A Brain Atlas for Mapping Connectivity in Focal Epilepsy

用于绘制局灶性癫痫连接性的大脑图谱

• 批准号: 9021699
• 负责人: Richard M Leahy
• 金额: $ 53.13万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9021699
• 关键词: Address; Affect; American; Area; Atlases; Brain; Brain region; Categories; Classification; Data; Databases; Disease; Electrodes; Electroencephalography; Epilepsy; Evoked Potentials; Excision; Exhibits; Frontal Lobe Epilepsy; Functional Magnetic Resonance Imaging; Goals; Health; Implant; Implanted Electrodes; Individual; Label; Left; Lesion; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Measurable; Measures; Modality; Monitor; Network-based; Neurologist; Neurosurgeon; Operative Surgical Procedures; Outcome; Partial Epilepsies; Patients; Pattern; Positron-Emission Tomography; Procedures; Property; Resected; Rest; Scalp structure; Seizures; Signal Transduction; Source; Techniques; Testing; X-Ray Computed Tomography; brain pathway; density; fluorodeoxyglucose positron emission tomography; improved; insight; interest; learning strategy; novel; preimplantation; prospective; relating to nervous system; response; single photon emission computed tomography

 DESCRIPTION (provided by applicant): Treatment of intractable focal epilepsy by resection of the seizure onset zone (SOZ) is often effective provided the SOZ can be reliably identified. Focal epilepsy, however, is fundamentally a network-based disease. The seizure onset zone is connected to a network whose other nodes may also exhibit abnormal neural activity either concurrently or subsequently. In patients without MRI detectable lesions, differentiation of the onset zone from these other nodes in the network can be difficult, even with the use of invasive recordings. The goal of this project is to improve SOZ identification, ultimately reducing the need for presurgical invasive recordings where possible, and guiding placement of electrodes in those patients who do need invasive monitoring. To achieve this goal, in Aim 1 we will build a functional connectivity atlas from a database of invasive Cortico-Cortical Evoked Potential (CCEP) recordings to identify common interaction networks in patients with partial epilepsy and to investigate the degree to which these are dependent on the location of the SOZ. To construct the atlas, patient data will be coregistered to a labelled anatomical atlas using a cortically constrained warping of each subject's structural MRI. In Aim 2, CCEPs data will be supplemented in the atlas with other data that provide additional insight into the brain regions involved in the seizure: regions of hypometabolism in interictal FDG PET, hypermetabolism in ictal SPECT, interictal spike localization from EEG and MEG and invasive recordings, functional areas associated with seizure semiology, MR-identified lesions, area of resection, post-surgical Engel classification. Using machine-learning methods, we will perform a sequence of tests to examine the degree to which the atlas can be used to identify the SOZ in individual subjects. Finally, in Aim 3, we will investigate the potential for using regional connectivity established frm noninvasive MEG data and resting state MRI in combination with the CCEPs atlas to identify these networks, with the ultimate goal of reducing the need for invasive monitoring. Retrospective analysis using a leave-one- out approach and comparison with outcomes will be used to quantify improvement in identification of the onset zone from both invasive and noninvasive recordings.

 描述（由申请人提供）：通过切除癫痫发作区（SOZ）治疗难治性局灶性癫痫通常是有效的，前提是可以可靠地识别SOZ。然而，局灶性癫痫基本上是一种基于网络的疾病。癫痫发作区连接到一个网络，该网络的其他节点也可能同时或随后表现出异常的神经活动。在没有MRI可检测病变的患者中，即使使用侵入性记录，也很难将网络中的这些其他节点与发病区区分开来。该项目的目标是改善SOZ识别，最终减少 在可能的情况下进行术前侵入性记录，并指导那些确实需要侵入性监测的患者放置电极。为了实现这一目标，在目标1中，我们将从侵入性皮质-皮质诱发电位（CCEP）记录的数据库中构建功能连接图谱，以识别部分癫痫患者的常见相互作用网络，并调查这些网络依赖于SOZ位置的程度。为了构建图谱，将使用每个受试者的结构MRI的皮质约束扭曲将患者数据配准到标记的解剖图谱。在目标2中，将在图谱中使用其他数据补充CCEP数据，这些数据提供了对癫痫发作涉及的脑区域的额外了解：发作间期FDG PET中的低代谢区域、发作SPECT中的高代谢、EEG和MEG的发作间期棘波定位以及侵入性记录、与癫痫发作症状学相关的功能区域、MR识别的病变、切除区域、术后Engel分类。使用机器学习方法，我们将进行一系列测试，以检查图谱可用于识别个体受试者中SOZ的程度。最后，在目标3中，我们将研究使用非侵入性MEG数据和静息状态MRI建立的区域连接与CCEP图谱相结合来识别这些网络的可能性，最终目标是减少对侵入性监测的需求。采用留一法进行回顾性分析，并与结局进行比较，以量化从有创和无创记录中识别发病区的改善。