Changes In Network Structure Research Articles

The spatial scaling of food web structure across European biogeographical regions.

The species-area relationship (SAR) is one of the most well-established scaling patterns in ecology. Its implications for understanding how communities change across spatial gradients are numerous, including the effects of habitat loss on biodiversity. However, ecological communities are not mere collections of species. They are the result of interactions between these species forming complex networks that tie them together. Should we aim to grasp the spatial scaling of biodiversity as a whole, it is fundamental to understand the changes in the structure of interaction networks with area. In spite of a few empirical and theoretical studies that address this challenge, we still do not know much about how network structure changes with area, or what are the main environmental drivers of these changes. Here, using the meta-network of potential interactions between all terrestrial vertebrates in Europe (1140 species and 67 201 feeding interactions), we analysed network-area relationships (NARs) that summarize how network properties scale with area. We do this across ten biogeographical regions, which differ in environmental characteristics. We found that the spatial scaling of network complexity strongly varied across biogeographical regions. However, once the variation in SARs was accounted for, differences in the shape of NARs vanished. On the other hand, the proportion of species across trophic levels remained remarkably constant across biogeographical regions and spatial scales, despite the great variation in species richness. Spatial variation in mean annual temperature and habitat clustering were the main environmental determinants of the shape of both SARs and NARs across Europe. Our results suggest new avenues in the exploration of the effects of environmental factors on the spatial scaling of biodiversity. We argue that NARs can provide new insights to analyse and understand ecological communities.

User‐driven supply chain business model innovation: The role of dynamic capabilities

AbstractThis research explores how focal tea companies facilitate supply chain business model innovation (SCBMI) to meet consumer demands and achieve sustainable development. By applying a case study method and dynamic capability theory, we find that: (1) the end‐user innovation‐driven is the precondition for tea SCBMI; (2) the user‐driven innovation leads to the change of the supply chain focal company dynamic capabilities and the tea supply chain network structure, and thus promote the tea SCBMI; (3) SCBMI will, in turn, enhance the focal company's dynamic capabilities and promote the change of tea supply chain network structure in order to meet the consumers' changing demands. Our research suggests that the supply chain should pay attention to the construction of dynamic capabilities and proactively manage supply chain network structure. Ultimately, the entire supply chain business model will continuously adapt to the external changes and achieve sustainable development in the long term.

A reaction-driven evolving network theory coupled with phase-field fracture to model polymer oxidative aging

High-temperature oxidation in polymers is a complex phenomenon, driven by the coupled diffusion–reaction process, causing changes in the amorphous network structure and resulting in property degradation. Prolonged oxidation in polymers results in the formation of a coarse, oxide layer on the outer surface and induces spontaneous cracking inside the material. In this paper, we present a chemical reaction-driven evolving network theory coupled with phase-field fracture to describe the effect of oxidation in polymers across different length scales. Guided by the statistical mechanics, the network theory has been introduced to model the reaction induced chain scissions and crosslinking events causing significant changes in the three-dimensional network structure. Further, these microscale events have been considered as the reason behind macroscopic mechanical property degradation, namely oxidative embrittlement. Finally the network theory is coupled with a phase-field fracture to model the macroscale damage initiation and propagation in the polymer under mechanical stress. The specific constitutive forms for all the physical–chemical processes are derived for the coupled system and numerically implemented in finite elements by writing ABAQUS user-defined element (UEL) subroutine. To present the model’s capability, various numerical examples with standard fracture geometries have been studied. The simulation results have demonstrated the model’s capability of predicting the effect of oxidative aging on the polymer’s response.

How the Global Trade Linkages are Evolving? -Insight from Social Network Analysis Based on Global Multi-Regional Input-Output Model

With the rise and fall of the trade shares of different countries in the world, does the trade network structure change at the same time? Do the dynamics of countries' positions differ in the evolution of trade network structure? Based on the latest world input-output database (WIOD), this paper illustrates the accounting models of international trade, and describes the dynamics of the global trade network structure and the countries' positions. Research shows that China and the emerging countries developed faster than the developed countries during 2000-2014, and play important roles, not only in the trade shares, but also in global trade networks. The innovation of this study is that we present a systemic and explicit portrait of the global pattern of trade linkages between countries based on a set of social network analysis methods, and we find that the dynamic of linkage network is more violative than that of linkage flow.

Muting, not fragmentation, of functional brain networks under general anesthesia

Changes in resting-state functional connectivity (rs-FC) under general anesthesia have been widely studied with the goal of identifying neural signatures of consciousness. This work has commonly revealed an apparent fragmentation of whole-brain network structure during unconsciousness, which has been interpreted as reflecting a break-down in connectivity and a disruption of the brain’s ability to integrate information. Here we show, by studying rs-FC under varying depths of isoflurane-induced anesthesia in nonhuman primates, that this apparent fragmentation, rather than reflecting an actual change in network structure, can be simply explained as the result of a global reduction in FC. Specifically, by comparing the actual FC data to surrogate data sets that we derived to test competing hypotheses of how FC changes as a function of dose, we found that increases in whole-brain modularity and the number of network communities – considered hallmarks of fragmentation – are artifacts of constructing FC networks by thresholding based on correlation magnitude. Taken together, our findings suggest that deepening levels of unconsciousness are instead associated with the increasingly muted expression of functional networks, an observation that constrains current interpretations as to how anesthesia-induced FC changes map onto existing neurobiological theories of consciousness.

Alterations of Brain Structural Network Connectivity in Type 2 Diabetes Mellitus Patients With Mild Cognitive Impairment.

Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to developing dementia, especially for those with mild cognitive impairment (MCI), but its underlying cause is still unclear. This study aims to investigate the early detection of white matter structural network changes in T2DM patients with MCI and assess the relationship between cognitive impairment and structural network alterations in T2DM patients. In this study, we performed a battery of neuropsychological tests and diffusion tensor MRI in 30 T2MD-MCI patients, 30 T2DM patients with normal cognition (T2DM-NC) and 30 age-, sex-, and education-matched healthy control (HC) individuals. Cognitive performance exhibited obvious differences among the three groups. The structural network was significantly disrupted in both global and regional levels in T2DM patients. The T2DM-MCI group showed more severe impairment of global network efficiency, and lower nodal efficiency and fewer connections within multiple regions like the limbic system, basal ganglia, and several cortical structures. Moreover, a subnetwork impaired in T2DM-MCI patients was characterized by cortical-limbic fibers, and commissural fibers and pathways within the frontal, temporal, and occipital lobes. These altered global and nodal parameters were significantly correlated with cognitive function in T2DM-MCI patients. In particular, executive dysfunction and working memory impairment in T2DM-MCI patients correlated with nodal efficiency in the right opercular part and triangular part of the inferior frontal gyrus, which indicated that white matter disruption in these regions may act as potential biomarkers for T2DM-associated MCI detection. Our investigation provides a novel insight into the neuropathological effects of white matter network disruption on cognition impairments induced by T2DM.

Sulfur speciation and network structural changes in silicate-phosphate glasses

Sulfur speciation and the network structure of glasses from the SiO2-P2O5-K2O-MgO-SO3 system were studied by Raman and MAS NMR techniques. Glasses were synthesized by melting powders containing reducing agents. Bulk composition and homogeneity of the glasses were confirmed by X-Ray Fluorescence Spectroscopy and Scanning Electron Microscopy, combined with analysis of microregions via Energy Dispersive X-Ray Spectroscopy.Raman spectra demonstrate that S-bearing glasses can contain more reduced forms of sulfur than SO42−, such as S2O42−, S2O32−, S2O52−, and polysulfides Sx2−, depending on the reducing conditions. The 31P and 29Si MAS NMR spectra did not reveal peaks corresponding to P-S and Si-S linkages and indicate slight polymerization of the silico-oxygen and phospho-oxygen subnetworks.

Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia.

Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical connectivity and investigate network organization using a graph theory approach. Relative to controls, bvFTD patients showed altered small-world properties and decreased global efficiency, suggesting a reduced ability to combine specialized information from distributed brain regions. At a local level, patients with bvFTD displayed lower values of local efficiency in the cortical thickness of the caudal and rostral middle frontal gyrus, rostral anterior cingulate, and precuneus, cuneus, and transverse temporal gyrus. A significant correlation was also found between the efficiency of caudal anterior cingulate thickness and Mini-Mental State Examination (MMSE) scores in bvFTD patients. Taken together, these findings confirm the selective disruption in structural brain networks of bvFTD patients, providing new insights on the association between cognitive decline and graph properties.

Neuroinflammation induces synaptic scaling through IL-1β-mediated activation of the transcriptional repressor REST/NRSF

Neuroinflammation is associated with synapse dysfunction and cognitive decline in patients and animal models. One candidate for translating the inflammatory stress into structural and functional changes in neural networks is the transcriptional repressor RE1-silencing transcription factor (REST) that regulates the expression of a wide cluster of neuron-specific genes during neurogenesis and in mature neurons. To study the cellular and molecular pathways activated under inflammatory conditions mimicking the experimental autoimmune encephalomyelitis (EAE) environment, we analyzed REST activity in neuroblastoma cells and mouse cortical neurons treated with activated T cell or microglia supernatant and distinct pro-inflammatory cytokines. We found that REST is activated by a variety of neuroinflammatory stimuli in both neuroblastoma cells and primary neurons, indicating that a vast transcriptional change is triggered during neuroinflammation. While a dual activation of REST and its dominant-negative splicing isoform REST4 was observed in N2a neuroblastoma cells, primary neurons responded with a pure full-length REST upregulation in the absence of changes in REST4 expression. In both cases, REST upregulation was associated with activation of Wnt signaling and increased nuclear translocation of β-catenin, a well-known intracellular transduction pathway in neuroinflammation. Among single cytokines, IL-1β caused a potent and prompt increase in REST transcription and translation in neurons, which promoted a delayed and strong synaptic downscaling specific for excitatory synapses, with decreased frequency and amplitude of spontaneous synaptic currents, decreased density of excitatory synaptic connections, and decreased frequency of action potential-evoked Ca2+ transients. Most important, the IL-1β effects on excitatory transmission were strictly REST dependent, as conditional deletion of REST completely occluded the effects of IL-1β activation on synaptic transmission and network excitability. Our results demonstrate that REST upregulation represents a new pathogenic mechanism for the synaptic dysfunctions observed under neuroinflammatory conditions and identify the REST pathway as therapeutic target for EAE and, potentially, for multiple sclerosis.

Machine Embroidered Sensors for Limb Joint Movement-Monitoring Smart Clothing.

In this study, a strain gauge sensor based on a change of contact or network structure between conductive materials was implemented using the handle-machine embroidery technique, and the variables (embroidery shape, embroidery distance, embroidery size, and implementation location) affecting its performance were studied. As a result of Experiment I on the structure of embroidery suitable for joint motion monitoring, the embroidery distance, rather than the embroidery size, was found to have a significant effect on the electric resistance changes caused by elongation. Based on the results of Experiment I, two types of zigzag embroideries, four types of embroideries with few contact points, and two types of embroideries with more contact points (all with short distances (2.0)) were selected for Experiment II (the dummy motion experiment). As a result of the dummy motion experiment, it was found that the locations of the suitable embroidered sensors for joint motion monitoring was the HJP (Hinge Joint Position) in the ‘types without a contact point’ (zigzag) and the LHJP (Lower Hinge Joint Position) in the ‘types with more contact points’. On the other hand, although there was no consistency among the ‘types with few contact points’, the resistance changes measured by the 2CP and 7CP embroidered sensors showed similar figures and patterns, and the HJP location was most suitable. The resistance changes measured by the 4CP and 6CP embroidered sensors exhibited no consistent patterns, but the LHJP locations were more suitable. These results indicate that the location of the HJP is suitable for measuring joint motion in the ‘type without a contact point’, and the location of the LHJP is suitable for measuring joint motion when the number of contact points exceeds a certain limit. Among them, the average resistance change of the 9CP sensor located at the LHJP was 40 Ω with the smallest standard deviation of less than 1, and it is thus considered to have the best performance among all the sensors.

Analysis and Visualization of Group Collaborative on Social Networks Relationships

<p indent=0mm>The study of social networks is widely applied, which is mainly focused on the change of the social network structure of node and link, and the research angle is relatively single. Group collaboration is common in social networks. To better explore the evolution of group-level network structure in the process of group collaboration, this paper first adds group-level tasks to the task classification for graphic visualization. A task taxonomy suitable for group collaboration relationship analysis is set up according to the characteristics of collaborative relationship networks and tasks to be completed for group collaboration are set up according to this task taxonomy. Then, relevant visual views are designed to display the features and evolution patterns of collaborative relationships among and within groups in a more intuitive way. Finally, a specific case analysis was carried out on the academic collaboration literature data set. Through user evaluation experiments, the Likert scale was used to analyze the questionnaire to verify the effectiveness and practicability of the design visualization view, so as to provide a visual analysis method of group collaboration for social network relationship analysts, facilitating further exploration and application of group collaboration network relationships.

Unraveling the comorbidity of depression and anxiety in a large inpatient sample: Network analysis to examine bridge symptoms.

Comorbidities in mental disorders are often understood by assuming a common cause. The network theory of mental disorders offers an alternative to this assumption by understanding comorbidities as mutually reinforced problems. In this study, we used network analysis to examine bridge symptoms between anxiety and depression in a large sample. Using data from a sample of patients diagnosed with both depression and an anxiety disorder before and after inpatient treatment (N = 5,614, mean age: 42.24, 63.59% female, average treatment duration: 48.12 days), network models of depression and anxiety symptoms are estimated. Topology, the centrality of nodes, stability, and changes in network structure are analyzed. Symptoms that drive comorbidity are determined by bridge node analysis. As an alternative to network communities based on categorical diagnosis, we performed a community analysis and propose empirically derived symptom subsets. The obtained network models are highly stable. Sad mood and the inability to control worry are the most central. Psychomotor agitation or retardation is the strongest bridge node between anxiety and depression, followed by concentration problems and restlessness. Changes in appetite and suicidality were unique to depression. Community analysis revealed four symptom groups. The estimated network structure of depression and anxiety symptoms proves to be highly accurate. Results indicate that some symptoms are considerably more influential than others and that only a small number of predominantly physical symptoms are strong candidates for explaining comorbidity. Future studies should include physiological measures in network models to provide a more accurate understanding.

Neuroanatomical Alterations in Patients With Tinnitus Before and After Sound Therapy: A Combined VBM and SCN Study.

Many neuroanatomical alterations have been detected in patients with tinnitus in previous studies. However, little is known about the morphological and structural covariance network (SCN) changes before and after long-term sound therapy. This study aimed to explore alterations in brain anatomical and SCN changes in patients with idiopathic tinnitus using voxel-based morphometry (VBM) analysis 24 weeks before and after sound therapy. Thirty-three tinnitus patients underwent magnetic resonance imaging scans at baseline and after 24 weeks of sound therapy. Twenty-six age- and sex-matched healthy control (HC) individuals also underwent two scans over a 24-week interval; 3.0T MRI and high-resolution 3D structural images were acquired with a 3D-BRAVO pulse sequence. Structural image data preprocessing was performed using the VBM8 toolbox. The Tinnitus Handicap Inventory (THI) score was assessed for the severity of tinnitus before and after treatment. Two-way mixed model analysis of variance (ANOVA) and post hoc analyses were performed to determine differences between the two groups (patients and HCs) and between the two scans (at baseline and on the 24th week). Student-Newman-Keuls (SNK) tests were used in the post hoc analysis. Interaction effects between the two groups and the two scans demonstrated significantly different gray matter (GM) volume in the right parahippocampus gyrus, right caudate, left superior temporal gyrus, left cuneus gyrus, and right calcarine gyrus; we found significantly decreased GM volume in the above five brain regions among the tinnitus patients before sound therapy (baseline) compared to that in the HC group. The 24-week sound therapy group demonstrated significantly greater brain volume compared with the baseline group among these brain regions. We did not find significant differences in brain regions between the 24-week sound therapy and HC groups. The SCN results showed that the left superior temporal gyrus and left rolandic operculum were significantly different in nodal efficiency, nodal degree centrality, and nodal betweenness centrality after FDR correction. This study characterized the effect of sound therapy on brain GM volume, especially in the left superior temporal lobe. Notably, sound therapy had a normalizing effect on tinnitus patients.

Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance

Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.

SiO&lt;sub&gt;4&lt;/sub&gt; network structure changes and crystallization of diatom shells in diatomaceous earth by heat treatment

SiO&lt;sub&gt;4&lt;/sub&gt; network structure changes and crystallization of diatom shells in diatomaceous earth by heat treatment

Application of Convolutional Neural Network in Fault Line Selection of Distribution Network

ABSTRACT Aiming at the problem that the effect of the existing fault line selection methods is mainly determined by the fault features constructed by manual extraction, and the fault feature extraction process is complex and time-consuming, a new method based on convolutional neural network (CNN) is proposed. Firstly, the fault voltage and current signal data are collected, and the time-frequency energy matrix of fault signal is constructed by HHT band-pass filtering method, which is regarded as the two-dimensional matrix form of input data of CNN. Then the time-frequency energy matrix is input into the CNN, and the fault features are extracted autonomously through convolution layer and pooling layer of the network, which is used to train the network to realize fault line selection and fault phase judgment. Finally, the results show that the method not only has high accuracy of fault line selection, but also can complete the fault line selection and fault phase judgment at the same time without adjusting any parameters, which realize the shared weights of the two non-independent problems. Meanwhile, under the influence of noise interference, compensation degree, network structure change and other factors, the proposed method has good robustness. However, compared with the FCM, SVM, DNN and DBN, CNN can still identify the faulty line and keep the optimal accuracy in the case of two-point grounding fault, and other methods have errors in line selection or low accuracy. Therefore, the experimental results also provide a new idea for fault line selection of distribution network.

Exit rights open complex pathways to cooperation

We study the evolutionary dynamics of the Prisoner's Dilemma game in which cooperators and defectors interact with another actor type called exiters. Rather than being exploited by defectors, exiters exit the game in favour of a small pay-off. We find that this simple extension of the game allows cooperation to flourish in well-mixed populations when iterations or reputation are added. In networked populations, however, the exit option is less conducive to cooperation. Instead, it enables the coexistence of cooperators, defectors, and exiters through cyclic dominance. Other outcomes are also possible as the exit pay-off increases or the network structure changes, including network-wide oscillations in actor abundances that may cause the extinction of exiters and the domination of defectors, although game parameters should favour exiting. The complex dynamics that emerges in the wake of a simple option to exit the game implies that nuances matter even if our analyses are restricted to incentives for rational behaviour.

Flatness Prediction of Cold Rolled Strip Based on EM-TELM

Flatness of cold rolled strip is an extremely important indicator of quality, and flatness control is the key technology of the modern high-accuracy rolling mill. The establishment of an efficient and accurate flatness prediction model is conducive to improving the flatness accuracy and realizing the effective control of flatness. Inspired by the error minimization principle, error minimized extreme learning machine with two hidden layers (EM-TELM) used to automatically determine the optimum hidden nodes is proposed in the paper, which is applied to establish the flatness prediction model of cold rolled strip. EM-TELM uses the block matrices to solve the output matrix of the second hidden layer. EM-TELM randomly adds one or a group of hidden nodes to the current network every time. During the increasing process of the network structure, the weights matrix connecting the hidden layer and the output layer are updated incrementally. Since EM-TELM is a no analytic method, it can be used in a kind of prediction problem for complex and difficult modeling systems. The experimental results indicated that the accuracy of EM-TELM is higher than that of EM-ELM, and EM-TELM reduces the computational complexity and training time compared to TELM which recalculates the parameters between different hidden layers when the network structure changes.

An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary DNA sequence. In addition to DNA sequences, histone modifications and chromatin accessibility are also important factors influencing their activity. They have been explored to predict TFBSs recently. However, current methods rarely take into account histone modifications and chromatin accessibility using CNN in an integrative framework. To this end, we developed a general CNN model to integrate these data for predicting TFBSs. We systematically benchmarked a series of architecture variants by changing network structure in terms of width and depth, and explored the effects of sample length at flanking regions. We evaluated the performance of the three types of data and their combinations using 256 ChIP-seq experiments and also compared it with competing machine learning methods. We find that contributions from these three types of data are complementary to each other. Moreover, the integrative CNN framework is superior to traditional machine learning methods with significant improvements.

Connectivity changes in major depressive disorder after rTMS: a review of functional and structural connectivity data

Abstract Aims In the search for effective therapeutic strategies for depression, repetitive transcranial magnetic stimulation (rTMS) emerged as a non-invasive, promising treatment. This is because the antidepressant effect of rTMS might be related to neuronal plasticity mechanisms possibly reverting connectivity alterations often observed in depression. Therefore, in this review, we aimed at providing an overview of the findings reported by studies investigating functional and structural connectivity changes after rTMS in depression. Methods A bibliographic search was conducted on PubMed, including studies that used unilateral, excitatory (⩾10 Hz) rTMS treatment targeted on the left dorsolateral prefrontal cortex (DLPFC) in unipolar depressed patients. Results The majority of the results showed significant TMS-induced changes in functional connectivity (FC) between areas important for emotion regulation, including the DLPFC and the subgenual anterior cingulate cortex, and among regions that are part of the major resting-state networks, such as the Default Mode Network, the Salience Networks and the Central Executive Network. Finally, in diffusion tensor imaging studies, it has been reported that rTMS appeared to increase fractional anisotropy in the frontal lobe. Limitations The small sample size, the heterogeneity of the rTMS stimulation parameters, the concomitant use of psychotropic drugs might have limited the generalisability of the results. Conclusions Overall, rTMS treatment induces structural and FC changes in brain regions and networks implicated in the pathogenesis of unipolar depression. However, whether these changes underlie the antidepressant effect of rTMS still needs to be clarified.