An F-ratio-based method for estimating the number of active sources in MEG.
An F-ratio-based method for estimating the number of active sources in MEG.
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DOI：
10.3389/fnhum.2023.1235192
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发表时间：
2023
影响因子：
2.9
通讯作者：
Pantazis D
中科院分区：
文献类型：
--
作者：
Giri A;Mosher JC;Adler A;Pantazis D
脑磁图（MEG）是研究人脑功能的有力手段。然而，准确地估计的来源，有助于MEG记录的数量仍然是一个具有挑战性的问题，由于低信噪比（SNR），相关的源的存在，头部建模的不准确性，在个人解剖结构的变化。为了解决这些问题，我们的研究引入了一种基于F比统计方法准确估计大脑中活动源数量的稳健方法，该方法允许在具有更多源的完整模型和具有更少源的简化模型之间进行比较。利用这种方法，我们开发了一个正式的统计过程，在多偶极子定位问题中，顺序增加源的数量，直到所有的源都被发现。我们的结果表明，阈值的选择在决定方法的整体性能中起着关键作用，和适当的阈值需要调整的源的数量和信噪比水平，而它们对于不同的源间相关性、平移建模的不准确性和不同的皮质解剖结构基本上保持不变。通过识别最佳阈值并在模拟、真实的体模和人体脑磁图数据中验证我们基于F比的方法，我们证明了我们基于F比的方法优于现有的最先进的统计方法，如赤池信息准则（AIC）和最小描述长度（MDL）。总的来说，当调整阈值的最佳选择时，我们的方法为研究人员提供了一种精确的工具来估计活跃大脑来源的真实数量并准确地模拟大脑功能。
Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Our results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.
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