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

抽象的。 MTLE是进行手术治疗的最常见的癫痫类型。大多数MTLE癫痫发作都是单方面开始的 对切除致痫组织反应良好。然而，手术的成功是建立在正确的基础上的 发作性偏侧的辨认。确定发病偏侧的非侵入性方法很少。 由于MTLE癫痫发作迅速从致痫区域蔓延到颞外区域，因此是决定性的。 功能网络建模是建立非侵入性生物标志物的一种很有前途的方法；在MTLE中，这是一种 方法检测所涉及的病理区域(节点)之间的功能连接性(边)的调制 暂时性癫痫发作蔓延。然而，许多建模技术通过基于任意图谱来定义节点 在连通性评估中引入不利于模型准确性的偏差的方法。一个 对MTLE连通性的不完全理解(特别是与病理性的颞外区域) 这也是为什么非侵入性成像生物标记物尚未被批准用于个别MTLE患者的原因。 一种用于开发功能网络模型的健壮方法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性。这种方法已经成功地使用了 构建经临床验证的多发性硬化症网络破坏的生物标记物。2013年，元分析 建模首先应用于MTLE。首先，发现内侧颞叶和丘脑内侧萎缩。 背核(MDN)。然后，这些萎缩区域之间的功能连接的模型是 构建并用于预测癫痫发作的偏侧性(敏感性为86%，特异性为100%)。直到最近， 文献不足限制了这一荟萃分析模型的准确性和临床实用性。然而，MTLE的主体 文学已经有了相当大的增长(超过两倍)，这为扩展这一模式提供了一个令人兴奋的机会， 理想情况下，将癫痫发作偏侧性的预测准确度提高到临床有用的水平。建议的战略 我将解释这样的假设：MTLE是一种基于网络的障碍，表现为功能的偏侧性改变 REST；这些变化将预测癫痫发作的偏侧性，敏感性为97%，而不会失去特异性。在这 建议，MTLE连接性将通过构建1)元分析模型来研究三个具体目标 健康和疾病连接性，2)MTLE患者和健康对照组的连接性分组模型 使用主要的静息状态fMRI数据，以及3)左右侧MTLE患者的每个受试者模型 使用rs-fmri。至关重要的是，所有模型都将使用元分析定义的节点进行数据驱动。使用这两种稀疏 以及针对每个分析级别(每个目标)的丰富建模方法，拟议的研究将确定 MTLE癫痫传播(目标1)，量化MTLE(对照健康对照)对网络连接的影响 (目标2)，并验证一种非侵入性诊断生物标记物预测MTLE中每个受试者癫痫发作的偏侧性 (目标3)。这些目标的完成将进一步建立开发非侵入性临床生物标记物的管道 用于其他癫痫，同时为未来的独立神经外科调查员提供关键培训。

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.