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Steven M Stufflebeam

Publications and source records attributed to Steven M Stufflebeam.

6 recordsLinked to original sources
PubMed2006

Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates.

Cerebral currents responsible for the extra-cranially recorded magnetoencephalography (MEG) data can be estimated by applying a suitable source model. A popular choice is the distributed minimum-norm estimate (MNE) which minimizes the l2-norm of the estimated current. Under the l2-norm constraint, the current estimate is related to the measurements by a linear inverse operator. However, the MNE has a bias towards superficial sources, which can be reduced by applying depth weighting. We studied the effect of depth weighting in MNE using a shift metric. We assessed the localization performance of the depth-weighted MNE as well as depth-weighted noise-normalized MNE solutions under different cortical orientation constraints, source space densities, and signal-to-noise ratios (SNRs) in multiple subjects. We found that MNE with depth weighting parameter between 0.6 and 0.8 showed improved localization accuracy, reducing the mean displacement error from 12 mm to 7 mm. The noise-normalized MNE was insensitive to depth weighting. A similar investigation of EEG data indicated that depth weighting parameter between 2.0 and 5.0 resulted in an improved localization accuracy. The application of depth weighting to auditory and somatosensory experimental data illustrated the beneficial effect of depth weighting on the accuracy of spatiotemporal mapping of neuronal sources.

Adult↗
PubMed2005

Functional MRI using regularized parallel imaging acquisition.

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.

Brain Mapping↗
PubMed2005

Application of magnetoencephalography in epilepsy patients with widespread spike or slow-wave activity.

PURPOSE: To examine whether magnetoencephalography (MEG) can be used to determine patterns of brain activity underlying widespread paroxysms of epilepsy patients, thereby extending the applicability of MEG to a larger population of epilepsy patients. METHODS: We studied two children with symptomatic localization-related epilepsy. Case 1 had widespread spikes in EEG with an operation scar from a resection of a brain tumor; Case 2 had hemispheric slow-wave activity in EEG with sensory auras. MEG was collected with a 204-channel helmet-shaped sensor array. Dynamic statistical parametric maps (dSPMs) were constructed to estimate the cortical distribution of interictal discharges for these patients. Equivalent current dipoles (ECDs) also were calculated for comparison with the results of dSPM. RESULTS: In case 1 with widespread spikes, dSPM presented the major activity at the vicinity of the operation scar in the left frontal lobe at the peak of the spikes, and some activities were detected in the left temporal lobe just before the peak in some spikes. In case 2 with hemispheric slow waves, the most active area was located in the left parietal lobe, and additional activity was seen at the ipsilateral temporal and frontal lobes in dSPM. The source estimates correlated well with the ictal manifestation and interictal single-photon emission computed tomography (SPECT) findings for this patient. In comparison with the results of ECDs, ECDs could not express a prior activity at the left temporal lobe in case 1 and did not model well the MEG data in case 2. CONCLUSIONS: We suggest that by means of dSPM, MEG is useful for presurgical evaluation of patients, not only with localized epileptiform activity, but also with widespread spikes or slow waves, because it requires no selections of channels and no time-point selection.

Adolescent↗
PubMed2005

Dynamic statistical parametric mapping for analyzing the magnetoencephalographic epileptiform activity in patients with epilepsy.

Our current purpose is to evaluate the applicability of dynamic statistical parametric mapping, a novel method for localizing epileptiform activity recorded with magnetoencephalography in patients with epilepsy. We report four pediatric patients with focal epilepsies. Magnetoencephalographic data were collected with a 306-channel whole-head helmet-shaped sensor array. We calculated equivalent current dipoles and dynamic statistical parametric mapping movies of the interictal epileptiform discharges that were based in the minimum-L2 norm estimate, minimizing the square sum of the dipole element amplitudes. The dynamic statistical parametric mapping analysis of interictal epileptiform discharges can demonstrate the rapid change and propagation of interical epileptiform discharges. According to these findings, specific epileptogenic lesion-focal cortical dysplasia could be found and patients could be operated on successfully. The presurgical analysis of interictal epileptiform discharges using dynamic statistical parametric mapping seems to be promising in patients with a possible underlying focal cortical dysplasia and might help to guide the placement of invasive electrodes.

Adolescent↗
PubMed2004

Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain.

This paper presents a computationally efficient source estimation algorithm that localizes cortical oscillations and their phase relationships. The proposed method employs wavelet-transformed magnetoencephalography (MEG) data and uses anatomical MRI to constrain the current locations to the cortical mantle. In addition, the locations of the sources can be further confined with the help of functional MRI (fMRI) data. As a result, we obtain spatiotemporal maps of spectral power and phase relationships. As an example, we show how the phase locking value (PLV), that is, the trial-by-trial phase relationship between the stimulus and response, can be imaged on the cortex. We apply the method to spontaneous, evoked, and driven cortical oscillations measured with MEG. We test the method of combining MEG, structural MRI, and fMRI using simulated cortical oscillations along Heschl's gyrus (HG). We also analyze sustained auditory gamma-band neuromagnetic fields from MEG and fMRI measurements. Our results show that combining the MEG recording with fMRI improves source localization for the non-noise-normalized wavelet power. In contrast, noise-normalized spectral power or PLV localization may not benefit from the fMRI constraint. We show that if the thresholds are not properly chosen, noise-normalized spectral power or PLV estimates may contain false (phantom) sources, independent of the inclusion of the fMRI prior information. The proposed algorithm can be used for evoked MEG/EEG and block-designed or event-related fMRI paradigms, or for spontaneous MEG data sets. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain can provide further understanding of large-scale neural activity and communication between different brain regions.

Acoustic Stimulation↗