Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Priors

Feng Liu, Li Wang, Yifei Lou, Ren Cang Li, Patrick L. Purdon

    Research output: Contribution to journalArticlepeer-review

    22 Scopus citations

    Abstract

    Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.

    Original languageEnglish
    Article number9201541
    Pages (from-to)321-334
    Number of pages14
    JournalIEEE Transactions on Medical Imaging
    Volume40
    Issue number1
    DOIs
    StatePublished - Jan 2021

    Keywords

    • EEG/MEG source imaging
    • graph structure learning
    • inverse problem
    • source localization

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