Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data

Owen, Julia P.; Wipf, David; Attias, Hagai; Sekihara, Kensuke; Nagarajan, Srikantan S.

doi:10.1016/j.neuroimage.2011.12.027

Cited by 75 publications

(83 citation statements)

References 29 publications

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“…First, the source localization algorithm Champagne (22) was used to compute the source strength at each voxel in the brain. MEG sensor data were third-order gradient denoised, detrended, and filtered from 4 to 40 Hz.…”

Section: Magnetoencephalographic Source Localizationmentioning

confidence: 99%

Temporal Cortical Plasticity in Single-Sided Deafness

Pross

Chang²,

Mizuiri³

et al. 2015

Otology &Amp; Neurotology

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SSD disrupts M100 latency difference between the two hemispheres to sound stimulation. This finding may represent maladaptive temporal cortical plasticity because of loss of a peripheral sensor. Based on this premise, a new generation of neurophysiologically inspired auditory treatments to correct or mitigate central consequences of SSD may be considered to optimize hearing in individuals with only one functional ear.

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Section: Magnetoencephalographic Source Localizationmentioning

confidence: 99%

Temporal Cortical Plasticity in Single-Sided Deafness

Pross

Chang²,

Mizuiri³

et al. 2015

Otology &Amp; Neurotology

Self Cite

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“…In addition, a recently-developed source reconstruction algorithm, Champagne (Owen et al, 2012), which uses an iterative approach to optimize a cost function related to the logarithm of the trace in data model covariance, performed better than several conventional approaches and was robust to noise, although its ability to accurately obtain the source time-courses was not evaluated (Owen et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images

Huang¹,

Huang²,

Robb³

et al. 2014

NeuroImage

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The present study developed a fast MEG source imaging technique based on Fast Vector-based Spatio-Temporal Analysis using a L1-minimum-norm (Fast-VESTAL) and then used the method to obtain the source amplitude images of resting-state magnetoencephalography (MEG) signals for different frequency bands. The Fast-VESTAL technique consists of two steps. First, L1-minimum-norm MEG source images were obtained for the dominant spatial modes of sensor-waveform covariance matrix. Next, accurate source time-courses with millisecond temporal resolution were obtained using an inverse operator constructed from the spatial source images of Step 1. Using simulations, Fast-VESTAL’s performance of was assessed for its 1) ability to localize multiple correlated sources; 2) ability to faithfully recover source time-courses; 3) robustness to different SNR conditions including SNR with negative dB levels; 4) capability to handle correlated brain noise; and 5) statistical maps of MEG source images. An objective pre-whitening method was also developed and integrated with Fast-VESTAL to remove correlated brain noise. Fast-VESTAL’s performance was then examined in the analysis of human mediannerve MEG responses. The results demonstrated that this method easily distinguished sources in the entire somatosensory network. Next, Fast-VESTAL was applied to obtain the first whole-head MEG source-amplitude images from resting-state signals in 41 healthy control subjects, for all standard frequency bands. Comparisons between resting-state MEG sources images and known neurophysiology were provided. Additionally, in simulations and cases with MEG human responses, the results obtained from using conventional beamformer technique were compared with those from Fast-VESTAL, which highlighted the beamformer’s problems of signal leaking and distorted source time-courses.

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“…These localization methods include Champagne (Owen et al 2012b), SAKETINI (Zumer et al 2007), and NSEFALoc (Zumer et al 2008), which all involve the idea of denoising and localizing data in one step, for improved spatial specificity and reduced sensitivity to correlated sources. Prior to inversion, data may also be preprocessed to remove artifacts.…”

Section: Inverse Methodsmentioning

confidence: 99%