51
|
Jiménez D, Labate D, Kakadiaris IA, Papadakis M. Improved Automatic Centerline Tracing for Dendritic and Axonal Structures. Neuroinformatics 2014; 13:227-44. [DOI: 10.1007/s12021-014-9256-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
52
|
Sümbül U, Zlateski A, Vishwanathan A, Masland RH, Seung HS. Automated computation of arbor densities: a step toward identifying neuronal cell types. Front Neuroanat 2014; 8:139. [PMID: 25505389 PMCID: PMC4243570 DOI: 10.3389/fnana.2014.00139] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Accepted: 11/06/2014] [Indexed: 11/17/2022] Open
Abstract
The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference.
Collapse
Affiliation(s)
- Uygar Sümbül
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA ; Department of Ophthalmology, Harvard Medical School Boston, MA, USA
| | - Aleksandar Zlateski
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge, MA, USA
| | - Ashwin Vishwanathan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA
| | - Richard H Masland
- Department of Ophthalmology, Harvard Medical School Boston, MA, USA ; Department of Neurobiology, Harvard Medical School Boston, MA, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA
| |
Collapse
|
53
|
Brown J, Pan WX, Dudman JT. The inhibitory microcircuit of the substantia nigra provides feedback gain control of the basal ganglia output. eLife 2014; 3:e02397. [PMID: 24849626 PMCID: PMC4067753 DOI: 10.7554/elife.02397] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2014] [Accepted: 05/17/2014] [Indexed: 12/26/2022] Open
Abstract
Dysfunction of the basal ganglia produces severe deficits in the timing, initiation, and vigor of movement. These diverse impairments suggest a control system gone awry. In engineered systems, feedback is critical for control. By contrast, models of the basal ganglia highlight feedforward circuitry and ignore intrinsic feedback circuits. In this study, we show that feedback via axon collaterals of substantia nigra projection neurons control the gain of the basal ganglia output. Through a combination of physiology, optogenetics, anatomy, and circuit mapping, we elaborate a general circuit mechanism for gain control in a microcircuit lacking interneurons. Our data suggest that diverse tonic firing rates, weak unitary connections and a spatially diffuse collateral circuit with distinct topography and kinetics from feedforward input is sufficient to implement divisive feedback inhibition. The importance of feedback for engineered systems implies that the intranigral microcircuit, despite its absence from canonical models, could be essential to basal ganglia function. DOI: http://dx.doi.org/10.7554/eLife.02397.001.
Collapse
Affiliation(s)
- Jennifer Brown
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn , United States Department of Physiology, Development and Neuroscience , University of Cambridge, Cambridge , United Kingdom
| | - Wei-Xing Pan
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn , United States
| | - Joshua Tate Dudman
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn , United States
| |
Collapse
|
54
|
Sholl analysis: A quantitative comparison of semi-automated methods. J Neurosci Methods 2014; 225:65-70. [DOI: 10.1016/j.jneumeth.2014.01.017] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Revised: 01/15/2014] [Accepted: 01/17/2014] [Indexed: 01/03/2023]
|
55
|
Misiak D, Posch S, Lederer M, Reinke C, Hüttelmaier S, Möller B. Extraction of protein profiles from primary neurons using active contour models and wavelets. J Neurosci Methods 2014; 225:1-12. [PMID: 24457055 DOI: 10.1016/j.jneumeth.2013.12.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2013] [Revised: 12/18/2013] [Accepted: 12/19/2013] [Indexed: 11/19/2022]
Abstract
The function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means. We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites. We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses.
Collapse
Affiliation(s)
- Danny Misiak
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle, Germany.
| | - Stefan Posch
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06099 Halle, Germany
| | - Marcell Lederer
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle, Germany
| | - Claudia Reinke
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle, Germany
| | - Stefan Hüttelmaier
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle, Germany
| | - Birgit Möller
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06099 Halle, Germany
| |
Collapse
|
56
|
Ming X, Li A, Wu J, Yan C, Ding W, Gong H, Zeng S, Liu Q. Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling. PLoS One 2013; 8:e84557. [PMID: 24391966 PMCID: PMC3877282 DOI: 10.1371/journal.pone.0084557] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 11/16/2013] [Indexed: 11/22/2022] Open
Abstract
Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.
Collapse
Affiliation(s)
- Xing Ming
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Jingpeng Wu
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Cheng Yan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- * E-mail:
| |
Collapse
|
57
|
Wang YC, Yang JS, Johnston R, Ren Q, Lee YJ, Luan H, Brody T, Odenwald WF, Lee T. Drosophila intermediate neural progenitors produce lineage-dependent related series of diverse neurons. Development 2013; 141:253-8. [PMID: 24306106 DOI: 10.1242/dev.103069] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Drosophila type II neuroblasts (NBs), like mammalian neural stem cells, deposit neurons through intermediate neural progenitors (INPs) that can each produce a series of neurons. Both type II NBs and INPs exhibit age-dependent expression of various transcription factors, potentially specifying an array of diverse neurons by combinatorial temporal patterning. Not knowing which mature neurons are made by specific INPs, however, conceals the actual variety of neuron types and limits further molecular studies. Here we mapped neurons derived from specific type II NB lineages and found that sibling INPs produced a morphologically similar but temporally regulated series of distinct neuron types. This suggests a common fate diversification program operating within each INP that is modulated by NB age to generate slightly different sets of diverse neurons based on the INP birth order. Analogous mechanisms might underlie the expansion of neuron diversity via INPs in mammalian brain.
Collapse
Affiliation(s)
- Yu-Chun Wang
- Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
58
|
NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model. Sci Rep 2013; 3:1414. [PMID: 23546385 PMCID: PMC3613804 DOI: 10.1038/srep01414] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Accepted: 02/22/2013] [Indexed: 11/30/2022] Open
Abstract
Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.
Collapse
|
59
|
Xiao H, Peng H. APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. ACTA ACUST UNITED AC 2013; 29:1448-54. [PMID: 23603332 DOI: 10.1093/bioinformatics/btt170] [Citation(s) in RCA: 146] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
MOTIVATION Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images. RESULTS We developed all-path-pruning 2.0 (APP2) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. We also design a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on ~700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods. AVAILABILITY The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Hang Xiao
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | | |
Collapse
|
60
|
A distance-field based automatic neuron tracing method. BMC Bioinformatics 2013; 14:93. [PMID: 23497429 PMCID: PMC3637550 DOI: 10.1186/1471-2105-14-93] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Accepted: 02/22/2013] [Indexed: 11/24/2022] Open
Abstract
Background Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex. Results We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can “push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology. Conclusion We have used DF-Tracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DF-Tracing produces better reconstructions.
Collapse
|
61
|
Hwang H, Lu H. Microfluidic tools for developmental studies of small model organisms--nematodes, fruit flies, and zebrafish. Biotechnol J 2013; 8:192-205. [PMID: 23161817 PMCID: PMC3918482 DOI: 10.1002/biot.201200129] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2012] [Revised: 09/13/2012] [Accepted: 09/24/2012] [Indexed: 12/15/2022]
Abstract
Studying the genetics of development with small model organisms such as the zebrafish (Danio Rerio), the fruit fly (Drosophila melanogaster), and the soil-dwelling nematode (Caenorhabditis elegans), provide unique opportunities for understanding related processes and diseases in humans. These model organisms also have potential for use in drug discovery and toxicity-screening applications. There have been sweeping developments in microfabrication and microfluidic technologies for manipulating and imaging small objects, including small model organisms, which allow high-throughput quantitative biological studies. Here, we review recent progress in microfluidic tools able to manipulate small organisms and project future directions and applications of these techniques and technologies.
Collapse
Affiliation(s)
- Hyundoo Hwang
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Dr. NW, Atlanta, GA, USA, Tel: +1-404-894-8473
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Dr. NW, Atlanta, GA, USA, Tel: +1-404-894-8473
| |
Collapse
|
62
|
Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 2012; 492:247-51. [PMID: 23143335 DOI: 10.1038/nature11601] [Citation(s) in RCA: 342] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2011] [Accepted: 09/19/2012] [Indexed: 11/09/2022]
Abstract
Active dendrites provide neurons with powerful processing capabilities. However, little is known about the role of neuronal dendrites in behaviourally related circuit computations. Here we report that a novel global dendritic nonlinearity is involved in the integration of sensory and motor information within layer 5 pyramidal neurons during an active sensing behaviour. Layer 5 pyramidal neurons possess elaborate dendritic arborizations that receive functionally distinct inputs, each targeted to spatially separate regions. At the cellular level, coincident input from these segregated pathways initiates regenerative dendritic electrical events that produce bursts of action potential output and circuits featuring this powerful dendritic nonlinearity can implement computations based on input correlation. To examine this in vivo we recorded dendritic activity in layer 5 pyramidal neurons in the barrel cortex using two-photon calcium imaging in mice performing an object-localization task. Large-amplitude, global calcium signals were observed throughout the apical tuft dendrites when active touch occurred at particular object locations or whisker angles. Such global calcium signals are produced by dendritic plateau potentials that require both vibrissal sensory input and primary motor cortex activity. These data provide direct evidence of nonlinear dendritic processing of correlated sensory and motor information in the mammalian neocortex during active sensation.
Collapse
|
63
|
Lee PC, Chuang CC, Chiang AS, Ching YT. High-throughput computer method for 3D neuronal structure reconstruction from the image stack of the Drosophila brain and its applications. PLoS Comput Biol 2012; 8:e1002658. [PMID: 23028271 PMCID: PMC3441491 DOI: 10.1371/journal.pcbi.1002658] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Accepted: 07/12/2012] [Indexed: 11/19/2022] Open
Abstract
Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100 000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16 000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network. It is now possible to image a single neuron in the fruit fly brain. However, manually reconstructing neuronal structures is tremendously time consuming. The proposed method avoids user interventions by first automatically identifying the end points and detecting the appropriate representative point of the soma, and then, by finding the shortest paths from the soma to the end points in an image stack. In the proposed algorithm, a tailor-made weighting function allows the resulting reconstruction to represent the neuron appropriately. Accuracy analysis and a robustness test demonstrated that the proposed method is accurate and robust to handle the noisy image data. Tract discovery is one of the most frequently mentioned potentials of reconstructed results. In addition to a method for neuronal structure reconstruction, this study presents a method for tract discovery and explores the tract-connecting olfactory neuropils using the reconstructed results. The discovered tracts are in agreement with the results of previous studies in the literature. Software for reconstructing the neuronal structures and the reconstruction results can be downloaded from the Web site http://www.flycircuit.tw. More details on acquiring the software and the reconstruction results are provided in Text S1.
Collapse
Affiliation(s)
- Ping-Chang Lee
- Department of Computer Science, National Chiao Tung University, HsinChu, Taiwan
| | - Chao-Chun Chuang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, HsinChu, Taiwan
- National Center for High-Performance Computing, HsinChu, Taiwan
| | - Ann-Shyn Chiang
- Institute of Biotechnology, National Tsing Hua University, HsinChu, Taiwan
- Brain Research Center, National Tsing Hua University, HsinChu, Taiwan
| | - Yu-Tai Ching
- Department of Computer Science, National Chiao Tung University, HsinChu, Taiwan
- * E-mail:
| |
Collapse
|
64
|
Rahim MSM, Razzali N, Sunar MS, Altameem A, Rehman A. Curve interpolation model for visualising disjointed neural elements. Neural Regen Res 2012; 7:1637-44. [PMID: 25657704 PMCID: PMC4308766 DOI: 10.3969/j.issn.1673-5374.2012.21.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2012] [Accepted: 05/14/2012] [Indexed: 11/18/2022] Open
Abstract
Neuron cell are built from a myriad of axon and dendrite structures. It transmits electrochemical signals between the brain and the nervous system. Three-dimensional visualization of neuron structure could help to facilitate deeper understanding of neuron and its models. An accurate neuron model could aid understanding of brain's functionalities, diagnosis and knowledge of entire nervous system. Existing neuron models have been found to be defective in the aspect of realism. Whereas in the actual biological neuron, there is continuous growth as the soma extending to the axon and the dendrite; but, the current neuron visualization models present it as disjointed segments that has greatly mediated effective realism. In this research, a new reconstruction model comprising of the Bounding Cylinder, Curve Interpolation and Gouraud Shading is proposed to visualize neuron model in order to improve realism. The reconstructed model is used to design algorithms for generating neuron branching from neuron SWC data. The Bounding Cylinder and Curve Interpolation methods are used to improve the connected segments of the neuron model using a series of cascaded cylinders along the neuron's connection path. Three control points are proposed between two adjacent neuron segments. Finally, the model is rendered with Gouraud Shading for smoothening of the model surface. This produce a near-perfection model of the natural neurons with attended realism. The model is validated by a group of bioinformatics analysts’ responses to a predefined survey. The result shows about 82% acceptance and satisfaction rate.
Collapse
Affiliation(s)
- Mohd Shafry Mohd Rahim
- UTMViCubeLab, Department of Computer Graphics and Multimedia, FSKSM, University of Technology, Skudai 81310, Malaysia
| | - Norhasana Razzali
- UTMViCubeLab, Department of Computer Graphics and Multimedia, FSKSM, University of Technology, Skudai 81310, Malaysia
| | - Mohd Shahrizal Sunar
- UTMViCubeLab, Department of Computer Graphics and Multimedia, FSKSM, University of Technology, Skudai 81310, Malaysia
| | - Ayman Altameem
- College of Applied Studies and Community Service, King Saud University, Riyadh 11451, Saudi Arabia
| | - Amjad Rehman
- College of Applied Studies and Community Service, King Saud University, Riyadh 11451, Saudi Arabia
| |
Collapse
|
65
|
Hogrebe L, Paiva AR, Jurrus E, Christensen C, Bridge M, Dai L, Pfeiffer R, Hof PR, Roysam B, Korenberg JR, Tasdizen T. Serial section registration of axonal confocal microscopy datasets for long-range neural circuit reconstruction. J Neurosci Methods 2012; 207:200-10. [PMID: 22465678 PMCID: PMC4981587 DOI: 10.1016/j.jneumeth.2012.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2011] [Revised: 03/02/2012] [Accepted: 03/15/2012] [Indexed: 12/19/2022]
Abstract
In the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships among the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information.
Collapse
Affiliation(s)
- Luke Hogrebe
- Scientific Computing and Imaging Institute, University of Utah, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, UT, United States
| | - Antonio R.C. Paiva
- Scientific Computing and Imaging Institute, University of Utah, UT, United States
| | - Elizabeth Jurrus
- Scientific Computing and Imaging Institute, University of Utah, UT, United States
- School of Computing, University of Utah, UT, United States
| | - Cameron Christensen
- Scientific Computing and Imaging Institute, University of Utah, UT, United States
| | | | - Li Dai
- Brain Institute, University of Utah, UT, United States
- Center for the Integration of Neuroscience and Human Behavior, University of Utah, UT, United States
- Department of Pediatrics, University of Utah, UT, United States
| | - Rebecca Pfeiffer
- Brain Institute, University of Utah, UT, United States
- Neuroscience Program, University of Utah, UT, United States
- Center for the Integration of Neuroscience and Human Behavior, University of Utah, UT, United States
| | - Patrick R. Hof
- Fishberg Department of Neuroscience and Friedman Brain Institute, Mount Sinai School of Medicine, NY, United States
| | - Badrinath Roysam
- Department of Electrical and Computer Engineering, University of Houston, TX, United States
| | | | - Tolga Tasdizen
- Scientific Computing and Imaging Institute, University of Utah, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, UT, United States
| |
Collapse
|
66
|
Mayerich D, Bjornsson C, Taylor J, Roysam B. NetMets: software for quantifying and visualizing errors in biological network segmentation. BMC Bioinformatics 2012; 13 Suppl 8:S7. [PMID: 22607549 PMCID: PMC3355337 DOI: 10.1186/1471-2105-13-s8-s7] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.
Collapse
Affiliation(s)
- David Mayerich
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL, USA.
| | | | | | | |
Collapse
|
67
|
Abstract
Reconstruction of the complete wiring diagram, or connectome, of a neural circuit provides an alternative approach to conventional circuit analysis. One major obstacle of connectomics lies in segmenting and tracing neuronal processes from the vast number of images obtained with optical or electron microscopy. Here I review recent progress in automated tracing algorithms for connectomic reconstruction with fluorescence and electron microscopy, and discuss the challenges to image analysis posed by novel optical imaging techniques.
Collapse
Affiliation(s)
- Ju Lu
- James H. Clark Center for Biomedical Engineering and Sciences, Department of Biological Sciences, Stanford University, Stanford, CA, USA.
| |
Collapse
|
68
|
Abstract
In 1873 Camillo Golgi discovered his eponymous stain, which he called la reazione nera. By adding to it the concepts of the Neuron Doctrine and the Law of Dynamic Polarisation, Santiago Ramon y Cajal was able to link the individual Golgi-stained neurons he saw down his microscope into circuits. This was revolutionary and we have all followed Cajal's winning strategy for over a century. We are now on the verge of a new revolution, which offers the prize of a far more comprehensive description of neural circuits and their operation. The hope is that we will exploit the power of computer vision algorithms and modern molecular biological techniques to acquire rapidly reconstructions of single neurons and synaptic circuits, and to control the function of selected types of neurons. Only one item is now conspicuous by its absence: the 21st century equivalent of the concepts of the Neuron Doctrine and the Law of Dynamic Polarisation. Without their equivalent we will inevitably struggle to make sense of our 21st century observations within the 19th and 20th century conceptual framework we have inherited.
Collapse
Affiliation(s)
- Rodney J Douglas
- Institute of Neuroinformatics, UZH/ETH, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | | |
Collapse
|
69
|
Halavi M, Hamilton KA, Parekh R, Ascoli GA. Digital reconstructions of neuronal morphology: three decades of research trends. Front Neurosci 2012; 6:49. [PMID: 22536169 PMCID: PMC3332236 DOI: 10.3389/fnins.2012.00049] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 03/02/2012] [Indexed: 01/16/2023] Open
Abstract
The importance of neuronal morphology has been recognized from the early days of neuroscience. Elucidating the functional roles of axonal and dendritic arbors in synaptic integration, signal transmission, network connectivity, and circuit dynamics requires quantitative analyses of digital three-dimensional reconstructions. We extensively searched the scientific literature for all original reports describing reconstructions of neuronal morphology since the advent of this technique three decades ago. From almost 50,000 titles, 30,000 abstracts, and more than 10,000 full-text articles, we identified 902 publications describing ∼44,000 digital reconstructions. Reviewing the growth of this field exposed general research trends on specific animal species, brain regions, neuron types, and experimental approaches. The entire bibliography, annotated with relevant metadata and (wherever available) direct links to the underlying digital data, is accessible at NeuroMorpho.Org.
Collapse
Affiliation(s)
- Maryam Halavi
- Molecular Neuroscience Department, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | | | | | | |
Collapse
|
70
|
Myatt DR, Hadlington T, Ascoli GA, Nasuto SJ. Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology. Front Neuroinform 2012; 6:4. [PMID: 22438842 PMCID: PMC3305991 DOI: 10.3389/fninf.2012.00004] [Citation(s) in RCA: 100] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2011] [Accepted: 02/20/2012] [Indexed: 02/05/2023] Open
Abstract
The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.
Collapse
Affiliation(s)
- Darren R Myatt
- School of Systems Engineering, University of Reading Reading, UK
| | | | | | | |
Collapse
|
71
|
mGRASP enables mapping mammalian synaptic connectivity with light microscopy. Nat Methods 2011; 9:96-102. [PMID: 22138823 DOI: 10.1038/nmeth.1784] [Citation(s) in RCA: 200] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2011] [Accepted: 09/29/2011] [Indexed: 12/19/2022]
Abstract
The GFP reconstitution across synaptic partners (GRASP) technique, based on functional complementation between two nonfluorescent GFP fragments, can be used to detect the location of synapses quickly, accurately and with high spatial resolution. The method has been previously applied in the nematode and the fruit fly but requires substantial modification for use in the mammalian brain. We developed mammalian GRASP (mGRASP) by optimizing transmembrane split-GFP carriers for mammalian synapses. Using in silico protein design, we engineered chimeric synaptic mGRASP fragments that were efficiently delivered to synaptic locations and reconstituted GFP fluorescence in vivo. Furthermore, by integrating molecular and cellular approaches with a computational strategy for the three-dimensional reconstruction of neurons, we applied mGRASP to both long-range circuits and local microcircuits in the mouse hippocampus and thalamocortical regions, analyzing synaptic distribution in single neurons and in dendritic compartments.
Collapse
|
72
|
Abstract
Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable. Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly). Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d. Contact:pengh@janelia.hhmi.org
Collapse
Affiliation(s)
- Hanchuan Peng
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | | | | |
Collapse
|