LiveMesh, a tool for real-time rendering of neuronal cells from morphologies

The project goal was to prove the feasibility of GPU-based tessellation to generate neuron membrane mesh representations from parametric descriptions of neurons. The developed prototype software produces a smooth, continuous and high-fidelity representation of neuron morphologies that can be used for scientific visualization. It is considered by the Blue Brain Project (BBP) visualization team as a replacement of their current offline mesh generation algorithm for real-time rendering. The implementation used C++, OpenGL and Qt

Chaste: an open source C++ library for computational physiology and biology

Chaste - Cancer, Heart And Soft Tissue Environment - is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to "re-invent the wheel" with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials

Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation

Creating high-quality polygonal meshes which represent the membrane surface of neurons for both visualization and numerical simulation purposes is an important yet nontrivial task, due to their irregular and complicated structures. In this paper, we develop a novel approach of constructing a watertight 3D mesh from the abstract point-and-diameter representation of the given neuronal morphology. The membrane shape of the neuron is reconstructed by progressively deforming an initial sphere with the guidance of the neuronal skeleton, which can be regarded as a digital sculpting process. To efficiently deform the surface, a local mapping is adopted to simulate the animation skinning. As a result, only the vertices within the region of influence (ROI) of the current skeletal position need to be updated. The ROI is determined based on the finite-support convolution kernel, which is convolved along the line skeleton of the neuron to generate a potential field that further smooths the overall surface at both unidirectional and bifurcating regions. Meanwhile, the mesh quality during the entire evolution is always guaranteed by a set of quasi-uniform rules, which split excessively long edges, collapse undersized ones, and adjust vertices within the tangent plane to produce regular triangles. Additionally, the local vertices density on the result mesh is decided by the radius and curvature of neurites to achieve adaptiveness

Proto-Plasm: parallel language for adaptive and scalable modelling of biosystems

This paper discusses the design goals and the first developments of Proto-Plasm, a novel computational environment to produce libraries of executable, combinable and customizable computer models of natural and synthetic biosystems, aiming to provide a supporting framework for predictive understanding of structure and behaviour through multiscale geometric modelling and multiphysics simulations. Admittedly, the Proto-Plasm platform is still in its infancy. Its computational framework—language, model library, integrated development environment and parallel engine—intends to provide patient-specific computational modelling and simulation of organs and biosystem, exploiting novel functionalities resulting from the symbolic combination of parametrized models of parts at various scales. Proto-Plasm may define the model equations, but it is currently focused on the symbolic description of model geometry and on the parallel support of simulations. Conversely, CellML and SBML could be viewed as defining the behavioural functions (the model equations) to be used within a Proto-Plasm program. Here we exemplify the basic functionalities of Proto-Plasm, by constructing a schematic heart model. We also discuss multiscale issues with reference to the geometric and physical modelling of neuromuscular junctions

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes

Dynamic Decomposition of Spatiotemporal Neural Signals

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals

Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform

Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species

Computational Approaches to Understanding Structure-Function Relationships at the Intersection of Cellular Organization, Mechanics, and Electrophysiology

The heart is a complex mechanical and electrical environment and small changes at the cellular and subcellular scale can have profound impacts at the tissue, organ, and organ system levels. The goal of this research is to better understand structure-function relationships at these cellular and subcellular levels of the cardiac environment. This improved understanding may prove increasingly important as medicine begins shifting toward engineered replacement tissues and organs. Specifically, we work towards this goal by presenting a framework to automatically create finite element models of cells based on optical images. This framework can be customized to model the effects of subcellular structure and organization on mechanical and electrophysiological properties at the cellular level and has the potential for extension to the tissue level and beyond. In part one of this work, we present a novel algorithm is presented that can generate physiologically relevant distributions of myofibrils within adult cardiomyocytes from confocal microscopy images. This is achieved by modelling these distributions as directed acyclic graphs, assigning a cost to each node based on observations of cardiac structure and function, and determining to minimum-cost flow through the network. This resulting flow represents the optimal distribution of myofibrils within the cell. In part two, these generated geometries are used as inputs to a finite element model (FEM) to determine the role the myofibrillar organization plays in the axal and transverse mechanics of the whole cell. The cardiomyocytes are modeled as a composite of fiber trusses within an elastic solid matrix. The behavior of the model is validated by comparison to data from combined Atomic Force Microscopy (AFM) and Carbon Fiber manipulation. Recommendations for extending the FEM framework are also explored. A secondary goal, discussed in part three of this work, is to make computational models and simulation tools more accessible to novice learners. Doing so allows active learning of complicated course materials to take place. Working towards this goal, we present CellSpark: a simulation tool developed for teaching cellular electrophysiology and modelling to undergraduate bioengineering students. We discuss the details of its implementation and implications for improved student learning outcomes when used as part of a discovery learning assignment

Head-to-nerve analysis of electromechanical impairments of diffuse axonal injury

The aim was to investigate mechanical and functional failure of diffuse axonal injury (DAI) in nerve bundles following frontal head impacts, by finite element simulations. Anatomical changes following traumatic brain injury are simulated at the macroscale by using a 3D head model. Frontal head impacts at speeds of 2.5-7.5 m/s induce mild-to-moderate DAI in the white matter of the brain. Investigation of the changes in induced electromechanical responses at the cellular level is carried out in two scaled nerve bundle models, one with myelinated nerve fibres, the other with unmyelinated nerve fibres. DAI occurrence is simulated by using a real-time fully coupled electromechanical framework, which combines a modulated threshold for spiking activation and independent alteration of the electrical properties for each three-layer fibre in the nerve bundle models. The magnitudes of simulated strains in the white matter of the brain model are used to determine the displacement boundary conditions in elongation simulations using the 3D nerve bundle models. At high impact speed, mechanical failure occurs at lower strain values in large unmyelinated bundles than in myelinated bundles or small unmyelinated bundles; signal propagation continues in large myelinated bundles during and after loading, although there is a large shift in baseline voltage during loading; a linear relationship is observed between the generated plastic strain in the nerve bundle models and the impact speed and nominal strains of the head model. The myelin layer protects the fibre from mechanical damage, preserving its functionalities

Numerical modelling in transcranial magnetic stimulation

Tese de doutoramento, Engenharia Biomédica e Biofísica, Universidade de Lisboa, Faculdade de Ciências, 2009In this work powerful numerical methods were used to study several problems that still remain unsolved in TMS.The first problem that was studied is related to the difficulties that arise when stimulating sub-cortical deep regions with TMS, due to the fact that the induced field rapidly decays and loses focality with depth. This study's approach to overcome this difficulty was to combine ferromagnetic cores with a coil designed to induce an electric field that decays slowly. The efficacy of this approach was tested by using the FEM to calculate the field induced by this coil / core design in a realistically shaped head model. The results show that the core might make this coil even more suited for deep brain stimulation.The second problem that was tackled is related to the lack of knowledge about the dominant mechanisms through which the induced electric field excites neurons in TMS. In this work the electric field along lines, representing trajectories of actual cortical neurons, was calculated using the FEM. The neurons were embedded in a realistically shaped sulcus model, with a figure-8 coil placed above the model. The electric field was then incorporated into the cable equation. The solution of the latter allowed the determination of the site and threshold of activation of the neurons. The results highlight the importance of axonal terminations and bends and tissue heterogeneities on stimulation of neurons.The third problem that was studied concerns TMS of small animals and the lack of knowledge about the optimal geometry, size and orientation of the used coils. This was studied by using the FEM to calculate the electric field induced in a realistically shaped mouse model by several commercially available coils. The results showed that the smaller coils induced fields with higher magnitude, better focality, and smaller decay than the bigger coils.These results highlight the importance of numerical modelling in TMS, either in coil design, determination of basic neurophysiologic mechanisms or optimization of experimental procedures