Structural Network Metrics Research Articles

Impacts of an invasion by green crab Carcinus maenas on the intertidal food web of a Patagonian rocky shore, Argentina

The green crabCarcinus maenas(L., 1758) is a highly invasive species that has altered various coastal marine ecosystems worldwide. Green crabs were first found on the Argentinian Patagonian coast in 2001 and were detected in Golfo Nuevo (Chubut, Argentina) in 2015. In this study, we described a rocky intertidal food web in the Golfo Nuevo and evaluated the effect of green crab invasion on the network. We assembled the intertidal food web from a compilation of diet studies and compared 2 scenarios: pre-invasion (without green crab) and post-invasion (with green crab). We calculated several structural network metrics to compare the scenarios. The green crab was positioned as a top predator in the intertidal food web, consuming prey from different trophic levels and exhibiting the capacity to exert top-down control. Green crab presence altered the structure and stability of the food web. Additionally, we evaluated the effect of potential competitive interactions between native species and the green crab on the food web. Possible competitive interactions increased the effect the green crab had on the structure of the food web. Our results show that green crab invasion decreased food web stability by reducing the capacity of the food web to contain small- and long-range disturbances. These results contribute to the assessment and monitoring of the possible effects of other factors of global change. The system is less stable than before green crab invasion, and the drastic changes in community composition already recorded in rocky intertidal ecosystems in Patagonia are more likely to amplify through the food web.

Voxel-based correlation of 18F-THK5351 accumulation with gray matter structural networks in cognitively normal older adults.

ObjectiveThe aim of this study was to evaluate tau-related structural network metrics derived from gray matter magnetic resonance imaging (MRI) scans in cognitively normal (CN) older adults.MethodsWe recruited 47 amyloid-negative CN older adults (mean age ± standard deviation, 65.0 ± 7.9 years; 26 women). All participants underwent 3D T1-weighted MRI and 11C-Pittsburgh compound-B and 18F-THK5351 positron emission tomography scans. Four local network metrics (betweenness centrality, clustering coefficient, characteristic path length, and degree) were computed and rendered on individual brain images. We then evaluated the correlations between 18F-THK5351 positron emission tomography images and local network metric images at the voxel level.ResultsSignificant positive correlations of the four local network metrics with 18F-THK5351 were detected in the bilateral caudate.ConclusionOur findings suggest that tau and neuroinflammation in CN older adults may influence the gray matter structural network in the caudate.

Geophysical templates modulate the structure of stream food webs dominated by omnivory

AbstractFood webs show the architecture of trophic relationships, revealing the biodiversity and species interactions in an ecosystem. Understanding which factors modulate the structure of food webs offers us the ability to predict how they will change when influential factors are altered. To date, most of the research about food webs has focused on species interactions whereas the influences of surrounding environments have been overlooked. Here, using network analysis, we identified how the structure of aquatic food webs varied across a range of geophysical conditions within a whole stream system. Within a headwater basin in the Cascade Mountain Range, Oregon, USA, macroinvertebrate and vertebrate composition was investigated at 18 sites. Predator–prey interactions were compiled based on existing literature and dietary analysis. Several structural network metrics were calculated for each food web. We show that the structure of food webs was predictable based on geophysical features at both local (i.e., slope) and broader (i.e., basin size) spatial extents. Increased omnivory, greater connectance, shorter path lengths, and ultimately greater complexity and resilience existed downstream compared to upstream in the stream network. Surprisingly, the variation in food web structure was not associated with geographic proximity. Structural metric values and abundance of omnivory suggest high levels of stability for these food webs. There is a predictable variation in the structure of food webs across the network that is influenced by both longitudinal position within streams and patchy discontinuities in habitat. Hence, findings illustrate that the slightly differing perspectives from the River Continuum Concept, Discontinuity Patch Dynamics, and Process Domains can be integrated and unified using food web networks. Our analyses extend ecologists’ understanding of the stability of food webs and are a vital step toward predicting how webs and communities may respond to both natural disturbances and current global environmental change.

Diffusion structural network metrics as a predictor for Alzheimer’s disease diagnosis in Down syndrome using support vector machine classification

AbstractBackgroundAlzheimer’s Disease (AD) is known to pathologically develop in Downs Syndrome in almost one hundred percent of cases, although at present structural changes to the brain are minimally utilized for diagnoses of neurodegeneration due to limited understanding of which white matter alterations are directly relevant to clinical presentation. By novel application of machine learning to metrics of the structural connectome in Downs Syndrome, this project aims to identify specific white matter networks that significantly impact classification of cases into AD positive or negative.MethodA pilot sample of 21 healthy Down’s syndrome participants and 4 Down’s syndrome participants with a diagnostic consensus of either mild cognitive impairment (MCI) or dementia underwent diffusion‐weighted and T1‐weighted MRI scanning at 3T. These data form part of a consortium, which will be utilised to increase sample size for future iterations of this analysis. Whole brain structural connectomes were produced for each participant, utilizing fibre orientation distributions and spherical deconvolution informed filtering of the tractograms. For accurate localisation of nodes to brain regions, the T1‐weighted scan was pre‐processed, segmented, parcellated and co‐registered to diffusion space. Networks were created by examining connection density between T1‐delineated structural nodes. Connection density matrices were used to train and test a support vector machine (SVM) classifier.ResultsPreliminary results show widespread reduction in network structural connectivity in the Down’s syndrome participants diagnosed with dementia or mild cognitive impairment (Fig. 1). The parietal, occipital and to a lesser extent the temporal areas of the network exhibit the most marked decreases in connection density, and no edges of the network displayed increased connection density in dementia or MCI patients compared to healthy Downs syndrome. Multivariate analyses indicate that although SVM classification of the networks has a >80% accuracy, false‐negative classifications were common due to the low number of Down’s syndrome dementia participants.ConclusionsThis pilot application of SVM to diffusion‐derived brain networks in Down’s syndrome demonstrates utility and feasibility as a diagnostic classifier. We expect that increased sample size should yield a more robust trained model.

Altered brain network integrity in patients with asthma: A structural connectomic diffusion tensor imaging study

Brain functional deficits had been reported in asthma patients. These deficits may be related to treatment resistance, inaccurate self-assessment and poor self-management. However, changes of the structural brain network in asthma patients remain largely unclear. Diffusion tensor imaging were acquired from 54 asthmatic patients and 44 controls. Then we calculated all the participants’ structural network metrics. All the participants underwent the test of Hamilton Rating Scale for Depression and Anxiety as well as a lung function. Multiple linear correlation analyses were conducted. At the global level, asthma patients had a higher path length and lower global efficiency than controls, implying a shift toward regular networks. At the local level, asthma patients exhibited abnormal nodal connectivity with other nodes involved the fronto-limbic regions. Our findings highlight more locally segregated but less efficiently integrated structural networks, particularly involving frontal-limbic networks, in asthmatic patients. These findings provide important evidence to support the role of brain networks in the pathophysiology of asthma.

Structural network disruption markers explain disability in multiple sclerosis

ObjectiveTo evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions.MethodsThis cross-sectional study included...

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer\u2019s disease

BackgroundAnimal models of Alzheimer’s disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present.MethodsCognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats.ResultsThe results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes.ConclusionsIn this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.

A diagnosis model for amnestic mild cognitive impairment based on structural network characteristics of the brain

The aim of this work is to select topological characteristics of structural network which were higher correlated with cognitive performance and estimate the classification models to classify normal aging and amenstic mild cognitive impairment( aMCI) patients. In two groups of diffusion tensor image( DTI) dataset,a group has 52 normal aging subjects and another group has 39 aMCI patients. We employed the diffusion tensor image to construct the structural network in each group and used the method of graph theory to extract the characteristics of structural network. We selected significant features by the correlation analysis between the characteristics of brain network and subject's mini-mental state examination( MMSE) score. These features were used to establish five kinds of classification model for evaluating the classification efficiency of the models.For normal aging dataset and aMCI dataset,18 characteristics of the structural network were selected as features,which are significantly associated with cognitive ability and locate in 9 brain areas according to the automated anatomical labeling( AAL) template in each group. However,the features and the related regions are different for the two datasets. Among 5 algorithms,sequential minimal optimization learning algorithm for support vector machine regression model was more accurate due to the higher specificity of 88. 46%,the higher sensitivity of 83. 05% and the higher accuracy of 85. 71%. Brain structural network metrics which were correlated greatly with the cognitive performance could be taken as biological marker pointers to establish the classification model to classify normal aging subjects and aMCI patients. Furthermore,these structural network features can provide the information of connection changes between corresponding brain regions.

Information flows and centrality among elite European newspapers

Combining citations and network analysis, this study examines information flows between 10 European elite newspapers from 2000 to 2009 and identifies this network’s most central actors, sub-groups and structural features. At the same time, the article contributes to the literature with an alternative and network approach to the study of the European public sphere. Results indicate that The Times and The Guardian are the most quoted by other foreign newspapers, while the top monitors of information are The Guardian and El País. A longitudinal analysis of structural network metrics indicates a less dense but more inclusive information exchange that can be interpreted as sign of a qualitative transformation of the European communication space in the direction of a horizontal integration.

Optimization of seed density in DTI tractography for structural networks

Diffusion tensor imaging (DTI) has been used for mapping the structural network of the human brain. The network can be constructed by choosing various brain regions as nodes and fiber tracts connecting those regions as links. The structural network generated from DTI data can be affected by noise in the scans and the choice of tractography algorithm. This study aimed to examine the effect of the number of seeds in tractography on the variance of structural networks. The variance of the network was characterized using an approach similar to the National Electrical Manufacturers Association (NEMA) standards for measurement of image noise. It was shown that the variance of the network is inversely related to the square root of seed density. Consequently, the number of seeds has a large impact on local characteristics and metrics of the network architecture. As the number of seeds increased, increased stability of structural network metrics was observed. However, more seeds can also lead to more spurious fibers and thus affect nodal degrees and edge weights, and proper thresholding may be necessary to create an appropriate weighted network. Because the variance of the network is also influenced by other imaging factors, further increase in the number of seeds has little effect in reducing the network variance. The selection of the seed number should be a balance between the network variance and computational effort.

The modified niche model: Including detritus in simple structural food web models

Food webs are constructed as structural directed graphs that describe “who eats whom,” but it is common to interpret them as energy flow diagrams where predation represents an energy transfer from the prey to the predator. It is the aim of this work to demonstrate that food webs are incomplete as energy flow diagrams if they ignore passive flows to detritus (dead organic material). While many ecologists do include detritus in conceptual and mathematical models, the detrital omission is still commonly found. Often detritus is either ignored or treated as an unlimited energy source, yet all organisms contribute to the detritus pool, which can be an energy source for other species in the system. This feedback loop is of high importance, since it increases the number of pathways available for energy flows, revealing the significance of indirect effects, and making the functional role of the top predators less clear. In this work we propose the modified niche model by adding a detritus compartment to the niche model. We demonstrate the effect of structural loops that result from feeding on detritus, by comparing empirical data sets to five different assembly models: (1) cascade, (2) constant connectance, (3) niche, (4) modified niche (original in this work), and (5) cyber-ecosystem. Of these models, only the last two explicitly include detritus. We show that when passive flows to detritus are included in the food web structure, the structure becomes more robust to the removal of individual nodes or connections. In addition, we show that food web models that include the detritus feedback loop perform better with respect to several structural network metrics.