Multistart Algorithms for MEG Empirical Data Analysis Reliably Characterize Locations and Time Courses of Multiple Sources

Aine, Cheryl J.; Huang, Mei‐Hua; Stephen, Julia M.; Christner, R.

doi:10.1006/nimg.2000.0616

Cited by 68 publications

(40 citation statements)

References 33 publications

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“…The time interval of 20-870 ms poststimulus was analyzed for all task conditions. We developed and used the automated Multistart Downhill Simplex Method for determining source locations, strengths, and magnitudes (Huang et al, 1998;Aine et al, 2000). Nelder and Mead's (1965) downhill simplex search was utilized along with the Sarvas (1987) formula for forward calculations.…”

Section: Source Localization and Statistical Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Aging: Compensation or maturation?

Aine

Woodruff²,

Knoefel

et al. 2006

NeuroImage

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Neuroimaging studies of healthy aging often reveal differences in neural activation patterns between young and elderly groups for episodic memory tasks, even though there are no differences in behavioral performance. One explanation typically offered is that the elderly compensate for their memory deficiencies through the recruitment of additional prefrontal regions. The present study of healthy aging compared magnetoencephalographic (MEG) time-courses localized to specific cortical regions in two groups of subjects (20-29 years and ≥65 years) during a visual delayed-match-to-sample (DMS) task. MR morphometrics and neuropsychological test results were also examined with the hope of providing insight into the nature of the age-related differences. The behavioral results indicated no differences in performance between young and elderly groups. Although there was a main effect of age on the latency of the initial peak in primary/secondary visual cortex, these longer latencies were not correlated with the performance of elderly on the DMS task. The lateral occipital gyrus (LOG) revealed qualitatively different patterns of activity for the two age groups corroborated by neuropsychological test results. Morphometric results for the young versus elderly groups revealed less white (WM) and gray matter (GM) volumes in the frontal lobes of the elderly. When a group of middle-aged subjects (33-43 years) was included in the morphometric analyses, the middle-aged subjects revealed statistically greater WM volumes in frontal and parietal cortex suggesting immature WM tracts in the young. Perhaps our elderly utilized a different strategy compared to the young due to the different brain maturation levels of these groups.

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Section: Source Localization and Statistical Methodsmentioning

confidence: 99%

“…A chi-squared minimization was performed for each of the 10,000 starting parameters. These methods have been tested previously using computer simulations and in numerous empirical studies (Huang et al, 1998;Aine et al, 2000Aine et al, , 2005.…”

Section: Source Localization and Statistical Methodsmentioning

confidence: 99%

Aging: Compensation or maturation?

Aine

Woodruff²,

Knoefel

et al. 2006

NeuroImage

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“…Various solutions have been proposed that attain varying degrees of success. These can be divided into two main categories: "equivalent current dipole" approaches, in which the EEG/MEG signals are assumed to be generated by a relatively small number of focal sources (Miltner et al, 1994;Scherg and Ebersole, 1994;Scherg et al, 1999;Aine et al, 2000), and the "linear distributed" approach, in which "all" possible source locations are considered simultaneously (Backus and Gilbert, 1970;Sarvas, 1987;Hä mä lä inen and Ilmoniemi, 1994;Grave de Peralta Menendez and Gonzalez Andino, 1999;Pascual-Marqui, 1999;Uutela et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

2002

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Distributed linear solutions of the EEG source localization problem are used routinely. Here we describe an approach based on the weighted minimum norm method that imposes constraints using anatomical and physiological information derived from other imaging modalities to regularize the solution. In this approach the hyperparameters controlling the degree of regularization are estimated using restricted maximum likelihood (ReML). EEG data are always contaminated by noise, e.g., exogenous noise and background brain activity. The conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimization of the residuals induced by noise and the improbability of the estimates as determined by their priors. This balance is specified by hyperparameters that control the relative importance of fitting and conforming to prior constraints. Here we introduce a systematic approach to this regularization problem, in the context of a linear observation model we have described previously. In this model, basis functions are extracted to reduce the solution space a priori in the spatial and temporal domains. The basis sets are motivated by knowledge of the evoked EEG response and information theory. In this paper we focus on an iterative "expectationmaximization" procedure to jointly estimate the conditional expectation of the source distribution and the ReML hyperparameters on which this solution rests. We used simulated data mixed with real EEG noise to explore the behavior of the approach with various source locations, priors, and noise levels. The results enabled us to conclude: (i) Solutions in the space of informed basis functions have a high face and construct validity, in relation to conventional analyses. (ii) The hyperparameters controlling the degree of regularization vary largely with source geometry and noise. The second conclusion speaks to the usefulness of using adaptative ReML hyperparameter estimates. © 2002 Elsevier Science (USA)

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“…Considerable variation is found in the latencies and durations of the effective dipole moments obtained from analysis of MEG evoked response experiments, from 5 ms in a short latency median nerve response to stimulation [Jun et al, 2006] to a range from 10 to 100 ms in a visual stimulus experiment [Aine et al, 2000].…”

Section: Choice Of Parametersmentioning

confidence: 99%

Modeling direct effects of neural current on MRI

Heller

Barrowes

George

2007

Human Brain Mapping

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Abstract:We investigate the effect of the magnetic field generated by neural activity on the magnitude and phase of the MRI signal in terms of a phenomenological parameter with the dimensions of length; it involves the product of the strength and duration of these currents. We obtain an analytic approximation to the MRI signal when the neuromagnetically induced phase is small inside the MRI voxel. The phase shift is the average of the MRI phase over the voxel, and therefore first order in that phase; and the reduction in the signal magnitude is one half the square of the standard deviation of the MRI phase, which is second order. The analytic approximation is compared with numerical simulations. For weak currents the agreement is excellent, and the magnitude change is generally much smaller than the phase shift. Using MEG data as a weak constraint on the current strength we find that for a net dipole moment of 10 nAm, a typical value for an evoked response, the reduction in the magnitude of the MRI signal is two parts in 10 5 , and the maximum value of the overall phase shift is % 4 Á 10 À3 , obtained when the MRI voxel is displaced 2/3 the size of the neuronal activity. We also show signal changes over a large range of values of the net dipole moment. We compare these results with others in the literature.

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Multistart Algorithms for MEG Empirical Data Analysis Reliably Characterize Locations and Time Courses of Multiple Sources

Cited by 68 publications

References 33 publications

Aging: Compensation or maturation?

Aging: Compensation or maturation?

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

Modeling direct effects of neural current on MRI

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