Average Clustering Coefficient Research Articles

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes.

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Comparative Transcriptomic Analysis of SARS, H1N1, Influenza, and Rhinovirus to Identify Significant Genes Involved in Altering Immune Response

Purpose: To identify significant genes responsible for altering immune response in viral infections, including SARS, H1N1, Influenza, and Rhinovirus, as there are no previous studies that have analyzed these viral infections together. Methods: Viral infection datasets pertaining to SARS, H1N1, Influenza, and Rhinovirus were obtained from the NCBI Gene Expression Omnibus. We have used three GEO datasets with accession numbers: GSE47962, GSE48466, and GSE71766. The Differentially Expressed Genes (DEG’s) were identified from each of the datasets, and then common DEGs were extracted. Protein-ProteinInteraction (PPI) network was constructed for the common DEGs obtained in all the virus datasets. Finally, we analyzed the PPI network to identify the hub genes that have high interconnectivity with other genes. The significantly enriched pathways are reported. Results: By performing the comparative analysis, we identified 463 common DEG’s among the viral infection datasets under study. The highly interconnected PPI network constructed from these genes contained 3396 edges with an average node degree of 14.7 and an average local clustering coefficient of 0.406. There were 51 nodes with degree>50. The highest interconnected node, STAT1 had degree 113. Conclusion: STAT 1 gene is identified as the most significant hub gene related to the immune response in all four viral infections, including SARS, H1N1, Influenza, and Rhinovirus. Its trivial role is already known in different viral infections, but being most significant in the four viruses together is a novel finding. It is thus identified as a central gene that is a potential therapeutic drug and vaccine target for viral infections.

A complex-network-based estimation of the representative elementary volume and equivalent permeability coefficient for fractured rock masses

The determination of the representative elementary volume (REV) and equivalent permeability coefficient (EPC) of fractured rock masses is crucial for studying the hydraulic behaviors of engineering rock masses. The complex network originated from graph theory and represents a rapidly developing interdisciplinary field in the 21st century. In this paper, we propose a novel method that utilizes complex-network-based metrics to estimate the REV and EPC of fractured rock masses. In this method, fractures and their intersections are treated as nodes and edges of a complex network, respectively. We propose a transformation procedure of a discrete fracture network (DFN) into a complex network, and the corresponding algorithms are conducted in MATLAB. Subsequently, we obtain a complex network, and several complex-network-based metrics, such as average degree, average clustering coefficient, and graph density, are calculated using Gephi software. We employ the average degree to estimate the REV size of DFN models, while the coefficient of variation is used to quantitatively assess changes in the average degree of multiple random models. Furthermore, we establish an empirical formula utilizing the average degree, average clustering coefficient, and graph density to estimate the EPC of a large-scale DFN model based on the EPC of a small-scale DFN model. To verify the developed method, we conduct numerical experiments using the MAFIC (Matrix/Fracture Interaction Code) solver within the FracMan software. This enabled us to calculate the EPC, which was then used to determine the REV. The results demonstrate that: (a) the REV size estimated by the average degree aligns with the REV size obtained through numerical simulations for both isotropic and anisotropic DFN models, and (b) the error rates between the average EPC calculated by the empirical formula and that obtained by numerical simulations are within 10%. Thus, the proposed seepage parameter estimation methods are deemed valid.

White matter connectivity and social functioning in survivors of pediatric brain tumor.

Survivors of pediatric brain tumors (SPBT) are at risk for social deficits, fewer friendships, and poor peer relations. SPBT also experience reduced brain connectivity via microstructural disruptions to white matter from neurological insults. Research with other populations implicates white matter connectivity as a key contributor to poor social functioning. This case-controlled diffusion-weighted imaging study evaluated structural connectivity in SPBT and typically developing controls (TDC) and associations between metrics of connectivity and social functioning. Diffusion weighted-imaging results from 19 SPBT and 19 TDC were analyzed using probabilistic white matter tractography. Survivors were at least 5years post-diagnosis and 2years off treatment. Graph theory statistics measured group differences across several connectivity metrics, including average strength, global efficiency, assortativity, clustering coefficient, modularity, and betweenness centrality. Analyses also evaluated the effects of neurological risk on connectivity among SPBT. Correlational analyses evaluated associations between connectivity and indices of social behavior. SPBT demonstrated reduced global connectivity compared to TDC. Several medical factors (e.g., chemotherapy, recurrence, multimodal therapy) were related to decreased connectivity across metrics of integration (e.g., average strength, global efficiency) in SPBT. Connectivity metrics were related to peer relationship quality and social challenges in the SPBT group and to social challenges in the total sample. Microstructural white matter connectivity is diminished in SPBT and related to neurological risk and peer relationship quality. Additional neuroimaging research is needed to evaluate associations between brain connectivity metrics and social functioning in SPBT.

Optimal placement of a dissimilar node for chaos suppression in networks

Abstract We demonstrate that the presence of a single dissimilar chaotic system suppresses chaos in networks of chaotic oscillators, in a diverse set of network topologies, for sufficiently strong coupling. The key property is determined to be the sum of the path lengths between the dissimilar node and all the other nodes (or its maximum, if coupled to unconnected networks), and there exists a linear relation between this quantity and the critical coupling strength for the onset of a spatiotemporal fixed point. This holds true for a chain with the dissimilar node at different locations, a ring and complete network with one embedded dissimilar node, as well as star networks with a dissimilar hub or dissimilar peripheral node. Furthermore, we show that networks with high average degree and high clustering coefficient are more resilient to the influence of an external dissimilar system. These findings will potentially aid in the design of optimally placed dissimilar nodes for controlling chaos in complex networks.

Structural properties of extended pseudo-fractal scale-free network with higher network efficiency

Abstract Complex networks, as an important model for studying complex systems, have been gradually studied in various extension models. Firstly, the network is proved to be sparse by calculating the density of the extending pseudo-fractal network. Secondly, it is proved that the network nodes conform the power-law distribution by enumerate the degree sequence of nodes with power law index $ 2 \lt \gamma \leq 2.585 $ shows that the network has scale-free feature. Thirdly, it is also demonstrated that the average clustering coefficient $ \overline{C_{g}} $ can reach a theoretical lower limit value 0.8182 and the increase of extension coefficient m makes the $ \overline{C_{g}} $ higher than traditional fractal networks. Fourthly, we derive the analytical expression and limit expression of the average path length, and conclude that it has small-world effect. Meanwhile, it is shown that the average path length $ \overline{D_{g}} $ is logarithmically related to Ng growth relationship in Fg. Finally, it is concluded that this extended pseudo-fractal network becomes more effective under the effect of m.

The bass diffusion model: agent-based implementation on arbitrary networks

ABSTRACT The goal of this study is to model Bass diffusion and its extensions on complex networks, including scale-free networks with arbitrary power-law exponent and assortative degree correlations. For this purpose we employ a combination of the free software packages networkX and NetLogo. Some new results obtained in the agent-based simulations (and differing from those in mean-field approximation) are the following. The introduction of assortative correlations in scale-free networks has the effect of delaying the adoption peak in the Bass model, compared to the uncorrelated case. The peak time depends strongly also on the maximum degree effectively present in the network. For diffusion models with threshold on signed network, a high level of clustering tends to cause adoption blockades. By analysing statistical ensembles of assortative networks generated via Newman rewiring one observes a remarkable strong correlation between the average degree of first neighbours k ˉ nn ( k ) and the average clustering coefficient C ˉ ( k ) .

Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN’s vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study’s insights can guide policy interventions to further strengthen the network against future shocks and promote global food security.

Collaboration network analysis for the Arctic issues based on a collection of international collaborative events

In recent years, global warming has accelerated the melting of Arctic ice. The value of the Arctic has been increasing in various aspects and has received widespread attention from the international community. The significance of globalization of the Arctic issue has become more and more prominent, the member states of the Arctic Council, observer states, and non-Arctic countries that have intensively developed and updated their respective Arctic strategies have actively participated about Arctic issues. The international collaboration has gradually become an effective way of addressing Arctic issues. In order to explore the pattern behind the collaborative events about Arctic issues, with the application of complex network analysis, the international collaborative events about Arctic issues from 2013 to 2023 are selected to construct the collaborative networks. Then the collaborative networks are quantitatively analyzed by the relevant parameters of complex network. These parameters for quantitative analysis include network density, average path length, clustering coefficient, degree centrality, closeness centrality and betweenness centrality. The results of the study reveal the changes in international collaboration about Arctic issues from 2013 to 2023.

Quorum sensing bacteria improve microbial networks stability and complexity in wastewater treatment plants

Quorum-sensing bacteria (QSB) are crucial factors for microbial communication, yet their ecological role in wastewater treatment plants (WWTPs) remains unclear. Here, we developed a method to identify QSB by comparing 16S rRNA gene sequences. QSB in 388 activated sludge samples collected from 130 WWTPs across China primarily were identified as rare taxa and conditionally rare taxa. A co-occurrence network shared by all sludge communities revealed that QSB exhibited higher average clustering coefficient (0.46) than non-QSB (0.15). Individual sludge networks demonstrated that quorum sensing microbiomes were positively correlated with network robustness and network complexity, including average clustering coefficient and link density. We confirmed that QSB keystones and QSB nodes have a positive impact on network complexity by influencing network modularity through a structural equation model. Meanwhile, QSB communities directly contributed to maintaining network robustness (r = 0.29, P < 0.05). Hence, QSB play an important role in promoting network complexity and stability. Furthermore, QSB communities were positively associated with the functional composition of activated sludge communities (r = 0.33, P < 0.01), especially the denitrification capacity (r = 0.45, P < 0.001). Overall, we elucidated the ecological significance of QSB and provided support for QS-based regulation of activated sludge microbial communities.

Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache

BackgroundIt is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated. MethodsThis study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed. ResultsMTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC). ConclusionsThese findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.

Clustering coefficients for networks with higher order interactions.

We introduce a clustering coefficient for nondirected and directed hypergraphs, which we call the quad clustering coefficient. We determine the average quad clustering coefficient and its distribution in real-world hypergraphs and compare its value with those of random hypergraphs drawn from the configuration model. We find that real-world hypergraphs exhibit a nonnegligible fraction of nodes with a maximal value of the quad clustering coefficient, while we do not find such nodes in random hypergraphs. Interestingly, these highly clustered nodes can have large degrees and can be incident to hyperedges of large cardinality. Moreover, highly clustered nodes are not observed in an analysis based on the pairwise clustering coefficient of the associated projected graph that has binary interactions, and hence higher order interactions are required to identify nodes with a large quad clustering coefficient.

Temporal dynamics of rhizosphere bacterial community in the Robinia pseudoacacia–Mesorhizobium loti symbiotic system for remediation of cadmium-contaminated soils

Rhizobia can be used to enhance the phytoremediation of cadmium-contaminated soils by legumes, and a symbiotic nitrogen-fixing system has been shown to reshape the structure of rhizosphere microbiota in a mining area. However, little is known about the temporal dynamics and possible roles of rhizosphere microbiota in rhizobia-enhanced phytoremediation. A rhizobox experiment was conducted using Robinia pseudoacacia alone or inoculated with Mesorhizobium loti HZ76 under greenhouse conditions. Rhizosphere bacterial communities were characterized over a 90-day period at three levels of cadmium (determined by the distance from the mining area; uncontaminated: 0.08 mg kg−1, contaminated: 0.39 and 1.72 mg kg−1). Rhizobial inoculation considerably improved the phytoremediation efficiency, such as increasing plant biomass and cadmium accumulation by approximately two times at 90 days after planting. The bacterial community structure shifted remarkably with increasing duration of remediation. Bacterial diversity exhibited a decreasing trend in the first 60 days and then recovered at 90 days. Despite having no discernible impact on the bacterial community structure in the rhizosphere, rhizobial inoculation enabled the enrichment of potentially beneficial taxa (e.g., Microvirga, Mesorhizobium, Diaphorobacter, Steroidobacter, Brevundimonas) with resistance to heavy metals and involvement in nutrient cycling. The bacterial co-occurrence networks of low and high cadmium-contaminated soils showed the highest average degree and clustering coefficient at 60 and 30 days, respectively. Some bacterial taxa involved in nitrogen fixation (e.g., Rhizobacter, Mesorhizobium), denitrification (e.g., Niastella, Nitrospira), and carbon turnover (e.g., Solirubrobacter, Brevundimonas) were found to be potential keystone taxa. The collective findings clarify possible mechanisms by which rhizosphere bacteria participate in phytoremediation of cadmium-contaminated mining soils using the R. pseudoacacia–M. loti symbiotic system.

Strategies to enhance the level of service and safety of rural roads: A case study.

Faced with the contradiction between the increasing traffic volume and the aging road infrastructures in the rural area, this paper aims to propose feasible strategies to enhance the level of service and safety, by a case study of the rural area in the north Jintan district. In order to figure out current issues related to rural roads, a carefully designed investigation has been conducted, and the results of the two-week investigation include roads' basic information, traffic signs and protective facilities, surrounding landscape, and etc. Based on the field driving tests, specific problems including signs category, signs installation and facility maintenance have been fully analyzed. Meanwhile, the problem of roadnet connectivity has also been pointed out through the theory of complex network, and results show that the average node clustering coefficient and shortest path length perform worse than the demonstration plot of other rural districts. For the sake of rural traffic safety and management efficiency, both quantified and qualified strategies have been put forward. The quantified strategies include the regular inspection indicators, the safety sight distance at T-type crossings, as well as the risk severity of sections and the crossings. The qualified strategies involve the management of trucks and roadworks, the setting of signalized intersections, and the timely updates of traffic signs and facilities. Finally, an intelligent management system framework has been established for rural road traffic, with highly interconnected modules of data acquisition, risk identification and information publishing.

Leaf trait network variations with woody species diversity and habitat heterogeneity in degraded karst forests

Leaf Trait Networks (LTNs) visually display the intricate trait connections in offering insights into how plants adapt to diverse environments. The pervasive environmental heterogeneity can influence plant diversity and functional traits. Various habitats sustain distinct species diversity and leaf trait differentiation, precisely as the degraded karst-heterogenous ecosystem caused by inhomogeneous microhabitats. However, how the LTNs respond to understory habitat heterogeneity and species diversity in degraded karst forests remains unclear. This study selected fifty-three forest plots for plant community survey and habitat investigation in a typical karst topography to visualize LTNs using twenty-one woody leaf traits. LTN architecture variations were analyzed in relation to habitat heterogeneity and species diversity through network-architectural parameters, including edge density, diameter, average path length, and average clustering coefficient, utilizing regression, principal component analysis, and structural equation model. The results showed a 35% variation in modularity across all plots, indicating distinct network architectural modularization differentiation. Network diameter and average path length displayed significant initial decreases followed by increases with ascending PC1 heterogeneity scores, indicating moderate heterogeneity sustained greater network connectivity. In addition, the network diameter, average path length, and average clustering coefficient significantly decreased with increasing species diversity, indicating that high species diversity enables greater connectivity and complexity. Moreover, the habitat heterogeneity significantly affected species diversity directly and affected the average clustering coefficient, diameter, average path length, and edge density indirectly through species diversity effects via SEM pathway analysis. Overall, habitat heterogeneity and woody plant diversity of karst forests significantly influence LTN architectures in degraded karst forests. In conclusion, moderate heterogeneity sustains greater network connectivity while species diversity jointly enhances connectivity and complexity by combining modular regulation of plant functional traits.

Microbial survival strategies in biological soil crusts of polymetallic tailing wetlands

The role of biological soil crusts (biocrusts) in ecological restoration is dominated by microbiota. This has been extensively studied in arid ecosystems characterized by nutrient deficiencies and poor soil substrates, including metal(loid)-rich mining regions. However, in the prior study areas, water constraints obscured the toxicity and restriction of succession by heavy metals, which were essential for soil remediation in mining regions using biocrusts. Accordingly, in the current study, we characterized biocrusts from a typical polymetallic tailing wetland and performed pot incubation to evaluate microbial community succession and microbial co-occurrence patterns. Results showed that adequate water and heavy-metal stress produced thicker and more hydrophobic biocrusts than did arid conditions, promoting stronger microbial activity in tailing wetlands. Moreover, the bioinformatic analyses during pot incubation suggest three potential survival strategies. (1) Cyanobacteria variation improved the succession level of cyanobacterial crusts. Even under heavy-metal stress, succession from light to dark cyanobacterial crusts still occurred, with Coleofasciculaceae decreasing 63% and Scytonema increasing 59% compared with the control groups. (2) Keystone taxa variation maintained high inorganic nitrogen turnover. Organisms potentially capable of inorganic nitrogen turnover, including Chloroflexi A4b and SBR1031, maintained a high relative abundance accounting for 42% (15/36) of the keystone taxa. (3) Variations in the interactions among microbiota created closer-knit microbial networks. Compared with the control groups, fewer nodes (15.2%) maintained higher average clustering coefficient values (1.8%) through 14% more positive interactions in the groups exposed to heavy-metal stress at the end of the incubation experiment. This study provides insights into the distinctive survival strategies of biocrusts in metal-rich extreme environments, offering theoretical support for biocrust-mediated ecological restoration in humid metal-contaminated regions.

The structural and spatial evolution of the China Railway Express network

To illustrate the structural and spatial evolution of the China Railway Express (CR-Express) network, this paper adopts the Space-P method to construct three complex networks of the CR-Express routes respectively by the block train data in 2013, 2016 and 2019, and the indices of overall network structure (average degree, average path length, and average clustering coefficient) and indices of node importance (degree centrality and betweenness centrality) in the networks are comparatively analyzed. It is shown that the CR-Express network has become more complicated, but in 2016 the overall structure was basically stable, and consistently presents clear small-world and scale-free characteristics. The nodes in the network can be partitioned into four groups based on their influence on network connectivity and the number of block trains departing from them, namely core nodes, freight demand nodes, key nodes and edge nodes. Among them, core nodes and freight demand nodes are located in the central and western regions during the whole study period, and the newly added nodes in the eastern region from 2016 to 2019 are edge nodes. Based on the above analysis, we capture the evolving nature of the CR-Express network across distinct development stages under the influence of policy, economy, geographical location and other factors, and put forward operational suggestions to optimize the CR-Express service, strengthen the maintenance of key nodes, and ensure the efficient operation of China-Europe trains.

Alterations in the multilayer network in patients with rapid eye movement sleep behaviour disorder.

This study aimed to reveal the pathophysiology of isolated rapid eye movement sleep behaviour disorder (RBD) in patients using multilayer network analysis. Participants eligible for isolated RBD were included and verified via polysomnography. Both iRBD patients and healthy controls underwent brain MRI, including T1-weighted imaging and diffusion tensor imaging. Grey matter matrix was derived from T1-weighted images using a morphometric similarity network. White matter matrix was formed from diffusion tensor imaging-based structural connectivity. Multilayer network analysis of grey and white matter was performed using graph theory. We studied 29 isolated RBD patients and 30 healthy controls. Patients exhibited a higher average overlap degree (27.921 vs. 23.734, p = 0.002) and average multilayer clustering coefficient (0.474 vs. 0.413, p = 0.002) compared with controls. Additionally, several regions showed significant differences in the degree of overlap and multilayer clustering coefficient between patients with isolated RBD and healthy controls at the nodal level. The degree of overlap in the left medial orbitofrontal, left posterior cingulate, and right paracentral nodes and the multilayer clustering coefficients in the left lateral occipital, left rostral middle frontal, right fusiform, right inferior posterior parietal, and right parahippocampal nodes were higher in patients with isolated RBD than in healthy controls. We found alterations in the multilayer network at the global and nodal levels in patients with isolated RBD, and these changes may be associated with the pathophysiology of isolated RBD. Multilayer network analysis can be used widely to explore the mechanisms underlying various neurological disorders.

Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia

Post-stroke aphasia recovery, especially in the chronic phase, is challenging to predict. Functional integrity of the brain and brain network topology have been suggested as biomarkers of language recovery. This study sought to investigate functional connectivity in four predefined brain networks (i.e., language, default mode, dorsal attention, and salience networks), in relation to aphasia severity and response to language therapy. Thirty patients with chronic post-stroke aphasia were recruited and received a treatment targeting word finding. Structural and functional brain scans were acquired at baseline and resting state functional connectivity for each network was calculated. Additionally, graph measures quantifying network properties were calculated for each network. These included global efficiency for all networks and average strength and clustering coefficient for the language network. Linear mixed effects models showed that mean functional connectivity in the default mode, dorsal attention, and salience networks as well as graph measures of all four networks are independent predictors of response to therapy. While greater mean functional connectivity and global efficiency of the dorsal attention and salience networks predicted greater treatment response, greater mean functional connectivity and global efficiency in the default mode network predicted poorer treatment response. Results for the language network were more nuanced with more efficient network configurations (as reflected in graph measures), but not mean functional connectivity, predicting greater treatment response. These findings highlight the prognostic value of resting-state functional connectivity in chronic treatment-induced aphasia recovery.