Abstract
Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.
Highlights
Planning resective brain surgery, whether for the removal of structural lesion or of seizure onset zone, requires the estimation of the location of functional regions of cortex in order to plan a strategy that maximizes benefit while minimizing risk of postsurgical functional deficit
Magnetoencephalography (MEG) is well-suited for functional preoperative brain mapping prior to surgery of tumor or vascular lesion because its results do not depend primarily on blood flow that can be altered by tumor growth and does not suffer from susceptibility artifacts and vascular confounds seen, e.g., with fMRI (Kreidenhuber et al 2019)
Over the last decade we have developed and gained experience with an empirical Bayesian algorithm with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al 2010; Owen et al 2012); in its current form, it is called Champagne
Summary
Whether for the removal of structural lesion or of seizure onset zone, requires the estimation of the location of functional regions of cortex in order to plan a strategy that maximizes benefit while minimizing risk of postsurgical functional deficit. Many semi- and fully automated algorithms have been elaborated to counter these difficulties These have included, for example, the application of the coherent Maximum Entropy of the Mean (cMEM) approach as described in Chowdhury et al (2013) to the localization of IEDs, a method termed distributed magnetic source imaging or dMSI, and found to be successful in comparison to ECD fitting by Pelligrino et al (2018). The Bayesian multidipole iterative Monte Carlo approach “SESAME” described by Sommariva and Sorrentino (2014) was evaluated in comparison to standard ECD methods by Luria et al (2020) for the localization of IEDs, likewise showing excellent performance These methods were noted to be advantageous because results were more operator-independent
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