Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

中文翻译：

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基于 Atlas 的结构和功能小鼠脑 MRI 成像数据分析的处理管道 (AIDAmri)

磁共振成像 (MRI) 是脑连通性和疾病病理学多模式动物研究中的一项关键技术。体内 MRI 提供包含结构和功能信息的非侵入性全脑宏观图像，从而补充侵入性体内高分辨率显微镜和离体分子技术。大脑映射，即标准脑图谱系统中多个大脑之间相应区域的相关性，广泛用于人类 MRI。然而，对于小动物 MRI，在预处理策略和基于图谱的神经信息学方面没有科学共识。因此，仍然难以比较和验证不同临床前研究的结果，这些研究是使用定制代码或临床 MRI 软件的个别调整处理的，并且没有标准的大脑参考图谱。这里，我们描述了 AIDAmri，这是一种新的基于 Atlas 的成像数据分析管道，用于处理结构和功能小鼠大脑数据，包括解剖 MRI、使用扩散张量成像 (DTI) 的纤维跟踪和使用静息状态功能 MRI (rs-fMRI) 的功能连接分析。AIDAmri 管道包括自动预处理步骤，例如原始数据转换、头骨剥离和偏置场校正，以及与 Allen Mouse Brain Reference Atlas (ARA) 的图像配准。遵循用 Python 脚本语言开发的模块化结构，管道集成了已建立和新开发的算法。每个处理步骤都针对需要最少用户输入和用户编程技能的高效数据处理进行了优化。分析原始数据并将结果传输到 ARA 坐标系，以便进行高效且高精度的基于区域的分析。AIDAmri 旨在为最相关的小鼠大脑 MRI 序列填补缺失的开放访问和跨平台工具箱的空白，从而促进大型队列和多中心研究中的数据处理。