机构：[1]School of Biological Science and Medical Engineering, Beihang University, Beijing, China,[2]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China,[3]Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China,[4]Hefei Innovation Research Institute, Beihang University, Hefei, China,[5]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China,神经内科首都医科大学宣武医院[6]Beijing Key Laboratory of Brain Functional Disease and Neuromodulation, Beijing, China
Beamforming techniques have played a prominent role in source imaging in neuroimaging and in locating epileptogenic zones. However, existing vector-beamformers are sensitive to noise on localization of epileptogenic zones. In this study, partial least square (PLS) was used to aid the minimum variance beamforming approach for source imaging with magnetoencephalography (MEG) arrays, and verified its effectiveness in simulated data and epilepsy data. First, PLS was employed to extract the components of the MEG arrays by maximizing the covariance between a linear combination of the predictors and the class variable. Noise was then removed by reconstructing the MEG arrays based on those components. The minimum variance beamforming method was used to estimate a source model. Simulations with a realistic head model and varying noise levels indicated that the proposed approach can provide higher spatial accuracy than other well-known beamforming methods. For real MEG recordings in 10 patients with temporal lobe epilepsy, the ratios of the number of spikes localized in the surgical excised region to the total number of spikes using the proposed method were higher than that of the dipole fitting method. These localization results using the proposed method are more consistent with the clinical evaluation. The proposed method may provide a new imaging marker for localization of epileptogenic zones.
基金：
This work was supported by the National Key Research
and Development Program of China (Grant Number:
2016YFF0201002), the Natural Science Foundation of China
(Grant Numbers: 61301005, 61572055), the project of the
Brain Functional Disease and Neuromodulation of Beijing
Key Laboratory, Hefei Innovation Research Institute, Beihang
University, Project of The Thousand Talents Plan for Young
Professionals, and The Thousand Talents Plan Workstation
between Beihang University and Jiangsu Yuwell Medical
Equipment and Supply Co. Ltd.
第一作者机构：[1]School of Biological Science and Medical Engineering, Beihang University, Beijing, China,[2]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China,[3]Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China,[4]Hefei Innovation Research Institute, Beihang University, Hefei, China,
通讯作者：
通讯机构：[1]School of Biological Science and Medical Engineering, Beihang University, Beijing, China,[2]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China,[3]Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China,[4]Hefei Innovation Research Institute, Beihang University, Hefei, China,[5]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China,[6]Beijing Key Laboratory of Brain Functional Disease and Neuromodulation, Beijing, China
推荐引用方式(GB/T 7714)：
Yegang Hu,Chunli Yin,Jicong Zhang,et al.Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging[J].FRONTIERS IN NEUROSCIENCE.2018,12(SEP):616.doi:10.3389/fnins.2018.00616.
APA：
Yegang Hu,Chunli Yin,Jicong Zhang&Yuping Wang.(2018).Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging.FRONTIERS IN NEUROSCIENCE,12,(SEP)
MLA：
Yegang Hu,et al."Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging".FRONTIERS IN NEUROSCIENCE 12..SEP(2018):616
