and the Chongqing VIP Chinese Science and Technology Periodical (VIP) Databases for Chinese articles published from the date of inception to 2 August 2022. Two researchers (BW, HZ) independently screened studies based on inclusion and exclusion criteria and assessed their quality. Results: A total of 25 157 studies were searched. Only 25 of them were ultimately included, containing 3336 subjects (1535 patients with MDD and NSSI, 1403 patients with MDD without NSSI and 398 HCs). Included studies were divided into 6 categories: psychosocial factors (11 studies), neuroimaging (8 studies), stress and hypothalamic-pituitary-adrenal (HPA) axis (2 studies), pain perception (1 study), electroencephalogram (EEG) (2 studies) and epigenetics (1 study). Conclusions: This systematic review indicates that patients with MDD and NSSI might have specific psychosocial factors, aberrant brain functions and neurochemical metabolisms, HPA axis dysfunctions, abnormal pain perceptions and epigenetic alterations.

White matter alterations in mild cognitive impairment revealed by meta-analysis of diffusion tensor imaging using tract-based spatial statistics.

The neuropathological mechanism of mild cognitive impairment (MCI) remains unclarified. Diffusion tensor imaging (DTI) studies revealed white matter (WM) microarchitecture alterations in MCI, but consistent findings and conclusions have not yet been drawn. The present coordinate-based meta-analysis (CBMA) of tract-based spatial statistics (TBSS) studies aimed to identify the most prominent and robust WM abnormalities in patients with MCI. A systematic search of relevant studies was conducted through January 2022 to identify TBSS studies comparing fractional anisotropy (FA) between MCI patients and healthy controls (HC). We used the seed-based d mapping (SDM) software to achieve the CBMA and analyze regional FA alterations in MCI. Meta-regression analysis was subsequently applied to explore the potential associations between clinical variables and FA changes. MCI patients demonstrated significantly decreased FA in widely distributed areas in the corpus callosum (CC), including the genu, body, and splenium of the CC, as well as one cluster in the left striatum. FA in the body of the CC and in three clusters in the splenium of the CC was negatively associated with the mean age. Additionally, FA in the genu of the CC and in three clusters in the splenium of the CC had negative correlations with the MMSE scores. Disrupted integrities of the CC and left striatum might play vital roles in the process of cognitive decline. These findings enhanced our understanding of the neural mechanism underlying WM neurodegeneration in MCI and provided perspectives for the early detection and intervention of dementia.Registration number: CRD42022235716.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.