scholarly journals Perspective: network-guided pattern formation of neural dynamics

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130522 ◽  
Author(s):  
Marc-Thorsten Hütt ◽  
Marcus Kaiser ◽  
Claus C. Hilgetag
Keyword(s):  
Pattern Formation ◽  
Network Architecture ◽  
Brain Connectivity ◽  
Activity Patterns ◽  
Neural Dynamics ◽  
Self Organized ◽  
Topological Features ◽  
Small Set ◽  
Spatio Temporal

The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.

scholarly journals Self-organized pattern formation increases functional diversity

2020 ◽  
Author(s):  
Janne Hülsemann ◽  
Toni Klauschies ◽  
Christian Guill
Keyword(s):  
Pattern Formation ◽  
Functional Diversity ◽  
Continuous Distribution ◽  
Stabilizing Selection ◽  
Chain Model ◽  
Self Organized ◽  
Local Functional ◽  
Spatio Temporal ◽  
Alternative Community ◽  
Food Chain Model

AbstractSelf-organized formation of spatial patterns is known from a variety of different ecosystems, yet little is known how these patterns affect functional diversity of local and regional communities. Here we use a food chain model in which autotroph diversity is described by a continuous distribution of a trait that affects both growth rate and defense against a heterotroph. On a single patch, stabilizing selection always promotes the dominance of a single autotroph species. Two alternative community states, with either defended or undefended species, are possible. In a metacommunity context, dispersal can destabilize these states, and complex spatio-temporal patterns emerge. This creates varying selection pressures on the local autotroph communities, which feed back on the trait dynamics. Local functional diversity increases ten-fold compared to a situation without self-organized pattern formation, thereby maintaining the adaptive potential of communities in an environment threatened by fragmentation and global change.

scholarly journals The role of phase separation for self-organized surface pattern formation by ion beam erosion and metal atom co-deposition

2012 ◽  
Vol 111 (2) ◽  
pp. 653-664 ◽  
Author(s):  
H. Hofsäss ◽  
K. Zhang ◽  
A. Pape ◽  
O. Bobes ◽  
M. Brötzmann
Keyword(s):  
Phase Separation ◽  
Pattern Formation ◽  
Metal Atom ◽  
Ion Beam ◽  
Surface Pattern ◽  
Self Organized

scholarly journals Network architecture determines vein fate during spontaneous reorganization, with a time delay

2021 ◽  
Author(s):  
Sophie Marbach ◽  
Noah Ziethen ◽  
Leonie Bastin ◽  
Felix Baeuerle ◽  
Karen Alim
Keyword(s):  
Time Delay ◽  
Network Architecture ◽  
Force Balance ◽  
Relative Pressure ◽  
Temporal Data ◽  
Vascular Networks ◽  
Local Shear ◽  
Flow Shear Stress ◽  
Spatio Temporal

Vascular networks continuously reorganize their morphology by growing new or shrinking existing veins to optimize function. Flow shear stress on vein walls has been set forth as the local driver for this continuous adaptation. Yet, shear feedback alone cannot account for the observed diversity of network dynamics -- a puzzle made harder by scarce spatio-temporal data. Here, we resolve network-wide vein dynamics and shear during spontaneous reorganization in the prototypical vascular networks of Physarum polycephalum. Our experiments reveal a plethora of vein dynamics (stable, growing, shrinking) that are not directly proportional to local shear. We observe (a) that shear rate sensing on vein walls occurs with a time delay of 1 to 3 min and (b) that network architecture dependent parameters -- such as relative pressure or relative vein resistance -- are key to determine vein fate. We derive a model for vascular adaptation, based on force balance at the vein walls. Together with the time delay, our model reproduces the diversity of experimentally observed vein dynamics, and confirms the role of network architecture. Finally, we observe avalanches of network reorganization events which cause entire clusters of veins to vanish. Such avalanches are consistent with architectural feedback as the vein connections perpetually change with reorganization. As these network architecture dependent parameters are intrinsically connected with the laminar fluid flow in the veins, we expect our findings to play a role across flow-based vascular networks.

scholarly journals Asamataxis: A cooperative relayed migration in response to subsurface inhomogeneity leads to long-range self-patterning of cells

2019 ◽  
Author(s):  
Akshada Khadpekar ◽  
Kanksha Mistry ◽  
Nehal Dwivedi ◽  
Aditya Paspunurwar ◽  
Parag Tandaiya ◽  
...  
Keyword(s):  
Pattern Formation ◽  
Long Range ◽  
Cellular Migration ◽  
Surface Structures ◽  
Dimensionless Number ◽  
Self Organized ◽  
Suitable Combination ◽  
Embedded Structure ◽  
Potential Tool

AbstractCells self-organize to give patterns that are essential for tissue functioning. While the effects of biochemical and mechanical cues are relatively well studied, the role of stiffness inhomogeneity on cellular patterning is unexplored. Using a rigid structure embedded in soft polyacrylamide (PAA) gel, we show that such mechanical inhomogeneity leads to long-range self-organized cellular patterns. Our results reveal that this patterning depends on cellular traction and cell morphology. Depending on a suitable combination of cellular morphology and traction, the information about the presence of embedded structure gets relayed outward. In response to this relay, the cells reorient their axis and migrate towards the embedded structure leading to the observed long-range (20-35 cell length) patterning. To predict the possibility of pattern formation, we present a dimensionless number ‘f’ combining the governing parameters. We have also shown that the pattern can be tailor-made by pre-designing sub-surface structures, a potential tool for tissue engineering. This mechanism of directed migration driven long-range pattern formation in response to mechanical inhomogeneity may be involved during several pathophysiological conditions, a proposition that needs further investigation.One Sentence SummarySubstrate inhomogeneity and cooperative cellular traction together lead to cellular migration and long-range pattern formation.

scholarly journals Nonlinear amplitude dynamics in flagellar beating

2017 ◽  
Vol 4 (3) ◽  
pp. 160698 ◽  
Author(s):  
David Oriola ◽  
Hermes Gadêlha ◽  
Jaume Casademunt
Keyword(s):  
Mathematical Models ◽  
Temporal Dynamics ◽  
Bending Waves ◽  
Self Organized ◽  
Dynein Motor ◽  
Spatio Temporal ◽  
Cilia And Flagella ◽  
Nonlinear Nature ◽  
Selection Of

The physical basis of flagellar and ciliary beating is a major problem in biology which is still far from completely understood. The fundamental cytoskeleton structure of cilia and flagella is the axoneme, a cylindrical array of microtubule doublets connected by passive cross-linkers and dynein motor proteins. The complex interplay of these elements leads to the generation of self-organized bending waves. Although many mathematical models have been proposed to understand this process, few attempts have been made to assess the role of dyneins on the nonlinear nature of the axoneme. Here, we investigate the nonlinear dynamics of flagella by considering an axonemal sliding control mechanism for dynein activity. This approach unveils the nonlinear selection of the oscillation amplitudes, which are typically either missed or prescribed in mathematical models. The explicit set of nonlinear equations are derived and solved numerically. Our analysis reveals the spatio-temporal dynamics of dynein populations and flagellum shape for different regimes of motor activity, medium viscosity and flagellum elasticity. Unstable modes saturate via the coupling of dynein kinetics and flagellum shape without the need of invoking a nonlinear axonemal response. Hence, our work reveals a novel mechanism for the saturation of unstable modes in axonemal beating.

scholarly journals Vulnerability to climate change of a microendemic lizard species from the central Andes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Laspiur ◽  
J. C. Santos ◽  
S. M. Medina ◽  
J. E. Pizarro ◽  
E. A. Sanabria ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution Models ◽  
Activity Patterns ◽  
Geographic Range ◽  
Behavioral Thermoregulation ◽  
Rapid Loss ◽  
Distribution Models ◽  
Lizard Species ◽  
Vulnerability To Climate Change

AbstractGiven the rapid loss of biodiversity as consequence of climate change, greater knowledge of ecophysiological and natural history traits are crucial to determine which environmental factors induce stress and drive the decline of threatened species. Liolaemus montanezi (Liolaemidae), a xeric-adapted lizard occurring only in a small geographic range in west-central Argentina, constitutes an excellent model for studies on the threats of climate change on such microendemic species. We describe field data on activity patterns, use of microhabitat, behavioral thermoregulation, and physiology to produce species distribution models (SDMs) based on climate and ecophysiological data. Liolaemus montanezi inhabits a thermally harsh environment which remarkably impacts their activity and thermoregulation. The species shows a daily bimodal pattern of activity and mostly occupies shaded microenvironments. Although the individuals thermoregulate at body temperatures below their thermal preference they avoid high-temperature microenvironments probably to avoid overheating. The population currently persists because of the important role of the habitat physiognomy and not because of niche tracking, seemingly prevented by major rivers that form boundaries of their geographic range. We found evidence of habitat opportunities in the current range and adjacent areas that will likely remain suitable to the year 2070, reinforcing the relevance of the river floodplain for the species’ avoidance of extinction.

scholarly journals Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Jin Li ◽  
◽  
Chenyuan Bian ◽  
Dandan Chen ◽  
Xianglian Meng ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Genetic Risk ◽  
Brain Connectivity ◽  
Persistent Homology ◽  
Brain Network ◽  
Resonance Imaging ◽  
Modeling Framework ◽  
Apoe Ε4 ◽  
Topological Features

Abstract Background Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Results Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. Conclusions We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

scholarly journals The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma
Keyword(s):  
European Union ◽  
Spatial Distribution ◽  
Spatial Information ◽  
National Level ◽  
Temporal Information ◽  
The European Union ◽  
Member States ◽  
Heavy Burden ◽  
Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

scholarly journals FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

2020 ◽  
Vol 15 (5) ◽  
pp. 791-801 ◽  
Author(s):  
Sophia Bano ◽  
Francisco Vasconcelos ◽  
Emmanuel Vander Poorten ◽  
Tom Vercauteren ◽  
Sebastien Ourselin ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Laser Photocoagulation ◽  
Short Term Memory ◽  
Total Duration ◽  
Frame Rate ◽  
Superior Performance ◽  
Computer Assisted ◽  
Convolutional Network ◽  
Spatio Temporal

Abstract Purpose Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos. Methods We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for the spatio-temporal identification of fetoscopic events. We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference. We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights. This may support computer-assisted interventions (CAI) during fetoscopic laser photocoagulation. Results We perform quantitative evaluation of our method using 7 in vivo fetoscopic videos captured from different human TTTS cases. The total duration of these videos was 5551 s (138,780 frames). To test the robustness of the proposed approach, we perform 7-fold cross-validation where each video is treated as a hold-out or test set and training is performed using the remaining videos. Conclusion FetNet achieved superior performance compared to the existing CNN-based methods and provided improved inference because of the spatio-temporal information modelling. Online testing of FetNet, using a Tesla V100-DGXS-32GB GPU, achieved a frame rate of 114 fps. These results show that our method could potentially provide a real-time solution for CAI and automating occlusion and photocoagulation identification during fetoscopic procedures.

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