Node Degree Centrality Research Articles

Effects of computerized cognitive training on structure‒function coupling and topology of multiple brain networks in people with mild cognitive impairment: a randomized controlled trial

BackgroundPeople with mild cognitive impairment (MCI) experience a loss of cognitive functions, whose mechanism is characterized by aberrant structure‒function (SC-FC) coupling and topological attributes of multiple networks. This study aimed to reveal the network-level SC-FC coupling and internal topological changes triggered by computerized cognitive training (CCT) to explain the therapeutic effects of this training in individuals with MCI.MethodsIn this randomized block experiment, we recruited 60 MCI individuals and randomly divided them into an 8-week multidomain CCT group and a health education control group. The neuropsychological outcome measures were the Montreal Cognitive Assessment (MoCA), Chinese Auditory Verbal Learning Test (CAVLT), Chinese Stroop Color–Word Test (SCWT), and Rey–Osterrieth Complex Figure Test (Rey CFT). The brain imaging outcome measures were SC-FC coupling and topological attributes using functional MRI and diffusion tensor imaging methods. We applied linear model analysis to assess the differences in the outcome measures and identify the correspondence between the changes in the brain networks and cognitive functions before and after the CCT.ResultsFifty participants were included in the analyses after the exclusion of three dropouts and seven participants with low-quality MRI scans. Significant group × time effects were found on the changes in the MoCA, CAVLT, and Rey CFT recall scores. The changes in the SC-FC coupling values of the default mode network (DMN) and somatomotor network (SOM) were higher in the CCT group than in the control group (P(unc.) = 0.033, P(unc.) = 0.019), but opposite effects were found on the coupling values of the visual network (VIS) (P(unc.) = 0.039). Increasing clustering coefficients in the functional DMN and SOM and subtle changes in the nodal degree centrality and nodal efficiency of the right dorsal medial prefrontal cortex, posterior cingulate cortex, left parietal lobe, somatomotor area, and visual cortex were observed in the CCT group (P < 0.05, Bonferroni correction). Significant correspondences were found between global cognitive function and DMN coupling values (P(unc.) = 0.007), between immediate memory and SOM as well as FPC coupling values (P(unc.) = 0.037, P(unc.) = 0.030), between delayed memory and SOM coupling values (P(unc.) = 0.030), and between visual memory and VIS coupling values (P(unc.) = 0.007).ConclusionsEight weeks of CCT effectively improved global cognitive and memory functions; these changes were correlated with increases in SC-FC coupling and changes in the topography of the DMN and SOM in individuals with MCI. The CCT regimen also modulated the clustering coefficient and the capacity for information transformation in functional networks; these effects appeared to underlie the cognitive improvement associated with CCT.Trial registrationChinese Clinical Trial Registry, ChiCTR2000034012. Registered on 21 June 2020.

A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

Menstrually-related migraine shapes the structural similarity network integration of brain.

Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.

A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply

With the continuous construction of urban infrastructure, urban multi-modal transportation system (UMTS) has gradually become complex and intertwined, which makes the system more uncertain and vulnerable, especially under disruptions. Improving the resilience of UMTS is critical to maintaining urban reliability and sustainability. This paper aims to build effective models to assess the resilience of UMTS. To develop the resilience assessment model, we provide a comprehensive, dynamic index that considers passenger demand and infrastructure supply. Three transportation modes are considered: bus, subway and taxi, and a cascading failure model is simulated. Data from Singapore are collected to analyse resilience indicators’ characteristics and explore relationship between resilience indicators and node measures. The results indicate that 1) when stations experience failure, it has the greatest impact on the accessibility of the transportation network, followed by efficiency and connectivity; 2) system resilience is negatively related to the node degree and node betweenness centrality of the attacked station; 3) beyond a certain threshold, continuing to increase stations’ additional capacity does not significantly increase system resilience. The proposed resilience index can be used as an optimization goal during system design and recovery. The conclusions and recommendations can inform the management and improvement of urban transportation system resilience.

The critical node detection problem in hypergraphs using weighted node degree centrality.

Network analysis is an indispensable part of today's academic field. Among the different types of networks, the more complex hypergraphs can provide an excellent challenge and new angles for analysis. This study proposes a variant of the critical node detection problem for hypergraphs using weighted node degree centrality as a form of importance metric. An analysis is done on both generated synthetic networks and real-world derived data on the topic of United States House and Senate committees, using a newly designed algorithm. The numerical results show that the combination of the critical node detection on hypergraphs with the weighted node degree centrality provides promising results and the topic is worth exploring further.

Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment.

Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data. The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed. Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI. Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.

Brain diffusion tensor imaging reveals altered connections and networks in epilepsy patients.

Accumulating evidence shows that epilepsy is a disease caused by brain network dysfunction. This study explored changes in brain network structure in epilepsy patients based on graph analysis of diffusion tensor imaging data. The brain structure networks of 42 healthy control individuals and 26 epilepsy patients were constructed. Using graph theory analysis, global and local network topology parameters of the brain structure network were calculated, and changes in global and local characteristics of the brain network in epilepsy patients were quantitatively analyzed. Compared with the healthy control group, the epilepsy patient group showed lower global efficiency, local efficiency, clustering coefficient, and a longer shortest path length. Both healthy control individuals and epilepsy patients showed small-world attributes, with no significant difference between groups. The epilepsy patient group showed lower nodal local efficiency and nodal clustering coefficient in the right olfactory cortex and right rectus and lower nodal degree centrality in the right olfactory cortex and the left paracentral lobular compared with the healthy control group. In addition, the epilepsy patient group showed a smaller fiber number of edges in specific regions of the frontal lobe, temporal lobe, and default mode network, indicating reduced connection strength. Epilepsy patients exhibited lower global and local brain network properties as well as reduced white matter fiber connectivity in key brain regions. These findings further support the idea that epilepsy is a brain network disorder.

Evaluation Method for Node Importance of Urban Rail Network Considering Traffic Characteristics

As a sustainable means of public transport, the safety of the urban rail transit is a significant section of public safety and is highly important in urban sustainable development. Research on the importance of urban rail stations plays an important role in improving the reliability of urban rail networks. This paper proposed an improved method for evaluating the importance of urban rail stations in a topology network, which was used to identify the key stations that affect the urban rail network performance. This method was based on complex network theory, considering the traffic characteristics of the urban rail network that runs on specific lines and integrating the structural characteristics and interrelationship of the lines where the stations are located. Hereafter, this method will be abbreviated as CLI. In order to verify that the high importance stations evaluated by this method were the key stations that had a great impact on the urban rail network performance, this paper designed a comparative attack experiment of betweenness centrality and CLI. The experiment was carried out by taking the Suzhou Rail Transit (SZRT) network as an example and the largest connected subgraph as well as the network efficiency as indicators to measure the network performance. The results showed that CLI had a greater impact on network performance and could better evaluate the key stations in the urban rail network than node degree and betweenness centrality.

Influence of link-addition strategies on network balance and passenger experience in rail networks

The performance of a network can be enhanced by adding links. The importance of the rail network augmentation scheme comparison is brought into the forefront because of the long lead time and disproportionate high cost. This work focuses on the influence of the addition link among the initial railway stations on passenger flow. The effectiveness of the network was evaluated by flow imbalance (FIB), average transfer times (ATT), average path length (APL), and the combined evaluation using three different strategies, namely, passenger flow load — subgraphs highest node (PFL-S), passenger flow load — least load (PFL-L), and node degree and betweenness centrality (Degree-BC). The case study on the real urban rail network with 2,211,468 trip records a day at 339 stations show that PFL-S and PFL-L have better performance than Degree-BC. Simulation experiments reveal that the link construction investment at the early stage during the expanding or in the network with less existing transfer stations for the same size can receive greater returns than those nearly completed networks. The proposed method not only provides a policy guidance for the sustainable development of rail networks but also offers a reference for other networks with limited flow loading capacity and high-cost constraints.

A Novel Video Propagation Strategy Fusing User Interests and Social Influences Based on Assistance of Key Nodes in Social Networks

Accurate video launching and propagation is significant for promotion and distribution of videos. In this paper, we propose a novel video propagation strategy that fuses user interests and social influences based on the assistance of key nodes in social networks (VPII). VPII constructs an estimation model for video distribution capacities in the process of video propagation by investigating interest preference and influence of social users: (1) An estimation method of user preferences for video content is designed by integrating a comparative analysis between current popular videos and historical popular videos. (2) An estimation method to determine the distribution capacities of videos is designed according to scale and importance of neighbor nodes covered. VPII further designs a multi-round video propagation strategy with the assistance of the selected key nodes, which enables these nodes to implement accurate video launching by estimating weighted levels based on available bandwidth and node degree centrality. The video propagation can effectively promote the scale and speed of video sharing and efficiently utilize network resources. Simulations-based testing shows how VPII outperforms other state-of-the-art solutions in terms of startup delay, caching hit ratio, caching cost and higher control overhead.

Meta-governance, uncertainty and self-organization in corporatist social service sectors: The case of Hong Kong

This article explores the relationship between meta-governance, uncertainty and governance network responses. A social network analysis was conducted on the interlocking directorate network among nonprofits before and after a market-oriented collibration triggered by a new public management reform to the corporatist social service provision system in Hong Kong. By unpacking and showing the distinct processes of network formation in response to substantive and strategic uncertainty arising from the market-oriented collibration, the study shows that although more nonprofit actors were directly included in the formalized policy venues after the reform, the network density and clustering coefficient dropped after the reform, potentially reducing the self-coordinating capacity of the governance network. However, for some nonprofit actors, node betweenness centrality increased and node degree centrality decreased, creating an enabling condition for adaption. Points for practitioners The uncertainties in meta-governance may bring about network processes at variance with the original intentions of public meta-governors that actors with complementary resources and competences combine their efforts in the provision of services and service innovation. To manage substantive and strategic uncertainties, public meta-governors need to find ways to increase the shared understandings of social problems, enhance the clarity of policy goals, and find a balance when collaborating between collaborative and market modes of governance.

Machine Learning Methods with Node-Level Features

In this paper we describe several methods for obtaining node features. They can also be grouped based on structure-based features, which again, do not require degrees, such as the simplest one, and which count edges, clusters, and triangles, as well as significance features like node degree and various centrality measures. A generalization that counts additional structures in which a particular node of interest participates is the Graphlet Degree Vector (GDV).

Low-dissipation optimization of the prefrontal cortex in the -12° head-down tilt position: A functional near-infrared spectroscopy study.

Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC. 40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded. Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest. In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

Study on the relationship between topological characteristics of ecological spatial network and soil conservation function in southeastern Tibet, China

Many regions on the globe are at risk of soil erosion, especially in the ecologically fragile areas of the Qinghai-Tibet Plateau. Enhancing the soil conservation capacity of regional vegetation is one of the main ways to address soil erosion. However, most studies ignore the relationship between the spatial distribution structure of vegetation and soil conservation capacity, and the construction of ecological spatial networks is an effective means to study the spatial distribution structure of vegetation. In this study, we extracted the ecological spatial network of Southeast Tibet by the minimum resistance model (MCR) and combined the ecological network with complex network theory to calculate the topological indices of the ecological spatial network and ecological nodes. Subsequently, the soil conservation capacity of ecological nodes is calculated based on the USLE model, and the relationship between it and topological indices is analyzed. It was shown that the degree, closeness centrality, betweenness centrality and eigenvector centrality of forest nodes showed a positive linear correlation with their soil conservation capacity, and the clustering coefficient of grassland nodes showed a positive linear correlation with their soil conservation capacity. This study calculates habitat quality based on the Invest model. We propose a Integrated Soil Conservation and Habitat Quality Evaluation (ISHE) Model to determine the direction of network optimization and propose an optimization strategy. We analyze the network robustness and soil conservation capacity and find that the optimized network robustness and soil conservation capacity are significantly improved. This study provides strategies and methods to protect ecologically vulnerable areas, optimize the spatial distribution of vegetation, and enhance soil conservation capacity. In addition, this can provide a theoretical reference for planning national ecological projects. We use the ISHE model to filter out three types of nodes that need to be focused on optimization and propose different optimization strategies.

Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning

The soil's compression modulus (Es) is one of the most critical mechanical parameters for studying land subsidence in urban strata. Meanwhile, the vertical heterogeneity and lateral discontinuity in the distribution of Es is one of the leading causes of TBM (tunnel-boring-machine) entrapment in the field. In this study, we first obtain the minimum redundancy input variables for Es prediction by comparing 20 purely data-driven models. Based on this, we propose a static directed and weighted graph based on spatial correlation and sequence stratigraphy for mechanism-driven learning, the spatial-geological stratigraphic graph (SG-SPG), where edges encode essential spatial-geological information in direction and weights. Then we built a spatial-geological graph attention network (SGGAT) for node-level estimation and edge-level correction of Es prediction, including two attention layers to adequately capture the dependencies between spatial location and lithology. The results show that our method outperforms state-of-the-art models (R2 = 0.99, RMSE = 0.0593 MPa). Furthermore, through network analysis of SG-SPG, we find that increasing global transitivity significantly improves accuracy, while decreasing local connectivity and degree centrality of nodes slightly increases the prediction residuals. Our approach provides interpretability for graph neural network (GNN) and is advantageous for geophysical property prediction problems with Spatio-temporal heterogeneity.

Aberrant Topological Properties of Brain Functional Network in Children with Obstructive Sleep Apnea Derived from Resting-State fMRI.

To examine the difference in the topological properties of brain functional network between the children with obstructive sleep apnea (OSA) and healthy controls, and to explore the relationships between these properties and cognitive scores of OSA children. Twenty-four OSA children (6.5 ± 2.8years, 15 males) and 26 healthy controls (8.0 ± 2.9years, 11 males) underwent resting-state fMRI (rs-fMRI), based on which brain functional networks were constructed. We compared the global and regional topological properties of the network between OSA children and healthy controls. Partial correlation analysis was performed between topological properties and cognitive scores across OSA children. When comparing the OSA children with the healthy controls, lower full-scale intelligent quotient (FIQ) and verbal intelligent quotient (VIQ) were observed. Additionally, nodal degree centrality decreased in the bilateral anterior cingulate and paracingulate gyrus, but increased in the right middle frontal gyrus, the left fusiform gyrus, and the left supramarginal gyrus. Nodal efficiency decreased in the right precentral gyrus, and the bilateral anterior cingulate and paracingulate gyrus, but increased in the left fusiform gyrus. Nodal betweenness centrality increased in the dorsolateral part of the right superior frontal gyrus, the left fusiform gyrus, and the left supramarginal gyrus. Further, the nodal degree centrality in the left supramarginal gyrus was positively correlated with FIQ. In contrast, none of global topological properties showed difference between those two groups. The outcomes of OSA may impaired the regional topological properties of the brain functional network of OSA children, which may be potential neural mechanism underlying the cognitive declines of these patients.

Aberrant resting-state functional connectivity and topological properties of the subcortical network in functional dyspepsia patients.

Functional dyspepsia (FD) is a disorder of gut-brain interaction. Previous studies have demonstrated a wide range of abnormalities in functional brain activity and connectivity patterns in FD. However, the connectivity pattern of the subcortical network (SCN), which is a hub of visceral information transmission and processing, remains unclear in FD patients. The study compared the resting-state functional connectivity (rsFC) and the global and nodal topological properties of SCN between 109 FD patients and 98 healthy controls, and then explored the correlations between the connectivity metrics and clinical symptoms in FD patients. The results demonstrated that FD patients manifested the increased rsFC in seventeen edges among the SCN, decreased small-worldness and local efficiency in SCN, as well as increased nodal efficiency and nodal degree centrality in the anterior thalamus than healthy controls (p < 0.05, false discovery rate corrected). Moreover, the rsFC of the right anterior thalamus-left nucleus accumbens edge was significantly correlated with the NDSI scores (r = 0.255, p = 0.008, uncorrected) and NDLQI scores (r = −0.241, p = 0.013, uncorrected), the nodal efficiency of right anterior thalamus was significantly correlated with NDLQI scores (r = 0.204, p = 0.036, uncorrected) in FD patients. This study indicated the abnormal rsFC pattern, as well as global and nodal topological properties of the SCN, especially the bilateral anterior thalamus in FD patients, which enhanced our understanding of the central pathophysiology of FD and will lay the foundation for the objective diagnosis of FD and the development of new therapies.

Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach.

ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC.ResultsIn patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model.ConclusionFunctional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM.

Disrupted topological organization of functional brain networks is associated with cognitive impairment in hypertension patients: a resting-state fMRI study.

To investigate the alterations of topological organization of the whole brain functional networks in hypertension patients with cognitive impairment (HTN-CI) and characterize its relationship with cognitive scores. Fifty-seven hypertension patients with cognitive impairment and 59 hypertension patients with normal cognition (HTN-NC), and 49 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Graph theoretical analysis was used to investigate the altered topological organization of the functional brain networks. The global topological properties and nodal metrics were compared among the three groups. Network-based statistic (NBS) analysis was used to determine the connected subnetwork. The relationships between network metrics and cognitive scores were also characterized. HTN-CI patients exhibited significantly decreased global efficiency, lambda, and increased shortest path length when compared with HCs. In addition, both HTN-CI and HTN-NC groups exhibited altered nodal degree centrality and nodal efficiency in the right precentral gyrus. The disruptions of global network metrics (lambda, Lp) and the nodal metrics (degree centrality and nodal efficiency) in the right precentral gyrus were positively correlated with the MoCA scores in HTN-CI. NBS analysis demonstrated that decreased subnetwork connectivity was present both in the HTN-CI and HTN-NC groups, which were mainly involved in the default mode network, frontoparietal network, and cingulo-opercular network. This study demonstrated the alterations of topographical organization and subnetwork connectivity of functional brain networks in HTN-CI. In addition, the global and nodal network properties were correlated with cognitive scores, which may provide useful insights for the understanding of neuropsychological mechanisms underlying HTN-CI.

Association of homotopic areas in the right hemisphere with language deficits in the short term after tumor resection.

It is important to identify language deficit and recovery in the week following a tumor resection procedure. The homotopic Broca's area and the superior longitudinal fasciculus in the right hemisphere participate in language functional compensation. However, the nodes in these structures, as well as their contributions to language rehabilitation, remain unknown. In this study, the authors investigated the association of homotopic areas in the right hemisphere with language deficit. The authors retrospectively reviewed the records of 50 right-handed patients with left hemispheric lower-grade glioma that had been surgically treated between June 2020 and May 2022. The patients were divided into normal and aphasia groups based on their postoperative aphasia quotient (AQ) from the Western Aphasia Battery. Preoperative (within 24 hours before surgery) and postoperative (7 days after tumor resection) diffusion tensor images were used to reveal alterations of structural networks by using graphic theory analysis. The shortest distance between the glioma and the nodes belonging to the language network (SDTN) was quantitatively assessed. Pearson's correlation and causal mediation analyses were used to identify correlations and mediator factors among SDTN, topological properties, and AQs. Postoperative nodal local efficiency of the node dorsal Brodmann area (BA) 44 (A44d; p = 0.0330), nodal clustering coefficient of the nodes A44d (p = 0.0402) and dorsal lateral BA6 (A6dl; p = 0.0097), and nodal degree centrality (p = 0.0058) of the node medial BA7 (A7m) were higher in the normal group than in the aphasia group. SDTN was positively correlated with postoperative AQ (r = 0.457, p = 0.0009) and ΔAQ (r = 0.588, p < 0.0001). The nodal local efficiency of node A44d and the nodal efficiency, nodal betweenness centrality, and degree centrality of node A7m were mediators of SDTN and postoperative AQs. The decreased ability of nodes A44d, A6dl, and A7m to convey information in the right hemisphere was associated with short-term language deficits after tumor resection. A smaller SDTN induced a worsened postoperative language deficit through a significant decrease in the ability to convey information from these three nodes.