scholarly journals Network analysis of mesoscale mouse brain structural connectome yields modular structure that aligns with anatomical regions and sensory pathways

2019 ◽  
Author(s):  
Bernard A. Pailthorpe
Keyword(s):  
Mouse Brain ◽  
3D Visualization ◽  
Visual Complexity ◽  
Network Models ◽  
Structural Features ◽  
Biologically Relevant ◽  
Sensory Pathways ◽  
Structural Connectome ◽  
Sensory Motor ◽  
Cortical Association Areas

AbstractThe Allen mesoscale mouse brain structural connectome is analysed using standard network methods combined with 3D visualizations. The full region-to-region connectivity data is used, with a focus on the strongest structural links. The spatial embedding of links and time evolution of signalling is incorporated, with two-step links included. Modular decomposition using the Infomap method produces 8 network modules that correspond approximately to major brain anatomical regions and system functions. These modules align with the anterior and posterior primary sensory systems and association areas. 3D visualization of network links is facilitated by using a set of simplified schematic coordinates that reduces visual complexity. Selection of key nodes and links, such as sensory pathways and cortical association areas together reveal structural features of the mouse structural connectome consistent with biological functions in the sensory-motor systems, and selective roles of the anterior and posterior cortical association areas of the mouse brain. Time progression of signals along sensory pathways reveals that close links are to local cortical association areas and cross modal, while longer links provide anterior-posterior coordination and inputs to non cortical regions. The fabric of weaker links generally are longer range with some having brain-wide reach. Cortical gradients are evident along sensory pathways within the structural network.Author’s SummaryNetwork models incorporating spatial embedding and signalling delays are used to investigate the mouse structural connectome. Network models that include time respecting paths are used to trace signaling pathways and reveal separate roles of shorter vs. longer links. Here computational methods work like experimental probes to uncover biologically relevant features. I use the Infomap method, which follows random walks on the network, to decompose the directed, weighted network into 8 modules that align with classical brain anatomical regions and system functions. Primary sensory pathways and cortical association areas are separated into individual modules. Strong, short range links form the sensory-motor paths while weaker links spread brain-wide, possibly coordinating many regions.

scholarly journals Applications of neutron reflectometry in biology

2020 ◽  
Vol 236 ◽  
pp. 04002 ◽  
Author(s):  
Yuri Gerelli
Keyword(s):  
Thin Films ◽  
Structural Features ◽  
Neutron Reflectometry ◽  
Protein Films ◽  
Biologically Relevant ◽  
Buried Interfaces ◽  
Biological Thin Films ◽  
Non Destructive

Over the last 10 years, neutron reflectometry (NR) has emerged as a powerful technique for the investigation of biologically relevant thin films. The great advantage of NR with respect to many other surface-sensitive techniques is its sub-nanometer resolution that enables structural characterizations at the molecular level. In the case of bio-relevant samples, NR is non-destructive and can be used to probe thin films at buried interfaces or enclosed in bulky sample environment equipment. Moreover, recent advances in biomolecular deutera-tion enabled new labeling strategies to highlight certain structural features and to resolve with better accuracy the location of chemically similar molecules within a thin film. In this chapter I will describe some applications of NR to bio-relevant samples and discuss some of the data analysis approaches available for biological thin films. In particular, examples on the structural characterization of biomembranes, protein films and protein-lipid interactions will be described.

scholarly journals Dynamic Transparency for 3D Visualization: Design and Evaluation

2009 ◽  
Vol 8 (1) ◽  
pp. 75-88 ◽  
Author(s):  
Niklas Elmqvist ◽  
Ulf Assarsson ◽  
Philippas Tsigas
Keyword(s):  
User Study ◽  
Negative Impact ◽  
3D Visualization ◽  
Visual Complexity ◽  
Superior Performance ◽  
Subjective Ratings ◽  
Human Aspects ◽  
Recent Developments ◽  
3D Environments ◽  
Empirical User Study

Recent developments in occlusion management for 3D environments often involve the use of dynamic transparency, or "virtual X-ray vision", to promote target discovery and access in complex 3D worlds. However, there are many different approaches to achieving this effect and their actual utility for the user has yet to be evaluated. Furthermore, the introduction of semitransparent surfaces adds additional visual complexity that may actually have a negative impact on task performance. In this paper, we report on an empirical user study investigating these human aspects of dynamic transparency. Our implementation of the technique is an image-space algorithm built using modern programmable shaders to achieve real-time performance and visually pleasing results. Results from the user study indicate that dynamic transparency provides superior performance for perceptual tasks in terms of both efficiency and correctness. Subjective ratings are also firmly in favor of the method.

Scaling of average weighted shortest path and average receiving time on weighted expanded Koch networks

2014 ◽  
Vol 28 (17) ◽  
pp. 1450111 ◽  
Author(s):  
Zikai Wu ◽  
Baoyu Hou ◽  
Hongjuan Zhang ◽  
Feng Jin
Keyword(s):  
Shortest Path ◽  
Linear Dependence ◽  
Path Length ◽  
Network Models ◽  
Structural Features ◽  
The Other ◽  
Large Network ◽  
Dynamical Processes ◽  
Current Generation ◽  
Evolutionary Step

Deterministic network models have been attractive media for discussing dynamical processes' dependence on network structural features. On the other hand, the heterogeneity of weights affect dynamical processes taking place on networks. In this paper, we present a family of weighted expanded Koch networks based on Koch networks. They originate from a r-polygon, and each node of current generation produces m r-polygons including the node and whose weighted edges are scaled by factor w in subsequent evolutionary step. We derive closed-form expressions for average weighted shortest path length (AWSP). In large network, AWSP stays bounded with network order growing (0 < w < 1). Then, we focus on a special random walks and trapping issue on the networks. In more detail, we calculate exactly the average receiving time (ART). ART exhibits a sub-linear dependence on network order (0 < w < 1), which implies that nontrivial weighted expanded Koch networks are more efficient than un-weighted expanded Koch networks in receiving information. Besides, efficiency of receiving information at hub nodes is also dependent on parameters m and r. These findings may pave the way for controlling information transportation on general weighted networks.

scholarly journals Cheminformatics Analysis of Natural Product Scaffolds: Comparison of Scaffolds Produced by Animals, Plants, Fungi and Bacteria

2020 ◽  
Author(s):  
Peter Ertl ◽  
Tim Schuhmann
Keyword(s):  
Natural Products ◽  
Natural Selection ◽  
Natural Product ◽  
Selection Process ◽  
Structural Features ◽  
New Drugs ◽  
Large Database ◽  
Biologically Relevant ◽  
Different Types ◽  
Selection Of

AbstractNatural products (NPs) have evolved over a very long natural selection process to form optimal interactions with biologically relevant macromolecules. NPs are therefore an extremely useful source of inspiration for the design of new drugs. In the present study we report the results of a cheminformatics analysis of a large database of NP structures focusing on their scaffolds. First, general differences between NP scaffolds and scaffolds from synthetic molecules are discussed, followed by a comparison of the properties of scaffolds produced by different types of organisms. Scaffolds produced by plants are the most complex and those produced by bacteria differ in many structural features from scaffolds produced by other organisms. The results presented here may be used as a guidance in selection of scaffolds for the design of novel NP-like bioactive structures or NP-inspired libraries.

scholarly journals Inferring network properties from time series using transfer entropy and mutual information: Validation of multivariate versus bivariate approaches

2020 ◽  
pp. 1-32
Author(s):  
Leonardo Novelli ◽  
Joseph T. Lizier
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Network Models ◽  
Small World ◽  
Structural Features ◽  
Transfer Entropy ◽  
Statistical Dependence ◽  
Neural Mass Model ◽  
Mass Model ◽  
Network Properties

Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting properties of these networks requires inferred network models to reflect key underlying structural features. However, even a few spurious links can severely distort network measures, posing a challenge for functional connectomes. We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/multivariate transfer entropy. The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular). Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality). We find that multivariate transfer entropy captures key properties of all network structures for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series but are unable to control false positives (lower specificity) as available data increases. This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.

scholarly journals 3D visualization and quantification of microvessels in the whole ischemic mouse brain using solvent-based clearing and light sheet microscopy

2017 ◽  
Vol 37 (10) ◽  
pp. 3355-3367 ◽  
Author(s):  
Erlen Lugo-Hernandez ◽  
Anthony Squire ◽  
Nina Hagemann ◽  
Alexandra Brenzel ◽  
Maryam Sardari ◽  
...  
Keyword(s):  
Mouse Brain ◽  
3D Visualization ◽  
Focal Cerebral Ischemia ◽  
Light Sheet ◽  
Artery Occlusion ◽  
Cerebral Microvessels ◽  
Light Sheet Microscopy ◽  
Low Viscosity ◽  
Ischemic Tissue ◽  
Cerebral Artery Occlusion

The visualization of cerebral microvessels is essential for understanding brain remodeling after stroke. Injection of dyes allows for the evaluation of perfused vessels, but has limitations related either to incomplete microvascular filling or leakage. In conventional histochemistry, the analysis of microvessels is limited to 2D structures, with apparent limitations regarding the interpretation of vascular circuits. Herein, we developed a straight-forward technique to visualize microvessels in the whole ischemic mouse brain, combining the injection of a fluorescent-labeled low viscosity hydrogel conjugate with 3D solvent clearing followed by automated light sheet microscopy. We performed transient middle cerebral artery occlusion in C57Bl/6j mice and acquired detailed 3D vasculature images from whole brains. Subsequent image processing, rendering and fitting of blood vessels to a filament model was employed to calculate vessel length density, resulting in 0.922 ± 0.176 m/mm3 in healthy tissue and 0.329 ± 0.131 m/mm3 in ischemic tissue. This analysis showed a marked loss of capillaries with a diameter ≤ 10 µm and a more moderate loss of microvessels in the range > 10 and ≤ 20 µm, whereas vessels > 20 µm were unaffected by focal cerebral ischemia. We propose that this protocol is highly suitable for studying microvascular injury and remodeling post-stroke.

scholarly journals Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

2017 ◽  
Vol 27 (4) ◽  
pp. 047405 ◽  
Author(s):  
Sarab S. Sethi ◽  
Valerio Zerbi ◽  
Nicole Wenderoth ◽  
Alex Fornito ◽  
Ben D. Fulcher
Keyword(s):  
Mouse Brain ◽  
Bold Signal ◽  
Structural Connectome

scholarly journals Individual structural features constrain the mouse functional connectome

2019 ◽  
Vol 116 (52) ◽  
pp. 26961-26969 ◽  
Author(s):  
Francesca Melozzi ◽  
Eyal Bergmann ◽  
Julie A. Harris ◽  
Itamar Kahn ◽  
Viktor Jirsa ◽  
...  
Keyword(s):  
Functional Mri ◽  
Mouse Brain ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Structural Data ◽  
Structural Features ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Functional Connectome ◽  
The Individual

Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure–function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.

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