scholarly journals Detection and skeletonization of single neurons and tracer injections using topological methods

2020 ◽  
Author(s):  
Dingkang Wang ◽  
Lucas Magee ◽  
Bing-Xing Huo ◽  
Samik Banerjee ◽  
Xu Li ◽  
...  
Keyword(s):  
Single Neuron ◽  
Image Data ◽  
Connectivity Matrix ◽  
Consensus Tree ◽  
Neuron Morphology ◽  
Tracer Injection ◽  
Topological Methods ◽  
Global Properties ◽  
Branching Patterns ◽  
Performance Gains

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory. However, the tree-like shapes of neurons cannot be described easily as points in a vector space (the subtraction of two neuronal shapes is not a meaningful operation), and methods from computational topology are better suited to their analysis. Here we introduce methods from Discrete Morse (DM) Theory to extract the tree-skeletons of individual neurons from volumetric brain image data, and to summarize collections of neurons labelled by tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons using a consensus tree-shape that provides a richer information summary than the traditional regional ‘connectivity matrix’ approach. The conceptually elegant DM approach lacks hand-tuned parameters and captures global properties of the data as opposed to previous approaches which are inherently local. For individual skeletonization of sparsely labelled neurons we obtain substantial performance gains over state-of-the-art non-topological methods (over 10% improvements in precision and faster proofreading). The consensus-tree summary of tracer injections incorporates the regional connectivity matrix information, but in addition captures the collective collateral branching patterns of the set of neurons connected to the injection site, and provides a bridge between single-neuron morphology and tracer-injection data.

scholarly journals Topological Skeletonization and Tree-Summarization of Neurons Using Discrete Morse Theory

2018 ◽  
Author(s):  
Suyi Wang ◽  
Xu Li ◽  
Partha Mitra ◽  
Yusu Wang
Keyword(s):  
Morse Theory ◽  
Stable Manifold ◽  
Image Data ◽  
Density Field ◽  
Discrete Morse Theory ◽  
Connectivity Matrix ◽  
Data Sets ◽  
Consensus Tree ◽  
Tracer Injection ◽  
Anterograde Tracer

AbstractNeuroscientific data analysis has classically involved methods for statistical signal and image processing, drawing on linear algebra and stochastic process theory. However, digitized neuroanatomical data sets containing labelled neurons, either individually or in groups labelled by tracer injections, do not fully fit into this classical framework. The tree-like shapes of neurons cannot mathematically be adequately described as points in a vector space (eg, the subtraction of two neuronal shapes is not a meaningful operation). There is therefore a need for new approaches. Methods from computational topology and geometry are naturally suited to the analysis of neuronal shapes. Here we introduce methods from Discrete Morse Theory to extract tree-skeletons of individual neurons from volumetric brain image data, or to summarize collections of neurons labelled by localized anterograde tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons labelled by a localized anterograde tracer injection using a consensus tree-shape. This consensus tree provides a richer information summary than the regional or voxel-based “connectivity matrix” approach that has previously been used in the literature.The algorithmic procedure includes an initial pre-processing step to extract a density field from the raw volumetric image data, followed by initial skeleton extraction from the density field using a discrete version of a 1-(un)stable manifold of the density field. Heuristically, if the density field is regarded as a mountainous landscape, then the 1-(un)stable manifold follows the “mountain ridges” connecting the maxima of the density field. We then simplify this skeletongraph into a tree using a shortest-path approach and methods derived from persistent homology. The advantage of this approach is that it uses global information about the density field and is therefore robust to local fluctuations and non-uniformly distributed input signals. To be able to handle large data sets, we use a divide-and-conquer approach. The resulting software DiMorSC is available on Github[40]. To the best of our knowledge this is currently the only publicly available code for the extraction of the 1-unstable manifold from an arbitrary simplicial complex using the Discrete Morse approach.

scholarly journals A high-throughput neurohistological pipeline for brain-wide mesoscale connectivity mapping of the common marmoset

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Meng Kuan Lin ◽  
Yeonsook Shin Takahashi ◽  
Bing-Xing Huo ◽  
Mitsutoshi Hanada ◽  
Jaimi Nagashima ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Common Marmoset ◽  
Connectivity Matrix ◽  
Tracer Injection ◽  
Common Reference ◽  
Connectivity Mapping ◽  
Primate Brain ◽  
The Common ◽  
The Individual

Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. Here we present an integrated neuro-histological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common marmoset (Callithrix jacchus). Individual brains are sectioned into ~1700 20 µm sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labeled sections. Systematic in-vivo MRI of the individual animals facilitates injection placement into reference-atlas defined anatomical compartments. Further, by combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to accurately map individual brains into a common reference atlas despite the significant individual variation. This approach will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain.

Eigenvector Rotation as an Estimation of Architectural Change

2016 ◽  
Author(s):  
Andy Dong ◽  
Somwrita Sarkar ◽  
Marie-Lise Moullec ◽  
Marija Jankovic
Keyword(s):  
System Architecture ◽  
Modular System ◽  
Connectivity Matrix ◽  
Present System ◽  
System Architectures ◽  
Architectural Change ◽  
Technical Innovations ◽  
Performance Gains ◽  
The Impact ◽  
Do So

Many important technical innovations occur through changes to existing system architectures. To manage the balance between performance gains by the innovation and the risk of change, companies estimate the degree of architectural change an innovation option could cause due to change propagation throughout the entire system. To do so, they must evaluate the innovation options for their integration cost given the present system architecture. This article presents a new algorithm and metrics based upon eigenvector rotations of the architectural connectivity matrix to assess the sensitivity of a system architecture to introduced innovations, modelled as perturbations on the system. The article presents studies of the impact of changes on synthetic system architectures to validate the method. The results show that there is no single architecture that is the most amenable to introduced innovation. Properties such as the density of existing connections and the number of changes that modify intra- or inter-module connections can introduce global effects that are not known in advance. Hierarchical modular system architectures tend to be relatively stable to introduced innovations and distributed changes to any architecture tends to cause the largest eigenvector rotations.

scholarly journals Robust automated protocol for extraction and comparison of single neuron morphology

2014 ◽  
Vol 8 ◽  
Author(s):  
Ai Hiroyuki ◽  
Haupt Stephan ◽  
Rautenberg Philipp ◽  
Stransky Michael ◽  
Wachtler Thomas ◽  
...  
Keyword(s):  
Single Neuron ◽  
Neuron Morphology ◽  
Automated Protocol

scholarly journals Cross-Modality Coherent Registration of Whole Mouse Brains

2021 ◽  
Author(s):  
Hanchuan Peng ◽  
Lei Qu ◽  
Yuanyuan Li ◽  
Peng Xie ◽  
Lijuan Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Cell Types ◽  
Brain Anatomy ◽  
Neuron Morphology ◽  
Brain Images ◽  
Whole Brain ◽  
Preparation Methods ◽  
Registration Method ◽  
3D Images

Abstract Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types.

scholarly journals 3D Reconstruction and Standardization of the Rat Vibrissal Cortex for Precise Registration of Single Neuron Morphology

2012 ◽  
Vol 8 (12) ◽  
pp. e1002837 ◽  
Author(s):  
Robert Egger ◽  
Rajeevan T. Narayanan ◽  
Moritz Helmstaedter ◽  
Christiaan P. J. de Kock ◽  
Marcel Oberlaender
Keyword(s):  
3D Reconstruction ◽  
Single Neuron ◽  
Neuron Morphology

Evolutionary remodeling in a visual system through extensive changes in the synaptic connectivity of homologous neurons

1989 ◽  
Vol 3 (5) ◽  
pp. 405-410 ◽  
Author(s):  
S. R. Shaw ◽  
D. Moore
Keyword(s):  
The Body ◽  
Synaptic Connectivity ◽  
Connectivity Matrix ◽  
Cellular Mechanisms ◽  
Microscopical Level ◽  
Unit Structure ◽  
Evolutionary Changes ◽  
Electron Microscopical Level ◽  
Branching Patterns ◽  
Electron Microscopical

AbstractThe cellular mechanisms by which nervous systems evolve to match evolutionary changes occurring in the rest of the body remain largely unexplored. In a distal visual neuropil of a previously unexamined ancient dipteran family, Stratiomyidae, homologues of all of the periodic neurons known already from more recent Diptera can be recognized, occupying the same locations within the unit structure. This points to extreme developmental stasis for more than 200 million years, conserving both cell identity and position. The arborizations that some neurons make also have remained conservative, but others show marked differences between families in both size and branching patterns. At the electron-microscopical level, extensive differences in synaptic connectivity are found, some sufficient to radically redefine the systems roles of particular neurons. The findings bear out an earlier prediction that changes in the connectivity matrix linking conserved neurons may have been a major factor in implementing evolutionary change in the nervous system.

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