Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands.
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital, Boston, Massachusetts.
Hum Brain Mapp. 2018 Jan;39(1):104-119. doi: 10.1002/hbm.23827. Epub 2017 Oct 8.
Studies using functional connectivity and network analyses based on magnetoencephalography (MEG) with source localization are rapidly emerging in neuroscientific literature. However, these analyses currently depend on the availability of costly and sometimes burdensome individual MR scans for co-registration. We evaluated the consistency of these measures when using a template MRI, instead of native MRI, for the analysis of functional connectivity and network topology.
Seventeen healthy participants underwent resting-state eyes-closed MEG and anatomical MRI. These data were projected into source space using an atlas-based peak voxel and a centroid beamforming approach either using (1) participants' native MRIs or (2) the Montreal Neurological Institute's template. For both methods, time series were reconstructed from 78 cortical atlas regions. Relative power was determined in six classical frequency bands per region and globally averaged. Functional connectivity (phase lag index) between each pair of regions was calculated. The adjacency matrices were then used to reconstruct functional networks, of which regional and global metrics were determined. Intraclass correlation coefficients were calculated and Bland-Altman plots were made to quantify the consistency and potential bias of the use of template versus native MRI.
Co-registration with the template yielded largely consistent relative power, connectivity, and network estimates compared to native MRI.
These findings indicate that there is no (systematic) bias or inconsistency between template and native MRI co-registration of MEG. They open up possibilities for retrospective and prospective analyses to MEG datasets in the general population that have no native MRIs available. Hum Brain Mapp, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. Hum Brain Mapp 39:104-119, 2018. © 2017 Wiley Periodicals, Inc.
基于磁共振源定位的功能连接和网络分析的研究在神经科学文献中迅速涌现。然而,这些分析目前依赖于昂贵且有时繁琐的个体磁共振扫描来进行配准。我们评估了使用模板磁共振成像(MRI)代替个体 MRI 进行功能连接和网络拓扑分析时这些测量的一致性。
17 名健康参与者接受了静息状态闭眼 MEG 和解剖 MRI。这些数据使用基于图谱的峰值体素和质心波束形成方法投影到源空间,方法分别使用(1)参与者的原始 MRI 或(2)蒙特利尔神经学研究所的模板。对于这两种方法,时间序列均从 78 个皮质图谱区域重建。每个区域的六个经典频带中确定相对功率,并进行全局平均。计算每个区域之间的功能连接(相位滞后指数)。然后使用邻接矩阵重建功能网络,并确定区域和全局指标。计算组内相关系数,并绘制 Bland-Altman 图以量化使用模板与原始 MRI 的一致性和潜在偏差。
与模板配准产生了与原始 MRI 相比,在相对功率、连接和网络估计方面具有高度一致性的结果。
这些发现表明,MEG 的模板与原始 MRI 配准之间没有(系统)偏差或不一致性。这为使用没有原始 MRI 的一般人群的 MEG 数据集进行回顾性和前瞻性分析开辟了可能性。
人类大脑映射,2017 年。©2017 作者 Wiley 期刊出版公司。人类大脑映射 39:104-119,2018.©2017 Wiley 期刊出版公司。