Nicolas Antille’s research while affiliated with Swiss Federal Institute of Technology in Lausanne and other places

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Publications (8)


Supplementary Data
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July 2018

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13 Reads

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Felix Schürmann
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Fig. 1. A high level overview of NeuroMorphoVis workflow. The framework imports morphological skeletons from standard file formats and applies a set of predefined filters to repair the skeletons from any tracing artifacts. The user can use the morphometric analysis tools for various purposes, for example, to compute the number of segments per branch or the average segment length per section. The morphology skeleton is converted into a threedimensional geometric representation based on a specific method selected by the user for visualization and certain analysis purposes. During the interactive visualization session, the user can manually apply other repairs, if required, and perform other visual analytics tasks. Afterwards, the repaired skeleton is used to reconstruct a three-dimensional surface mesh using piecewise watertight meshing (Abdellah et al., 2017b). The resulting mesh can be exported into various file formats for applying simulation data on a vertex basis. This surface mesh is converted into a volumetric model tagged with user-defined optical properties using conservative voxelization. The volume can be exported into common file formats including binary and byte volumes. All the tasks can be executed from a user-friendly graphical interface (GUI) or via command-line interface (CLI) and batch scripts for large scale analysis 
Fig. 5. Wireframe polygonal mesh models of a neocortical neuron created at (A) 100%, (B) 50%, (C) 25% and (D) 10% tessellation levels. Lower level-of-detail meshes are essential for large scale simulation experiments, making it possible to load and visualize a massive amount of neurons on-the-fly and also in volumetric studies where multiple triangles along the surface span the same extent of a single voxel 
Fig. 6. The effect of using vertex smoothing filters to improve the realism of the reconstructed neuronal mesh models. The shown meshes are generated without (top row) and with (bottom row) smoothing 
Fig. 7. The sequence followed to reconstruct a solid volumetric model of a neocortical neuron from its morphological skeleton. (A) A geometric representation of the neurites of the morphology is extracted. (B) The three-dimensional profile of the soma is reconstructed on a physically plausible basis. (C) The different components of the neuron (axon, dendrites and soma) are grouped together into a single connected polygonal surface mesh that is piecewise watertight. (D) A volumetric shell of the polygonal mesh is obtained using conservative rasterization and surface voxelization. (E) The intracellular space of the neuron is filled and annotated with user-specified optical properties based on the flood-filling algorithm 
Fig. 8. Three-dimensional mesh models simulating the variations of the soma of a layer V pyramidal neuron using different values for number of soft body subdivisions and stiffness. The simulation parameters are as follows (number of subdivisions, stiffnesses): (A: 2, 0.05), (B: 3, 0.05), (C: 2, 0.5), (D: 4, 0.1), (E: 4, 0.5) and (F: 6, 0.5) 

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NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks

June 2018

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494 Reads

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50 Citations

Bioinformatics

Motivation: From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled. Results: We present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments. Availability and implementation: The source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface). Supplementary information: Supplementary data are available at Bioinformatics online.


From Big Data to Big Displays High-Performance Visualization at Blue Brain

October 2017

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373 Reads

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3 Citations

Lecture Notes in Computer Science

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions.


Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

September 2017

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568 Reads

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15 Citations

BMC Bioinformatics

Background We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender. Results Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback. Conclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters. AMS Subject Classification Modelling and Simulation Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1788-4) contains supplementary material, which is available to authorized users.


Figure 2: Soma progressive reconstruction. The soma is modeled by a soft body sphere in (A). The initial and final locations of the primary branches are illustrated by the green and red points respectively. The first-order sections are projected to the sphere to find out the vertices where the hooks will be created. The faces from each hook are merged into a single face and shaped into a circle (B). The hooks are pulled and the circles are scaled to match the size of the sections (C-E). The final soma is reconstructed in (F).
Figure 3: Physically-plausible reconstruction of the somata of diverse neocortical neurons labeled by their morphological type. The initial shape of the soma is defined by a soft body sphere that is deformed by pulling the corresponding vertices of each primary branch. The algorithm uses the soft body toolbox and the hook modifier in Blender [Ble16].
Figure 4: Reconstruction of a piecewise watertight polygonal mesh model of a pyramidal neuron in (B) from its morphological skeleton in (A). In (C), the applicability of the proposed meshing algorithm is demonstrated with multiple neurons having diverse morphological types to validate its generality. The reconstruction results of the 55 exemplar neurons are provided in high resolution with the supplementary materials. The somata, basal dendrites, apical dendrites and axons are colored in yellow, red, green and blue respectively.
Figure 6: Volumetric reconstructions of multiple neocortical circuits with solid voxelization. The presented workflow is capable of creating large scale volumetric models for circuits with different complexity. (A) Single cell volume. (B) A group of five pyramidal neurons. (C) 5% of the pyramidal neurons that exist in layer five in the neocortical column. (D) 5% of all the neurons in a single column (containing ∼31,000 neurons). (E) A uniformlysampled selection of only 1% of the neurons in a digital slice composed of seven columns (containing ∼210,000 neurons) stacked together. The resolution of the largest dimension of each volume is set to 8000 voxels. The area covered by the orange box in (E) represents the maximum volumetric extent that could be simulated in similar previous studies [ABE * 15b, ABE * 17].
Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

July 2017

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48 Reads

Background We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender. Results Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback. Conclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.


From Big Data to Big Displays: High-Performance Visualization at Blue Brain

June 2017

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209 Reads

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4 Citations

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.


Reconstruction and Simulation of Neocortical Microcircuitry

October 2015

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1,530 Reads

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1,382 Citations

Cell

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies. Paperclip: VIDEO ABSTRACT.


The Neocortical Microcircuit Collaboration Portal: A Resource for Rat Somatosensory Cortex

October 2015

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462 Reads

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167 Citations

We have established a multi-constraint, data-driven process to digitally reconstruct, and simulate prototypical neocortical microcircuitry, using sparse experimental data. We applied this process to reconstruct the microcircuitry of the somatosensory cortex in juvenile rat at the cellular and synaptic levels. The resulting reconstruction is broadly consistent with current knowledge about the neocortical microcircuit and provides an array of predictions on its structure and function. To engage the community in exploring, challenging, and refining the reconstruction, we have developed a collaborative, internet-accessible facility-the Neocortical Microcircuit Collaboration portal (NMC portal; https://bbp.epfl.ch/nmc-portal). The NMC portal allows users to access the experimental data used in the reconstruction process, download cellular and synaptic models, and analyze the predicted properties of the microcircuit: six layers, similar to 31,000 neurons, 55 morphological types, 11 electrical types, 207 morpho-electrical types, 1941 unique synaptic connection types between neurons of specific morphological types, predicted properties for the anatomy and physiology of similar to 40 million intrinsic synapses. It also provides data supporting comparison of the anatomy and physiology of the reconstructed microcircuit against results in the literature. The portal aims to catalyzee consensus on the cellular and synaptic organization of neocortical microcircuitry (ion channel, neuron and synapse types and distributions, connectivity, etc.). Community feedback will contribute to refined versions of the reconstruction to be released periodically. We consider that the reconstructions and the simulations they enable represent a major step in the development of in silica neuroscience.

Citations (5)


... Additionally, we display the Gaussian curvature of the soma surface to show that it is a non-spherical geometry (always positive but not constant). A limitation of the current approach (and the majority of existing tools [48,49,50]) is the slightly inaccurate definition of the soma surface, as shown in the top right corner (arrows). ...

Reference:

Decoding Gray Matter: large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals
NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks

Bioinformatics

... The first is concerned with creating mesh models that are convenient for visual analytics. Depending on the objective, these meshes could be highly tessellated if being used for single cell visualization and analysis, or low tessellated when a visualization of full-compartmental simulation of large scale circuit is essential (Eilemann et al., 2012(Eilemann et al., , 2017Markram et al., 2015). Although they might have various tessellation levels, these meshes are accurate enough to capture all the details of a given morphology, however, they are not required to be watertight (Brito et al., 2013;Garcia-Cantero et al., 2017;Lasserre et al., 2012). ...

From Big Data to Big Displays High-Performance Visualization at Blue Brain

Lecture Notes in Computer Science

... Neuromorphovis (Abdellah et al., 2018) extends an earlier meshing algorithm (Abdellah et al., 2017) and is capable of reconstructing piecewise watertight meshes that could be employed to visualize detailed electrophysiological activities obtained from voltage dynamics simulations. However, too many metaobjects have to be placed for creating a smoothvarying implicit field for extracting a neuronal membrane iso-surface. ...

Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

BMC Bioinformatics

... Moreover, studying and clarifying neuronal morphology will allow progress in the knowledge of both molecular and physiological properties. Recordings from and morphological reconstructions of thousands of neurons from cortical brain slices have been used to classify them into hundreds of morpho-electrical types 32 . Here, we questioned whether quantum computers may provide the same output as classic computers for the morphological classification of neurons. ...

Reconstruction and Simulation of Neocortical Microcircuitry
  • Citing Article
  • October 2015

Cell

... 25,[28][29][30][31] The model was implemented using the NEURON simulation environment 32 and incorporated morphological parameters and channel distributions as described by Ramaswamy and Markram. 33 Electrophysiological properties were based on previously reported values. 30 To simulate an early developing neuronal model, we modified the model to include 50% neonatal variant and 50% adult variant wherever Na V 1.2 was expressed in the model 34 ; for the Na V 1.2-Met1770Leu variant model, both 50% neonatal and 50% adult Na V 1.2 were included to obtain an equal contribution of WT and Na V 1.2-Met1770Leu subunits representing the heterozygous condition observed in patients (with the total amount of sodium channels the same between the WT and Na V 1.2-Met1770Leu condition). ...

The Neocortical Microcircuit Collaboration Portal: A Resource for Rat Somatosensory Cortex