Registering and analyzing rat fMRI data in the stereotaxic framework by exploiting intrinsic anatomical features
Registering and analyzing rat fMRI data in the stereotaxic framework by exploiting intrinsic anatomical features
复制标题
DOI：
10.1016/j.mri.2009.05.019
复制
发表时间：
2010-01-01
影响因子：
2.5
通讯作者：
Yang, Yihong
中科院分区：
文献类型：
--
作者：
Lu, Hanbing;Scholl, Clara A.;Yang, Yihong
在组水平上分析神经成像数据的价值已经在人类研究中得到了很好的确立。然而，在啮齿动物功能磁共振成像（fMRI）研究中，没有标准的程序将功能磁共振成像（fMRI）数据配准和分析到公共空间。提出了一种在立体定位框架中进行大鼠成像数据分析的方法。该方法基于生物学观察，即只要重量在一定范围内，大鼠脑的头骨形状和大小基本相同。注册使用刚体变换，没有缩放或剪切，保持大鼠大脑结构中固有的稳定的形状和大小的独特属性。此外，它不需要脑组织掩蔽，并且不偏向表面线圈灵敏度曲线。使用标准的大鼠脑图谱来促进识别共同空间中的激活区域，从而允许准确的感兴趣区域分析。对一组进行常规MRI扫描的大鼠（n=11）进行评价，估计配准精度在400 gm以内。前爪电刺激模型获得的fMRI数据的分析表明，这种技术的实用性。该方法是在功能神经影像分析（AFNI）框架内实现的，可以很容易地扩展到其他研究。由爱思唯尔公司出版
The value of analyzing neuroimaging data on a group level has been well established in human studies. However, there is no standard procedure for registering and analyzing functional magnetic resonance imaging (fMRI) data into common space in rodent fMRI studies. An approach for performing rat imaging data analysis in the stereotaxic framework is presented. This method is rooted in the biological observation that the skull shape and size of rat brain are essentially the same as long as their weights are within certain range. Registration is performed using rigid-body transformations without scaling or shearing, preserving the unique properties of the stable shape and size inherent in rat brain structure. Also, it does not require brain tissue masking and is not biased towards surface coil sensitivity profile. A standard rat brain atlas is used to facilitate the identification of activated areas in common space, allowing accurate region of interest analysis. This technique is evaluated from a group of rats (n=11) undergoing routine MRI scans; the registration accuracy is estimated to be within 400 gm. The analysis of fMRI data acquired with an electrical forepaw stimulation model demonstrates the utility of this technique. The method is implemented within the Analysis of Functional NeuroImages (AFNI) framework and can be readily extended to other studies. Published by Elsevier Inc.