Informed MEG/EEG source imaging reveals the locations of interictal spikes missed by SEEG

NeuroImage

Shen Luo¹  Ke Xu¹  Jia-Hong Gao²  Li Zheng²  Miao Cao³  Su Shu³  Xiongfei Wang⁴  Lang Qin⁴  Jing Xu⁵ 

[1] Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China;McGovern Institute for Brain Research, Peking University, Beijing 100871, China;Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China;Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China;Department of Neurosurgery, Sanbo Brain Hospital of Capital Medical University, Beijing 100093, China;

关键词: Interictal spike;    Stereo-electroencephalography;    Missing problem;    Magnetoencephalography;    Inverse problem;   

DOI  :  

来源: DOAJ

【 摘 要 】

Determining the accurate locations of interictal spikes has been fundamental in the presurgical evaluation of epilepsy surgery. Stereo-electroencephalography (SEEG) is able to directly record cortical activity and localize interictal spikes. However, the main caveat of SEEG techniques is that they have limited spatial sampling (covering <5% of the whole brain), which may lead to missed spikes originating from brain regions that were not covered by SEEG. To address this problem, we propose a SEEG-informed minimum-norm estimates (SIMNE) method by combining SEEG with magnetoencephalography (MEG) or EEG. Specifically, the spike locations determined by SEEG offer as a priori information to guide MEG source reconstruction. Both computer simulations and experiments using data from five epilepsy patients were conducted to evaluate the performance of SIMNE. Our results demonstrate that SIMNE generates more accurate source estimation than a traditional minimum-norm estimates method and reveals the locations of spikes missed by SEEG, which would improve presurgical evaluation of the epileptogenic zone.

【 授权许可】

Unknown