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基于变形的大鼠脑形态计量学。

Deformation-based brain morphometry in rats.

机构信息

Hans Berger Clinic for Neurology, Jena University Hospital, Erlanger Allee 101, 07747 Jena, Germany.

出版信息

Neuroimage. 2012 Oct 15;63(1):47-53. doi: 10.1016/j.neuroimage.2012.06.066. Epub 2012 Jul 6.

Abstract

Magnetic resonance imaging (MRI)-based morphometry provides in vivo evidence for macro-structural plasticity of the brain. Experiments on small animals using automated morphometric methods usually require expensive measurements with ultra-high field dedicated animal MRI systems. Here, we developed a novel deformation-based morphometry (DBM) tool for automated analyses of rat brain images measured on a 3-Tesla clinical whole body scanner with appropriate coils. A landmark-based transformation of our customized reference brain into the coordinates of the widely used rat brain atlas from Paxinos and Watson (Paxinos Atlas) guarantees the comparability of results to other studies. For cross-sectional data, we warped images onto the reference brain using the low-dimensional nonlinear registration implemented in the MATLAB software package SPM8. For the analysis of longitudinal data sets, we chose high-dimensional registrations of all images of one data set to the first baseline image which facilitate the identification of more subtle structural changes. Because all deformations were finally used to transform the data into the space of the Paxinos Atlas, Jacobian determinants could be used to estimate absolute local volumes of predefined regions-of-interest. Pilot experiments were performed to analyze brain structural changes due to aging or photothrombotically-induced cortical stroke. The results support the utility of DBM based on commonly available clinical whole-body scanners for highly sensitive morphometric studies on rats.

摘要

基于磁共振成像(MRI)的形态计量学为大脑的宏观结构可塑性提供了体内证据。使用自动化形态计量方法进行小动物实验通常需要昂贵的超高场专用动物 MRI 系统进行测量。在这里,我们开发了一种新的基于变形的形态计量学(DBM)工具,用于对使用适当线圈在 3 特斯拉临床全身扫描仪上测量的大鼠脑图像进行自动分析。我们自定义参考大脑的基于地标变换到 Paxinos 和 Watson(Paxinos 图谱)的广泛使用的大鼠脑图谱的坐标中,保证了结果与其他研究的可比性。对于横截面数据,我们使用 MATLAB 软件包 SPM8 中实现的低维非线性配准将图像配准到参考大脑上。对于纵向数据集的分析,我们选择将所有图像的高维配准到第一个基线图像,以利于识别更微妙的结构变化。由于所有变形最终都用于将数据转换到 Paxinos 图谱的空间中,雅可比行列式可用于估计预定义感兴趣区域的绝对局部体积。进行了初步实验以分析由于衰老或光血栓诱导的皮质中风引起的大脑结构变化。结果支持基于常用临床全身扫描仪的 DBM 用于对大鼠进行高度敏感的形态计量学研究的实用性。

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