Haupt Dirk, Vanni Matthieu P, Bolanos Federico, Mitelut Catalin, LeDue Jeffrey M, Murphy Tim H
University of British Columbia, Kinsmen Laboratory of Neurological Research, Faculty of Medicine, Department of Psychiatry, Vancouver, Canada.
University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, Canada.
Neurophotonics. 2017 Jul;4(3):031210. doi: 10.1117/1.NPh.4.3.031210. Epub 2017 May 19.
Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.
中尺度脑活动成像用于绘制脑区之间的相互作用。这项工作受益于格林瓦尔德等人的开创性研究，他们利用内在光信号和小分子电压敏感染料的特性，采用光学方法对脑功能进行成像。中尺度脑区间成像技术因细胞靶向和神经元活动的选择性重组指示剂而得到了发展。在中尺度成像过程中，通常会收集自发静息态活动，以便为使用相关性绘制连接关系提供基础。然而，中尺度数据集的信息量巨大，鉴于需要将测量限制在一组固定的频率、感兴趣区域和其他参数上，在稿件中只是表面地呈现了这些信息。我们描述了一个用Python编写的新开源工具，称为中尺度脑探索器（MBE），它提供了一个处理和探索这些大型数据集的接口。该平台支持自动图像处理管道，能够评估多个试验并合并来自不同动物的数据。该工具提供了时间滤波、平均以及使用时间相关的相关性对功能连接关系进行可视化的功能。在这里，我们描述了该工具并展示了应用，其中使用MBE对先前发表的数据集进行了重新分析。
