A Brain Atlas for Mapping Connectivity in Focal Epilepsy

 描述(由申请人提供)：通过切除癫痫发作起始区(SOZ)来治疗顽固性局灶性癫痫通常是有效的，前提是能够可靠地识别SOZ。然而，局灶性癫痫从根本上说是一种基于网络的疾病。发作起始区连接到一个网络，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可检测到病变的患者中，即使使用侵入性记录，也很难将起始区与网络中的这些其他结节区分开来。该项目的目标是改进SOZ识别，最终减少对 在可能的情况下，用于术前有创性记录，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，我们将从侵袭性皮质-皮质诱发电位(CCEP)记录的数据库中建立功能连接图谱，以确定部分癫痫患者中常见的相互作用网络，并调查这些网络对SOZ位置的依赖程度。为了构建图谱，患者数据将使用每个受试者的结构MRI的皮质约束扭曲被共同配准到标记的解剖图谱。在目标2中，CCEPS数据将在图谱中补充其他数据，以提供对癫痫相关脑区的更多洞察力：发作间期FDG PET的低代谢区域、发作期SPECT的高代谢区域、发作间期EEG和MEG的棘波定位和侵入性记录、与癫痫症状相关的功能区、MR识别的病变、切除区域、手术后Engel分类。使用机器学习方法，我们将进行一系列测试，以检查地图集在多大程度上可以用来确定个别对象的SOZ。最后，在目标3中，我们将研究使用非侵入性脑磁图数据和静息状态MRI建立的区域连接的可能性，并结合CCEPS图谱来识别这些网络，最终目标是减少侵入性监测的需要。使用留一法的回顾分析和与结果的比较将被用来量化从有创和非侵入性记录识别起始区的进步。

 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.