Automated Analysis of Movement Disorders from Diffusion and Functional MRI

磁共振成像(MRI)是最广泛使用的诊断成像工具，用于检测神经退行性疾病，如帕金森氏病。该项目将开发新的自动化方法来检测可以通过MRI揭示的细微影响，包括人脑组织水分扩散特性的变化和大脑功能活动的变化。为了评估与正常大脑的偏差，将开发一种计算高效的算法，从正常大脑群体构建特定于群体的大脑结构模板。此外，将开发一种新的算法，以便于使用弥散磁共振数据检测帕金森氏症。最后，将开发新的算法来建立来自扩散的信息和功能MRI数据之间的相关性，从而能够在给定解剖信息的情况下预测功能活动，反之亦然。推断这种相关性将使预测神经退行性疾病引起的组织微结构变化引起的功能变化成为可能，反之亦然。综上所述，该项目的精确目标是：(I)开发一种计算高效的模板脑图构建算法，用于提取来自弥散磁共振的特征。在此背景下，提出了集合平均传播算子(EAP)，它同时捕捉了扩散MRI数据中每个体素的扩散过程的方向和形状信息。将使用基于模板的分割的标准评估指标对构建的模板进行验证。(Ii)开发新的方法，使用EAP场和柯西变形张量(捕捉EAP场中的变化)自动区分控制组和帕金森组。对分类器的验证将使用标准的k-out策略。(Iii)提出了一种新的基于核函数的非线性回归算法，用于从磁共振扩散图得到的EAP场与从功能磁共振激活图得到的标量场之间的非线性回归。该算法将能够预测给定EAP字段的激活程度，反之亦然。这些预测将使用先验标记的数据集进行验证。根据结构信息预测功能反应，反之亦然，这将对帕金森病和其他神经退行性疾病患者的治疗计划产生重大影响。该项目的多学科性质将提供机会，在该项目的STEM相关领域对不同背景的研究生进行集体培训。

Magnetic resonance imaging (MRI) is the most widely used diagnostic imaging tool for detecting neurodegenerative disorders such as Parkinson's Disease. This project will develop new automated methods for detecting subtle effects that can be revealed by MRI, including changes in water diffusional properties of human brain tissue, and functional brain activity. To assess the deviation from the normal brains, a computationally efficient algorithm will be developed to construct a population-specific brain structural template from a normal brain population. Further, a new algorithm will be developed to facilitate the detection of Parkinson's using diffusion MRI data. Finally, novel algorithms for establishing the correlation between the information derived from diffusion and functional MRI data will be developed, enabling prediction of functional activity given the anatomical information and vice-versa. Inferring such a correlation will make it possible to predict functional changes due to changes in tissue microstructure caused by neurodegenerative disorders and vice-versa.In summary, the precise project goals are: (i) To develop a computationally efficient template brain map construction algorithm for features derived from diffusion MRI. In this context, the ensemble average propagator (EAP), which captures both orientation and shape information of the diffusion process at each voxel in the diffusion MRI data, is proposed. Validation of the constructed template will be performed using standard evaluation metrics for template-based segmentation. (ii) To develop novel methods to automatically discriminate between control and Parkinson's groups using the EAP fields as well as Cauchy deformation tensors (that capture the changes in EAP fields). Validation of the classifier will be achieved using the standard leave-k-out strategy. (iii) To develop a novel algorithm for kernel-based nonlinear regression between EAP fields derived from diffusion MRI and scalar-valued fields derived from functional MRI activation maps. The algorithm will be able to predict the level of activation given the EAP fields and vice-versa. These predictions will be validated using a priori labeled data sets. Predicting functional responses from structural information and vice-versa will significantly impact treatment planning of patients with Parkinson's Disease and other neurodegenerative disorders. The multidisciplinary nature of this project will provide the opportunity to collectively train graduate students from diverse backgrounds in the STEM related fields of this project.