Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation.
Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation.
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DOI：
10.1109/embc46164.2021.9629686
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发表时间：
2021-11
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影响因子：
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通讯作者：
Papadelis C
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文献类型：
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作者：
Corona L;Tamilia E;Madsen JR;Stufflebeam SM;Pearl PL;Papadelis C
患有难治性癫痫(MRE)的儿童需要切除神经外科手术以实现癫痫的自由发作，其成功与否取决于对致痫区域(EZ)的准确描绘。功能连接性(FC)可以评估癫痫脑网络的程度，因为颅内EEG(IcEEG)研究表明它与EZ有关，并对这些患者的手术结果有预测价值。在这里，我们提出了一种新的基于脑磁图(MEG)和高密度(HD-EEG)数据的非侵入性方法，通过植入虚拟传感器(VSS)来估计源级别的FC度量。我们分析了8名患有MRE的儿童的脑磁图、HD-EEG和icEEG数据，这些儿童接受了手术，结果良好，并在非侵入性数据上进行了源定位(波束形成器)，以在icEEG电极位置建立VSS。我们分析了不同频段有无发作间期癫痫样放电(IED)的数据，并计算了以下FC矩阵：幅度包络相关(AEC)、相关(CORR)和锁相值(PLV)。每个矩阵被用来使用最小生成树(MST)来生成图，并且对于每个节点(即每个传感器)，我们计算了四个中心性度量：介数、贴近度、度和特征向量。我们通过线性相关检验了VSS测量相对于ICEEG(作为基准)的可靠性，并比较了切除内和切除外的FC值。我们观察到，对于有IED的数据，AEC[α(8-12赫兹)、贝塔(12-30赫兹)和宽带(1-50赫兹)]的内侧FC高于外切除(p<；0.05)；对于icEEG，对于没有IED的数据，对于没有IED的数据，对于ICEEG，对于AEC theta(4-8赫兹)，对于MEG-VSS，对于所有的中心性测量，对于icEEG和MEG/HD-EEG-VSS，对于AEC宽带(1-50赫兹)。此外，ICEEG和VSS指标表现出高度的相关性(0.6-0.9，p<；0.05)。我们的数据支持这一观点，即所提出的方法可以潜在地复制icEEG的能力，以映射MRE儿童的癫痫网络。通过VSS利用脑磁图和HD-EEG等非侵入性技术评估FC是一种很有前途的工具，它可以在不等待癫痫发作的情况下描绘EZ，从而帮助术前评估FC，并有可能改善接受手术的MRE患者的手术结果。
Children with medically refractory epilepsy (MRE) require resective neurosurgery to achieve seizure freedom, whose success depends on accurate delineation of the epileptogenic zone (EZ). Functional connectivity (FC) can assess the extent of epileptic brain networks since intracranial EEG (icEEG) studies have shown its link to the EZ and predictive value for surgical outcome in these patients. Here, we propose a new noninvasive method based on magnetoencephalography (MEG) and high-density (HD-EEG) data that estimates FC metrics at the source level through an “implantation” of virtual sensors (VSs). We analyzed MEG, HD-EEG, and icEEG data from eight children with MRE who underwent surgery having good outcome and performed source localization (beamformer) on noninvasive data to build VSs at the icEEG electrode locations. We analyzed data with and without Interictal Epileptiform Discharges (IEDs) in different frequency bands, and computed the following FC matrices: Amplitude Envelope Correlation (AEC), Correlation (CORR), and Phase Locking Value (PLV). Each matrix was used to generate a graph using Minimum Spanning Tree (MST), and for each node (i.e., each sensor) we computed four centrality measures: betweenness, closeness, degree, and eigenvector. We tested the reliability of VSs measures with respect to icEEG (regarded as benchmark) via linear correlation, and compared FC values inside vs. outside resection. We observed higher FC inside than outside resection (p<0.05) for AEC [alpha (8-12 Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data with IEDs and AEC theta (4-8 Hz) on data without IEDs for icEEG, AEC broadband (1-50 Hz) on data without IEDs for MEG-VSs, as well as for all centrality measures of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG and VSs metrics presented high correlation (0.6-0.9, p<0.05). Our data support the notion that the proposed method can potentially replicate the icEEG ability to map the epileptogenic network in children with MRE. The estimation of FC with noninvasive techniques, such as MEG and HD-EEG, via VSs is a promising tool that would help the presurgical evaluation by delineating the EZ without waiting for a seizure to occur, and potentially improve the surgical outcome of patients with MRE undergoing surgery.