Measures Of Network Topology Research Articles

Impaired Topographic Organization in Patients With Idiopathic Blepharospasm.

Background: Idiopathic blepharospasm (BSP) is a common adult-onset focal dystonia. Neuroimaging technology can be used to visualize functional and microstructural changes of the whole brain.Method: We used resting-state functional MRI (rs-fMRI) and graph theoretical analysis to explore the functional connectome in patients with BSP. Altogether 20 patients with BSP and 20 age- and gender-matched healthy controls (HCs) were included in the study. Measures of network topology were calculated, such as small-world parameters (clustering coefficient [Cp], the shortest path length [Lp]), network efficiency parameters (global efficiency [Eglob], local efficiency [Eloc]), and the nodal parameter (nodal efficiency [Enod]). In addition, the least absolute shrinkage and selection operator (LASSO) regression was adopted to determine the most critical imaging features, and the classification model using critical imaging features was constructed.Results: Compared with HCs, the BSP group showed significantly decreased Eloc. Imaging features of nodal centrality (Enod) were entered into the LASSO method, and the classification model was constructed with nine imaging nodes. The area under the curve (AUC) was 0.995 (95% CI: 0.973–1.000), and the sensitivity and specificity were 95% and 100%, respectively. Specifically, four imaging nodes within the sensorimotor network (SMN), cerebellum, and default mode network (DMN) held the prominent information. Compared with HCs, the BSP group showed significantly increased Enod in the postcentral region within the SMN, decreased Enod in the precentral region within the SMN, increased Enod in the medial cerebellum, and increased Enod in the precuneus within the DMN.Conclusion: The network model in BSP showed reduced local connectivity. Baseline connectomic measures derived from rs-fMRI data may be capable of identifying patients with BSP, and regions from the SMN, cerebellum, and DMN may provide key insights into the underlying pathophysiology of BSP.

Identification of key miRNAs in prostate cancer progression based on miRNA-mRNA network construction

Prostate cancer (PC) is one of the major male cancers. Differential diagnosis of PC is indispensable for the individual therapy, i.e., Gleason score (GS) that describes the grade of cancer can be used to choose the appropriate therapy. However, the current techniques for PC diagnosis and prognosis are not always effective.To identify potential markers that could be used for differential diagnosis of PC, we analyzed miRNA-mRNA interactions and we build specific networks for PC onset and progression. Key differentially expressed miRNAs for each GS were selected by calculating three parameters of network topology measures: the number of their single regulated mRNAs (NSR), the number of target genes (NTG) and NSR/NTG. miRNAs that obtained a high statistically significant value of these three parameters were chosen as potential biomarkers for computational validation and pathway analysis.20 miRNAs were identified as key candidates for PC. 8 out of 20 miRNAs (miR-25-3p, miR-93-3p, miR-122-5p, miR-183-5p, miR-615-3p, miR-7-5p, miR-375, and miR-92a-3p) were differentially expressed in all GS and proposed as biomarkers for PC onset. In addition, “Extracellular-receptor interaction”, “Focal adhesion”, and “microRNAs in cancer” were significantly enriched by the differentially expressed target genes of the identified miRNAs. miR-10a-5p was found to be differentially expressed in GS 6, 7, and 8 in PC samples.3 miRNAs were identified as PC GS-specific differentially expressed miRNAs: miR-155-5p was identified in PC samples with GS 6, and miR-142-3p and miR-296-3p in PC samples with GS 9.The efficacy of 20 miRNAs as potential biomarkers was revealed with a Random Forest classification using an independent dataset. The results demonstrated our 20 miRNAs achieved a better performance (AUC: 0.73) than miRNAs selected with Boruta algorithm (AUC: 0.55), a method for the automated feature extraction.Studying miRNA-mRNA associations, key miRNAs were identified with a computational approach for PC onset and progression. Further experimental validations are needed for future translational development.

An introduction to network analysis for studies of medication use.

Network Analysis (NA) is a method that has been used in various disciplines such as Social sciences and Ecology for decades. So far, NA has not been used extensively in studies of medication use. Only a handful of papers have used NA in Drug Prescription Networks (DPN). We provide an introduction to NA terminology alongside a guide to creating and extracting results from the medication networks. To introduce the readers to NA as a tool to study medication use by demonstrating how to apply different NA measures on 3 generated medication networks. We used the Norwegian Prescription Database (NorPD) to create a network that describes the co-medication in elderly persons in Norway on January 1, 2013. We used the Norwegian Electronic Prescription Support System (FEST) to create another network of severe drug-drug interactions (DDIs). Lastly, we created a network combining the two networks to show the actual use of drugs with severe DDIs. We used these networks to elucidate how to apply and interpret different network measures in medication networks. Interactive network graphs are made available online, Stata and R syntaxes are provided. Various useful network measures for medication networks were applied such as network topological features, modularity analysis and centrality measures. Edge lists data used to generate the networks are openly available for readers in an open data repository to explore and use. We believe that NA can be a useful tool in medication use studies. We have provided information and hopefully inspiration for other researchers to use NA in their own projects. While network analyses are useful for exploring and discovering structures in medication use studies, it also has limitations. It can be challenging to interpret and it is not suitable for hypothesis testing.

The Deficits of Individual Morphological Covariance Network Architecture in Schizophrenia Patients With and Without Violence.

Background: Schizophrenia is associated with a significant increase in the risk of violence, which constitutes a public health concern and contributes to stigma associated with mental illness. Although previous studies revealed structural and functional abnormalities in individuals with violent schizophrenia (VSZ), the neural basis of psychotic violence remains controversial.Methods: In this study, high-resolution structural magnetic resonance imaging (MRI) data were acquired from 18 individuals with VSZ, 23 individuals with non-VSZ (NSZ), and 22 age- and sex-matched healthy controls (HCs). Whole-brain voxel-based morphology and individual morphological covariance networks were analysed to reveal differences in gray matter volume (GMV) and individual morphological covariance network topology. Relationships among abnormal GMV, network topology, and clinical assessments were examined using correlation analyses.Results: GMV in the hypothalamus gradually decreased from HCs and NSZ to VSZ and showed significant differences between all pairs of groups. Graph theory analyses revealed that morphological covariance networks of HCs, NSZ, and VSZ exhibited small worldness. Significant differences in network topology measures, including global efficiency, shortest path length, and nodal degree, were found. Furthermore, changes in GMV and network topology were closely related to clinical performance in the NSZ and VSZ groups.Conclusions: These findings revealed the important role of local structural abnormalities of the hypothalamus and global network topological impairments in the neuropathology of NSZ and VSZ, providing new insight into the neural basis of and markers for VSZ and NSZ to facilitate future accurate clinical diagnosis and targeted treatment.

Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks

Electric Power Networks (EPNs) play a fundamental role in the wellbeing of modern societies and recovery of societal functions after an earthquake. Risk and resilience analyses may identify useful network characteristics to improve EPN response and recovery during and after a severe seismic event. This work computes different functional measures in order to: (i) estimate the actual risk and resilience of EPNs; and (ii) evaluate the predictive capacity of different topological measures (TMs) relative to the EPN earthquake risk performance. The analysis is carried out on the Chilean EPN at the national, regional and substation level, by using a detailed model of the network. EPN operation was modeled using the DC optimal power flow model from the time of earthquake occurrence until full system recovery using the Seismic Probabilistic Risk Assessment framework. Seismic risk and resilience estimations of Energy Not Supplied (ENS) and number of hours with ENS have been correlated with six network TMs. Linear correlation results show that TMs provide, in general, limited insight into the criticality of the Chilean EPN. In spite of that, the strongest correlation was observed for the degree TM. Moreover, the Damage Consequence Index confirmed the rather uniformly distributed seismic risk along the country.

Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation.

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

Structural network topology and microstructural alterations of the anterior insula associate with cognitive and affective impairment in Parkinson\u2019s disease

The aim of the current study was to assess the structural centrality and microstructural integrity of the cortical hubs of the salience network, the anterior insular cortex (AIC) subregions and anterior cingulate cortex (ACC), and their relationship to cognitive and affective impairment in PD. MRI of 53 PD patients and 15 age-matched controls included 3D-T1 for anatomical registration, and diffusion tensor imaging for probabilistic tractography. Network topological measures of eigenvector and betweenness centrality were calculated for ventral (vAI) and dorsal (dAI) AIC. Microstructural tract integrity between vAI, dAI and the ACC was quantified with fractional anisotrophy (FA) and mean diffusivity (MD). Structural integrity and connectivity were related to cognitive and affective scores. The dAI had significantly higher eigenvector centrality in PD than controls (p < 0.01), associated with higher depression scores (left dAI only, rs = 0.28, p < 0.05). Tracts between dAI and ACC showed lower FA and higher MD in PD (p < 0.05), and associated with lower semantic fluency, working memory and executive functioning, and higher anxiety scores (range 0.002 < p < 0.05). This study provides evidence for clinically relevant structural damage to the cortical hubs of the salience network in PD, possibly due to extensive local neuropathology and loss of interconnecting AIC-ACC tracts.

Functional network topology of the right insula affects emotion dysregulation in hyperactive-impulsive attention-deficit/hyperactivity disorder

Emotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in young adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficits in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroIMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and was associated with emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in young adult ADHD.

Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input–output tables

A multi-regional input–output table (MRIOT) containing the transactions among the region-sectors in an economy defines a weighted and directed network. Using network analysis tools, we analyze the regional and sectoral structure of the Chinese economy with the province-sector MRIOTs of China in 2007 and 2012. Global analyses are done with network topology measures. Growth-driving province-sector clusters are identified with community detection methods. Influential province-sectors are ranked by weighted PageRank scores. The results have revealed a few interesting and telling insights. The level of inter-province activities increased with the rapid growth of the national economy, but not as fast as that of intra-province economic activities. Regional community structures were deeply associated with geographical factors. The community heterogeneity across the regions was high and the regional fragmentation increased during the study period. Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.

Topological Aberrance of Structural Brain Network Provides Quantitative Substrates of Post-Traumatic Brain Injury Attention Deficits in Children.

Background: Traumatic brain injury (TBI)-induced attention deficits are among the most common long-term cognitive consequences in children. Most of the existing studies attempting to understand the neuropathological underpinnings of cognitive and behavioral impairments in TBI have utilized heterogeneous samples and resulted in inconsistent findings. The current research proposed to investigate topological properties of the structural brain network in children with TBI and their relationship with post-TBI attention problems in a more homogeneous subgroup of children who had severe post-TBI attention deficits (TBI-A). Materials and Methods: A total of 31 children with TBI-A and 35 group-matched controls were involved in the study. Diffusion tensor imaging-based probabilistic tractography and graph theoretical techniques were used to construct the structural brain network in each subject. Network topological properties were calculated in both global level and regional (nodal) level. Between-group comparisons among the topological network measures and analyses for searching brain-behavioral were all corrected for multiple comparisons using Bonferroni method. Results: Compared with controls, the TBI-A group showed significantly higher nodal local efficiency and nodal clustering coefficient in left inferior frontal gyrus and right transverse temporal gyrus, whereas significantly lower nodal clustering coefficient in left supramarginal gyrus and lower nodal local efficiency in left parahippocampal gyrus. The temporal lobe topological alterations were significantly associated with the post-TBI inattentive and hyperactive symptoms in the TBI-A group. Conclusion: The results suggest that TBI-related structural re-modularity in the white matter subnetworks associated with temporal lobe may play a critical role in the onset of severe post-TBI attention deficits in children. These findings provide valuable input for understanding the neurobiological substrates of post-TBI attention deficits, and have the potential to serve as quantitatively measurable criteria guiding the development of more timely and tailored strategies for diagnoses and treatments to the affected individuals. Impact statement This study provides a new insight into the neurobiological substrates associated with post-traumatic brain injury attention deficits (TBI-A) in children, by evaluating topological alterations of the structural brain network. The results demonstrated that relative to group-matched controls, the children with TBI-A had significantly altered nodal local efficiency and nodal clustering coefficient in temporal lobe, which strongly linked to elevated inattentive and hyperactive symptoms in the TBI-A group. These findings suggested that white matter structural re-modularity in subnetworks associated with temporal lobe may serve as quantitatively measurable biomarkers for early prediction and diagnosis of post-TBI attention deficits in children.

Do the mobility patterns for city taxicabs impact road safety?

Recently, large investments have been made by cities such as Singapore, New York City, and London towards creating smart city initiatives in the areas of traffic safety enhancement and higher mobility. In this article, we investigate the impact of various network topology measures on the number of vehicle crashes in a city mobility network. Extant literature on mobility in city traffic networks has not studied the impact of network structure on road accidents. We fill this important gap by identifying the structural properties of critical zones in the city traffic network, which have a high risk of vehicle crashes. We use econometric methods to analyze a large dataset on city mobility from the New York Taxi and Limousine Commission, and a dataset on motor vehicle collisions from the New York Police Department; and derive various insights on the scope of traffic safety issues in a smart city. Our dataset has information on about 100,000,000 taxi trips over the year 2018. In this year, around 1,500,000 vehicle crash events were reported in New York City. One would expect that due to a large number of shortest paths, the number of accidents should be significantly more in the high betweenness centrality zones in the traffic mobility network. However, our analysis reveals that zones with high betweenness centrality tend to have a lower number of accidents. Furthermore, zones with a high degree centrality in the traffic mobility network are associated with a higher number of vehicle crash incidents. Our study reveals some crucial pointers for smart city policymakers and the operations managers of ride-sharing companies on how information on the mobility patterns of the high accident risk zones can be leveraged to reduce motor vehicle collisions.

Brain network analysis reveals that amyloidopathy affects comorbid cognitive dysfunction in older adults with depression

Late-life depression (LLD) may increase the risk of Alzheimer’s dementia (AD). While amyloidopathy accelerates AD progression, its role in such patients has not yet been elucidated. We hypothesized that cerebral amyloidopathy distinctly affects the alteration of brain network topology and may be associated with distinct cognitive symptoms. We recruited 26 and 27 depressed mild cognitive impairment (MCI) patients with (LLD-MCI-A(+)) and without amyloid accumulation (LLD-MCI-A(−)), respectively, and 21 normal controls. We extracted structural brain networks using their diffusion-weighted images. We aimed to compare the distinct network deterioration in LLD-MCI with and without amyloid accumulation and the relationship with their distinct cognitive decline. Thus, we performed a group comparison of the network topological measures and investigated any correlations with neurocognitive testing scores. Topological features of brain networks were different according to the presence of amyloid accumulation. Disrupted network connectivity was highly associated with impaired recall and recognition in LLD-MCI-A(+) patients. Inattention and dysexecutive function were more influenced by the altered networks involved in fronto-limbic circuitry dysfunction in LLD-MCI-A(−) patients. Our results show that alterations in brain network topology may reflect different cognitive dysfunction depending on amyloid accumulation in depressed older adults with MCI.

Event-related network changes unfold the dynamics of cortical integration during face processing.

Face perception arises from a collective activation of brain regions in the occipital, parietal and temporal cortices. Despite the wide acknowledgment that these regions act in an intertwined network, the network behavior itself is poorly understood. Here we present a study in which time-varying connectivity estimated from EEG activity elicited by facial expressions presentation was characterized using graph-theoretical measures of node centrality and global network topology. Results revealed that face perception results from a dynamic reshaping of the network architecture, characterized by the emergence of hubs located in the occipital and temporal regions of the scalp. The importance of these nodes can be observed from the early stages of visual processing and reaches a climax in the same time-window in which the face-sensitive N170 is observed. Furthermore, using Granger causality, we found that the time-evolving centrality of these nodes is associated with ERP amplitude, providing a direct link between the network state and local neural response. Additionally, investigating global network topology by means of small-worldness and modularity, we found that face processing requires a functional network with a strong small-world organization that maximizes integration, at the cost of segregated subdivisions. Interestingly, we found that this architecture is not static, but instead, it is implemented by the network from stimulus onset to ~200 ms. Altogether, this study reveals the event-related changes underlying face processing at the network level, suggesting that a distributed processing mechanism operates through dynamically weighting the contribution of the cortical regions involved.

Assessing the impact of taxon resolution on network structure.

Constructing ecological networks has become an indispensable approach in understanding how different taxa interact. However, the methods used to generate data in network research vary widely among studies, potentially limiting our ability to compare results meaningfully. In particular, methods of classifying nodes vary in their precision, likely altering the architecture of the network studied. For example, rather than being classified as Linnaean species, taxa are regularly assigned to morphospecies in observational studies, or to molecular operational taxonomic units (MOTUs) in molecular studies, with the latter defined based on an arbitrary threshold of sequence similarity. Although the use of MOTUs in ecological networks holds great potential, especially for allowing rapid construction of large data sets of interactions, it is unclear how the choice of clustering threshold can influence the conclusions obtained. To test the impact of taxonomic precision on network architecture, we obtained and analyzed 16 data sets of ecological interactions, inferred from metabarcoding and observations. Our comparisons of networks constructed under a range of sequence thresholds for assigning taxa demonstrate that even small changes in node resolution can cause wide variation in almost all key metric values. Moreover, relative values of commonly used metrics such as robustness were seen to fluctuate continuously with node resolution, thereby potentially causing error in conclusions drawn when comparing multiple networks. In observational networks, we found that changing node resolution could, in some cases, lead to substantial changes to measurements of network topology. Overall, our findings highlight the importance of classifying nodes to the greatest precision possible, and demonstrate the need for caution when comparing networks that differ with respect to node resolution, even where taxonomic groups and interaction types are similar. In such cases, we recommend that comparisons of networks should focus on relative differences rather than absolute values between the networks studied.

Exploring the dimensions of metapopulation persistence: a comparison of structural and temporal measures

The spatial arrangement of habitat patches in a metapopulation and the dispersal connections among them influence metapopulation persistence. Metapopulation persistence emerges from a dynamic process, namely the serial extinctions and recolonizations of local habitat patches, while measures of persistence are typically based solely on structural properties of the spatial network (e.g., spatial distance between sites). Persistence estimators based on static properties may be unable to capture the dynamic nature of persistence. Understanding the shape of the distribution of extinction times is a central goal in population ecology. Here, we examine the goodness of fit of the power law to patch persistence time distributions using data on a foundational metapopulation system—the Glanville fritillary butterfly in the Åland islands. Further, we address the relationship between structural measures of metapopulation persistence (i.e., metapopulation capacity) and our temporal distributional fits to patch persistence times based on a power law. Patch persistence time distributions were well fit by a power law for the majority of semi-independent networks. Power law fits to persistence time distributions were related to metapopulation capacity, linking structural and temporal measures of metapopulation persistence. Several environmental variables and measures of network topology were correlated with both measures of metapopulation persistence, though correlations tended to be stronger for the structural measure of metapopulation persistence (i.e., metapopulation capacity). Together, our findings suggest that persistence time distributions are useful dynamic properties of metapopulations, and provide evidence of a relationship between metapopulation structure and metapopulation dynamics.

Text structuring methods based on complex network: a systematic review

Currently, there is a large amount of text being shared through the Internet. These texts are available in different forms—structured, unstructured and semi structured. There are different ways of analyzing texts, but domain experts usually divide this process in some steps such as pre-processing, feature extraction and a final step that could be classification, clustering, summarization, and keyword extraction, depending on the purpose over the text. For this processing, several approaches have been proposed in the literature based on variations of methods like artificial neural network and deep learning. In this paper, we conducted a systematic review of papers dealing with the use of complex networks approaches for the process of analyzing text. The main results showed that complex network topological properties, measures and modeling can capture and identify text structures concerning different purposes such as text analysis, classification, topic and keyword extraction, and summarization. We conclude that complex network topological properties provide promising strategies with respect of processing texts, considering their different aspects and structures.

Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment

There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer’s disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive (‘Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.

Network topology changes in chronic mild traumatic brain injury (mTBI)

BackgroundIn mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. ObjectiveFew studies have assessed longitudinal changes in network topology in chronic mTBI. We utilized a graph theoretical approach to investigate alterations in global network topology based on resting-state functional connectivity in veterans with chronic mTBI. Methods50 veterans with chronic mTBI (mean of 20.7 yrs. from trauma) and 40 age-matched controls underwent two functional magnetic resonance imaging scans 18 months apart. Graph theory analysis was used to quantify network topology measures (density, clustering coefficient, global efficiency, and modularity). Hierarchical linear mixed models were used to examine longitudinal change in network topology. ResultsWith all network measures, we found a significant group × time interaction. At baseline, brain networks of individuals with mTBI were less clustered (p = 0.03) and more modular (p = 0.02) than those of HC. Over time, the mTBI networks became more densely connected (p = 0.002), with increased clustering (p = 0.001) and reduced modularity (p < 0.001). Network topology did not change across time in HC. ConclusionThese findings demonstrate that brain networks of individuals with mTBI remain plastic decades after injury and undergo significant changes in network topology even at the later phase of the disease.

Topological network measures for drug repositioning.

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

White matter integrity and structural brain network topology in cerebral small vessel disease: The Hamburg city health study.

Cerebral small vessel disease is a common finding in the elderly and associated with various clinical sequelae. Previous studies suggest disturbances in the integration capabilities of structural brain networks as a mediating link between imaging and clinical presentations. To what extent cerebral small vessel disease might interfere with other measures of global network topology is not well understood. Connectomes were reconstructed via diffusion weighted imaging in a sample of 930 participants from a population based epidemiologic study. Linear models were fitted testing for an association of graph‐theoretical measures reflecting integration and segregation with both the Peak width of Skeletonized Mean Diffusivity (PSMD) and the load of white matter hyperintensities of presumed vascular origin (WMH). The latter were subdivided in periventricular and deep for an analysis of localisation‐dependent correlations of cerebral small vessel disease. The median WMH volume was 0.6 mL (1.4) and the median PSMD 2.18 mm2/s x 10−4 (0.5). The connectomes showed a median density of 0.880 (0.030), the median values for normalised global efficiency, normalised clustering coefficient, modularity Q and small‐world propensity were 0.780 (0.045), 1.182 (0.034), 0.593 (0.026) and 0.876 (0.040) respectively. An increasing burden of cerebral small vessel disease was significantly associated with a decreased integration and increased segregation and thus decreased small‐worldness of structural brain networks. Even in rather healthy subjects increased cerebral small vessel disease burden is accompanied by topological brain network disturbances. Segregation parameters and small‐worldness might as well contribute to the understanding of the known clinical sequelae of cerebral small vessel disease.