Detection of epileptogenic cortical malformations with surface-based MRI morphometry.
Detection of epileptogenic cortical malformations with surface-based MRI morphometry.
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DOI：
10.1371/journal.pone.0016430
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发表时间：
2011-02-04
期刊：
影响因子：
3.7
通讯作者：
Kuzniecky R
中科院分区：
文献类型：
--
作者：
Thesen T;Quinn BT;Carlson C;Devinsky O;DuBois J;McDonald CR;French J;Leventer R;Felsovalyi O;Wang X;Halgren E;Kuzniecky R
磁共振成像已经彻底改变了癫痫患者结构异常的检测。然而，许多局灶性异常在常规目视检查中仍未被发现。在这里，我们使用一个自动化的，基于表面的方法来量化的形态特征相关的致癫痫皮质畸形，以检测异常的皮质厚度和模糊的灰白质边界。使用MRI形态测量在3T与表面为基础的球形平均技术，精确地对齐个体大脑之间的解剖结构，我们比较了单个患者与已知病变的一个大的正常对照组，以检测集群异常皮质厚度，灰白质对比，局部回旋，脑沟深度，雅可比距离和曲率。为了评估阈值和平滑对检测灵敏度和特异性的影响，我们用不同的阈值和平滑水平系统地改变这些参数。为了测试该技术的有效性，以检测病变的致痫性，我们比较了检测到的结构异常，专家跟踪，颅内脑电图，病理和手术结果在一个同质的患者样本。使用最佳参数并结合厚度和GWC，基于表面的检测方法识别了92%的皮质病变（灵敏度），几乎没有假阳性（96%特异性），成功区分了94%的患者和对照组。在所有手术患者中，检测到的结构异常与癫痫发作区、异常组织学和阳性结局相关。然而，该方法未能充分描述病变范围在大多数情况下。自动化的基于表面的MRI形态测量，如果使用优化的参数，可能是一个有价值的额外的临床工具，以提高检测微妙的或以前隐藏的畸形，因此可以提高识别难治性局灶性癫痫患者谁可能受益于手术。
Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery.
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