Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study.
Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study.
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DOI：
10.1002/hbm.26118
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发表时间：
2023-01
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
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癫痫是一种高度异质性的神经系统疾病，具有不同的病因，表现和治疗反应。癫痫样脑活动的新模型必须解释这种变异性，以确定个体需求并允许临床医生策划个性化护理。在这里，我们使用隐马尔可夫模型（HMM）创建一个独特的统计模型发作间期的大脑活动的10名儿科患者。我们使用脑磁图（MEG）数据作为费城儿童医院患者标准临床护理的一部分。这些数据通常使用过量峰度图（EKM）进行分析;然而，随着病例变得更加复杂（极端多灶性和/或多态性活动），它们变得更难用EKM解释。我们评估了三个患者组的HMM与EKM的性能，这些患者组的表现越来越复杂。两种方法的致痫灶定位差异为7 ± 2 mm（所有10例患者的平均值± SD）;通过HMM状态访视匹配了94% ± 13%的EKM时间标记。HMM定位致痫区域（与EKM一致），并提供有关这些区域之间关系的额外信息。与当前方法相比，HMM的一个关键优势是它是一个数据驱动的模型，因此输出会针对每个人进行调整。最后，模型输出是直观的，允许用户（临床医生）查看结果并手动选择HMM癫痫样状态，与以前的方法相比具有多种优势，并允许在难治性癫痫患者的手术决策中更广泛地实施MEG癫痫样分析。在这项研究中，我们使用隐马尔可夫模型（HMM）创建一个独特的统计模型，发作间期脑活动的10名儿童癫痫患者。该数据驱动模型为每位患者生成唯一的输出，并用于定位致癫痫区域。在两个病人，其中一个以上的焦点被确定，HMM提供了额外的信息之间的关系，癫痫样活动在这些地区出现。
Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy. In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas.
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影响因子：
2.2
作者：
Gofshteyn, J. S.;Lee, T.;Marsh, E. D.
通讯作者：
Marsh, E. D.
影响因子：
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作者：
Kim, Hunmin;Chung, Chun Kee;Hwang, Hee
通讯作者：
Hwang, Hee
影响因子：
2.2
作者：
Nissen, I. A.;Stam, C. J.;Hillebrand, A.
通讯作者：
Hillebrand, A.
影响因子：
16.6
作者：
Hill, Ryan M.;Boto, Elena;Brookes, Matthew J.
通讯作者：
Brookes, Matthew J.
影响因子：
5.7
作者：
Seedat, Zelekha A.;Quinn, Andrew J.;Brookes, Matthew J.
通讯作者：
Brookes, Matthew J.