Establishing an Atrophy-Based Functional Network Model as a Biomarker for Seizure-Onset Laterality

建立基于萎缩的功能网络模型作为癫痫发作偏侧性的生物标志物

• 批准号: 10751261
• 负责人: Jonathan Michael Towne
• 金额: $ 3.69万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751261
• 关键词: Address; Amnesia; Animal Model; Anterograde Amnesia; Atlases; Atrophic; Bilateral; Biological Markers; Brain region; Caring; Classification; Clinical; Data; Data Analyses; Disease; Electroencephalography; Epilepsy; Equation; Excision; Exhibits; Fellowship; Functional Magnetic Resonance Imaging; Future; Goals; Handedness; Health; Human; Individual; Left; Literature; Logistic Regressions; Magnetic Resonance Imaging; Medial; Medial Dorsal Nucleus; Meta-Analysis; Methodology; Methods; Modeling; Multiple Sclerosis; Network-based; Operative Surgical Procedures; Pathologic; Pathology; Patients; Physiology; Predisposition; Property; Publishing; Reporting; Reproducibility; Research; Research Personnel; Resort; Rest; Risk; Seizures; Specificity; Techniques; Temporal Lobe; Temporal Lobe Epilepsy; Thalamic structure; Tissues; Training; Validation; Visual; Weight; With laterality; biomarker validation; cerebral atrophy; clinical biomarkers; detection method; diagnostic biomarker; experimental study; graph theory; imaging biomarker; improved; innovation; network models; neuroimaging; neurosurgery; non-invasive imaging; noninvasive diagnosis; predictive modeling; regional atrophy; skills; statistics; success

Abstract . MTLE is the most common type of epilepsy referred for surgery. Most MTLE seizures begin unilaterally and respond well to resection of the epileptogenic tissue. Surgical success, however, is predicated on correct identification of seizure-onset laterality. Non-invasive methods for determining onset laterality are seldom definitive since MTLE seizures rapidly spread beyond the epileptogenic zone to extratemporal regions. Functional network modeling is a promising method for establishing non-invasive biomarkers; in MTLE, this method detects modulations in functional connectivity (edges) between pathologic regions (nodes) involved in extratemporal seizure spread. However, many modeling techniques define nodes by arbitrary atlas-based approaches that introduce bias in assessments of connectivity that is detrimental to model accuracy. An incomplete understanding of MTLE connectivity (particularly with extratemporal regions of pathology) has contributed to why non-invasive imaging biomarkers have yet to be approved for use in individual MTLE patients. One robust approach for developing functional network models uses meta-analysis to define nodes and then computes functional connectivity between those data-driven regions. This approach has been successfully used to construct a clinically validated biomarker for network disruption in multiple sclerosis. In 2013, meta-analytic modeling was first applied to MTLE. First, atrophy was identified in the medial temporal lobe and thalamic medial dorsal nucleus (MDN). Then, a model of the functional connectivity between these atrophic regions was constructed and used to predict seizure-onset laterality (86% sensitivity, 100% specificity). Until recently, insufficient literature limited this meta-analytic model’s accuracy and clinical utility. However, the body of MTLE literature has grown considerably (over double), presenting an exciting opportunity to expand this model and, ideally, improve prediction accuracy of seizure-onset laterality to clinically useful levels. The proposed strategy will address the hypothesis that: MTLE is a network-based disorder exhibiting lateralized changes in function at rest; these changes will predict seizure-onset laterality with >97% sensitivity without loss of specificity. In this proposal, MTLE connectivity will be studied in three specific aims by constructing 1) meta-analytic models of health and disease connectivity, 2) group-wise models of connectivity in MTLE patients and healthy controls using primary resting-state fMRI data, and 3) per-subject models of left- and right- lateralized MTLE patients using rs-fMRI. Crucially, all models will be data-driven using nodes defined by meta-analysis. Using both sparse and rich modeling approaches for each level of analysis (each aim), the proposed studies will identify paths of MTLE seizure propagation (Aim 1), quantify the effects of MTLE (versus healthy controls) on network connectivity (Aim 2), and validate a non-invasive diagnostic biomarker to predict seizure-onset laterality, per-subject, in MTLE (Aim 3). Completion of these aims will further establish a pipeline for developing noninvasive clinical biomarkers for other epilepsies while providing critical training for a future independent neurosurgical investigator.

抽象的。 MTLE 是转诊手术的最常见癫痫类型，大多数 MTLE 癫痫发作都是单侧发作。 然而，手术成功的前提是手术的正确性。 确定发作偏侧性的非侵入性方法很少。 由于 MTLE 癫痫发作迅速从致痫区蔓延至颞外区域，因此具有确定性。 功能网络建模是在 MTLE 中建立非侵入性生物标志物的一种有前景的方法； 方法检测参与的病理区域（节点）之间功能连接（边缘）的调制 然而，许多建模技术通过任意基于图集来定义节点。 在连通性评估中引入偏差的方法会影响模型的准确性。 对 MTLE 连接性（尤其是颞外病理区域）的不完全理解 这就是为什么非侵入性成像生物标志物尚未被批准用于个体 MTLE 患者的原因。 开发功能网络模型的一种强大方法是使用元分析来定义节点，然后 计算这些数据驱动区域之间的功能连接这种方法已被成功使用。 2013 年，荟萃分析构建了一种经临床验证的多发性硬化症网络破坏生物标志物。 模型首先应用于 MTLE，首先发现内侧颞叶和丘脑内侧萎缩。 然后，建立了这些萎缩区域之间的功能连接模型。 构建并用于预测癫痫发作的偏侧性（86％的敏感性，100％的特异性）。 文献不足限制了这种荟萃分析模型的准确性和临床实用性，但是 MTLE 的主体。 文献数量大幅增长（超过两倍），为扩展该模型提供了令人兴奋的机会，并且， 理想情况下，将癫痫发作偏侧性的预测准确性提高到临床有用的水平。 将解决以下假设： MTLE 是一种基于网络的疾病，在功能上表现出侧化变化 休息；这些变化将以 >97% 的敏感性预测癫痫发作的侧向性，而不会损失特异性。 提案中，MTLE 连接性将通过构建 1）元分析模型来研究三个具体目标 健康和疾病连通性，2) MTLE 患者和健康对照的分组连通性模型 使用主要静息态 fMRI 数据，以及 3) 左侧和右侧 MTLE 患者的每个受试者模型 至关重要的是，所有模型都将使用荟萃分析定义的节点进行数据驱动。 以及每个分析级别（每个目标）的丰富建模方法，拟议的研究将确定以下路径： MTLE 癫痫传播（目标 1），量化 MTLE（与健康对照）对网络连接的影响 （目标 2），并验证非侵入性诊断生物标志物以预测 MTLE 中每个受试者的癫痫发作偏侧性 （目标 3） 完成这些目标将进一步建立开发非侵入性临床生物标志物的管道。 治疗其他癫痫症，同时为未来的独立神经外科研究者提供关键培训。