Distance Connectivity Research Articles

Contracted functional connectivity profiles in autism

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes.MethodsWe studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types.ResultsCompared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores.LimitationsDespite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts.ConclusionsOur study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Bridge Sampling for Connections via Multiple Scattering Events

AbstractExplicit sampling of and connecting to light sources is often essential for reducing variance in Monte Carlo rendering. In dense, forward‐scattering participating media, its benefit declines, as significant transport happens over longer multiple‐scattering paths around the straight connection to the light. Sampling these paths is challenging, as their contribution is shaped by the product of reciprocal squared distance terms and the phase functions. Previous work demonstrates that sampling several of these terms jointly is crucial. However, these methods are tied to low‐order scattering or struggle with highly‐peaked phase functions.We present a method for sampling a bridge: a subpath of arbitrary vertex count connecting two vertices. Its probability density is proportional to all phase functions at inner vertices and reciprocal squared distance terms. To achieve this, we importance sample the phase functions first, and subsequently all distances at once. For the latter, we sample an independent, preliminary distance for each edge of the bridge, and afterwards scale the bridge such that it matches the connection distance. The scale factor can be marginalized out analytically to obtain the probability density of the bridge. This approach leads to a simple algorithm and can construct bridges of any vertex count. For the case of one or two inserted vertices, we also show an alternative without scaling or marginalization.For practical path sampling, we present a method to sample the number of bridge vertices whose distribution depends on the connection distance, the phase function, and the collision coefficient. While our importance sampling treats media as homogeneous we demonstrate its effectiveness on heterogeneous media.

Computational Model of Functional Connectivity Distance Predicts Neural Alterations

Computational Model of Functional Connectivity Distance Predicts Neural Alterations

Enhancing secret key distribution through advanced color modulation in visible light communication

Visible light communication (VLC) has emerged as a dynamic area of research poised to revolutionize high-speed wireless communication. VLC technology uses light-emitting diodes (LEDs) within existing infrastructure to emit light within the visible spectrum. VLC complements traditional radio frequency (RF) communications, addressing its inherent limitations and drawbacks. To navigate the demands of modern urban environments, VLC systems must prioritize secure data transmission, accessibility, and economic feasibility, particularly within the framework of smart cities. We introduce what is to our knowledge a novel privacy-enhanced VLC system for optical wireless communication. Leveraging color data modulation techniques and the intricacies of a hyperchaotic three-dimensional map, this innovative approach ensures robust security. By employing diverse LED colors for data transmission and exploiting the unpredictable mathematical properties of hyperchaotic maps, enhanced privacy is achieved. The performance of the proposed system was rigorously evaluated through various tests, manipulating initial control parameters of the encryption process with the hyperchaotic map, as well as adjusting message length and content. Tests were conducted over a 1 m connection distance at a symbol transmission rate of 2 baud. Remarkably, the proposed system demonstrated high accuracy in message recovery, achieving a symbol error rate (SER) of only 0.02 at an incident optical power of 22 µW. We highlight the critical importance of precise decryption parameter values in the proposed method, demonstrating the necessity for accuracy within the range of 10−15 for each decryption parameter; it underscores the indispensability of meticulous parameter calibration to ensure the correct decryption of transmitted symbols. These results pave the way for applications where absolute security is imperative, particularly in smart city environments, such as for key distribution purposes.

Investigation of Car following and Lane Changing Behavior in Diverging Areas of Tunnel–Interchange Connecting Sections Based on Driving Simulation

Tunnel–interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers’ car-following and lane-changing behavior in tunnel–interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using the Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel–interchange scenarios, the reduction in velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units of increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F > 1.60, p < 0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability.

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

Bootstrap percolation, connectivity, and graph distance

Bootstrap percolation, connectivity, and graph distance

Revealing the time-dependent electromechanically coupled performances of viscoelastic MWCNT/polyethylene nanocomposite stress sensors

While highly sensitive elastic strain sensors have been widely investigated, the time-dependent stress sensitivity of viscoelastic MWCNT/polyethylene nanocomposite stress sensors remains to be explored. In this paper, we develop an electromechanically coupled homogenization scheme to reveal the time-dependent stress sensing performances of viscoelastic MWCNT/polyethylene nanocomposite sensors. In the time-dependent context, the complex moduli and electrical conductivity are selected as the dual homogenization parameters. The time-dependent stress sensitivity is illustrated through the viscoelastic imperfect interface connection and stress-induced tunneling distance. The predicted stress sensing capacities of viscoelastic MWCNT/polyethylene nanocomposite stress sensors are shown to be consistent with the experiments under the constant stress loading. It reveals that the stress sensitivity factor increases with the loading time under constant stress. The optimal MWCNT aspect ratio for high sensing capacities exhibits an increasing trend regarding the MWCNT volume fraction. The uncovered sensing characteristics can provide microstructural design guidance in high-performance nanocomposite stress sensors.

Long-range connections damage in white matter hyperintensities affects information processing speed.

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

A k-hop Constrained Reachability Based Proactive Connectivity Maintaining Mechanism of UAV Swarm Networks

<p>Harsh or hostile environments may lead to node failure and connectivity degradation of a UAV swarm system. In order to maintain or restore the connectivity of a network in case of node failure, this paper proposes a mechanism to adjust the network topology to resist the impact caused by node failure. Firstly, a network model of a UAV swarm network based on k-hop constrained reachability is proposed. Secondly, a k-hop constrained reachability based proactive connectivity maintaining mechanism of UAV swarm network is presented. In this mechanism, each node identifies the network abnormality distributed according to k-hop reachability, and reports the observed abnormality to the master node; then, a virtual edge-based topology reconstruction algorithm is put forward for the master node to derive a topology reconstruction solution in a centralized way; afterwards, the solution is delivered to the slave nodes to reconfigure the network topology in parallel. Thirdly, a quantitative method is introduced to optimize the total travel distance of nodes, and a spanning tree-based method is designed to maintain the connectivity during the topology transformation process. Both theoretical analysis and simulation results have shown that: on the one hand, the proposed mechanism are effective in maintaining a UAV swarm’s connectivity in case of node failure; on the other hand, the proposed mechanism outperforms existing mechanisms in terms of fault tolerance, connectivity, and total travel distance, and it’s less affected by the failure rate.</p> <p> </p>

Legacy effects of wheat season organic fertilizer addition on microbial co-occurrence networks, soil function, and yield of the subsequent maize season in a wheat-maize rotation system

Organic fertilizer can alleviate soil degradation. While numerous studies have explored the immediate impacts of organic fertilizer on soil properties and crop production, the legacy effects of organic fertilizer addition remain less understood. This research investigated the subsequent effects of organic fertilizer addition during the winter wheat season on soil microbial community structure, co-occurrence networks, soil function, and summer maize yield from 2018 to 2020. Six fertilization treatments were implemented as chemical nitrogen fertilizer (N) alone or combined sheep manure and nitrogen fertilizer (SMN) at low, medium, and high fertilization levels during the winter wheat season, with only N fertilizer applied during the maize season. The findings revealed significant variations in bacterial and fungal community structures between the SMN and N treatments. The SMN treatments increased the relative abundance of Proteobacteria, Actinobacteria, and Bacteroidetes and decreased the relative abundance of Rokubacteria, Acidobacteria, Gemmatimonadetes, Chloroflexi, and Nitrospirae compared to the N treatment. The SMN treatments had higher fungal network connectivity and lower mean path distance and modularity than the N treatment, resulting in heightened sensitivity of fungi to environmental changes. The legacy effects of organic fertilizer changed the functional potential of the N and C cycles, with keystone taxa such as Proteobacteria, Actinomycetes, Acidobacteria, Gemmatimonadetes, Bacteroides, and Ascomycota significantly correlating with functional genes related to the C and N cycles. Surprisingly, no significant differences in summer maize yield occurred between the SMN and N treatments. However, the random forest model revealed that the SMN treatments had significantly higher explanatory power of soil microbial community structure for maize yield (74.31%) than the N treatment (13.07%). These results were corroborated in subsequent studies and underscore the legacy effects of organic fertilizer addition on soil microbial communities. This research offers valuable insights into organic fertilizer use for enhancing soil quality and sustaining agricultural productivity.

Fuzzy based clustering and improved ant colony optimization for collecting data via mobile sink in wireless sensor networks

<span>Energy efficient routing of data from sensor to base station (BS) is attained utilizing the clustering of sensor nodes, thus minimizing the number of hops and circling the task of the cluster head (CH) sporadically. In addition, the clusters near to the BS take a substantial load over multi-hop communication. The hot spot problem affects wireless sensor networks (WSNs) with BS nodes, which is caused by sensor nodes close to the BS allowing for increased traffic load. So, the entire network lifespan is minimized owing to the element some nodes drain their energy resources much faster equated to the break. To solve these issues fuzzy based clustering and form the optimal route (FCOR) by mobile sink (MS) approach for efficient data collection is proposed. The fuzzy logic method is used for elected the CH by node remaining energy, node connectivity and node distance parameters. Discovering an optimal movable trajectory for the MS is serious so as to attain energy efficiency. Improved ant colony optimization (IACO) method is a better solution to discovering an optimal traversal route. Simulation results proves that FCOR increases the energy efficiency, throughput and minimized the network delay in the WSN.</span>

Boundary green infrastructure: a green infrastructure connecting natural and artificial spaces

As a naturally-based solution (NBS), green infrastructural network constructing can improve urban ecological resilience and support sustainable urban development. However, as the Frontier of urban expansion, the boundary of built-up areas has little research on the boundary green infrastructure (BGI) connecting natural and artificial spaces. In order to make up for the shortcomings of relevant research, we propose a method for identifying BGI and analyze its landscape pattern characteristics. We selected 15 European cities as cases to extract the boundaries of built-up areas. Moreover, we used morphological pattern analysis (MSPA) to identify the ecological source and select the best distance threshold for the landscape connectivity model to identify the BGI range. Through the gradient area method and MSPA, the BGI landscape pattern characteristics of the case cities were analyzed quantitatively. The BGI scale was affected by the area of the built-up area and the threshold of GI landscape connectivity distance. Additionally, the BGI space contained a small number of large ecological sources and many scattered and small fragmented patches. The best landscape model of BGI was the surrounding pattern, followed by the aggregation pattern, which had good landscape connectivity; however, the fragmentation of the scattered pattern was high. Lastly, the ecological core area in BGI was the main landscape type; it has a high landscape connection function for the GI network inside and outside the built-up area and promotes biological exchange inside and outside the built-up area. This study proves that BGI has an important ecological significance, can guarantee the scientific nature of the NBS method, and ensures the ecological security pattern of cities.

Multiple Probability Ecological Network and County-Scale Management

Ecological networks are threatened by rapid urbanization; the implementation of ecological network management and maintenance strategies is essential in the county units of urban agglomerations. This study focused on the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) and objectively identified and evaluated the regional ecological networks. Connectivity distance thresholds were determined using goodness-of-fit experiments. The Probability of becoming an Ecological Source (PES) was constructed to investigate the ecological networks at different probabilities, and the network weight in each county was analyzed based on the gravity model. The results show that: (1) The distance thresholds were 2500, 2700, and 2300 m in 2000, 2010, and 2020, respectively. Source degradation and corridor disconnection were most significant at a PES > 50%. (2) At a PES > 50%, the high gravitational value increased from 39,972 to 31,4642, owing to fragmentation. Fourteen counties received weights, and fluctuations were most significant at a PES > 90%. (3) The trends in the PES and gravitational value were not always synergistic, and the negative impact of source degradation on the gravitational value was significantly less than the positive impact of the reduced distance between sources. The gravity center movement under the high PES determined the ecological network status of neighboring counties. The findings can quantify ecological information flow and provide a basis for county ecological management and restoration.

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. The experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, and a high consistency between the region of detected clump and that of simulated clump, and the experiments demonstrate that FacetClumps is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises 13CO (J = 1−0) emission line of the MWISP within 11.°7 ≤ l ≤ 13.°4, 0.°22 ≤ b ≤ 1.°05, and 5 km s−1 ≤ v ≤ 35 km s−1 and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.

Phytoplankton diversity explained by connectivity across a mesoscale frontal system in the open ocean

Phytoplankton community composition is important in establishing ecosystem structure and function. Intuitively, we recognize that water movements must be important for modifying spatial gradients and plankton diversity. However, identifying boundaries and exchange between habitats in the open ocean is not straightforward. Here, we use the abundance of nine phytoplankton species closely sampled in a mesoscale frontal system in the northeastern North Sea as a proxy for community composition and explore the relationship between phytoplankton biogeography and transport patterns. Subsurface community distributions could be related to modeled patterns in water movement. A methodology for analyzing pelagic diversity that includes a representation of plankton community composition and an Eulerian connectivity tracer was developed, and the relative importance of connectivity and geographical distance for phytoplankton species composition analyzed. The connectivity tracer identifies timescales and dispersal barriers in the open ocean. Connectivity was found to be superior in explaining pelagic plankton diversity and found to be a prerequisite for understanding the pelagic phytoplankton composition. This approach is a valuable tool for establishing the link between ocean transports, ecosystem structure and biodiversity and for informing the placement of marine protected areas.

A design-support framework to assess urban green spaces for human wellbeing

Rapid and high-density urban development has led to limited Urban Green Space (UGS), which is crucial for human wellbeing and sustainable urban development. To avoid compromising human wellbeing while promoting environmental sustainability, we propose a novel research framework that quantifies the importance of each UGS on-site for human wellbeing, to support multi-objective UGS design. This paper presents a framework for spatial parameterised evaluation of human wellbeing based on accessibility and usability. The evaluation considers connectivity and walking distance for accessibility, and space enclosure and availability for usability. The proposed thresholds and weights can be adjusted to fit different study contexts and reflect various needs. The use case demonstrated that the framework allows for ranking of UGS sites facilitating integration of human needs and other sustainable environmental needs into a multi-objective UGS design workflow. This approach provides designers with evidence to accurately allocate UGS resources according to human needs on each UGS and space conditions of UGS. It also presents an opportunity to integrate with other quantified analyses for sustainable urban development. The framework provides a useful approach to promoting human wellbeing by identifying the most important UGS for safeguarding human needs.

Identifying changes in the hydrological connectivity and their drivers in the Liaohe Delta wetland

Hydrological connectivity is of great significance to the formation and development of wetlands. Identifying the causes of reduced hydrological connectivity is also critical for restoring wetland systems. Despite its importance, research to identify connectivity or blockage and quantify the impact of different drivers on wetland hydrological connectivity remains a challenge due to the lack of appropriate data at large scales. This paper quantitatively analyzed the hydrological connectivity in the Liaohe Delta wetland using multispectral Landsat series datasets from 1986 to 2020 in the Google Earth Engine (GEE) by combining water area, connectivity function value (CFV), average monthly inundation frequency. The paper also defined identification criteria for connectivity or blockage and quantified the drivers of changes in hydrological connectivity by combining the identified results with wetland classification data. The results showed that (1) over the past 35 years, water area has slightly decreased and has shown a seasonal difference, with summer, autumn, and spring in decreasing order. The spatial distribution of water has expanded from land to sea. (2) CFV decreased and connectivity blockage occurred. The maximum hydrological connectivity distance has undergone fluctuations, exhibiting a decrease in its threshold value from 62.37 km between 1986 and 2011, to 55.2 km between 2011 and 2013. Subsequently, the threshold increased back to 62.37 km between 2013 and 2014, before declining to 55.17 km between 2014 and 2020. Heavy inundation and total inundation were mainly concentrated in nascent estuary areas and shallow sea areas. (3) Connectivity blockage has been a major trend in connectivity change over the past 35 years, with the most severe blockage occurring from 2006 to 2015. The factors influencing connectivity blockage were more natural than human factors before 2006, after which human factors gradually became the dominant driving factor. Over the past 35 years, hydrological connectivity has decreased and connectivity has been blocked. Human activities have become the main factor in the blockage of connectivity in the Liaohe Delta wetland since 2006. This study provides valuable insights for the conservation and restoration of hydrological connectivity in the Liaohe Delta wetland.

Intercellular mechanical signalling in a 3D nonlinear fibrous network model

The extracellular matrix (ECM) consists of a network of polymeric fibres in which cells are embedded and interact with each other. The nonlinear elasticity of the ECM aids in directing the transmission of cell-induced mechanical loads over long distances, thereby facilitating mechanical communication between cells that are not in direct contact. Previous computational models have established the role of compression-buckling of the ECM fibres in increasing the propagation range of mechanical loads and fibre remodelling, however, most of them are two-dimensional (2D). Here, we generate a more realistic, 3D finite-element simulation of two cells contracting within the ECM by means of two contractile spheres embedded within a network of beam elements. We explore the effects of the mechanical behaviour of the fibres (linear-elastic, compression-bucklable), network connectivity (3.7, 8, 12) and intercellular distance (1–5 cell radii) on the structural remodelling and mechanical loads occurring on the cell surface and within the intercellular region of the ECM. When cells were embedded in a matrix of bucklable rather than linear-elastic fibres, contraction-induced loads were more directed towards the neighbouring cell, and the fibre rearrangement in the intercellular ECM was more eminent up to an intercellular distance of ∼2.5–5 cell radii. Our 3D discrete model highlights the role of the ECM nonlinear elasticity in the efficient transfer of mechanical signals between cells, a mechanism governing numerous multicellular processes such as tissue morphogenesis, wound healing, angiogenesis and cancer metastasis. The findings reemphasise the importance of using nonlinear elastic gels in in vitro cellular models.

Altered resting-state neurovascular coupling in patients with pontine infarction

The risk for motor and cognitive impairment is increased in patients with chronic pontine infarction (PI). In this study, we attempted to explore the alterations of neurovascular coupling (NVC) in order to understand the neural basis of behavioral impairment after PI. Three-dimensional pseudo-continuous arterial spin labeling (3D-pcASL) and resting-state functional magnetic resonance imaging (rs-fMRI) were applied in 49 patients with unilateral PI (left-sided, n = 26; right-sided, n = 23) and 30 matched normal subjects to assess whole-brain cerebral blood flow (CBF) and functional connectivity strength (FCS). We evaluated NVC in each subject by calculating the correlation coefficient between the whole-brain CBF and FCS (CBF-FCS coupling) and the ratio between voxel-wise CBF and FCS (CBF/FCS ratio). The FCS maps were then divided into long-range and short-range FCS to identify the influence of connection distance. The results indicated that the CBF-FCS coupling in the whole-brain level was significantly interrupted in PI patients, and the CBF/FCS ratio in cognition-related brain regions was abnormal. Distance-dependent results demonstrated that PI had a more serious effect on long-range neurovascular coupling. Correlation analysis revealed that the changes in neurovascular coupling were correlated with working memory scores. These findings imply that disruption of neurovascular coupling in the remote-infarction brain regions may underlie the impaired cognitive functions in chronic PI.