Corrigendum: A preliminary exploration of the regression equation for performance in amateur half-marathon runners: a perspective based on respiratory muscle function.

[This corrects the article DOI: 10.3389/fphys.2024.1340513.].

Combined Endurance and Resistance Exercise Mitigates Age-Associated Cardiac Dysfunction.

Aging is associated with a decline in cardiac function. Exercise has been shown to effectively reduce the risks of cardiovascular diseases. Here whether a combination of endurance and resistance exercises can improve cardiac function in aged mice during late life is investigated. Through transcriptome analysis, several signaling pathways activated in the hearts of 22-month-old mice after combined exercise, including cardiac muscle contraction, mitophagy, and longevity regulation are identified. Combined exercise training mitigated age-associated pathological cardiac hypertrophy, reduced oxidative stress, cardiac senescence, and enhanced cardiac function. Upstream stimulatory factor 2 (Usf2) is upregulated in the aged mouse hearts with combined exercise compared to sedentary mice. In the human cardiomyocytes senescent model, overexpression of Usf2 led to anti-senescence effects, while knockdown of Usf2 exacerbated cellular senescence. The results suggest that a combination of endurance and resistance exercises, such as swimming and resistance running, can mitigate age-related pathological cardiac remodeling and cardiac dysfunction in late life. These cardioprotective effects are likely due to the activation of Usf2 and its anti-senescence effect. Therefore, Usf2 can potentially be a novel therapeutic target for mitigating age-related cardiac dysfunction.

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS: In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS: Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS: Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

A comparison of the effects of two protocols of concurrent resistance and aerobic training on physical fitness in middle school students.

Objective: This study aimed to compare the effects of two concurrent training (CT) protocols on the physical fitness of middle school students. Method: A 12-week quasi-experimental pre-test/post-test study was conducted with 157 middle school students (age = 12.48 ± 0.34, n = 90 females) divided into three groups: CT group A (CT-0h) received combined resistance training (RT) and aerobic training (AT) in each physical education session, CT group B (CT-48h) received RT and AT across two separate physical education classes 48 h apart, and a control group (Con) received no training. Training occurred twice a week. Test indicators included cardiorespiratory fitness (CRF) measured by estimated VO2max and 20 m shuttle run (laps), as well as muscle strength assessed through long jump, vertical jump, and handgrip strength. Results: The intervention groups exhibited significant increases in estimated VO2max and muscle strength compared to their baseline values (p < 0.05). Both CT-0h and CT-48h groups demonstrated significant improvements in 20 m shuttle run (laps) (mean difference: 8.88 laps, p < 0.01; mean difference: 4.81 laps, p < 0.01, respectively), standing long jump (mean difference: 6.20 cm, p < 0.01; mean difference: 3.68 cm, p < 0.01, respectively), vertical jump (mean difference: 4.95 cm, p < 0.01; mean difference: 4.04 cm, p < 0.01, respectively), and handgrip strength (mean difference: 11.17 kg, p < 0.01; mean difference: 6.99 kg, p < 0.01, respectively). CT-0h group exhibited significantly increased estimated VO2max (mean difference: 1.47 ml/kg/min, p < 0.01) compared to the CT-48h group. Conclusion: Both CT programs effectively improved adolescents' physical fitness indicators. However, the program that integrated RT and AT within the same physical education class demonstrated superior enhancement in adolescents' CRF.

Current role of magnetic resonance imaging on assessing and monitoring the efficacy of phototherapy.

Phototherapy, also known as photobiological therapy, is a non-invasive and highly effective physical treatment method. Its broad use in clinics has led to significant therapeutic results. Phototherapy parameters, such as intensity, wavelength, and duration, can be adjusted to create specific therapeutic effects for various medical conditions. Meanwhile, Magnetic Resonance Imaging (MRI), with its diverse imaging sequences and excellent soft-tissue contrast, provides a valuable tool to understand the therapeutic effects and mechanisms of phototherapy. This review explores the clinical applications of commonly used phototherapy techniques, gives a brief overview of how phototherapy impacts different diseases, and examines MRI's role in various phototherapeutic scenarios. We argue that MRI is crucial for precise targeting, treatment monitoring, and prognosis assessment in phototherapy. Future research and applications will focus on personalized diagnosis and monitoring of phototherapy, expanding its applications in treatment and exploring multimodal imaging technology to enhance diagnostic and therapeutic precision and effectiveness.

A preliminary exploration of the regression equation for performance in amateur half-marathon runners: a perspective based on respiratory muscle function.

This document presents a study on the relationship between physical characteristics, respiratory muscle capacity, and performance in amateur half-marathon runners. The aim of this study was to establish a preliminary predictive model to provide insights into training and health management for runners. Participants were recruited from the 2023 Beijing Olympic Forest Park Half-Marathon, comprising 233 individuals. Personal information including age, gender, height, weight, and other relevant factors were collected, and standardized testing methods were used to measure various parameters. Correlation analysis revealed significant associations between gender, height, weight, maximum expiratory pressure, maximal inspiratory pressure, and half-marathon performance. Several regression equations were developed to estimate the performance of amateur marathon runners, with a focus on gender, weight, maximum expiratory pressure, and height as predictive factors. The study found that respiratory muscle training can delay muscle fatigue and improve athletic performance. Evaluating the level of respiratory muscle capacity in marathon athletes is crucial for defining the potential speed limitations and achieving optimal performance. The information from this study can assist amateur runners in optimizing their training methods and maintaining their physical wellbeing.

Microfluidics-enabled fluorinated assembly of EGCG-ligands-siTOX nanoparticles for synergetic tumor cells and exhausted t cells regulation in cancer immunotherapy.

Immune checkpoint inhibitor (ICI)-derived evolution offers a versatile means of developing novel immunotherapies that targets programmed death-ligand 1 (PD-L1)/programmed death-1 (PD-1) axis. However, one major challenge is T cell exhaustion, which contributes to low response rates in "cold" tumors. Herein, we introduce a fluorinated assembly system of LFNPs/siTOX complexes consisting of fluorinated EGCG (FEGCG), fluorinated aminolauric acid (LA), and fluorinated polyethylene glycol (PEG) to efficiently deliver small interfering RNA anti-TOX (thymus high mobility group box protein, TOX) for synergistic tumor cells and exhausted T cells regulation. Using a microfluidic approach, a library of LFNPs/siTOX complexes were prepared by altering the placement of the hydrophobe (LA), the surface PEGylation density, and the siTOX ratio. Among the different formulations tested, the lead formulation, LFNPs3-3/siTOX complexes, demonstrated enhanced siRNA complexation, sensitive drug release, improved stability and delivery efficacy, and acceptable biosafety. Upon administration by the intravenous injection, this formulation was able to evoke a robust immune response by inhibiting PD-L1 expression and mitigating T cell exhaustion. Overall, this study provides valuable insights into the fluorinated assembly and concomitant optimization of the EGCG-based delivery system. Furthermore, it offers a promising strategy for cancer immunotherapy, highlighting its potential in improving response rates in ''cold'' tumors.

Factors influencing the accuracy and safety of preoperative computed tomography (CT)-guided soft hook-wire localization for pulmonary nodules: a comprehensive analysis.

Background: The necessity of localization of pulmonary nodules lies in ensuring the ability to locate the nodule quickly and accurately during surgery, thereby improving the success rate of the operation. The accuracy and risk of preoperative localization of pulmonary nodules need further exploration. Therefore, the purpose of this study was to investigate the factors of accuracy and safety of computed tomography (CT)-guided localization of pulmonary nodules using a flexible wire hook positioner. Methods: In this retrospective cross-sectional analysis, 281 patients with a single pulmonary nodule underwent video-assisted thoracoscopic surgery (VATS) following localization with a soft hook-wire guided by CT scan from January 2021 to July 2022 at Nanjing Drum Tower Hospital. The patients underwent VATS to remove pulmonary nodules within 24 hours after localization. The demographic, pulmonary nodule, and technical factors were analyzed retrospectively. Univariate and multivariate analysis were used to analyze the identified factors that influence pulmonary nodule localization accuracy and complications. Results: Localization was successfully performed in 280 patients, with only 1 patient being excluded due to a displaced positioner and the hook wire failing to enter the lung parenchyma as a result of pneumothorax. Out of the total cases, 191 (68.2%) were accurately positioned in group G0, whereas 89 cases (31.7%) were inaccurately positioned in group G1. Hemorrhage and self-limited hemoptysis were observed in 64 patients (22.8%), whereas pneumothorax was observed in 84 patients (29.9%). There were no serious complications such as air embolism or death. The accuracy of localization was found to be influenced by both the depth of pulmonary nodules [odds ratio (OR) =22.610, 95% confidence interval (CI): 10.351-49.391, P=0.001] and the depth of the needle used (OR =0.322, 95% CI: 0.136-0.765, P=0.010). Additionally, postoperative hemorrhage was found to be affected by several important factors, including the diameter (P=0.036) and depth of the nodule (P=0.011), as well as the thickness of the chest wall (P=0.043) and the depth of the needle used (P=0.005). Conclusions: The CT-guided flexible wire hook positioner has been found to be a safe and effective device for locating pulmonary nodules. The depth of pulmonary nodules and needle penetration are key factors affecting the accuracy of lung nodule localization under CT guidance and are important factors affecting postoperative bleeding.

Improved YOLOv7-based steel surface defect detection algorithm.

In response to the limited detection ability and low model generalization ability of the YOLOv7 algorithm for small targets, this paper proposes a detection algorithm based on the improved YOLOv7 algorithm for steel surface defect detection. First, the Transformer-InceptionDWConvolution (TI) module is designed, which combines the Transformer module and InceptionDWConvolution to increase the network's ability to detect small objects. Second, the spatial pyramid pooling fast cross-stage partial channel (SPPFCSPC) structure is introduced to enhance the network training performance. Third, a global attention mechanism (GAM) attention mechanism is designed to optimize the network structure, weaken the irrelevant information in the defect image, and increase the algorithm's ability to detect small defects. Meanwhile, the Mish function is used as the activation function of the feature extraction network to improve the model's generalization ability and feature extraction ability. Finally, a minimum partial distance intersection over union (MPDIoU) loss function is designed to locate the loss and solve the mismatch problem between the complete intersection over union (CIoU) prediction box and the real box directions. The experimental results show that on the Northeastern University Defect Detection (NEU-DET) dataset, the improved YOLOv7 network model improves the mean Average precision (mAP) performance by 6% when compared to the original algorithm, while on the VOC2012 dataset, the mAP performance improves by 2.6%. These results indicate that the proposed algorithm can effectively improve the small defect detection performance on steel surface defects.

How plants manage pathogen infection.

To combat microbial pathogens, plants have evolved specific immune responses that can be divided into three essential steps: microbial recognition by immune receptors, signal transduction within plant cells, and immune execution directly suppressing pathogens. During the past three decades, many plant immune receptors and signaling components and their mode of action have been revealed, markedly advancing our understanding of the first two steps. Activation of immune signaling results in physical and chemical actions that actually stop pathogen infection. Nevertheless, this third step of plant immunity is under explored. In addition to immune execution by plants, recent evidence suggests that the plant microbiota, which is considered an additional layer of the plant immune system, also plays a critical role in direct pathogen suppression. In this review, we summarize the current understanding of how plant immunity as well as microbiota control pathogen growth and behavior and highlight outstanding questions that need to be answered.

Commensal lifestyle regulated by a negative feedback loop between Arabidopsis ROS and the bacterial T2SS.

Despite the plant health-promoting effects of plant microbiota, these assemblages also comprise potentially detrimental microbes. How plant immunity controls its microbiota to promote plant health under these conditions remains largely unknown. We find that commensal bacteria isolated from healthy Arabidopsis plants trigger diverse patterns of reactive oxygen species (ROS) production dependent on the immune receptors and completely on the NADPH oxidase RBOHD that selectively inhibited specific commensals, notably Xanthomonas L148. Through random mutagenesis, we find that L148 gspE, encoding a type II secretion system (T2SS) component, is required for the damaging effects of Xanthomonas L148 on rbohD mutant plants. In planta bacterial transcriptomics reveals that RBOHD suppresses most T2SS gene expression including gspE. L148 colonization protected plants against a bacterial pathogen, when gspE was inhibited by ROS or mutation. Thus, a negative feedback loop between Arabidopsis ROS and the bacterial T2SS tames a potentially detrimental leaf commensal and turns it into a microbe beneficial to the host.

Comparative multi-omics analysis of broomcorn millet in response to Anthracocystis destruens infection.

Anthracocystis destruens is the causal agent of broomcorn millet (Panicum miliaceum) smut disease, which results in serious yield losses in broomcorn millet production. However, the molecular basis underlying broomcorn millet defense against A. destruens is less understood. In this study, we investigated how broomcorn millet responds to infection by A. destruens employing a comprehensive multi-omics approach. We examined the responses of broomcorn millet across transcriptome, metabolome, and microbiome levels. Infected leaves (ILs) exhibited an upregulation of genes related to photosynthesis, accompanied by a higher accumulation of photosynthesis-related compounds and alterations in hormonal levels. However, broomcorn millet genes involved in immune response were downregulated post A. destruens infection, suggesting A. destruens may suppress broomcorn millet immunity. In addition, we show that the immune suppression and altered host metabolism induced by A. destruens have no effect on microbial community structure of broomcorn millet leaf, thus providing a new perspective for understanding the tripartite interaction between plant, pathogen, and microbiota.

A Multi-Scale Recursive Attention Feature Fusion Network for Image Super-Resolution Reconstruction Algorithm.

In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems with underutilization of feature information and loss of feature details. In this paper, a multi-scale recursive attention feature fusion network (MSRAFFN) is proposed for this purpose. The network consists of three parts: a shallow feature extraction module, a multi-scale recursive attention feature fusion module, and a reconstruction module. The shallow features of the image are first extracted by the shallow feature extraction module. Then, the feature information at different scales is extracted by the multi-scale recursive attention feature fusion network block (MSRAFFB) to enhance the channel features of the network through the attention mechanism and fully fuse the feature information at different scales in order to improve the network's performance. In addition, the image features at different levels are integrated through cross-layer connections using residual connections. Finally, in the reconstruction module, the upsampling capability of the deconvolution module is used to enlarge the image while extracting its high-frequency information in order to obtain a sharper high-resolution image and achieve a better visual effect. Through extensive experiments on a benchmark dataset, the proposed network model is shown to have better performance than other models in terms of both subjective visual effects and objective evaluation metrics.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.

Objective: To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. Methods: 131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index. Results: After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560). Conclusion: This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

Diagnostic value of combined magnetic resonance imaging techniques in the evaluation of Parkinson disease.

Background: The incidence of Parkinson disease (PD) has been increasing each year. The development of new magnetic resonance imaging (MRI) technology can help understand its pathogenesis and identify more effective imaging-based biological indicators. Methods: The clinical and MRI imaging data of 40 patients with PD and 40 healthy controls were analyzed. All participants underwent susceptibility-weighted imaging (SWI), neuromelanin-sensitive magnetic resonance imaging (NM-MRI), and T2*mapping sequence examination. The diagnostic value of single and combined multiparameter indicators was analyzed using the receiver operating characteristic curve. Results: Compared with the healthy control group, the PD group showed significant differences in the disappearance of bilateral "swallow tail sign", the distribution volume of melanocytes in the substantia nigra and the smaller volume in the bilateral substantia nigra, the maximum signal of the locus coeruleus and the smaller and average volume in the bilateral substantia nigra, and the values of T2* and R2* in the bilateral substantia nigra (P<0.01). The maximum and smaller value and the average value of the bilateral locus coeruleus signal were negatively correlated with the disease course duration (P<0.05), and the smaller distribution volume of the melanin neurons in the bilateral substantia nigra was negatively correlated with Hoehn and Yahr (H-Y) grade (P<0.05). In the joint diagnosis with multiple indicators, some composite parameters were found to be negatively correlated with H-Y grading (P<0.05), while others were negatively correlated with disease course duration (P<0.05). Joint use of multiple parameter indicators greatly improved diagnostic efficacy [area under the curve (AUC) =0.958]. Conclusions: The distribution volume of melanin in substantia nigra and the maximum value of locus coeruleus signal may be the biological imaging indicators for the early diagnosis, severity, and follow-up evaluation of PD. Compared with a single indicator, composite indicators used in combination with multiple techniques have a significantly better diagnostic efficacy for PD.

Divide and conquer: Spatiotemporal plant innate immunity at single-cell resolution.

Plants have evolved an innate immune system to cope with devastating plant diseases jeopardizing food security. In this issue of Cell Host and Microbe, Tang et al. use single-cell approaches to disentangle spatiotemporal dynamics and cell-type-specific functionalities of plant immunity, providing strategies for precise crop engineering.

Impact of inflammation and anti-inflammatory modalities on diabetic cardiomyopathy healing: From fundamental research to therapy.

Diabetic cardiomyopathy (DCM) is a prevalent cardiovascular complication of diabetes mellitus, characterized by high morbidity and mortality rates worldwide. However, treatment options for DCM remain limited. For decades, a substantial body of evidence has suggested that the inflammatory response plays a pivotal role in the development and progression of DCM. Notably, DCM is closely associated with alterations in inflammatory cells, exerting direct effects on major resident cells such as cardiomyocytes, vascular endothelial cells, and fibroblasts. These cellular changes subsequently contribute to the development of DCM. This article comprehensively analyzes cellular, animal, and human studies to summarize the latest insights into the impact of inflammation on DCM. Furthermore, the potential therapeutic effects of current anti-inflammatory drugs in the management of DCM are also taken into consideration. The ultimate goal of this work is to consolidate the existing literature on the inflammatory processes underlying DCM, providing clinicians with the necessary knowledge and tools to adopt a more efficient and evidence-based approach to managing this condition.

Muscle-Brain crosstalk in cognitive impairment.

Sarcopenia is an age-related, involuntary loss of skeletal muscle mass and strength. Alzheimer's disease (AD) is the most common cause of dementia in elderly adults. To date, no effective cures for sarcopenia and AD are available. Physical and cognitive impairments are two major causes of disability in the elderly population, which severely decrease their quality of life and increase their economic burden. Clinically, sarcopenia is strongly associated with AD. However, the underlying factors for this association remain unknown. Mechanistic studies on muscle-brain crosstalk during cognitive impairment might shed light on new insights and novel therapeutic approaches for combating cognitive decline and AD. In this review, we summarize the latest studies emphasizing the association between sarcopenia and cognitive impairment. The underlying mechanisms involved in muscle-brain crosstalk and the potential implications of such crosstalk are discussed. Finally, future directions for drug development to improve age-related cognitive impairment and AD-related cognitive dysfunction are also explored.

Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain modulation and rehabilitation technique used in patients with neuropsychiatric diseases. rTMS can structurally remodel or functionally induce activities of specific cortical regions and has developed to an important therapeutic method in such patients. Magnetic resonance imaging (MRI) provides brain data that can be used as an explanation tool for the neural mechanisms underlying rTMS effects; brain alterations related to different functions or structures may be reflected in changes in the interaction and influence of brain connections within intrinsic specific networks. In this review, we discuss the technical details of rTMS and the biological interpretation of brain networks identified with MRI analyses, comprehensively summarize the neurobiological effects in rTMS-modulated individuals, and elaborate on changes in the brain network in patients with various neuropsychiatric diseases receiving rehabilitation treatment with rTMS. We conclude that brain connectivity network analysis based on MRI can reflect alterations in functional and structural connectivity networks comprising adjacent and separated brain regions related to stimulation sites, thus reflecting the occurrence of intrinsic functional integration and neuroplasticity. Therefore, MRI is a valuable tool for understanding the neural mechanisms of rTMS and practically tailoring treatment plans for patients with neuropsychiatric diseases.