Computational methods and challenges for large-scale circuit mapping

被引:59
作者
Helmstaedter, Moritz [1 ]
Mitra, Partha P. [2 ]
机构
[1] Max Planck Inst Neurobiol, Struct Neocort Circuits Grp, D-82152 Martinsried, Germany
[2] Cold Spring Harbor Lab, Cold Spring Harbor, NY 11724 USA
关键词
SCANNING-ELECTRON-MICROSCOPY; MOUSE-BRAIN; 3-DIMENSIONAL STRUCTURE; DENDRITIC SPINES; NERVOUS-SYSTEM; IMAGE DATA; DATA SETS; IN-VIVO; DROSOPHILA; RECONSTRUCTION;
D O I
10.1016/j.conb.2011.11.010
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The connectivity architecture of neuronal circuits is essential to understand how brains work, yet our knowledge about the neuronal wiring diagrams remains limited and partial. Technical breakthroughs in labeling and imaging methods starting more than a century ago have advanced knowledge in the field. However, the volume of data associated with imaging a whole brain or a significant fraction thereof, with electron or light microscopy, has only recently become amenable to digital storage and analysis. A mouse brain imaged at light-microscopic resolution is about a terabyte of data, and 1 mm(3) of the brain at EM resolution is about half a petabyte. This has given rise to a new field of research, computational analysis of large-scale neuroanatomical data sets, with goals that include reconstructions of the morphology of individual neurons as well as entire circuits. The problems encountered include large data management, segmentation and 3D reconstruction, computational geometry and workflow management allowing for hybrid approaches combining manual and algorithmic processing. Here we review this growing field of neuronal data analysis with emphasis on reconstructing neurons from EM data cubes.
引用
收藏
页码:162 / 169
页数:8
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