Structural Connectivity Networks Research Articles

Structural brain connectivity predicts early acute pain after mild traumatic brain injury.

Mild traumatic brain injury (mTBI), is a leading cause of disability worldwide, with acute pain manifesting as one of its most debilitating symptoms. Understanding acute postinjury pain is important because it is a strong predictor of long-term outcomes. In this study, we imaged the brains of 157 patients with mTBI, following a motorized vehicle collision. We extracted white matter structural connectivity networks and used a machine learning approach to predict acute pain. Stronger white matter tracts within the sensorimotor, thalamiccortical, and default-mode systems predicted 20% of the variance in pain severity within 72 hours of the injury. This result generalized in 2 independent groups: 39 mTBI patients and 13 mTBI patients without whiplash symptoms. White matter measures collected at 6 months after the collision still predicted mTBI pain at that timepoint (n = 36). These white matter connections were associated with 2 nociceptive psychophysical outcomes tested at a remote body site-namely, conditioned pain modulation and magnitude of suprathreshold pain-and with pain sensitivity questionnaire scores. Our findings demonstrate a stable white matter network, the properties of which determine an important amount of pain experienced after acute injury, pinpointing a circuitry engaged in the transformation and amplification of nociceptive inputs to pain perception.

Exploring (peri-) lesional and structural connectivity tissue damage through T1/T2-weighted ratio in iron rim multiple sclerosis lesions

ObjectiveIn multiple sclerosis (MS), iron rim lesions (IRLs) on magnetic resonance imaging (MRI) are associated with pronounced intralesional tissue damage. The aim of this study was to investigate (peri-)lesional and structural connectivity tissue damage in IRLs compared to non-IRLs. Material and methodsMRI was acquired on a 3 T system. Tissue integrity was assessed using the T1/T2-weighted (T1/T2w) ratio. Furthermore, we assessed the impact on structural network connectivity accounting for differences in lesion volumes and T1/T2w values. ResultsSeventy-six patients (38 with at least one IRL and 38 age- and sex-matched patients without IRLs) were included. In the IRL-group, T1/T2w ratios of IRLs were significantly lower compared to non-IRLs (p < 0.05). When comparing the T1/T2w ratios in non-IRLs between the IRL-group and non-IRL group, there was no significant difference (p = 0.887). We observed a centrifugal decrease in microstructural damage from lesions to the perilesional white matter. In the IRL-group, T1/T2w ratios in the perilesional white matter 3–8 mm distant to the lesion were significantly lower in IRLs compared to non-IRLs. We found no significant differences in the amount of network disruption between both lesion types (p = 0.122). ConclusionT1/T2w represents an interesting candidate to capture a pronounced intra- and perilesional tissue damage of IRLs. However, our preliminary results suggest that a pronounced tissue damage might not result in a higher disruption to structural connectivity networks in IRLs.

Constraining functional coactivation with a cluster-based structural connectivity network.

In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints from the structural connectivity network. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest (ROIs), and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network. The coactivations of ROIs and their clusters can be studied by correlations between factors, which can largely differ by ongoing cognitive task. We provide a simulation study to validate that the pipeline can recover the underlying structural and functional network. We also apply the proposed pipeline to empirical data to explore the structural network of ROIs obtained by the Gordon parcellation and study their functional coactivations across eight cognitive tasks and a resting-state condition.

Identifying influential nodes in complex networks based on spreading probability

The identification of node importance is a challenging topic in network science, and plays a critical role in understanding the structure and function of networks. Various centrality methods have been proposed to define the influence of nodes. However, most existing works do not directly use the node propagation capacity for measuring the importance of nodes. Moreover, those methods do not have a high enough ability to distinguish nodes with minor differences, and are not applicable to a wide range of network types. To address the issues, we first define a method to calculate the propagation capability of nodes and divide the nodes in the network into an infected source and the uninfected nodes. The propagation capability of a source node is calculated from the probability that uninfected nodes are infected by the source, either directly or indirectly. Based on measuring the propagation ability of each node in the network, we propose a novel centrality method based on node spreading probability (SPC). Empirical analysis is performed by Susceptible–Infected–Recovered (SIR) model and static attacking simulation. We use six classical networks, and five typical methods to validate SPC. The results demonstrate that our method balances the measurement of node importance in the network connectivity and propagation structure with superior ability to discriminate nodes.

The impact of sparsity in low-rank recurrent neural networks.

Neural population dynamics are often highly coordinated, allowing task-related computations to be understood as neural trajectories through low-dimensional subspaces. How the network connectivity and input structure give rise to such activity can be investigated with the aid of low-rank recurrent neural networks, a recently-developed class of computational models which offer a rich theoretical framework linking the underlying connectivity structure to emergent low-dimensional dynamics. This framework has so far relied on the assumption of all-to-all connectivity, yet cortical networks are known to be highly sparse. Here we investigate the dynamics of low-rank recurrent networks in which the connections are randomly sparsified, which makes the network connectivity formally full-rank. We first analyse the impact of sparsity on the eigenvalue spectrum of low-rank connectivity matrices, and use this to examine the implications for the dynamics. We find that in the presence of sparsity, the eigenspectra in the complex plane consist of a continuous bulk and isolated outliers, a form analogous to the eigenspectra of connectivity matrices composed of a low-rank and a full-rank random component. This analogy allows us to characterise distinct dynamical regimes of the sparsified low-rank network as a function of key network parameters. Altogether, we find that the low-dimensional dynamics induced by low-rank connectivity structure are preserved even at high levels of sparsity, and can therefore support rich and robust computations even in networks sparsified to a biologically-realistic extent.

To what extent do network effects moderate the relationship between social media propagated news and investors’ perceptions?

As a prominent social media tool, Twitter enables prompt dissemination of financial news and information, which can have a substantial impact on investors’ perceptions and decision-making processes. The propagation of financial news and information through Twitter can either positively or negatively affect investors’ perceptions. As per network theory, the impact of information on one's perception and behavior is known as the network effect. Since Twitter is also a network, we tried to contribute more to this theory in this study by considering other factors that can have an impact on the perceptions of investors. We argue that the impact of financial information and news on investors’ perceptions is moderated by other factors such as connectivity, social ties, and network size of the network. To establish the links between them, we considered three key factors in investors’ networks: (1) network connectivity (network structure); (2) social ties circle (friends, family, colleagues); and (3) size of the network (number of contacts). The results of this study indicate that highly connected investors receive more information and hence, the impact of news is derived from the connectivity of investors within the network. The findings of the study also show that the social ties circle plays a crucial role in determining the impact of the news. The findings further indicate that the impact of news on investors’ perceptions also depends on the theme of the news.

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns.

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.

Drawing the web structure and content analysis beyond the Tor darknet: Freenet as a case of study

The World Wide Web is the most widely used service on the Internet, although only a small part of it, the Surface Web, is indexed and accessible. The rest of the content, the Deep Web, is split between that unable to be indexed by usual search engines and content that needs to be accessed through specific methods and techniques. The latter is deployed in the so-called darknets, which have been the subject of much less study, where anonymity and privacy security services are preserved. Although there are several darknets, Tor is the most well-known and widely analyzed. Hence, the current work presents an analysis of web site connectivity, relationships and content of one of the less known and explored darknets: Freenet. Given the special features of this study, a new crawling tool, called c4darknet, was developed for the purpose of this work. This tool is, in turn, used in the experimentation that was carried out in a real distributed environment. Our results can be summarized as follows: there is great general availability of websites on Freenet; there are significant nodes within the network connectivity structure; and underage porn or child pornography is predominant among illegal content. Finally, the outcomes are compared against a similar study for the I2P darknet, showing special features and differences between both darknets.

Neuroimaging Parameters Are Not Associated With Chronic Post-stroke Fatigue in Young Stroke Patients.

IntroductionPost-stroke fatigue is frequently present in young adults, but its underlying mechanism is still unclear. The aim of the study was to investigate the association between lesion location, network efficiency and chronic post-stroke fatigue based on voxel-based lesion-symptom mapping and structural network connectivity analysis.Patients and MethodsOne hundred and thirty five young patients, aged 18–50 years, with a first-ever transient ischemic attack or cerebral infarction from the Follow-Up of Transient ischemic attack and stroke patients and Unelucidated Risk factor Evaluation (FUTURE) study, underwent 1.5T MRI and were assessed for fatigue using the self-report Checklist Individual Strength. Stroke lesions were manually segmented, and structural network efficiency was calculated using the diffusion MRI-based brain networks and graph theory for each patient. Univariate and multivariate analyses was performed to study the associations between MRI parameters and chronic post-stroke fatigue. In addition, we used voxel-based lesion-symptom mapping to analyze the relationship between the lesion location and chronic post-stroke fatigue.ResultsMean age at index event was 39.0 years (SD ± 8.2), and mean follow-up duration was 11.0 years (SD ± 8.0). 50 patients (37%) had post-stroke fatigue. Voxel-based lesion-symptom mapping showed no significant relation between stroke lesions and the presence of chronic post-stroke fatigue. Furthermore, there were no significant associations between the lesion size or network efficiency, and the presence of chronic post-stroke fatigue.DiscussionWe did not find any association between stroke characteristics (lesion location and size) and chronic post-stroke fatigue (CIS20-R), nor associations between structural brain network connectivity and post-stroke fatigue on the long term in young stroke patients.

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.

Fixed Point Attractor Theory Bridges Structure and Function in C. elegans Neuronal Network.

Understanding the structure–function relationship in a neuronal network is one of the major challenges in neuroscience research. Despite increasing researches at circuit connectivity and neural network structure, their structure-based biological interpretability remains unclear. Based on the attractor theory, here we develop an analytical framework that links neural circuit structures and their functions together through fixed point attractor in Caenorhabditis elegans. In this framework, we successfully established the structural condition for the emergence of multiple fixed points in C. elegans connectome. Then we construct a finite state machine to explain how functions related to bistable phenomena at the neural activity and behavioral levels are encoded. By applying the proposed framework to the command circuit in C. elegans, we provide a circuit level interpretation for the forward-reverse switching behaviors. Interestingly, network properties of the command circuit and first layer amphid interneuron circuit can also be inferred from their functions in this framework. Our research indicates the reliability of the fixed point attractor bridging circuit structure and functions, suggesting its potential applicability to more complex neuronal circuits in other species.

Multiple scenario simulation and optimization of an urban green infrastructure network based on complex network theory: a case study in Harbin City, China

BackgroundUrban green infrastructure (GI) networks play a significant role in ensuring regional ecological security; however, they are highly vulnerable to the influence of urban development, and the optimization of GI networks with better connectivity and resilience under different development scenarios has become a practical problem that urgently needs to be solved. Taking Harbin, a megacity in Northeast China, as the case study, we set five simulation scenarios by adjusting the economic growth rate and extracted the GI network in multiple scenarios by integrating the minimal cumulative resistance model and the gravity model. The low-degree-first (LDF) strategy of complex network theory was introduced to optimize the GI network, and the optimization effect was verified by robustness analysis.ResultsThe results showed that in the 5% economic growth scenario, the GI network structure was more complex, and the connectivity of the network was better, while in the other scenarios, the network structure gradually degraded with economic growth. After optimization by the LDF strategy, the average degree of the GI network in multiple scenarios increased from 2.368, 2.651, 2.189, 1.972, and 1.847 to 2.783, 3.125, 2.643, 2.414, and 2.322, respectively, and the GI network structure connectivity and resilience were significantly enhanced in all scenarios.ConclusionsEconomic growth did not necessarily lead to degradation of the GI network; there was still room for economic development in the study area, but it was limited under existing GI conditions, and the LDF strategy was an effective method to optimize the GI network. The research results provide a new perspective for the study of GI network protection with urban economic growth and serve as a methodological reference for urban GI network optimization.

A ketogenic intervention improves dorsal attention network functional and structural connectivity in mild cognitive impairment

Ketones, the brain's alternative fuel to glucose, bypass the brain glucose deficit and improve cognition in mild cognitive impairment (MCI). Our goal was to assess the impact of a 6-month ketogenic intervention on the functional connectivity within eight major brain resting-state networks, and its possible relationship to improved cognitive outcomes in the BENEFIC trial. MCI participants were randomized to a placebo (n = 15) or ketogenic medium chain triglyceride (kMCT; n = 17) intervention. kMCT was associated with increased functional connectivity within the dorsal attention network (DAN), which correlated to improvement in cognitive tests targeting attention. Ketone uptake (11C-acetoacetate PET) specifically in DAN cortical regions was highly increased in the kMCT group and was directly associated with the improved DAN functional connectivity. Analysis of the structural connectome revealed increased fiber density within the DAN following kMCT. Our findings suggest that ketones in MCI may prove beneficial for cognition at least in part because they improve brain network energy status, functional connectivity and axonal integrity.

Strengthened and posterior-shifted structural rich-club organization in people who use cocaine

BackgroundPeople with cocaine use disorder (CUD) often have abnormal cognitive function and brain structure. Cognition is supported by brain networks that typically have characteristics like rich-club organization, which is a group of regions that are highly connected across the brain and to each other, and small worldness, which is a balance between local and long-distance connections. However, it is unknown whether there are abnormalities in structural brain network connectivity of CUD. MethodsUsing diffusion-weighted imaging, we measured structural connectivity in 37 people with CUD and 38 age-matched controls. We identified differences in rich-club organization and whether such differences related to small worldness and behavior. We also tested whether rich-club reorganization was associated with caudate and putamen structural connectivity due to the relevance of the dopamine system to cocaine use. ResultsPeople with CUD had a higher normalized rich-club coefficient than controls, more edges connecting rich-club nodes to each other and to non-rich-club nodes, and fewer edges connecting non-rich-club nodes. Rich-club nodes were shifted posterior and lateral. Rich-club reorganization was related to lower clustered connectivity around individual nodes found in CUD, to increased impulsivity, and to a decrease in caudate connectivity. ConclusionsThese findings are consistent with previous work showing increased rich-club connectivity in conditions associated with a hypofunctional dopamine system. The posterior shift in rich-club nodes in CUD suggests that the structural connectivity of posterior regions may be more impacted than previously recognized in models based on brain function and morphology.

Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping.

Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.

Brain Network Associated with Three Types of Remote Associations: Graph Theory Analysis

ABSTRACT Remote association, the ability to form new relations between independent elements, was assessed using the Remote Associates Test (RAT). The Chinese RAT (CRAT) includes the Chinese radical RAT (CRRAT), Chinese word RAT (CWRAT), and Chinese compound RAT (CCRAT). Behavioral research indicates that an individual’s performance on the CRAT reflects different ways of remote associations, although there are few studies on brain networks. Therefore, this study analyzed the correlation between structural network connectivity and CRAT performance in 52 adults. The findings showed that CCRAT performance had a positive correlation with clustering coefficients, local efficiency, and global efficiency, but a negative correlation with the characteristic path length. CWRAT performance was positively correlated with normalized clustering coefficients and small-world-ness, and CRRAT performance was positively correlated with characteristic path length. This study enriches our understanding of the process of remote association by exploring different remote associations from a brain network perspective.

Age-specificity and generalization of behavior-associated structural and functional networks and their relevance to behavioral domains.

Behavior‐associated structural connectivity (SC) and resting‐state functional connectivity (rsFC) networks undergo various changes in aging. To study these changes, we proposed a continuous dimension where at one end networks generalize well across age groups in terms of behavioral predictions (age‐general) and at the other end, they predict behaviors well in a specific age group but fare poorly in another age group (age‐specific). We examined how age generalizability/specificity of multimodal behavioral associated brain networks varies across behavioral domains and imaging modalities. Prediction models consisting of SC and/or rsFC networks were trained to predict a diverse range of 75 behavioral outcomes in a young adult sample (N = 92). These models were then used to predict behavioral outcomes in unseen young (N = 60) and old (N = 60) subjects. As expected, behavioral prediction models derived from the young age group, produced more accurate predictions in the unseen young than old subjects. These behavioral predictions also differed significantly across behavioral domains, but not imaging modalities. Networks associated with cognitive functions, except for a few mostly relating to semantic knowledge, fell toward the age‐specific end of the spectrum (i.e., poor young‐to‐old generalizability). These findings suggest behavior‐associated brain networks are malleable to different degrees in aging; such malleability is partly determined by the nature of the behavior.

Structural connectivity of the sensorimotor network within the non-lesioned hemisphere of children with perinatal stroke

Perinatal stroke occurs early in life and often leads to a permanent, disabling weakness to one side of the body. To test the hypothesis that non-lesioned hemisphere sensorimotor network structural connectivity in children with perinatal stroke is different from controls, we used diffusion imaging and graph theory to explore structural topology between these populations. Children underwent diffusion and anatomical 3T MRI. Whole-brain tractography was constrained using a brain atlas creating an adjacency matrix containing connectivity values. Graph theory metrics including betweenness centrality, clustering coefficient, and both neighbourhood and hierarchical complexity of sensorimotor nodes were compared to controls. Relationships between these connectivity metrics and validated sensorimotor assessments were explored. Eighty-five participants included 27 with venous stroke (mean age = 11.5 ± 3.7 years), 26 with arterial stroke (mean age = 12.7 ± 4.0 years), and 32 controls (mean age = 13.3 ± 3.6 years). Non-lesioned primary motor (M1), somatosensory (S1) and supplementary motor (SMA) areas demonstrated lower betweenness centrality and higher clustering coefficient in stroke groups. Clustering coefficient of M1, S1, and SMA were inversely associated with clinical motor function. Hemispheric betweenness centrality and clustering coefficient were higher in stroke groups compared to controls. Hierarchical and average neighbourhood complexity across the hemisphere were lower in stroke groups. Developmental plasticity alters the connectivity of key nodes within the sensorimotor network of the non-lesioned hemisphere following perinatal stroke and contributes to clinical disability.

Encoding time in neural dynamic regimes with distinct computational tradeoffs.

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

Effect of low aspect ratio one-dimensional nanoparticles on properties of photocrosslinked alginate nanocomposite hydrogels

One-dimensional nanoparticles such as fibers, wires, represent an exciting type of filler for obtaining nanocomposite hydrogels due to their high aspect ratio. Unlike continuous fibers, in composites with short fibers, stress transfer occurs not only at the cylindrical surface of the fiber but also at the fiber ends. This work aimed to study the influence of the length of low aspect ratio one-dimensional nanoparticles on the rheological, swelling and thermal properties of nanocomposite hydrogels. We synthesized nanocomposite hydrogels via photopolymerization of methacrylated alginate macromonomers and aluminum oxide nanoparticles, nanowires and nanofibers as fillers. The main difference between the nanoparticles lies in their length. Longer nanoparticles (nanofibers) provided a glassier structure and a more significant reinforcement to the hydrogel, for example, at the concentration of 0.5 wt% the complex modulus increased approximately 2-fold and 8-fold for nanowires and nanofibers, respectively. Similarly, the incorporation of nanofibers produced hydrogels with a lower swelling capacity (2.5-fold decrease), which was attributed to a more connected network structure due to the higher aspect ratio of the nanofibers. Finally, both nanoparticle types produced an increase in the activation energy of thermal degradation.