High Throughput web-base Image Analysis of Mouse Brain MR Imaging Studies

描述（由申请人提供）：在过去的十年中，人脑形态测量学的MRI研究已用于调查精神病学研究中的多种病理学和药物相关效应。区分患者群体或跟踪纵向变化的形态测量通常是微妙的，需要大量的受试者或重复研究来检测和统计学建模。成本、患者依从性、对患者的风险以及某些疾病的罕见性通常限制了传统的临床形态学研究。这些并发症促使使用模型生物，如小鼠和大鼠。动物研究也非常受欢迎，因为它们体积小，发育周期快，基因型和表型数据丰富，以及操纵其遗传信息以诱发疾病的技术成熟。大鼠和小鼠的脑形态测量模型通常涉及组织学切片、行为数据、基因测试，以及越来越多的MRI扫描。特别是，除了脑形态测量，MRI扫描被用作一种假设生成方法，用于集中的组织学和分子学检查，并用于应变比较。有效的方法已被开发用于从人类MRI扫描中提取脑形态测量。我们是其开发和应用领域的领导者。我们已经开发了Legrendre多项式方法的MRI偏差校正方法，基于图集的方法进行组织分类，和球谐函数技术的形状参数化。我们已经应用这些方法来关联海马形状的变化，区分患有精神分裂症的患者。相比之下，小动物MRI的自动定量分析方法很少。标准是在大量动物的MRI切片中手动勾勒大脑特征，这种手动方法缺乏可重复性并且非常耗时。缺乏自动化MRI分析方法是许多动物研究的限制因素。我们建议开发自动的，可靠的，高通量的MR图像分析方法，小动物，脑形态计量学研究。此外，我们建议开发一个直观的基于Web的界面，用于收集和分发小动物研究的成像数据，以及启动分布式处理网络上的数据处理。基于网络的数据共享和处理系统还支持对正在进行的处理的检查和对计算结果的检查。这种基于网络的处理系统本质上是通用的，并且可以扩展到托管和处理人类MRI数据以及来自其他模态和其他应用的数据。为了证明和评估数据系统，我们将其应用于脆性X综合征小鼠模型的神经解剖学研究。该小鼠模型是基于FMR 1小鼠模型的敲除，并且它已经显示出与脆性X/自闭症人类表型一致的行为缺陷。拟议的软件将推进神经发育和神经退行性精神疾病的形态测量和连接的小鼠MRI研究。对整个大脑研究的MR图像的分析将成为在网络界面上点击几下鼠标的事情。

DESCRIPTION (provided by applicant): In the last decade, MRI studies of human brain morphometry have been used to investigate a multitude of pathologies and drug-related effects in psychiatric research. The morphometric measures that differentiate patient populations or track longitudinal changes are often subtle and require a large number of subjects or repeated studies to detect and statistically model with significance. Cost, patient compliance, risks to the patients, and the rarity of certain diseases often limit traditional, clinical morphometric studies. These complications have motivated the use of model organisms such as of mice and rats. Animal studies are also very popular due to their small size and rapid development cycle, the wealth of genotype and phenotype data, as well as the maturity of the technology to manipulate their genetic information to induce disease. Brain morphometry models of rat and mice typically involve histological slides, behavioral data, genetic testing, and, increasingly, MRI scans. In particular, in addition to brain morphometry, MRI scans are being employed as a hypothesis generation method for focused histological and molecular examinations, and for strain comparisons. Effective methods have been developed for extracting brain morphometry from human MRI scans. We are leaders in the field for their development and their application. We have developed Legrendre polynomial methods for MRI bias correction methods, atlas-based methods for tissue classification, and spherical harmonics techniques for shape parameterization. We have applied these methods to correlate hippocampus shape variations that distinguish patients suffering from schizophrenia. By contrast, few automated quantitative analysis methods exist for small animal MRI. The standard is to manually outline brain features in MRI slices for a large number of animals, and such manual methods lack reproducibility and are extremely time consuming. The lack of automated MRI analysis methods is the limiting factor in many animal studies. We propose to develop automatic, reliable, high-throughput MR image analysis methods for small animal, brain morphometry studies. Additionally, we propose to develop an intuitive web-based interface for collecting and distributing the imaging data of small animal studies as well as initiating the processing of that data on a distributed processing network. The web-based data sharing and processing system also supports the inspection of the ongoing processing and the examination of the computed results. This web-based processing system is generic in nature and can be extended to host and process human MRI data as well as data from other modalities and other applications. To demonstrate and evaluate the data system, we will apply it to the study of the neuroanatomy of a fragile-X syndrome mouse model. This mouse model Is based on a knockout of the FMR1 mouse model, and it has shown behavioral deficits consistent with a Fragile X/autism human phenotype. The proposed software will advance murine MRI studies of morphometry and connectivity for neuro-developmental, and neuro-degenerative psychiatry diseases. The analysis of MR images of entire brain studies will become the matter of a few mouseclicks on a web-interface.