An F-ratio-based method for estimating the number of active sources in MEG

Frontiers in Human Neuroscience

Human Neuroscience

John C. Mosher¹  Amita Giri²  Dimitrios Pantazis²  Amir Adler³ 

[1] Department of Neurology, McGovern Medical School, Texas Institute for Restorative Neurotechnologies, UTHealth, Houston, TX, United States;McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States;McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States;Department of Electrical Engineering, Braude College of Engineering, Karmiel, Israel;

关键词: F-ratio;    source localization;    Alternating Projection (AP);    source enumeration;    MEG;    AIC;    MDL;   

DOI  :  10.3389/fnhum.2023.1235192

 received in 2023-06-05, accepted in 2023-08-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionMagnetoencephalography (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.MethodsTo 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.ResultsOur 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).DiscussionOverall, 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.

【 授权许可】

Unknown   
Copyright © 2023 Giri, Mosher, Adler and Pantazis.