Exploratory Analysis Tools for Developmental Studies of Brain Microstructure with Diffusion MRI

项目摘要 磁共振弥散分析是研究脑白质微结构的主要工具。青春期大脑 由于大脑重组和神经修剪，发育涉及重大的微观结构变化。这个 这些变化的轨迹可以通过精神障碍的发作而改变，并对 需要扩散成像来捕捉这些变化。我们的建议旨在使开放源码可用 用于纵向扩散磁共振数据集探索性分析的软件平台，并将其应用于研究 青少年大脑认知发育(ABCD)数据中双相情感障碍的早期发病。 弥散张量成像不足以区分 青少年大脑发育，因此我们的软件工具将支持更高级的扩散模型，如 轴突的定向、弥散和密度成像。这利用了高角度分辨率的 Z1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1D1A1A1D1D1D1D1D1D1D1D1D1D1D1D1A1A1A1A1E1A1E1D1D1D1A1A1A0。我们开发的勘探工具将使用广泛使用的基于区域的空间 统计(TBSS)技术，这是一种非常适合于探索性分析的降维方法。 超越标准的TBSS，我们将使该技术与改进的注册技术一起工作，以 使用更高级的扩散模型，并提供基于纵向的发展轨迹 成像数据。 TBSS方法需要高质量的图像对齐。为了实现这一目标，我们将制定一项 扩散数据集成对配准的深度学习模型，加快了扩散的重要一步 MRI分析。通过从ABCD扩散成像生成纤维取向分布，我们的注册 除了解剖结构外，模型还将对齐纤维方向。 这些工具将在一个开放源码软件平台中提供。虽然有许多工具可用于 探索扩散磁共振数据，这将是独一无二的，因为它支持高角度的纵向数据集 分辨率成像。

Project Summary Diffusion MRI analysis is the primary tool for studying brain white matter microstructure. Adolescent brain development involves significant microstructural changes due to brain reorganization and neural pruning. The trajectory of these changes can be altered by the onset of mental disorders, and longitudinal analysis of diffusion imaging is needed to capture these alterations. Our proposal aims to make available an open source software platform for exploratory analysis of longitudinal diffusion MRI datasets, and to apply it to study the early onset of bipolar disorder in the Adolescent Brain Cognitive Development (ABCD) data. Diffusion tensor imaging is not sufficient to distinguish the different types of changes that occur during adolescent brain development, so our software tool will support more advanced diffusion models such as Neurite Orientation Dispersion and Density Imaging. This takes advantage of the high angular resolution of ABCD diffusion images. The exploratory tools we develop will employ the widely used Tract-Based Spatial Statistics (TBSS) technique, which is a dimensionality reduction that is well suited for exploratory analysis. Going beyond standard TBSS, we will adapt the technique to work with improved registration techniques, to work with more advanced diffusion models, and to provide developmental trajectories based on longitudinal imaging data. The TBSS methodology requires high quality image alignment. In order to achieve this, we will develop a deep learning model for pairwise registration of diffusion datasets, speeding up an important step in diffusion MRI analysis. By generating fiber orientation distributions from the ABCD diffusion imaging, our registration model will align fiber orientations in addition to anatomical structures. These tools will be made available in an open source software platform. While there are many tools for exploring diffusion MRI data, this one will be unique in its support for longitudinal datasets with high angular resolution imaging.