Background: Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. While these recordings afford detailed information about local brain activity, putting this activity in context and comparing results across patients is challenging. Non-invasive whole-brain Magnetoencephalography (MEG) could help translate iEEG in the context of overall brain activity, and thereby aid group analysis and interpretation. Methods: Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy. Pre-processed MEG sensor data was projected to source space. The time delay embedded hidden Markov model (HMM) technique was applied to find recurrent sub-second patterns of network activity in a completely data-driven way. To relate MEG and iEEG results, correlations were computed between HMM state time courses and iEEG power envelopes in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Results: Five HMM states were inferred from MEG. Two of them corresponded to the left and right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. The majority of iEEG contacts were also located in left and right temporal areas and the theta/alpha power of the local field potentials (LFP) recorded from these contacts correlated with the time course of the HMM state corresponding to the temporal lobe of the respective hemisphere. Discussion: Our findings are consistent with the fact that most subjects were diagnosed with temporal epilepsy and implanted with temporal electrodes. As the placement of electrodes between patients was inconsistent, their modulation by HMM states could help group the contacts into functional clusters. This is the first time that HMM was applied to simultaneously recorded iEEG-MEG and our pipeline could be used in future similar studies.

中文翻译：

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通过隐马尔可夫模型同时分析iEEG-MEG数据的动态

背景：颅内脑电图（iEEG）记录用于在手术切除癫痫病发作的重点之前进行临床评估，并为了解正常的脑功能提供了一个窗口。尽管这些录音提供了有关局部脑部活动的详细信息，但是将这种活动置于上下文中并比较患者之间的结果具有挑战性。非侵入性全脑脑磁图（MEG）可以帮助在整体脑部活动的背景下翻译iEEG，从而有助于小组分析和解释。方法：11例癫痫患者在休息时同时进行MEG-iEEG记录。预处理的MEG传感器数据被投影到源空间。应用时延嵌入式隐马尔可夫模型（HMM）技术以完全数据驱动的方式查找网络活动的复发性亚秒级模式。为了关联MEG和iEEG结果，计算了HMM状态时程与iEEG功率包络在等距频率区间内的相关性，并将其表示为各个状态和iEEG通道的相关性谱。结果：从MEG推断出五个HMM状态。它们中的两个对应于左右时间激活，并且主要在θ/α频带具有光谱特征。大部分iEEG接触点也位于左右颞区，从这些接触点记录的局部场电位（LFP）的theta / alpha功率与HMM状态的时程相关，该HMM状态对应于各个半球的颞叶。讨论：我们的发现与大多数受试者被诊断为颞癫痫并植入颞电极的事实是一致的。由于患者之间电极的放置不一致，因此，通过HMM状态对其进行的调制可以帮助将触点分组为功能簇。这是首次将HMM应用于同时记录的iEEG-MEG，并且我们的管道可用于将来的类似研究。我们的发现与大多数受试者被诊断为颞癫痫并植入颞电极的事实是一致的。由于患者之间电极的放置不一致，因此通过HMM状态对其进行的调制可以帮助将触点分组为功能簇。这是首次将HMM应用于同时记录的iEEG-MEG，我们的管道可用于将来的类似研究。我们的发现与大多数受试者被诊断为颞癫痫并植入颞电极的事实是一致的。由于患者之间电极的放置不一致，因此通过HMM状态对其进行的调制可以帮助将触点分组为功能簇。这是首次将HMM应用于同时记录的iEEG-MEG，并且我们的管道可用于将来的类似研究。