Pallast Niklas, Diedenhofen Michael, Blaschke Stefan, Wieters Frederique, Wiedermann Dirk, Hoehn Mathias, Fink Gereon R, Aswendt Markus
Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
In-vivo-NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany.
Front Neuroinform. 2019 Jun 4;13:42. doi: 10.3389/fninf.2019.00042. eCollection 2019.
Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive high-resolution microscopy and 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.
磁共振成像(MRI)是大脑连接性和疾病病理学多模态动物研究中的一项关键技术。MRI提供包含结构和功能信息的非侵入性全脑宏观图像,从而补充侵入性高分辨率显微镜和分子技术。脑图谱绘制,即在标准脑图谱系统中多个大脑相应区域的相关性分析,在人类MRI中被广泛应用。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尚无科学共识。因此,仍然难以比较和验证来自不同临床前研究的结果,这些研究使用定制代码或对临床MRI软件进行个别调整且没有标准脑参考图谱进行处理。在此,我们描述了AIDAmri,一种基于图谱的新型成像数据分析流程,用于处理结构和功能小鼠脑数据,包括解剖MRI、使用扩散张量成像(DTI)的纤维追踪以及使用静息态功能MRI(rs-fMRI)的功能连接性分析。AIDAmri流程包括自动预处理步骤,如原始数据转换、去颅骨和偏置场校正,以及与艾伦小鼠脑参考图谱(ARA)的图像配准。该流程遵循用Python脚本语言开发的模块化结构,集成了已建立的和新开发的算法。每个处理步骤都针对高效数据处理进行了优化,所需用户输入和用户编程技能最少。对原始数据进行分析,并将结果转换到ARA坐标系,以便进行高效且高精度的基于区域的分析。AIDAmri旨在填补针对最相关小鼠脑MRI序列的缺失的开放获取和跨平台工具箱的空白,从而促进大型队列研究和多中心研究中的数据处理。