title: Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy.
abstract: Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source.
authors.affiliation: Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Abbas
authors.initials: A
authors.lastname: Sohrabpour
authors.affiliation: Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Department of Neurology, Mayo Clinic, Rochester, MN, USA.
authors.forename: Gregory
authors.initials: G
authors.lastname: Worrell
authors.affiliation: Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA. Electronic address: binhe@umn.edu.
authors.forename: Bin
authors.initials: B
authors.lastname: He
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008962:Models, Theoretical; D059906:Neuroimaging / Q000379:methods*; D012815:Signal Processing, Computer-Assisted
keywords: Convex optimization; EEG; Extent; Inverse problem; Iterative reweighting; MEG; Source extent; Sparsity