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<title cf:type="text"><![CDATA[Progress in Biochemistry and Biophysics -->Special Topic: Brain Imaging and Brain Networks]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Editorial: Brain Imaging and Brain Networks]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20120280]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[揭示脑的奥秘是人类面临的最大挑战之一。神经元是构成神经系统结构与功能的基本单位。神经元与神经元之间通过突触实现信息交互，并构成神经环路或神经网络。神经环路有局部的，也有跨脑区或长程的，甚至全脑尺度的。神经环路则是脑实现神经信息处理的基本单元。若干神经环路构成脑网络。脑网络研究已经成为脑功能与脑疾病研究领域的热点。<br>
在国家自然科学基金委员会和科技部“973计划”等项目的支持下，我国科学家在这一领域已经开展了卓有成效的工作。2011年第393次香山科学会议“脑网络组及其临床应用的前沿科学问题”曾对此进行过比较深入的研讨。为促进对该领域现状及发展的了解，本期汇集了2篇述评和2篇研究论文，作为脑成像与脑网络专题发表，以飨读者。<br>
利用9.4T功能磁共振成像(fMRI)获得轻度麻醉状态下大鼠静息状态及刺激激活的数据，通过互相关分析构建节点之间的相关系数矩阵并计算相应的网络参数，赖永秀等人报道了大鼠感觉运动系统静息态脑网络的研究成果，发现感觉运动系统在静息态时的脑网络具有小世界属性。<br>
扩散磁共振成像(dMRI)的出现为大脑结构与功能研究提供了全新的检测手段，雷皓等报道了小动物高分辨扩散磁共振成像数据分析方法，为小动物脑dMRI研究提供了统一图像模板与完善的计算方法，对于检测神经纤维微观结构的变化，以及临床诊断，将具有极其重要的意义。<br>
神经环路功能变化的实时在体监测是研究脑网络不可或缺的手段，曾绍群等评述了基于声光偏转器的快速无惯性随机扫描双光子显微成像技术的研究进展及发展趋势，指出该技术的进一步发展将为神经活动观测提供一种全新的方法，从而极大地推动脑科学研究的发展。<br>
针对哺乳动物全脑的神经元网络成像，龚辉等从空间分辨率、探测范围、数据配准和成像速度等方面评述了光学显微水平全脑成像方法的研究进展，并讨论所面临的挑战。他们指出，要在全脑尺度获取突起水平分辨率的结构与功能数据，光学成像方法最为成熟。华中科技大学研制的MOST系统，率先获得了一系列高分辨率的完整大脑解剖数据集，该成果将在神经元网络的构建和脑功能与疾病研究中发挥重要作用。<br>
我们期待更多、更好的有关脑成像与脑网络的论文发表，以更广泛和深入地促进我国脑科学研究领域的学术交流。]]></description>
<pubDate>2012/6/21 0:00:00</pubDate>
<category><![CDATA[Special Topic: Brain Imaging and Brain Networks]]></category>
<author><![CDATA[LUO Qing-Ming]]></author>
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<atom:name>LUO Qing-Ming</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Review: Progress on Whole Brain Imaging Methods at The Level of Optical Microscopy]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20120237]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[One of the basic goals of modern neuroscience is to study the brain-wide complex neural network of mammals' brain. Because of the technological limitations, the traditional tools can only study the local circuits of the higher animals or the small brain network of the lower animals. Here we review recent efforts to resolve the contradiction between large specimen and high-resolution, and some of them have been applied to image the neural network in mammals' whole brain. We focus on some technology index includes spatial resolution, detection range, data registration and throughput, and also discuss the challenges of the emerging methods.]]></description>
<pubDate>2012/6/21 0:00:00</pubDate>
<category><![CDATA[Special Topic: Brain Imaging and Brain Networks]]></category>
<author><![CDATA[LI An-An and GONG Hui]]></author>
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<atom:name>LI An-An and GONG Hui</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Review: Random-access Two-photon Microscopy for Neural Activity Observation]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20120234]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Two-photon microscope has become an important instrument in neuroscience research. However, the current commercial instruments can hardly meet the need for the detection of neural signal in millisecond scale due to their low imaging rates. Fast random-access two-photon microscopy based on acousto-optic deflector (AOD) has the potential for increasing the observation speed while maintaining adequate signal to noise ratio (SNR). We summarize the latest related research progress. It is demonstrated from four parts, including the spatio-temporal evolution theory of the femtosecond laser after passing the angular dispersion devices, dispersion compensation method for AOD, random-access two-photon microscopy instrument, and calcium signal identification method in the instrument applications. In the end, the future development trends for random-access two-photon microscopy are discussed. The systematic and in-deep research on this technology will provide a new tool for the neural activity observation and boost the development of brain science.]]></description>
<pubDate>2012/6/21 0:00:00</pubDate>
<category><![CDATA[Special Topic: Brain Imaging and Brain Networks]]></category>
<author><![CDATA[JIIANG Run-Hua,LV Xiao-Hua,LI De-Rong,QUAN Ting-Wei,LIU Xiu-Li,LUO Qing-Ming and 曾绍群]]></author>
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<atom:name>JIIANG Run-Hua,LV Xiao-Hua,LI De-Rong,QUAN Ting-Wei,LIU Xiu-Li,LUO Qing-Ming and 曾绍群</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Research Paper: Templates and Analysis Methods for Small Animal High-resolution Diffusion Magnetic Resonance Imaging]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20120235]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Diffusion magnetic resonance imaging (dMRI) is a non-invasive imaging technique capable of characterizing the diffusion properties of water molecules <i>in vivo</i> and detecting microstructural changes in brain tissue. It provides new tools to investigate the integrity of brain white matter. Although dMRI is widely used in clinical studies, and has become a common method in clinical radiology, it is infrequently used in preclinical brain imaging studies on small animal models. In this paper, we implemented computational methods for small animal dMRI. First, a high-resolution dMRI template for rat was constructed. Secondly, voxel-based analysis and tract-based spatial statistics methods for small animal dMRI were implemented. Last but not least, the deterministic and probabilistic tractography methods for small animal imaging were implemented. The implementation of these methods will facilitate the applications of dMRI in preclincal small animal imaging.]]></description>
<pubDate>2012/6/21 0:00:00</pubDate>
<category><![CDATA[Special Topic: Brain Imaging and Brain Networks]]></category>
<author><![CDATA[LIN Fu-Chun,WANG Xu-Xia,ZHAO Qian-Cheng and LEI Hao]]></author>
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<atom:name>LIN Fu-Chun,WANG Xu-Xia,ZHAO Qian-Cheng and LEI Hao</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Research Paper: Study on Resting-State Functional Connectivity of Rat Sensorimotor System]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20120236]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In order to better understand functional connectivity of rodent brain, we analyzed the functional magnetic resonance imaging (fMRI) data of the resting-state and forepaw electrical stimulation from lightly anesthetized rat at 9.4 T using cross-correlation analysis. The results showed that the primary somatosensory cortex (S1) and thalamus(Tha) were significantly positively activated, the caudate putamen nucleus(CPu) was significantly negatively activated during forepaw stimulation. The higher connectivity bilateral intra-sensory/motor cortices and bilateral intra- thalamus, but lower connectivity between sensory/motor cortex and thalamus were found during the resting state. Synchronized low frequency fluctuation (LFF) was observed between functionally related brain regions. In addition, the brain network of sensorimotor system showed small-world feature during resting-state. These results suggested that rodents have some similar properties with human in functional separation and integration during brain information processing, and strongly support the view that the underlying physiology of CNS is conserved across mammalian species.]]></description>
<pubDate>2012/6/21 0:00:00</pubDate>
<category><![CDATA[Special Topic: Brain Imaging and Brain Networks]]></category>
<author><![CDATA[XIA Yang,DONG Kai,LAI Yong-Xiu,LUO Cheng,LEI Lei and YAO De-Zhong]]></author>
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<atom:name>XIA Yang,DONG Kai,LAI Yong-Xiu,LUO Cheng,LEI Lei and YAO De-Zhong</atom:name>
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