Rapidly spreading seizures arise from large-scale functional brain networks in focal epilepsy
Rapidly spreading seizures arise from large-scale functional brain networks in focal epilepsy
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局灶性癫痫中大规模功能性脑网络引起快速蔓延的癫痫发作
DOI：
10.1016/j.neuroimage.2021.118104
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发表时间：
2021
期刊：
影响因子：
5.7
通讯作者：
Kira Jun-ichi
中科院分区：
文献类型：
--
作者：
Uehara Taira;Shigeto Hiroshi;Mukaino Takahiko;Yokoyama Jun;Okadome Toshiki;Yamasaki Ryo;Ogata Katsuya;Mukae Nobutaka;Sakata Ayumi;Tobimatsu Shozo;Kira Jun-ichi
目前尚不清楚局灶性癫痫的致痫网络是否在生理网络上发展。这项工作的目的是探索快速传播的发作快速活动（IFA），一个建议的生物标志物的癫痫网络，和功能连接或网络的健康受试者之间的关联。我们回顾了45例局灶性癫痫患者，他们接受了皮质电图（ECoG）记录，以确定显示IFA快速扩散的患者。IFA功率量化为归一化β-γ波段功率。使用已发表的静息态功能磁共振成像数据库，我们估计了健康受试者（RSFC-HS）和健康受试者（RSNs-HS）在对应于患者电极位置的静息态功能连接。我们使用最近开发的称为活动流映射的方法，基于电极位置之间的RSFC-HS（基于RSFC-HS的预测）预测每个电极的IFA功率。使用基于种子和基于图谱的方法鉴定RSNs-HS。我们使用非参数相关系数将IFA功率与基于RSFC-HS的预测或RSNs-HS进行了比较。还使用发作间期ECoG数据估计了每例患者的RSFC和基于种子的RSN（RSFC-PT和基于种子的RSNs-PT），并以与RSFC-HS和基于种子的RSNs-HS相同的方式与IFA功率进行比较。进行空间自相关保留随机化检验以进行显著性检验。9例患者符合入选标准。所有患者均未出现反射性癫痫发作。6例患者表现出结构性病因的病理学证据。我们总共分析了49次癫痫发作（每例患者2-13次癫痫发作）。我们分别在28例（57.1%）、21例（42.9%）和28例（57.1%）癫痫发作中观察到IFA功率与基于RSFC-HS的预测、基于种子的RSNs-HS或基于图谱的RSNs-HS之间存在显著相关性。32例（65.3%）癫痫发作与基于种子或基于图谱的RSNs-HS显著相关，但这一比例因患者而异：6例患者的29次癫痫发作中有27次（93.1%）与其中任何一次相关。在基于图谱的RSNs-HS中，与IFA功率相关的RSNs-HS包括默认模式、控制、背侧注意、躯体运动和颞顶网络。我们无法获得RSFC-PT和RSNs-PT在一个病人，由于频繁的发作间期癫痫样放电。在其余8例患者中，大多数癫痫发作显示IFA功率与基于RSFC-PT的预测或基于种子的RSNs-PT之间存在显著相关性。我们的研究提供了证据表明，IFA在局灶性癫痫中的快速传播可能源于生理性RSN。这一发现表明癫痫和功能网络之间的重叠，这可能解释了为什么局灶性癫痫患者的功能网络经常中断。
It remains unclear whether epileptogenic networks in focal epilepsy develop on physiological networks. This work aimed to explore the association between the rapid spread of ictal fast activity (IFA), a proposed biomarker for epileptogenic networks, and the functional connectivity or networks of healthy subjects. We reviewed 45 patients with focal epilepsy who underwent electrocorticographic (ECoG) recordings to identify the patients showing the rapid spread of IFA. IFA power was quantified as normalized beta-gamma band power. Using published resting-state functional magnetic resonance imaging databases, we estimated resting-state functional connectivity of healthy subjects (RSFC-HS) and resting-state networks of healthy subjects (RSNs-HS) at the locations corresponding to the patients' electrodes. We predicted the IFA power of each electrode based on RSFC-HS between electrode locations (RSFC-HS-based prediction) using a recently developed method, termed activity flow mapping. RSNs-HS were identified using seed-based and atlas-based methods. We compared IFA power with RSFC-HS-based prediction or RSNs-HS using non-parametric correlation coefficients. RSFC and seed-based RSNs of each patient (RSFC-PT and seed-based RSNs-PT) were also estimated using interictal ECoG data and compared with IFA power in the same way as RSFC-HS and seed-based RSNs-HS. Spatial autocorrelation-preserving randomization tests were performed for significance testing. Nine patients met the inclusion criteria. None of the patients had reflex seizures. Six patients showed pathological evidence of a structural etiology. In total, we analyzed 49 seizures (2–13 seizures per patient). We observed significant correlations between IFA power and RSFC-HS-based prediction, seed-based RSNs-HS, or atlas-based RSNs-HS in 28 (57.1%), 21 (42.9%), and 28 (57.1%) seizures, respectively. Thirty-two (65.3%) seizures showed a significant correlation with either seed-based or atlas-based RSNs-HS, but this ratio varied across patients: 27 (93.1%) of 29 seizures in six patients correlated with either of them. Among atlas-based RSNs-HS, correlated RSNs-HS with IFA power included the default mode, control, dorsal attention, somatomotor, and temporal-parietal networks. We could not obtain RSFC-PT and RSNs-PT in one patient due to frequent interictal epileptiform discharges. In the remaining eight patients, most of the seizures showed significant correlations between IFA power and RSFC-PT-based prediction or seed-based RSNs-PT. Our study provides evidence that the rapid spread of IFA in focal epilepsy can arise from physiological RSNs. This finding suggests an overlap between epileptogenic and functional networks, which may explain why functional networks in patients with focal epilepsy frequently disrupt.
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