Clarke Maggie D, Larson Eric, Peterson Erica R, McCloy Daniel R, Bosseler Alexis N, Taulu Samu
Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States.
Department of Physics, University of Washington, Seattle, WA, United States.
Front Neurol. 2022 Mar 23;13:827529. doi: 10.3389/fneur.2022.827529. eCollection 2022.
We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-processing, including movement compensation, suppression of magnetic interference from sources inside and outside the magnetically shielded room, suppression of specific physiological artifact components such as cardiac artifacts. In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. We discuss the importance of taking the noise covariance structure into account in inverse modeling and present the proper treatment of the noise covariance matrix to accurately reflect the processing that was applied to the data. Using example cases, we investigate the level of source localization error before and after processing. One of our main findings is that statistical metrics of source reconstruction may erroneously indicate that the results are reliable even in cases where the data are severely distorted by head movements. As a consequence, we stress the importance of proper signal processing in infant MEG.
我们讨论了婴儿脑磁图（MEG）中的特定挑战和解决方案，婴儿MEG是MEG研究中技术难度最大的领域之一。我们的结果可推广到MEG数据采集的各种具有挑战性的场景，包括临床环境。我们涵盖了预处理中的广泛步骤，包括运动补偿、抑制磁屏蔽室内外源的磁干扰、抑制特定的生理伪影成分，如心脏伪影。在评估预处理算法的结果时，我们专注于比较预处理前后的信号表示，并讨论主要处理步骤不同组件的重要性。我们讨论了在逆建模中考虑噪声协方差结构的重要性，并介绍了对噪声协方差矩阵的适当处理，以准确反映应用于数据的处理过程。通过示例案例，我们研究了处理前后源定位误差的水平。我们的主要发现之一是，即使在数据因头部运动而严重失真的情况下，源重建的统计指标可能会错误地表明结果是可靠的。因此，我们强调了婴儿MEG中适当信号处理的重要性。
