Directional multiscale representations and applications in digital neuron reconstruction

2019 ◽  
Vol 349 ◽  
pp. 482-493 ◽  
Author(s):  
Cihan Kayasandik ◽  
Kanghui Guo ◽  
Demetrio Labate
Author(s):  
Zihao Tang ◽  
Donghao Zhang ◽  
Siqi Liu ◽  
Yang Song ◽  
Hanchuan Peng ◽  
...  

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 159-166
Author(s):  
Rodrigo Muñoz-Castañeda ◽  
Brian Zingg ◽  
Katherine S. Matho ◽  
Xiaoyin Chen ◽  
Quanxin Wang ◽  
...  

AbstractAn essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.


eNeuro ◽  
2015 ◽  
Vol 2 (1) ◽  
pp. ENEURO.0049-14.2014 ◽  
Author(s):  
Linqing Feng ◽  
Ting Zhao ◽  
Jinhyun Kim

Author(s):  
Shuxia Guo ◽  
Xuan Zhao ◽  
Shengdian Jiang ◽  
Liya Ding ◽  
Hanchuan Peng

Abstract Motivation To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast and the large dynamic range of signal and background both within and across images. Results We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high signal-background contrast and better within- and between image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. Additionally, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased 5 times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience providing better 3D morphological reconstruction and lower cost of data storage and transfer. Availability The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality, and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN’s Brain Image Library (BIL) (https://www.brainimagelibrary.org). Supplementary information Supplementary data are available at Bioinformatics online.


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