scholarly journals Network analysis shows decreased ipsilesional structural connectivity in glioma patients

2021 ◽  
Author(s):  
Lucius Samo Fekonja ◽  
Ziqian Wang ◽  
Alberto Cacciola ◽  
Timo Roine ◽  
Baran D. Aydogan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Information Transfer ◽  
Structural Connectivity ◽  
Local Network ◽  
Neurological Assessment ◽  
Multiple Systems ◽  
Local Efficiency ◽  
Network Properties ◽  
Global And Local ◽  
Theoretical Analyses

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to 25 assess local network differences (1) and graph theoretical analyses (2) enable investigation of global and local network properties. Here, we used network measures to characterize gliomarelated decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long 30 connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks. In network science, reduced global and local efficiency reflect the impairment of information transfer across different regions of a network.

scholarly journals Amplitude and phase coupling optimize information transfer between brain networks that function at criticality

2021 ◽  
Author(s):  
Arthur-Ervin Avramiea ◽  
Anas Masood ◽  
Huibert D Mansvelder ◽  
Klaus Linkenkaer-Hansen
Keyword(s):  
Information Processing ◽  
Long Range ◽  
Information Transfer ◽  
Distributed Processing ◽  
Brain Networks ◽  
Local Network ◽  
Phase Coupling ◽  
Scale Free ◽  
Functional Connections ◽  
Network Properties

Brain function depends on segregation and integration of information processing in brain networks often separated by long-range anatomical connections. Neuronal oscillations orchestrate such distributed processing through transient amplitude and phase coupling; however, little is known about local network properties facilitating these functional connections. Here, we test whether criticality—a dynamical state characterized by scale-free oscillations—optimizes the capacity of neuronal networks to couple through amplitude or phase, and transfer information. We coupled in silico networks with varying excitatory and inhibitory connectivity, and found that phase coupling emerges at criticality, and that amplitude coupling, as well as information transfer, are maximal when networks are critical. Our data support the idea that criticality is important for local and global information processing and may help explain why brain disorders characterized by local alterations in criticality also exhibit impaired long-range synchrony, even prior to degeneration of physical connections.

scholarly journals Emergent properties in microbiome networks reveal the anthropogenic disturbance of farming practices in vineyard soil fungal communities

2020 ◽  
Author(s):  
Rüdiger Ortiz-Álvarez ◽  
Hector Ortega-Arranz ◽  
Vicente J. Ontiveros ◽  
Charles Ravarani ◽  
Alberto Acedo ◽  
...  
Keyword(s):  
Small World ◽  
Fungal Communities ◽  
Local Network ◽  
Emergent Properties ◽  
Farming Practices ◽  
Natural Systems ◽  
Vineyard Soil ◽  
Global Agriculture ◽  
Network Properties ◽  
The Impact

AbstractAgro-ecosystems are human-managed natural systems, and therefore are subject to generalized ecological rules. A deeper understanding of the factors impacting on the biotic component of ecosystem stability is needed for promoting the sustainability and productivity of global agriculture. Here we propose a method to determine ecological emergent properties through the inference of network properties in local microbial communities, and to use them as biomarkers of the anthropogenic impact of different farming practices on vineyard soil ecosystem functioning. In a dataset of 350 vineyard soil samples from USA and Spain we observed that fungal communities ranged from random to small-world network arrangements with differential levels of niche specialization. Some of the network properties studied were strongly correlated, defining patterns of ecological emergent properties that are influenced by the intensification level of the crop management. Low-intervention practices (from organic to biodynamic approaches) promoted densely clustered networks, describing an equilibrium state based on mixed (generalist-collaborative) communities. Contrary, in conventionally managed vineyards, we observed highly modular (niche-specialized) low clustered communities, supported by a higher degree of selection (more co-exclusion proportion). We also found that, although geographic factors can explain the different fungal community arrangements in both countries, the relationship between network properties in local fungal communities better capture the impact of farming practices regardless of the location. Thus, we hypothesize that local network properties can be globally used to evaluate the effect of ecosystem disturbances in crops, but also in when evaluating the effect of clinical interventions or to compare microbiomes of healthy vs. disturbed conditions.

scholarly journals Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

2020 ◽  
Author(s):  
Kyesam Jung ◽  
Simon B. Eickhoff ◽  
Oleksandr V. Popovych
Keyword(s):  
Data Processing ◽  
Resting State ◽  
Optimal Parameter ◽  
Structural Connectivity ◽  
Model Performance ◽  
Brain Dynamics ◽  
Dynamical Modeling ◽  
Whole Brain ◽  
Brain Connectome ◽  
Network Properties

AbstractDynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

scholarly journals Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy

2017 ◽  
Author(s):  
Eli Kinney-Lang ◽  
Michael Yoong ◽  
Matthew Hunter ◽  
Krishnaraya Kamath Tallur ◽  
Jay Shetty ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Network Analysis ◽  
Nearest Neighbor ◽  
Rank Correlation ◽  
Population Based ◽  
Classification Model ◽  
Phase Slope ◽  
Network Properties ◽  
Normal Cognition

AbstractObjective: Cognitive impairment (CI) is common in children with epilepsy and can have devastating effects on their quality of life and that of their family. Early identification of CI is a priority to improve outcomes, but the current gold standard of detection with psychometric assessment is resource intensive and not always available. This paper proposes a novel technique of network analysis using routine clinical electroencephalography (EEG) to help identify CI in children with early-onset epilepsy (CWEOE) (0-5 y.o.).Methods: We analyzed functional networks from routinely acquired EEGs of 51 newly diagnosed CWEOE from a prospective population-based study. Combinations of connectivity metrics (e.g. phase-slope index (PSI)) with sub-network analysis (e.g. cluster-span threshold (CST)) identified significant correlations between network properties and cognition scores via rank correlation analysis with Kendall’s τ. Predictive properties were investigated using a 5-fold cross-validated K-Nearest Neighbor classification model with normal cognition, mild/moderate CI and severe CI classes.Results: Phase-dependent connectivity metrics had higher sensitivity to cognition scores, with sub-networks identifying significant functional network changes over a broad range of spectral frequencies. Approximately 70.5% of all children were appropriately classified as normal cognition, mild/moderate CI or severe CI using CST network features. CST classification predicted CI classes 55% better than chance, and reduced misclassification penalties by half.Conclusions: CI in CWEOE can be detected with sensitivity at 85% (with respect to identifying either mild/moderate or severe CI) and specificity of 84%, by EEG network analysis.Significance: This study outlines a data-driven methodology for identifying candidate biomarkers of CI in CWEOE from network features. Following additional replication, the proposed method and its use of routinely acquired EEG forms an attractive proposition for supporting clinical assessment of CI.

scholarly journals Altered topology of large-scale structural brain networks in chronic stroke

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Bastian Cheng ◽  
Eckhard Schlemm ◽  
Robert Schulz ◽  
Marlene Boenstrup ◽  
Arnaud Messé ◽  
...  
Keyword(s):  
Information Transfer ◽  
Large Scale ◽  
Neuronal Degeneration ◽  
Brain Networks ◽  
Brain Regions ◽  
Local Network ◽  
Structural Connectome ◽  
The Impact ◽  
Structural Brain ◽  
Topological Changes

Abstract Beyond disruption of neuronal pathways, focal stroke lesions induce structural disintegration of distant, yet connected brain regions via retrograde neuronal degeneration. Stroke lesions alter functional brain connectivity and topology in large-scale brain networks. These changes are associated with the degree of clinical impairment and recovery. In contrast, changes of large scale, structural brain networks after stroke are less well reported. We therefore aimed to analyse the impact of focal lesions on the structural connectome after stroke based on data from diffusion-weighted imaging and probabilistic fibre tracking. In total, 17 patients (mean age 64.5 ± 8.4 years) with upper limb motor deficits in the chronic stage after stroke and 21 healthy participants (mean age 64.9 ± 10.3 years) were included. Clinical deficits were evaluated by grip strength and the upper extremity Fugl-Meyer assessment. We calculated global and local graph theoretical measures to characterize topological changes in the structural connectome. Results from our analysis demonstrated significant alterations of network topology in both ipsi- and contralesional, primarily unaffected, hemispheres after stroke. Global efficiency was significantly lower in stroke connectomes as an indicator of overall reduced capacity for information transfer between distant brain areas. Furthermore, topology of structural connectomes was shifted toward a higher degree of segregation as indicated by significantly higher values of global clustering and modularity. On a level of local network parameters, these effects were most pronounced in a subnetwork of cortico-subcortical brain regions involved in motor control. Structural changes were not significantly associated with clinical measures. We propose that the observed network changes in our patients are best explained by the disruption of inter- and intrahemispheric, long white matter fibre tracts connecting distant brain regions. Our results add novel insights on topological changes of structural large-scale brain networks in the ipsi- and contralesional hemisphere after stroke.

scholarly journals On the Network Analysis of the State Space of Discrete Dynamical Systems

2017 ◽  
Vol 27 (04) ◽  
pp. 1750062 ◽  
Author(s):  
Cheng Xu ◽  
Chengqing Li ◽  
Jinhu Lü ◽  
Shi Shu
Keyword(s):  
Dynamical Systems ◽  
Network Analysis ◽  
Cellular Automata ◽  
State Space ◽  
Discrete Dynamical Systems ◽  
The State ◽  
Dynamical Complexity ◽  
Two Parameters ◽  
Theoretical Analyses

This paper discusses the letter entitled “Network analysis of the state space of discrete dynamical systems” by A. Shreim et al. [Phys. Rev. Lett. 98, 198701 (2007)]. We found that some theoretical analyses are wrong and the proposed indicators based on two parameters of the state-mapping network cannot discriminate the dynamical complexity of the discrete dynamical systems composed of a 1D cellular automata.

Athletes as Advocates: Influencing Eating-Disorder Beliefs and Perceptions Through Social Media

2018 ◽  
Vol 11 (4) ◽  
pp. 433-446 ◽  
Author(s):  
Fallon R. Mitchell ◽  
Sara Santarossa ◽  
Sarah J. Woodruff
Keyword(s):  
Social Media ◽  
Eating Disorder ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Text Analysis ◽  
Social Issues ◽  
Penn State ◽  
Network Properties

The present study aimed to explore the interactions and influences that occurred on Twitter after Joey Julius’s (NCAA athlete, Penn State Football) and Mike Marjama’s (MLB player, Seattle Mariners) eating-disorder (ED) diagnoses were revealed. Corresponding with the publicizing of each athlete’s ED, all publicly tagged Twitter media using @joey_julius, Joey Julius, @MMarjama, and Mike Marjama were collected using Netlytic software and analyzed. Text analysis revealed that the conversation was supportive and focused on feelings and size. Social network analysis, based on 5 network properties, showed that Joey Julius invoked a larger conversation but that both athletes’ conversations were single sided. Athlete advocacy on social media should be further explored, as it may contribute to changing societal opinion regarding social issues such as EDs.

Dynamical estimation of neuron and network properties III: network analysis using neuron spike times

2014 ◽  
Vol 108 (3) ◽  
pp. 261-273 ◽  
Author(s):  
Chris Knowlton ◽  
C. Daniel Meliza ◽  
Daniel Margoliash ◽  
Henry D. I. Abarbanel
Keyword(s):  
Network Analysis ◽  
Neuron Spike ◽  
Network Properties

scholarly journals Heterogeneities in afferent connectivity dominate local heterogeneities in the emergence of response decorrelation in the dentate gyrus

2017 ◽  
Author(s):  
Poonam Mishra ◽  
Rishikesh Narayanan
Keyword(s):  
Olfactory Bulb ◽  
Dentate Gyrus ◽  
Adult Neurogenesis ◽  
Information Transfer ◽  
Granule Cells ◽  
Dominant Role ◽  
Critical Role ◽  
Local Network ◽  
Afferent Inputs ◽  
The Impact

ABSTRACTThe ability of a neuronal population to effectuate response decorrelation has been identified as an essential prelude to efficient neural encoding. To what extent are diverse forms of local and afferent heterogeneities essential in accomplishing such response decorrelation in the dentate gyrus (DG)? Here, we incrementally incorporated four distinct forms of biological heterogeneities into conductance-based network models of the DG and systematically delineate their relative contributions to response decorrelation. We incorporated intrinsic heterogeneities by stochastically generating several electrophysiologically-validated basket and granule cell models that exhibited significant parametric variability, and introduced synaptic heterogeneities through randomized local synaptic strengths. In including adult neurogenesis, we subjected the valid model populations to randomized structural plasticity and matched neuronal excitability to electrophysiological data. We assessed networks comprising different combinations of these three local heterogeneities with identical or heterogeneous afferent inputs from the entorhinal cortex. We found that the three forms of local heterogeneities were independently and synergistically capable of mediating significant response decorrelation when the network was driven by identical afferent inputs. Strikingly, however, when we incorporated afferent heterogeneities into the network to account for the unique divergence in DG afferent connectivity, the impact of all three forms of local heterogeneities were significantly suppressed by the dominant role of afferent heterogeneities in mediating response decorrelation. Our results unveil a unique convergence of cellular- and network-scale degeneracy in the emergence of response decorrelation in the DG, and constitute a significant departure from the literature that assigns a critical role for local network heterogeneities in input discriminability.SIGNIFICANCE STATEMENTThe olfactory bulb and the dentate gyrus (DG) networks assimilate new neurons in adult rodents, with adult neurogenesis postulated to subserve efficacious information transfer by reducing correlations in neuronal responses to afferent inputs. Heterogeneities emerging from the lateral dendro-dendritic synapses, mediated by locally-projecting neurogenic inhibitory granule cells, are known to play critical roles in channel decorrelation in the olfactory bulb. However, the contributions of different heterogeneities in mediating response decorrelation in DG, comprising neurogenic excitatory granule cells projecting beyond DG and endowed with uniquely divergent afferent inputs, have not been delineated. Here, we quantitatively demonstrate the dominance of afferent heterogeneities, over multiple local heterogeneities, in the emergence of response decorrelation in DG, together unveiling cross-region degeneracy in accomplishing response decorrelation.

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