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，人类大脑形态测量的MRI研究已被用于研究精神病学研究中的多种病理和药物相关效应。区分患者群体或跟踪纵向变化的形态计量学测量通常是微妙的，需要大量的受试者或重复研究来检测和统计模型具有显著性。成本、患者依从性、对患者的风险以及某些疾病的罕见性往往限制了传统的临床形态计量学研究。这些并发症促使人们使用小鼠和大鼠等模式生物。动物研究由于其规模小、发展周期快、基因型和表型数据丰富，以及操纵其遗传信息诱导疾病的技术成熟，也非常受欢迎。大鼠和小鼠的脑形态测量模型通常包括组织学切片、行为数据、基因测试，以及越来越多的MRI扫描。特别是，除了脑形态测量学，MRI扫描被用作集中的组织学和分子检查以及应变比较的假设生成方法。从人类核磁共振扫描中提取脑形态的有效方法已经开发出来。我们是其开发和应用领域的领导者。我们已经开发了用于MRI偏差校正方法的Legrendre多项式方法，用于组织分类的基于图谱的方法，以及用于形状参数化的球面谐波技术。我们已经应用这些方法来关联海马体形状的变化，以区分患有精神分裂症的患者。相比之下，小动物MRI的自动化定量分析方法很少。目前的标准是在大量动物的MRI切片中手工勾勒出大脑特征，这种手工方法缺乏可重复性，而且非常耗时。缺乏自动化的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、可靠、高通量的磁共振图像分析方法，用于小动物脑形态学研究。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，并在分布式处理网络上启动该数据的处理。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和计算结果的检查。这种基于网络的处理系统本质上是通用的，可以扩展到宿主和处理人类MRI数据以及来自其他模式和其他应用的数据。为了演示和评估数据系统，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FMR1小鼠模型的敲除，它显示出与脆性X/自闭症人类表型一致的行为缺陷。提出的软件将推进小鼠的MRI研究形态学和连接神经发育和神经退行性精神疾病。整个大脑的核磁共振图像分析将变成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.