A study of macroinvertebrate community structure and diversity in response to land use type in the Yiluo River Basin.

Land use types have a significant impact on river ecosystems. The Yiluo River is the largest tributary below Xiaolangdi Reservoir in the middle reaches of the Yellow River, and is one of the important water conservation areas in the Yellow River Basin. Studying the ecological status of the Yiluo River under varied land use types in this basin is crucial for both ecological protection and the high-quality development of the Yellow River Basin. This study investigated the impacts of land use types on the macroinvertebrate community and functional structure in the Yiluo River Basin and introduced the concept of the land use health index (LUI). During the survey period, a total of 11,894 macroinvertebrates were collected, and 143 species were identified, belonging to 4 phyla, 7 orders, 22 families, and 75 families. The results showed that LUI had the most significant impact on macroinvertebrate community structure, with substrate type, dry plant weight, total phosphorus, turbidity, and attached algae biomass also playing significant roles in affecting macroinvertebrate communities. The species richness, the Shannon-Wiener index, and the Margalef richness index exhibited a nonlinear positive correlation with LUI of the sampling site, increasing as LUI enhancing and eventually reaching a plateau. Functional richness showed a linear and positive correlation with LUI, increasing with its enhancement, while functional evenness and functional divergence exhibited a nonlinear correlation with LUI. Functional evenness initially increased and then decreased with the enhancement of LUI, while functional divergence decreased with LUI enhancement. This study can provide a scientific reference for river ecological management under various land use scenarios.The Yiluo River is the largest tributary below Xiaolangdi Reservoir in the middle reaches of the Yellow River, and is one of the important water conservation areas in the Yellow River Basin. Studying the ecological status of the Yiluo River under varied land use types in this basin is crucial for both ecological protection and the high-quality development of the Yellow River Basin. This study investigated the impacts of land use types on the macroinvertebrate community and functional structure in the Yiluo River Basin and introduced the concept of the land use health index (LUI). During the survey period, a total of 11,894 macroinvertebrates were collected, and 143 species were identified, belonging to 4 phyla, 7 orders, 22 families, and 75 families. The results showed that LUI had the most significant impact on macroinvertebrate community structure, with substrate type, dry plant weight, total phosphorus, turbidity, and attached algae biomass also playing significant roles in affecting macroinvertebrate communities. The species richness, the Shannon-Wiener index, and the Margalef richness index exhibited a nonlinear positive correlation with LUI of the sampling site, increasing as LUI enhancing and eventually reaching a plateau. Functional richness showed a linear and positive correlation with LUI, increasing with its enhancement, while functional evenness and functional divergence exhibited a nonlinear correlation with LUI. Functional evenness initially increased and then decreased with the enhancement of LUI, while functional divergence decreased with LUI enhancement. This study can provide a scientific reference for river ecological management under various land use scenarios.

Liver fibrosis automatic diagnosis utilizing dense-fusion attention contrastive learning network.

BACKGROUND: Liver fibrosis poses a significant public health challenge given its elevated incidence and associated mortality rates. Diffusion-Weighted Imaging (DWI) serves as a non-invasive diagnostic tool for supporting the identification of liver fibrosis. Deep learning, as a computer-aided diagnostic technology, can assist in recognizing the stage of liver fibrosis by extracting abstract features from DWI images. However, gathering samples is often challenging, posing a common dilemma in previous research. Moreover, previous studies frequently overlooked the cross-comparison information and latent connections among different DWI parameters. Thus, it is becoming a challenge to identify effective DWI parameters and dig potential features from multiple categories in a dataset with limited samples. PURPOSE: A self-defined Multi-view Contrastive Learning Network is developed to automatically classify multi-parameter DWI images and explore synergies between different DWI parameters. METHODS: A Dense-fusion Attention Contrastive Learning Network (DACLN) is designed and used to recognize DWI images. Concretely, a multi-view contrastive learning framework is constructed to train and extract features from raw multi-parameter DWI. Besides, a Dense-fusion module is designed to integrate feature and output predicted labels. RESULTS: We evaluated the performance of the proposed model on a set of real clinical data and analyzed the interpretability by Grad-CAM and annotation analysis, achieving average scores of 0.8825, 0.8702, 0.8933, 0.8727, and 0.8779 for accuracy, precision, recall, specificity and F-1 score. Of note, the experimental results revealed that IVIM-f, CTRW-ß, and MONO-ADC exhibited significant recognition ability and complementarity. CONCLUSION: Our method achieves competitive accuracy in liver fibrosis diagnosis using the limited multi-parameter DWI dataset and finds three types of DWI parameters with high sensitivity for diagnosing liver fibrosis, which suggests potential directions for future research.

Stimulator of interferon genes mediated immune senescence reveals the immune microenvironment and prognostic characteristics of bladder cancer.

Background: Studies have shown that the stimulator of interferon genes (STING) is critical in tumorigenesis, and development. This study aimed to investigate the immune profile and prognostic significance of STING-mediated immune senescence in bladder cancer (BLCA). Methods: We identified differential genes between tumor and normal tissue based on the Cancer Genome Atlas database, and used consensus clustering to identify BLCA subtypes. The genes most associated with overall survival were screened by further analysis and used to construct risk models. Then, comparing the immune microenvironment, tumor mutational load (TMB), and microsatellite instability (MSI) scores between different risk groups. Eventually, a nomogram was constructed based on clinical information and risk scores. The model was validated using receiver operating curves (ROC) and calibration plots. Results: We identified 160 differential genes, including 13 genes most associated with prognosis. Three subtypes of bladder cancer with different clinical and immunological features were identified. Immunotherapy was more likely to benefit the low-risk group, which had higher TMB and MSI scores. The nomogram was found to be highly predictive based on ROC analysis and calibration plots. Conclusion: The risk model and nomogram not only predict the prognosis of BLCA patients but also can guide the treatment.

Monocyte-Derived Macrophages Aggravate Cardiac Dysfunction After Ischemic Stroke in Mice.

BACKGROUND: Cardiac damage induced by ischemic stroke, such as arrhythmia, cardiac dysfunction, and even cardiac arrest, is referred to as cerebral-cardiac syndrome (CCS). Cardiac macrophages are reported to be closely associated with stroke-induced cardiac damage. However, the role of macrophage subsets in CCS is still unclear due to their heterogeneity. Sympathetic nerves play a significant role in regulating macrophages in cardiovascular disease. However, the role of macrophage subsets and sympathetic nerves in CCS is still unclear. METHODS AND RESULTS: In this study, a middle cerebral artery occlusion mouse model was used to simulate ischemic stroke. ECG and echocardiography were used to assess cardiac function. We used Cx3cr1GFPCcr2RFP mice and NLRP3-deficient mice in combination with Smart-seq2 RNA sequencing to confirm the role of macrophage subsets in CCS. We demonstrated that ischemic stroke-induced cardiac damage is characterized by severe cardiac dysfunction and robust infiltration of monocyte-derived macrophages into the heart. Subsequently, we identified that cardiac monocyte-derived macrophages displayed a proinflammatory profile. We also observed that cardiac dysfunction was rescued in ischemic stroke mice by blocking macrophage infiltration using a CCR2 antagonist and NLRP3-deficient mice. In addition, a cardiac sympathetic nerve retrograde tracer and a sympathectomy method were used to explore the relationship between sympathetic nerves and cardiac macrophages. We found that cardiac sympathetic nerves are significantly activated after ischemic stroke, which contributes to the infiltration of monocyte-derived macrophages and subsequent cardiac dysfunction. CONCLUSIONS: Our findings suggest a potential pathogenesis of CCS involving the cardiac sympathetic nerve-monocyte-derived macrophage axis.

Decreased connexin 40 expression of the sinoatrial node mediates ischemic stroke-induced arrhythmia in mice.

BACKGROUND: Arrhythmia is the most common cardiac complication after ischemic stroke. Connexin 40 is the staple component of gap junctions, which influences the propagation of cardiac electrical signals in the sinoatrial node. However, the role of connexin 40 in post-stroke arrhythmia remains unclear. METHODS: In this study, a permanent middle cerebral artery occlusion model was used to simulate the occurrence of an ischemic stroke. Subsequently, an electrocardiogram was utilized to record and assess variations in electrocardiogram measures. In addition, optical tissue clearing and whole-mount immunofluorescence staining were used to confirm the anatomical localization of the sinoatrial node, and the sinoatrial node tissue was collected for RNA sequencing to screen for potential pathological mechanisms. Lastly, the rAAV9-Gja5 virus was injected with ultrasound guidance into the heart to increase Cx40 expression in the sinoatrial node. RESULTS: We demonstrated that the mice suffering from a permanent middle cerebral artery occlusion displayed significant arrhythmia, including atrial fibrillation, premature ventricular contractions, atrioventricular block, and abnormal electrocardiogram parameters. Of note, we observed a decrease in connexin 40 expression within the sinoatrial node after the ischemic stroke via RNA sequencing and western blot. Furthermore, rAAV9-Gja5 treatment ameliorated the occurrence of arrhythmia following stroke. CONCLUSIONS: In conclusion, decreased connexin 40 expression in the sinoatrial node contributed to the ischemic stroke-induced cardiac arrhythmia. Therefore, enhancing connexin 40 expression holds promise as a potential therapeutic approach for ischemic stroke-induced arrhythmia.

Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study.

Depression ranks among the most prevalent mood-related psychiatric disorders. Existing clinical diagnostic approaches relying on scale interviews are susceptible to individual and environmental variations. In contrast, the integration of neuroimaging techniques and computer science has provided compelling evidence for the quantitative assessment of major depressive disorder (MDD). However, one of the major challenges in computer-aided diagnosis of MDD is to automatically and effectively mine the complementary cross-modal information from limited datasets. In this study, we proposed a few-shot learning framework that integrates multi-modal MRI data based on contrastive learning. In the upstream task, it is designed to extract knowledge from heterogeneous data. Subsequently, the downstream task is dedicated to transferring the acquired knowledge to the target dataset, where a hierarchical fusion paradigm is also designed to integrate features across inter- and intra-modalities. Lastly, the proposed model was evaluated on a set of multi-modal clinical data, achieving average scores of 73.52% and 73.09% for accuracy and AUC, respectively. Our findings also reveal that the brain regions within the default mode network and cerebellum play a crucial role in the diagnosis, which provides further direction in exploring reproducible biomarkers for MDD diagnosis.

Effects of second language acquisition on brain functional networks at different developmental stages.

Previous studies have shown that language acquisition influences both the structure and function of the brain. However, whether the acquisition of a second language at different periods of life alters functional network organization in different ways remains unclear. Here, functional magnetic resonance imaging data from 27 English-speaking monolingual controls and 52 Spanish-English bilingual individuals, including 22 early bilinguals who began learning a second language before the age of ten and 30 late bilinguals who started learning a second language at age fourteen or later, were collected from the OpenNeuro database. Topological metrics of resting-state functional networks, including small-world attributes, network efficiency, and rich- and diverse-club regions, that characterize functional integration and segregation of the networks were computed via a graph theoretical approach. The results showed obvious increases in network efficiency in early bilinguals and late bilinguals relative to the monolingual controls; for example, the global efficiency of late bilinguals and early bilinguals was improved relative to that of monolingual controls, and the local efficiency of early bilinguals occupied an intermediate position between that of late bilinguals and monolingual controls. Obvious increases in rich-club and diverse-club functional connectivity were observed in the bilinguals relative to the monolingual controls. Three network metrics were positively correlated with Spanish proficiency test scores. These findings demonstrated that early and late acquisition of a second language had different impacts on the functional networks of the brain.

Decreased structural pathways mediating functional connectivity in obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep breathing disorder that is often accompanied by changes in structural connectivity (SC) and functional connectivity (FC). However, the current understanding of the interaction between SC and FC in OSA is still limited. METHODS: The aim of this study is to integrate complementary neuroimaging modalities into a unified framework using multi-layer network analysis methods and to reveal their complex interrelationships. We introduce a new graph metric called SC-FC bandwidth, which measures the throughput of SC mediating FC in a multi-layer network. The bandwidth differences between two groups are evaluated using the network-based statistics (NBS) method. Additionally, we traced and analyzed the SC pathways corresponding to the abnormal bandwidth. RESULTS: In both the healthy control and patients with OSA, the majority offunctionally synchronized nodes were connected via SC paths of length 2. With the NBS method, we observed significantly lower bandwidth between the right Posterior cingulate gyrus and right Cuneus, bilateral Middle frontal gyrus, bilateral Gyrus rectus in OSA patients. By tracing the high-proportion SC pathways, it was found that OSA patients typically exhibit a decrease in direct SC-FC, SC-FC triangles, and SC-FC quads intra- and inter-networks. CONCLUSION: Complex interrelationship changes have been observed between the SC and FC in patients with OSA, which might leads to abnormal information transmission and communication in the brain network.

Rich Club Reorganization in Nurses Before and After the Onset of Occupational Burnout: A Longitudinal MRI Study.

BACKGROUND: Studies on potential disruptions in rich club structure in nursing staff with occupational burnout are lacking. Moreover, existing studies on nurses with burnout are limited by their cross-sectional design. PURPOSE: To investigate rich club reorganization in nursing staff before and after the onset of burnout and the underlying impact of anatomical distance on such reconfiguration. STUDY TYPE: Prospective, longitudinal. POPULATION: Thirty-nine hospital nurses ( 23.67 ± 1.03 $$ 23.67\pm 1.03 $$ years old at baseline, 24.67 ± 1.03 $$ 24.67\pm 1.03 $$ years old at a follow-up within 1.5 years, 38 female). FIELD STRENGTH/SEQUENCE: Magnetization-prepared rapid gradient-echo and gradient-echo echo-planar imaging sequences at 3.0 T. ASSESSMENT: The Maslach Burnout Inventory and Symptom Check-List 90 testing were acquired at each MRI scan. Rich club structure was assessed at baseline and follow-up to determine whether longitudinal changes were related to burnout and to changes in connectivities with different anatomical distances (short-, mid-, and long range). STATISTICAL TESTS: Chi-square, paired-samples t, two-sample t, Mann-Whitney U tests, network-based statistic, Spearman correlation analysis, and partial least squares regression analysis. Significance level: Bonferroni-corrected P < 0.05 $$ P<0.05 $$ . RESULTS: In nurses who developed burnout: 1) Strengths of rich club, feeder, local, short-, mid-, and long-range connectivities were significantly decreased at follow-up compared with baseline. 2) At follow-up, strengths of above connectivities and that between A5m.R and dlPu.L were significantly correlated with emotional exhaustion (r ranges from -0.57 to -0.73) and anxiety scores (r = -0.56), respectively. 3) Longitudinal change (follow-up minus baseline) in connectivity strength between A5m.R and dlPu.L reflected change in emotional exhaustion score (r = 0.87). Longitudinal changes in strength of connectivities mainly involving parietal lobe were significantly decreased in nurses who developed burnout compared with those who did not. DATA CONCLUSION: In nurses after the onset of burnout, rich club reorganization corresponded to significant reductions in strength of connectivities with different anatomical distances. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder. / é‡åº¦æŠ‘éƒéšœç¢æ‚£è€ ä¸˜è„‘çš®å±‚çŽ¯è·¯çš„åŠ¨æ€åŠŸèƒ½è¿žæŽ¥å¼‚å¸¸.

Thalamocortical circuitry has a substantial impact on emotion and cognition. Previous studies have demonstrated alterations in thalamocortical functional connectivity (FC), characterized by region-dependent hypo- or hyper-connectivity, among individuals with major depressive disorder (MDD). However, the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear. Hence, we analyzed dynamic FC (dFC) between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls (HCs) to investigate time-varying changes in thalamocortical FC in patients with MDD. Moreover, dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time. Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated. We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks, compared with the HCs. In addition, MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration. The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort. Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms. Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients. The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.

Preliminary study on the role of the CSMD2 gene in bladder cancer.

Background: CSMD2 has been reported as a potential prognostic factor in several cancers. However, whether CSMD2 affects bladder cancer (BC) remains unclear. Methods: Public data were obtained from the TCGA (https://cancergenome.nih.gov) databases. CSMD2expression and its prognostic value were analyzed using bioinformatics methods. CSMD2 mRNA level in patients with BC and BC cell lines was evaluated via quantitative reverse transcriptase polymerase chain reaction. CSMD2 protein level in patients with BC was evaluated via immunohistochemistry. BC cell lines T24 and UMUC-3 were selected for loss-of-function assays targeting CSMD2. Cell viability was determined by CCK8 and clone formation experiments. Cell migration and invasion were evaluated using Transwell assays. Furthermore, the transcriptome of UMUC-3 with CSMD2 knockdown was sequenced to analyze potential signaling network pathways. Finally, the TIMER2.0 database was employed to identify the correlation between CSMD2 and immune cells in the tumor microenvironment. Results: CSMD2 expression was up-regulated in BC tissues compared to adjacent tissues. High CSMD2 expression was associated with poor survival and could serve as an independent predictor for survival in patients with BC. Furthermore, down-regulation of CSMD2 notably restrained the viability, migration, and invasion abilities of T24 and UMUC-3 cells. Moreover, transcriptomic sequencing after CSMD2 knockdown in UMUC-3 cells revealed its involvement in the regulation of the malignant phenotype in BC. Finally, public databases suggest a connection between CSMD2 and immune cell infiltration in BC. Conclusions: These findings suggest that CSMD2 may promote proliferation and tumorigenicity, and could represent a potential target for improving the prognosis of BC.

Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN.

Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features. In addition, we introduce a fully convolutional network, which simplifies the extraction of deep convolutional features and overcomes the high computational memory requirements associated with deep learning. Our approach outperforms several widely-used models on eight publicly available datasets (n = 2501), with a mean absolute error (MAE) of 2.20 years in predicting brain age. Comparisons with deep learning models lacking the AFA module demonstrate that our fusion model effectively improves overall performance. These findings provide a promising approach for combining anatomical and deep convolutional features from sMRI data to predict brain age, with potential applications in clinical diagnosis and treatment, particularly for populations with age-related cognitive decline or neurological disorders.

Fine-grained features characterize hippocampal and amygdaloid change pattern in Parkinson's disease and discriminate cognitive-deficit subtype.

AIMS: To extract vertex-wise features of the hippocampus and amygdala in Parkinson's disease (PD) with mild cognitive impairment (MCI) and normal cognition (NC) and further evaluate their discriminatory efficacy. METHODS: High-resolution 3D-T1 data were collected from 68 PD-MCI, 211 PD-NC, and 100 matched healthy controls (HC). Surface geometric features were captured using surface conformal representation, and surfaces were registered to a common template using fluid registration. The statistical tests were performed to detect differences between groups. The disease-discriminatory ability of features was also tested in the ensemble classifiers. RESULTS: The amygdala, not the hippocampus, showed significant overall differences among the groups. Compared with PD-NC, the right amygdala in MCI patients showed expansion (anterior cortical, anterior amygdaloid, and accessory basal areas) and atrophy (basolateral ventromedial area) subregions. There was notable atrophy in the right CA1 and hippocampal subiculum of PD-MCI. The accuracy of classifiers with multivariate morphometry statistics as features exceeded 85%. CONCLUSION: PD-MCI is associated with multiscale morphological changes in the amygdala, as well as subtle atrophy in the hippocampus. These novel metrics demonstrated the potential to serve as biomarkers for PD-MCI diagnosis. Overall, these findings from this study help understand the role of subcortical structures in the neuropathological mechanisms of PD cognitive impairment.

Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework.

Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD.

The α1 adrenoceptor antagonist prazosin potentiates morphine induced conditioned place preference in rats.

The norepinephrine (NE) system is involved in pathways that regulate morphine addiction. Here, we investigated the role of α1 adrenoceptor in the ventrolateral orbital cortex (VLO) of rats with repeated morphine treatment and underlying molecular mechanisms. The rewarding properties of morphine were assessed by the conditioned place preference (CPP) paradigm. Prazosin, an α1 adrenoceptor antagonist, was microinjected into the VLO. The expression of α1 adrenoceptor, p-CaMKII/CaMKII, CRTC1, BDNF and PSD95 in the VLO were determined by immunohistochemistry or western blotting. Neurotransmitter NE in the VLO and inflammatory factors in serum were detected separately through high-performance liquid chromatography and enzyme-linked immunosorbent assay. Our experimental results showed that repeated morphine administration induced stable CPP and prazosin promoted the morphine-induced CPP. Microinjection of prazosin in the VLO not only blocked the activity of α1 adrenoceptor, decreased CaMKII phosphorylation and CRTC1, which eventually resulted in a regression of synaptic plasticity-related proteins, but also was accompanied by significantly decreasing of NE in the VLO and increasing of inflammatory cytokines in peripheral blood. These findings suggested that prazosin potentiates the addictive effects of morphine. The effect of increased CPP through reducing α1 adrenoceptor and NE was associated with the CaMKII-CRTC1 pathway and synaptic plasticity-related proteins in the VLO and inflammatory cytokines in the peripheral blood. The NE system may therefore be an underlying therapeutic target in morphine addiction. Additionally, we believe that the clinical use of prazosin in hypertensive patients with morphine abuse may be a potential risk because of its reinforcing effect on addiction.

An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis.

Objective.Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD.Approach.In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis.Main results.This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD.Significance.The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.

Functional Connectome Hierarchy Distortions in Female Nurses With Occupational Burnout and Its Gene Expression Signatures.

BACKGROUND: Burnout has become a serious public health issue worldwide, particularly during the COVID-19 pandemic. Functional connectome impairments associated with occupational burnout were widely distributed, involving both low-level sensorimotor cortices and high-level association cortices. PURPOSE: To investigate whether there are hierarchical perturbations in the functional connectomes and if these perturbations are potentially influenced by genetic factors in nurses who feel "burned out." STUDY TYPE: Prospective, case control. POPULATION: Thirty-three female nurses with occupational burnout (aged 27-40, 32.42 ± 3.37) and 32 matched nurses who were not feeling burned out (aged 27-42, 32.50 ± 4.21). FIELD STRENGTH/SEQUENCE: 3.0 T, gradient-echo echo-planar imaging sequence (GE-EPI). ASSESSMENT: Gradient-based techniques were used to depict the perturbations in the multi-dimensional hierarchical structure of the macroscale connectome. Gene expression data were acquired from the Allen Human Brain Atlas. STATISTICAL TESTS: Cortex-wide multivariate analyses were used for between-group differences in gradients as well as association analyses between the hierarchy distortions and the MBI score (FDR corrected). Partial least squares, spin test and bootstrapping were utilized together to select the gene sets (FDR corrected). Gene enrichment analyses (GO, KEGG and cell-type) were further performed. Significance level: P < 0.05. RESULTS: There were significant gradient distortions, with strong between-group effects in the somatosensory network and moderate effects in the higher-order default-mode network, which were significantly correlated with the gene expression profiles (r = 0.3171). The most related genes were broadly involved in the cellular response to minerals, neuronal plasticity, and the circadian rhythm pathway (q value < 0.01). Significant enrichments were found in excitatory (r = 0.2588), inhibitory neurons (r = 0.2610), and astrocytes cells (r = 0.2633). Regions affected by burnout severity were mainly distributed in the association and visual cortices. DATA CONCLUSION: By connecting in vivo imaging to genes, cell classes, and clinical data, this study provides a framework to understand functional impairments in occupational burnout and how the microscopic genetic architecture drive macroscopic distortions. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection.

Introduction: As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. Methods: The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Results: Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Discussion: Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.

Association of aberrant brain network dynamics with gut microbial composition uncovers disrupted brain-gut-microbiome interactions in irritable bowel syndrome: Preliminary findings.

BACKGROUND AND PURPOSE: Growing evidence suggests that abnormalities in brain-gut-microbiome (BGM) interactions are involved in the pathogenesis of irritable bowel syndrome (IBS). Our study aimed to explore alterations in dynamic functional connectivity (DFC), the gut microbiome and the bidirectional interaction in the BGM. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI), fecal samples and clinical chacteristics were collected from 33 IBS patients and 32 healthy controls. We performed a systematic DFC analysis on rs-fMRI. The gut microbiome was analyzed by 16S rRNA gene sequencing. Associations between DFC characteristics and microbial alterations were explored. RESULTS: In the DFC analysis, four dynamic functional states were identified. IBS patients exhibited increased mean dwell and fraction time in State 4, and reduced transitions from State 3 to State 1. Aberrant temporal properties in State 4 were only evident when choosing a short window (36 s or 44 s). Decreased functional connectivity (FC) variability was found in State 1 and State 3 in IBS patients, two of which (independent component [IC]51-IC91, IC46-IC11) showed significant correlations with clinical characteristics. Additionally, we identified nine significantly differential abundances in microbial composition. We also found that IBS-related microbiota were associated with aberrant FC variability, although these exploratory results were obtained at an uncorrected threshold of significance. CONCLUSIONS: Although future studies are needed to confirm our results, the findings not only provide a new insight into the dysconnectivity hypothesis in IBS from a dynamic perspective, but also establish a possible link between DFC and the gut microbiome, which lays the foundation for future research on disrupted BGM interactions.

Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model.

Differences in model application effectiveness, insufficient numbers of disaster samples, and unreasonable selection of non-hazard samples are common problems in landslide susceptibility studies. Therefore, in this paper, we propose a semi-integrated supervised approach to improve the prediction performance of machine learning (ML) models in landslide susceptibility studies. First, taking the lower reaches of the Jinsha River as the study area, a geospatial dataset consisting of 349 landslides, an equal number of randomly selected non-landslide points, and 12 environmental factors were randomly divided into training (70%) and testing (30%) datasets. Then, K-nearest neighbors (KNN), random forest (RF), and Bayesian-regularized neural network (BRNN) models were built. Second, the three models were combined to form an integrated weighted model. Very high- and low-prone areas were selected and, combined with the prediction results and remote sensing images, landslide and non-landslide samples were identified. The identified samples were then combined with the original samples to form new samples, which were used to sequentially construct the ensemble-supervised K-nearest neighbors (ESKNN), ensemble-supervised random forest (ESRF), and ensemble-supervised Bayesian-regularized neural network (ESBRNN) models. Finally, the area under the curve (AUC), true skill statistic (TSS), and frequency ratio (FR) values were used to test the accuracy of each model. The traditional ML model results and accuracy were improved by the semi-integrated supervised method. The ESRF model had the best prediction effect (AUC = 0.939, TSS = 0.440, and FR = 95.8%). The proposed semi-integrated supervised ML model solved the problems observed in traditional landslide susceptibility studies and provided insights for reducing variations in model applications, expanding landslide data sources, and improving non-landslide sample selection.