Development and application of cognitive neuroimaging tools for quantitative white matter analyses

弥散张量成像(DTI)、磁化传递成像(MTI)、髓鞘水成像(MWI)等脑白质定量成像方法对脑微结构的改变比常规MRI更敏感。然而，尽管MRI硬件(例如，更高的磁场强度和更多的接收器通道)、改进的数据采集方案(例如，3D脉冲序列和并行成像技术)以及DTI、MTI和MWI采集参数的精细化，用于量化受试者间差异和/或白质结构的纵向变化的数据分析方法仍然相对静止。 特别是，对人类脑白质核磁共振数据的分析传统上采取了两种方法之一。体素分析具有高空间分辨率的优势，因此能够检测到微小的局部性白质变化。然而，如果白质的大小或位置因个体而异，这种方法在交叉受试者分析中会受到很大影响，因此不适合于对患有创伤性脑损伤或白质障碍(如多发性硬化症)的患者进行分组分析。另一方面，感兴趣区域(ROI)分析从较大的预先确定的大脑区域提取数据，从而允许不同受试者的病变大小和位置的微小差异(即，只要它们位于选定的ROI内)，但代价是对微小的局部变化的敏感度。然而，这些ROI方法也需要关于选择哪个大脑区域(或集合或多个区域)以及每个ROI应该有多大的先验假设，从而导致固有的敏感性和特异性之间的权衡。 为了解决这些限制中的几个问题，我的小组将追求以下三个相互关联的研究目标： 1.使用fMRI引导的DTI创建一组功能定义的白质图谱； 2.开发新的基于脑束的分析方法来测量白质感兴趣区；以及 3.使用上述图谱和基于区域的分析方法，结合功能磁共振成像和认知测验，研究结构连通性、功能连通性与认知成绩之间的关系。 总共有4名研究生将通过生物医学工程和生理学研究生计划接受培训(即第一个目标2名硕士学生，第二和第三个目标各1名博士生)。 最后，通过这项研究获得的知识将进一步阐明大脑结构、大脑功能和认知表现之间的复杂关系；由于由此产生的脑图谱和分析软件可能在整个系统和认知神经科学中得到广泛应用，我们计划免费分发这些工具，以便它们可以被加拿大国内外的其他神经成像研究人员使用。

Quantitative white matter imaging methods such as diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), and myelin water imaging (MWI) are more sensitive than conventional MRI methods to changes in brain microstructure. However, despite advances in MRI hardware (e.g., higher magnetic field strengths and more receiver channels), improved data acquisition schemes (e.g., 3D pulse sequences and parallel imaging techniques), and the refinement of DTI, MTI and MWI acquisition parameters, data analysis approaches to quantify inter-subject differences and/or longitudinal changes in white matter structure have remained relatively static. In particular, analyses of human white matter MRI data have traditionally taken one of two approaches. Voxel-wise analyses have the advantage of high spatial resolution and are therefore able to detect small, localized white matter changes. However, this approach suffers greatly in cross-subject analyses if sizes or locations of the white matter changes vary from one individual to the next, and are therefore not well-suited for group-wise analysis of patients with traumatic brain injuries or white matter disorders such as Multiple Sclerosis. On the other hand, region-of-interest (ROI) analyses extract data from larger pre-determined brain areas, thereby allowing for small differences in lesion sizes and locations across subjects (i.e., as long as they are somewhere within the chosen ROI) at the expense of sensitivity to small, localized changes. However, these ROI approaches also require a priori assumptions about which brain region (or set or regions) to choose and how large each ROI should be, leading to inherent sensitivity vs. specificity trade-offs. In order to address several of these limitations, my group will pursue the following three interrelated research objectives: 1. Creating a set of functionally-defined white matter atlases using fMRI-guided DTI; 2. Developing novel tract-based analysis methods to measure along white matter ROIs; and 3. Using the aforementioned atlases and tract-based analysis methods in conjunction with fMRI and cognitive testing to study relationships between structural connectivity, functional connectivity and cognitive performance. In total, 4 graduate students will be trained through the Biomedical Engineering and Physiology Graduate Programs (i.e., 2 MSc students for the first objective, and 1 PhD student each for the second and third objectives). Finally, the knowledge gained through this research will further elucidate the complex relationships between brain structure, brain function and cognitive performance; and since the resulting brain atlases and analysis software are likely to have wide-ranging applications throughout systems and cognitive neuroscience, we plan to freely distribute these tools so that they can be used by other neuroimaging researchers within Canada and abroad.