"CRCNS" Automatic Prediction of the Onset of Epilepsy via Analysis of HARD-MRI

描述（由申请人提供）：癫痫由40多种临床综合征组成，影响全球5000万人。大约25%至30%的接受药物治疗的患者癫痫发作控制不足。在CNS感染、头部创伤或长时间癫痫发作（癫痫持续状态）与后来出现癫痫之间存在所谓的沉默间期（通常持续数年），这提示了进行性变化。这个过程被称为癫痫发生，被认为是一系列动态生物事件，改变了神经网络中兴奋和抑制之间的平衡。了解这些变化是预防癫痫发作的关键。为此，高角分辨率弥散加权MR成像（HARDI）提供了无创跟踪边缘结构（齿状回等）的结构变化的可能性。我们的目标是开发数学模型和有效的算法来处理从癫痫发生期间成像的大鼠大脑中获得的HARDI数据，并获得可用于预测癫痫发作的结构特征。请注意，在预测癫痫发作的结构特征方面，这项工作没有先例。 我们的数学模型的特点是多个纤维束在一个体素的连续概率密度超过2张量，而不是现在流行的多张量模型。在体素处没有多个纤维的情况下，所提出的密度模型默认为高斯，其表征单个纤维的存在。该公式的新奇在于通过众所周知的拉普拉斯变换和Wishart密度将信号和2-张量的概率密度联系起来，从而得到封闭形式的解。此外，我们建议分割概率密度的3D网格，以提取ROI并绘制出将使用组织学数据进行验证的纤维。一些新的性质（Renyi熵等）然后将从分割的ROI计算构成表征癫痫发生期的结构签名。然后，这些特征将用于基于核的聚类，以标记癫痫发生期间的聚类。然后通过贝叶斯优化方案对新的数据集进行预测。预测结果的验证将在癫痫发作时间已知的数据上进行。拟议的研究将大大推进我们对边缘系统重组的理解，这不仅是由长时间癫痫发作引起的，也是反复癫痫发作和进一步海马损伤的影响。

DESCRIPTION (provided by applicant): Epilepsy consists of more than 40 clinical syndromes affecting 50 million people worldwide. Approximately 25 to 30% of the patients receiving medication have inadequate seizure control. Progressive changes are suggested by the existence of a so-called silent interval, often years in duration, between CNS infection, head trauma, or prolonged seizure (status epilepticus) and the later appearance of epilepsy. This process is known as epileptogenesis and is thought of as a cascade of dynamic biological events altering the balance between excitation and inhibition in neural networks. Understanding these changes is key to preventing the onset of epilepsy. To this end, high angular resolution diffusion weighted MR-imaging (HARDI) offers the possibility to non-invasively track structural changes in limbic structures (dentate gyrus etc.). Our goal is to develop mathematical models and efficient algorithms to process HARDI data acquired from rat brains that have been imaged during the epileptogenetic period and derive structural signatures that can be used to predict the onset of epilepsy. Note that there is no precedence to this work on structural signatures for use in prediction of the onset of epilepsy. Our mathematical model characterizes multiple fiber tracts at a voxel by a continuous probability density over 2-tensors instead of the now popular multi-tensor model. In the absence of multiple fibers at a voxel, the proposed density model defaults to a Gaussian which characterizes the presence of a single fiber. The novelty of this formulation lies in relating the signal and the probability density of the 2-tensors via the well known Laplace transform and for the Wishart densities, leads to a closed form solution. Additionally we propose to segment the 3D lattice of probability densities to extract the ROI and map out the fibers which will be validated using histology data. Several novel properties (Renyi entropy etc.) constituting the structural signature characterizing the epileptogenetic period will then be computed from the segmented ROI. These features will then be used in a Kernel-based clustering to label clusters over the epileptogenetic period. Prediction will then be achieved for a novel data set via a Bayesian optimization scheme. Validation of the prediction results will be done on data for which onset times of epilepsy are already known. The proposed research will significantly advance our understanding of limbic system reorganization caused not only by prolonged seizures, but also the effects of recurrent seizures and further hippocampus damage.