Network community structure and resilience to localized damage: Application to brain microcirculation

Goirand, Florian; Georgeot, Bertrand; Giraud, Olivier; Lorthois, Sylvie

doi:10.1016/j.brain.2021.100028

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…This suggests that robustness to sporadic occlusion is of relatively low importance in tree vasculature. A possible interpretation is that although intervessel connections between xylem vessels can confer robustness of flow against loss of xylem vessels by providing redundant pathways for water transport, these connections can also threaten water transport efficiency during drought stress periods so that there is evolutionary pressure to limit such connectivity (9,32). Under high tension, embolisms can spread across pits into adjoining xylem conduits by aspiration, further preventing water transport.…”

Section: Discussionmentioning

confidence: 99%

“…In such fluid transport networks, topology impacts hydraulic transport efficiency, dictates the distribution of fluid throughput, and determines vulnerability in response to disruptions (1)(2)(3). Representing a network in a graph facilitates quantitative descriptors and the analysis of functional efficiency using many different methods (4)(5)(6)(7)(8)(9). Therefore, the ability to accurately capture biological networks as graphs is a crucial step toward understanding the robustness of these complex systems.…”

Section: Introductionmentioning

confidence: 99%

“…Because intervessel pit connections are not only permeable to water but also to air, embolisms can, under increasing tension, be aspirated through pits into neighboring conduits and spread through the xylem networks (18). Consequently, connected networks, with many pit connections, could make networks more vulnerable (9). However, it is also possible that the redundancy of multiple paths for water flow provided by pit connections facilitates the redirection of the water flow around embolism events, making the network more robust against conductance loss (32).…”

Section: Introductionmentioning

confidence: 99%

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A multivariate network analysis of ring- and diffuse-porous tree xylem vasculature segmented by convolutional neural networks

Huber

Haft‐Javaherian

Berg

et al. 2023

Preprint

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The xylem network, the water conduction system in wood determines the ability of trees to avoid hydraulic failure during drought stress. The capability to withstand embolisms, disruptions of the water column by gas bubbles that contribute to hydraulic failure, is mainly determined by the anatomical arrangement and connectedness (topology) of xylem vessels. However, the quantification of xylem network characteristics has been difficult, so that relating network properties and topology to hydraulic vulnerability and predicting xylem function remains challenging. We studied the xylem vessel networks of three diffuse- (Fagus sylvatica, Liriodendron tulipifera, Populus x canadensis) and three ring-porous (Carya ovata, Fraxinus pennsylvatica, Quercus montana) tree species using volumetric images of xylem from laser ablation tomography (LATscan). Using convolutional neural networks for image segmentation, we generated three-dimensional, high-resolution maps of xylem vessels, with detailed measurements of morphology and topology. We studied the network topologies by incorporating multiple network metrics into a multidimensional analysis and simulated the robustness of these networks against the loss of individual vessel elements that mimic the obstruction of water flow from embolisms. This analysis suggested that networks in Populus x canadensis and Carya ovata are quite similar despite being different wood types. Similar networks had comparable experimental measurements of P50 values (pressure inducing 50% hydraulic conductivity loss) obtained from hydraulic vulnerability curves, a common tool to quantify the cavitation resistance of xylem networks. This work produced novel data on plant xylem vessel networks and introduces new methods for analyzing the biological impact of these network structures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A multivariate network analysis of ring- and diffuse-porous tree xylem vasculature segmented by convolutional neural networks

Huber

Haft‐Javaherian

Berg

et al. 2023

Preprint

Self Cite

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“…Then, it is also a piping system in which lymph flows, conveying crucial immune system molecules such as antigens, inflammatory soluble mediators and cytokines across the lymph node. Thus its structure could also be analyzed from a hydrodynamics point of view, such as modeled in the cortex capillary network for example [ 36 , 37 ]. We anticipate that our finding would be consistent with a problem of optimal transport within the conduit network.…”

Section: Discussionmentioning

confidence: 99%

Random walk informed heterogeneity detection reveals how the lymph node conduit network influences T cells collective exploration behavior

et al. 2023

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Random walks on networks are widely used to model stochastic processes such as search strategies, transportation problems or disease propagation. A prominent example of such process is the dynamics of naive T cells within the lymph node while they are scanning for antigens. The observed T cells trajectories in small sub-volumes of the lymph node are well modeled as a random walk and they have been shown to follow the lymphatic conduit network as substrate for migration. One can then ask how does the connectivity patterns of the lymph node conduit network affect the T cells collective exploration behavior. In particular, does the network display properties that are uniform across the whole volume of the lymph node or can we distinguish some heterogeneities? We propose a workflow to accurately and efficiently define and compute these quantities on large networks, which enables us to characterize heterogeneities within a very large published dataset of Lymph Node Conduit Network. To establish the significance of our results, we compared the results obtained on the lymph node to null models of varying complexity. We identified significantly heterogeneous regions characterized as “remote regions” at the poles and next to the medulla, while a large portion of the network promotes uniform exploration by T cells.

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“…Graph theory serves as a foundational mathematical framework for examining graphs [37][38][39] , which are structures composed of nodes representing brain regions interconnected by edges signifying functional connectivity between these regions. We converted the undirected graphs into sparse ones by applying a threshold that retained the top 19% of the strongest weights 40,41 .…”

Section: Graph Theorymentioning

confidence: 99%

Between-networks hyperconnectivity is induced by beta-amyloid and may facilitate tau spread

Hojjati,

Butler,

Leon

et al. 2024

Preprint

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Alzheimer’s disease (AD) is characterized by the buildup of neurofibrillary tau tangles and beta-amyloid (Aβ) plaques. While it has been hypothesized that Aβ facilitates the spread of tau outside of the medial temporal lobe (MTL), the specific pathological processes and mechanisms by which this occurs remain poorly understood. Our study employed advanced neuroimaging techniques, integrating 18F-Florbetaben Aβ and 18F-MK6240 tau positron emission tomography (PET) with resting-state functional magnetic resonance imaging (rs-fMRI) to characterize these mechanisms in two distinct datasets, that included 481 healthy elderly subjects, 46 of whom came with longitudinal data. Our research highlighted an intricate internetwork relationship between Aβ and tau accumulation, across spatially distinct functional networks. Additionally, we observed compelling evidence supporting the existence of a compensatory mechanism triggered by Aβ accumulation, resulting in hyperconnectivity between functional networks. Finally, the longitudinal findings indicate that between-networks hyperconnectivity is associated with future tau elevation and mediates the relationship between cortical Aβ and early-stage tau. Understanding this early brain alteration in response to the accumulation of Aβ could guide treatments early in the disease course and potentially prevent future tau accumulation.

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Network community structure and resilience to localized damage: Application to brain microcirculation

Cited by 7 publications

References 42 publications

A multivariate network analysis of ring- and diffuse-porous tree xylem vasculature segmented by convolutional neural networks

A multivariate network analysis of ring- and diffuse-porous tree xylem vasculature segmented by convolutional neural networks

Random walk informed heterogeneity detection reveals how the lymph node conduit network influences T cells collective exploration behavior

Between-networks hyperconnectivity is induced by beta-amyloid and may facilitate tau spread

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