Vector-based spatial–temporal minimum L1-norm solution for MEG

Huang, Mei‐Hua; Dale, Anders M.; Song, Tao; Halgren, Eric; Harrington, Deborah L.; Podgorny, I. A.; Cañive, José M.; Lewis, Stephen F.; Lee, Roland R.

doi:10.1016/j.neuroimage.2006.01.029

Cited by 101 publications

(130 citation statements)

References 97 publications

Supporting

Mentioning

125

Contrasting

Order By: Relevance

“…Furthermore, the statistical analysis of the median distance from the CCD maxima to the central sulcus indicates that there is significant difference between the group of algorithms based on L 1 norm and the others. The results using the L 1 norm based CCD techniques are consistent with the previous literatures (Matsuura and Okaba, 1995;Uutela etal., 1999;Huang et al, 2006). This is also in agreement with previous suggestion derived from computer simulation studies and human studies using noninvasive SEP and MRP data (Yao & Dewald, 2005).…”

Section: Discussionsupporting

confidence: 90%

“…The finding that the CCD distributions estimated by the L p -norm and LORETA-L p methods were smoother when p value increased is in accordance with previous reports (Matsuura and Okabe, 1995;Beucker and Schlitt, 1996;Uutela etal., 1999;Huang et al, 2006). Secondly, comparing the present results with other works about LORETA (Pascual-Marqui and Michel, 1994a;Fuchs et al, 1999;Pascual-Marqui, 2002), it further indicates that the LORETA based on the L 1 norm is more suitable to use for imaging well localized sources than the LORETA-L 2 method.…”

Section: Discussionsupporting

confidence: 77%

“…Later, this approach has been further developed into a weighted minimum-norm least squares solution (MNLS), which can avoid the intrinsic bias toward superficial currents (Pascual-Marqui et al, 1994a;Gorodnitsky et al, 1995). Among weighted minimum norm (WMN) estimations, the following solutions have been developed, including the low-resolution electromagnetic tomography (LORETA) (Pascual-Marqui et al, 1994a), L 1 -norm (Matsuura and Okabe 1995), minimum L p -norm (Beucker and Schlitt, 1996), minimum current estimate based on L 1 -norm (Uutela et al, 1999), standardized low-resolution electromagnetic tomography (sLORETA) (Pascual-Marqui, 2002), and vector-based spatial-temporal minimum L 1 -norm solution (Huang et al, 2006). Efforts have been made in an attempt to evaluate the reported EEG/MEG source reconstruction algorithms by computer simulations or experiments (Pascual-Marqui et al, 1994a;Fuchs et al, 1999;Hann et al, 2000;He et al, 2002b;Phillips et al, 2002;Stenbacka et al, 2002;Waberski et al, 2002b;Wagner et al, 2004;Yao & Dewald, 2005;Grova et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Xiang

Towle

et al. 2007

NeuroImage

View full text Add to dashboard Cite

Objective-EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.Methods-Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with L p -norm (p equal to 1, 1.5 and 2), sLORETA, the minimum L p -norm (p equal to 1 and 1.5. When p = 2, the MNLS method is mathematically equivalent to the minimum L p -norm.), and L 1 -norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.Results-The CCD distributions estimated by the L p -norm and LORETA-L p methods were smoother when the p values were high. The LORETA based on the L 1 norm performed better than the LORETA-L 2 method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum L p -norm (with p equal to 1), L 1 -norm (the Linear programming) and LORETA-L p (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with L p -norm (p equal to 1.5 and 2.0), sLORETA, and the minimum L p -norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.Conclusions-The present experimental study suggests that L 1 -norm based algorithms provide better performance than L 2 and L 1.5 norm based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Xiang

Towle

et al. 2007

NeuroImage

View full text Add to dashboard Cite

show abstract

“…Source reconstruction of each session for each subject was performed using the Frequency-Domain VESTAL approach. 30,57,58 RMS amplitude per grid point was then computed for each subject and saved in a three-dimensional Nifty format file, which was then interpolated into 1 · 1 · 1 images in native MR coordinates. RMS reconstructions in native scanner space were then smoothed (5-mm gaussian kernel) and averaged across all sessions for each subject.…”

Section: Statistical Analysis Of Neuropsychological Testsmentioning

confidence: 99%

Magnetoencephalography Slow-Wave Detection in Patients with Mild Traumatic Brain Injury and Ongoing Symptoms Correlated with Long-Term Neuropsychological Outcome

Swan

Nichols

Drake

et al. 2015

Journal of Neurotrauma

Self Cite

View full text Add to dashboard Cite

Mild traumatic brain injury (mTBI) is common in the United States, accounting for as many as 75-80% of all TBIs. It is recognized as a significant public health concern, but there are ongoing controversies regarding the etiology of persistent symptoms post-mTBI. This constellation of nonspecific symptoms is referred to as postconcussive syndrome (PCS). The present study combined results from magnetoencephalography (MEG) and cognitive assessment to examine group differences and relationships between brain activity and cognitive performance in 31 military and civilian individuals with a history of mTBI + PCS and 33 matched healthy control subjects. An operator-free analysis was used for MEG data to increase reliability of the technique. Subjects completed a comprehensive neuropsychological assessment, and measures of abnormal slow-wave activity from MEG were collected. Results demonstrated significant group differences on measures of executive functioning and processing speed. In addition, significant correlations between slow-wave activity on MEG and patterns of cognitive functioning were found in cortical areas, consistent with cognitive impairments on exams. Results provide more objective evidence that there may be subtle changes to the neurobiological integrity of the brain that can be detected by MEG. Further, these findings suggest that these abnormalities are associated with cognitive outcomes and may account, at least in part, for long-term PCS in those who have sustained an mTBI.

show abstract

“…Such a prior is often considered to be fixed and known, as in the case of minimum ℓ 2 -norm approaches [2], minimum current estimation (MCE) [18], [44], FOCUSS [7], [16], sLORETA [30], and minimum variance beamformers [45]. Alternatively, a number of empirical Bayesian approaches have been proposed that attempt a form of model selection by using the data to guide the search for an appropriate p(S).…”

Section: Introductionmentioning

confidence: 99%

A Unified Bayesian Framework for MEG/EEG Source Imaging

Sekihara

Nagarajan

2015

Electromagnetic Brain Imaging

View full text Add to dashboard Cite

The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions. However, the means by which these priors are chosen, as well as the estimation and inference procedures that are subsequently adopted to affect localization, have led to a daunting array of algorithms with seemingly very different properties and assumptions. From the vantage point of a simple Gaussian scale mixture model with flexible covariance components, this paper analyzes and extends several broad categories of Bayesian inference directly applicable to source localization including empirical Bayesian approaches, standard MAP estimation, and multiple variational Bayesian (VB) approximations. Theoretical properties related to convergence, global and local minima, and localization bias are analyzed and fast algorithms are derived that improve upon existing methods. This perspective leads to explicit connections between many established algorithms and suggests natural extensions for handling unknown dipole orientations, extended source configurations, correlated sources, temporal smoothness, and computational expediency. Specific imaging methods elucidated under this paradigm include weighted minimum ℓ 2 -norm, FOCUSS, MCE, VESTAL, sLORETA, ReML and covariance component estimation, beamforming, variational Bayes, the Laplace approximation, and automatic relevance determination (ARD). Perhaps surprisingly, all of these methods can be formulated as particular cases of covariance component estimation using different concave regularization terms and optimization rules, making general theoretical analyses and algorithmic extensions/improvements particularly relevant.

show abstract

Vector-based spatial–temporal minimum L1-norm solution for MEG

Cited by 101 publications

References 97 publications

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Magnetoencephalography Slow-Wave Detection in Patients with Mild Traumatic Brain Injury and Ongoing Symptoms Correlated with Long-Term Neuropsychological Outcome

A Unified Bayesian Framework for MEG/EEG Source Imaging

Contact Info

Product

Resources

About