Scanning Brain Networks
Figure 5 : (a) Skeletonized model of a neuronal network of the human frontal cortex. The brain surface is toward the top. Soma positions are indicated with closed circles. Neurons are color-coded. Total width: 240 µm. (b) A pair of neurons 1020 (magenta) and 1023 (green) were extracted from the model. Possible synaptic connec- tions are indicated with black dots. (c) Neurons 1020 and 1023 connect their inputs and outputs to each other to form a feedback loop. Dendrites are drawn with thin lines, and axons with arrows. Possible synaptic connections are indicated with diamonds. Reprinted with permission from [5]. (d) A similar feedback circuit composed of transistors is known as an astable multivibrator.
brain disorders are being administrated without a complete understanding of their mechanisms of action on the circuits of the brain. Better diagnosis, prevention, and treatment of brain disorders should thus be possible by analyzing brain networks and identifying neuronal circuits responsible for individual brain disorders.
Conclusion
Neurons make up a 3D network in brain tissue. T is article reports on the 3D structures in human and fruit fl y brain tissues as measured with a microscopic version of medical computed tomography (CT) called X-ray tomographic microscopy (or microtomography or micro-CT). T e obtained structures were analyzed by building skeletonized models of neurons. T e resulting quantitative description of the 3D networks provided information about the functional mechanisms of the brain.
16
Acknowledgments T is work was supported in part by Grants-in-Aid for
Scientifi c Research from the Japan Society for the Promotion of Science (nos. 25282250 and 25610126). T e synchrotron radiation experiments were performed at SPring-8 with the approval of the Japan Synchrotron Radiation Research Institute (JASRI) (proposal nos. 2008B1261, 2013B0034, 2013B0041, 2014B1083, 2014B1096, and 2015A1160).
References [1] R Mizutani et al ., Cereb Cortex 20 ( 2010 ) 1739 – 48 . [2] R Mizutani et al ., J Struct Biol 184 ( 2013 ) 271 – 9 . [3] R Mizutani et al ., Nucl Instrum Meth A 621 ( 2010 ) 615 – 9 . [4] Y Bengio , Foundations and Trends in Machine Learning 2 ( 2009 ) 1 – 127 .
[5] R Mizutani et al ., KENBIKYO 49 ( 2014 ) 222 – 25 .
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