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

“CRCNS”通过 HARD-MRI 分析自动预测癫痫发作

• 批准号: 7432500
• 负责人: Baba C Vemuri
• 金额: $ 31.32万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7432500
• 关键词: Affect; Algorithms; Anisotropy; Appearance; Atlases; Biological; Biological Neural Networks; Brain; Characteristics; Class; Clinical; Computer software; Condition; Craniocerebral Trauma; Data; Data Set; Diffusion; Diffusion Magnetic Resonance Imaging; Drug Formulations; Entropy; Epilepsy; Epileptogenesis; Equilibrium; Event; Facility Construction Funding Category; Fiber; Functional Magnetic Resonance Imaging; Goals; Heart; Hippocampus (Brain); Histology; Image; Image Analysis; Infection; Invasive; Label; Limbic System; Magnetic Resonance Imaging; Maps; Measures; Metric; Modeling; Neighborhoods; Pathology; Patients; Pharmaceutical Preparations; Probability; Process; Property; Purpose; Rattus; Recurrence; Research; Resolution; Scheme; Sclerosis; Seizures; Shapes; Signal Transduction; Solutions; Staging; Status Epilepticus; Structure; Syndrome; Testing; Thinking; Time; Validation; Vision; Water; Weight; Work; base; density; dentate gyrus; in vivo; injured; interest; mathematical model; novel; prevent; research study; tool; vector

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.

描述（由申请人提供）：癫痫由40多种影响全球5000万人的临床综合征组成。接受药物治疗的患者中约有25％至30％的癫痫发作控制不足。 CNS感染，头部外伤或长时间的癫痫发作（癫痫持续状态）和后来的癫痫病的出现表明了渐进的变化，通常在CNS感染，头部外伤或长时间的癫痫发作或长期癫痫发作之间存在所谓的无声间隔。该过程称为癫痫发生，被认为是一系列动态生物事件，改变了神经网络中激发与抑制之间的平衡。了解这些变化是防止癫痫发作的关键。为此，高角度分辨率扩散加权MR成像（HARDI）为非侵入性跟踪边缘结构的结构变化（齿状回）提供了可能性。我们的目标是开发数学模型和有效算法，以处理从癫痫发育时期成像的大鼠大脑中获取的HARDI数据，并得出可用于预测癫痫发作的结构特征。请注意，这项工作没有优先考虑的结构特征，用于预测癫痫发作。 我们的数学模型通过在2次量的连续概率密度而不是现在流行的多张量模型来表征在体素上的多个纤维道。在体素处没有多个纤维的情况下，提出的密度模型默认为高斯，该高斯表征了单个纤维的存在。这种表述的新颖性在于通过众所周知的拉普拉斯变换和WishArt密度将2张量的信号和概率密度与封闭式溶液相关联。此外，我们建议将概率密度的3D晶格分割，以提取ROI并绘制将使用组织学数据验证的纤维。然后将从分段的ROI中计算出构成表征癫痫发育时期的结构特征的几种新型特性（Renyi熵等）。然后，这些特征将用于基于核的聚类中，以在癫痫发育时期标记簇。然后，将通过贝叶斯优化方案为新的数据集实现预测。预测结果的验证将在已知的癫痫发作时间的数据上完成。拟议的研究将显着提高我们对边缘系统重组的理解，不仅是由于持续性癫痫发作而导致的，而且还引起了复发性癫痫发作和进一步的海马损害的影响。