The circuit architecture of whole brains at the mesoscopic scale.
The circuit architecture of whole brains at the mesoscopic scale.
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DOI：
10.1016/j.neuron.2014.08.055
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发表时间：
2014-09-17
期刊：
影响因子：
16.2
通讯作者：
Mitra, Partha P.
中科院分区：
文献类型：
--
作者：
Mitra, Partha P.
即使是中等大小的脊椎动物大脑也由天文数字数量的神经元组成，并且在微观尺度上表现出很大程度的个体差异。这种变异可能是表型可塑性和个体经验的结果。然而，在更大的尺度上，在神经元结构中观察到相对稳定的物种典型空间模式，例如，胞体和轴突投射模式的空间分布，可能是遗传编码的发育程序的结果。脑结构分析的中观尺度是个体差异显著的微观尺度和观察到稳定的、物种典型的神经结构的宏观水平之间的过渡点。神经解剖学图谱中隐含的这种尺度的经验存在，加上计算资源的进步，使得研究整个大脑的电路结构成为一项实际任务。先前已经提出了一种方法，该方法采用类似猎枪的基于网格的方法来系统地覆盖整个脑体积，注射神经元示踪剂。这种方法被用来获得中尺度电路图在小鼠，并应适用于其他脊椎动物类群。由此产生的大型数据集提出了数据表示，分析和解释的问题，这些问题必须得到解决。即使对于数据表示的挑战是不平凡的：传统的方法，使用区域连接矩阵未能捕获的投射神经元的侧支分支模式。这个有前途的研究企业未来的成功取决于以前的神经解剖学知识的整合，部分通过开发合适的计算工具，封装这样的专业知识。
Vertebrate brains of even moderate size are composed of astronomically large numbers of neurons and show a great degree of individual variability at the microscopic scale. This variation is presumably the result of phenotypic plasticity and individual experience. At a larger scale, however, relatively stable species-typical spatial patterns are observed in neuronal architecture, e.g., the spatial distributions of somata and axonal projection patterns, probably the result of a genetically encoded developmental program. The mesoscopic scale of analysis of brain architecture is the transitional point between a microscopic scale where individual variation is prominent and the macroscopic level where a stable, species-typical neural architecture is observed. The empirical existence of this scale, implicit in neuroanatomical atlases, combined with advances in computational resources, makes studying the circuit architecture of entire brains a practical task. A methodology has previously been proposed that employs a shotgun-like grid-based approach to systematically cover entire brain volumes with injections of neuronal tracers. This methodology is being employed to obtain mesoscale circuit maps in mouse and should be applicable to other vertebrate taxa. The resulting large data sets raise issues of data representation, analysis, and interpretation, which must be resolved. Even for data representation the challenges are nontrivial: the conventional approach using regional connectivity matrices fails to capture the collateral branching patterns of projection neurons. Future success of this promising research enterprise depends on the integration of previous neuroanatomical knowledge, partly through the development of suitable computational tools that encapsulate such expertise.
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DOI：
10.1073/pnas.1312098111
发表时间：
2014-04-08
影响因子：
11.1
作者：
Grange, Pascal;Bohland, Jason W.;Mitra, Partha P.
通讯作者：
Mitra, Partha P.
影响因子：
13.9
作者：
Fenno L;Yizhar O;Deisseroth K
通讯作者：
Deisseroth K
影响因子：
56.9
作者：
ANDERSON, PW
通讯作者：
ANDERSON, PW
影响因子：
9.2
作者：
Chiang, Ann-Shyn;Lin, Chih-Yung;Hwang, Jenn-Kang
通讯作者：
Hwang, Jenn-Kang
影响因子：
4.3
作者：
Bohland JW;Wu C;Barbas H;Bokil H;Bota M;Breiter HC;Cline HT;Doyle JC;Freed PJ;Greenspan RJ;Haber SN;Hawrylycz M;Herrera DG;Hilgetag CC;Huang ZJ;Jones A;Jones EG;Karten HJ;Kleinfeld D;Kötter R;Lester HA;Lin JM;Mensh BD;Mikula S;Panksepp J;Price JL;Safdieh J;Saper CB;Schiff ND;Schmahmann JD;Stillman BW;Svoboda K;Swanson LW;Toga AW;Van Essen DC;Watson JD;Mitra PP
通讯作者：
Mitra PP