发作间期痫样放电的检测：头皮 MEG 与常规 MEG 测量的比较。
Detection of interictal epileptiform discharges: A comparison of on-scalp MEG and conventional MEG measurements.
机构信息
NatMEG, Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
Department of Microtechnology and Nanoscience - MC2, Chalmers University of Technology, Gothenburg, Sweden.
出版信息
Clin Neurophysiol. 2020 Aug;131(8):1711-1720. doi: 10.1016/j.clinph.2020.03.041. Epub 2020 May 11.
OBJECTIVE
Conventional MEG provides an unsurpassed ability to, non-invasively, detect epileptic activity. However, highly resolved information on small neuronal populations required in epilepsy diagnostics is lost and can be detected only intracranially. Next-generation on-scalp magnetencephalography (MEG) sensors aim to retrieve information unavailable to conventional non-invasive brain imaging techniques. To evaluate the benefits of on-scalp MEG in epilepsy, we performed the first-ever such measurement on an epilepsy patient.
METHODS
Conducted as a benchmarking study focusing on interictal epileptiform discharge (IED) detectability, an on-scalp high-temperature superconducting quantum interference device magnetometer (high-Tc SQUID) system was compared to a conventional, low-temperature SQUID system. Co-registration of electroencephalopraphy (EEG) was performed. A novel machine learning-based IED-detection algorithm was developed to aid identification of on-scalp MEG unique IEDs.
RESULTS
Conventional MEG contained 24 IEDs. On-scalp MEG revealed 47 IEDs (16 co-registered by EEG, 31 unique to the on-scalp MEG recording).
CONCLUSION
Our results indicate that on-scalp MEG might capture IEDs not seen by other non-invasive modalities.
SIGNIFICANCE
On-scalp MEG has the potential of improving non-invasive epilepsy evaluation.
目的
常规 MEG 提供了一种无与伦比的能力，可以非侵入性地检测癫痫活动。然而，在癫痫诊断中所需的小神经元群体的高分辨率信息丢失了，只能在颅内检测到。新一代头皮磁强计（MEG）传感器旨在获取常规非侵入性脑成像技术无法获取的信息。为了评估头皮 MEG 在癫痫中的益处，我们对一名癫痫患者进行了首次此类测量。
方法
作为一项专注于发作间期癫痫样放电（IED）可检测性的基准研究，我们将头皮高温超导量子干涉仪磁强计（高-Tc SQUID）系统与传统的低温 SQUID 系统进行了比较。进行了脑电图（EEG）配准。开发了一种基于机器学习的 IED 检测算法，以帮助识别头皮 MEG 特有的 IED。
结果
常规 MEG 包含 24 个 IED。头皮 MEG 显示出 47 个 IED（16 个与 EEG 同步，31 个仅在头皮 MEG 记录中出现）。
结论
我们的结果表明，头皮 MEG 可能会捕捉到其他非侵入性方式无法看到的 IED。
意义
头皮 MEG 有可能改善非侵入性癫痫评估。
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