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Gregory L Barkley

Publications and source records attributed to Gregory L Barkley.

5 recordsLinked to original sources
PubMed2004

Controversies in neurophysiology. MEG is superior to EEG in localization of interictal epileptiform activity: Pro.

UNLABELLED: Both EEG and magnetoencephalography (MEG), with a time resolution of 1 ms or less, provide unique neurophysiologic data not obtainable by other neuroimaging techniques. MEG and EEG have often been compared to each other now although the two are complementary. Now that MEG has emerged as a mature clinical technology, it is worthwhile to compare the relative strengths of each for the localization of interictal epileptiform activity and to describe the strengths of MEG relative to EEG in the localization of interictal epileptiform activity. The sources of MEG and EEG signals will first be reviewed. Issues relevant to solving the forward problem and the inverse problem in MEG and EEG will be addressed followed by a comparison of research concerning the detection and localization of interictal epileptiform activity by MEG and EEG. The emphasis will be upon techniques and software routinely used in clinical applications but some emerging areas of MEG research which are entering clinical practice will also be reviewed. SIGNIFICANCE: MEG is a new noninvasive neurophysiologic technique which provides unique information for the clinical evaluation of patients with epilepsy, revealing aspects of neuronal function that previously could only be obtained by invasive EEG monitoring, and giving a new window for research of neuronal activity.

Cerebral Cortex↗
PubMed2003

Magnetoencephalographic validation parameters for clinical evaluation of interictal epileptic activity.

The authors demonstrate that the confidence volume (the spatial volume that encompasses the 95% probability of source localization) of the single equivalent current dipole is helpful in validating magnetoencephalographic epileptic spike mapping. Such mapping involves distinguishing spikes from other neuronal events. The usual criteria for validating dipole fit reliability involve four parameters-correlation coefficient (R > or =0.98), goodness of fit (> or =0.95), root mean square magnetic field value (>400 fT), and dipole moment (Q value > 200 nAm)-but other parameters (direction of dipole moment, location of dipole, and confidence volume) can be considered. In 21 patients with epilepsy, the average correlation coefficient for 608 epileptic spikes was 0.99; average goodness of fit, 0.98; average root mean square, 1,198 fT; and the average Q value, 370 nAm. The mean average confidence volume was 0.30 +/- 0.27 cm3. Correlation coefficient values for quiet brain activity were less than 0.90; goodness of fit values, less than 0.85; and confidence volumes were large (>5 cm3); and for noise runs (no subject) they were even larger (>100 cm3), although correlation coefficient values were more than 0.80 and goodness of fit values were more than 0.85. Confidence volumes for noise data are large-for background brain activity even larger-but confidence volumes for epileptic spikes are small. Confidence volume, in conjunction with other parameters, may be a robust parameter for spike selection.

Algorithms↗
PubMedDate not supplied

MEG and EEG in epilepsy.

Both EEG and magnetoencephalogram (MEG), with a time resolution of 1 ms or less, provide unique neurophysiologic data not obtainable by other neuroimaging techniques. MEG has now emerged as a mature clinical technology. While both EEG and MEG can be performed with more than 100 channels, MEG recordings with 100 to 300 channels are more easily done because of the time needed to apply a large number of EEG electrodes. EEG has the advantage of the long-term video EEG recordings, which facilitates extensive temporal sampling across all periods of the sleep/wake cycle. MEG and EEG seem to complement each other for the detection of interictal epileptiform discharges, because some spikes can be recorded only on MEG but not on EEG and vice versa. Most studies indicate that MEG seems to be more sensitive for neocortical spike sources. Both EEG and MEG source localizations show excellent agreement with invasive electrical recordings, clarify the spatial relationship between the irritative zone and structural lesions, and finally, attribute epileptic activity to lobar subcompartments in temporal lobe and to a lesser extent in extratemporal epilepsies. In temporal lobe epilepsy, EEG and MEG can differentiate between patients with mesial, lateral, and diffuse seizure onsets. MEG selectively detects tangential sources. EEG measures both radial and tangential activity, although the radial components dominate the EEG signals at the scalp. Thus, while EEG provides more comprehensive information, it is more complicated to model due to considerable influences of the shape and conductivity of the volume conductor. Dipole localization techniques favor MEG due to the higher accuracy of MEG source localization compared to EEG when using the standard spherical head shape model. However, if special care is taken to address the above issues and enhance the EEG, the localization accuracy of EEG and MEG actually are comparable, although these surface EEG analytic techniques are not typically approved for clinical use in the United States. MEG dipole analysis is approved for clinical use and thus gives information that otherwise usually requires invasive intracranial EEG monitoring. There are only a few dozen whole head MEG units in operation in the world. While EEG is available in every hospital, specialized EEG laboratories capable of source localization techniques are nearly as scarce as MEG facilities. The combined use of whole-head MEG systems and multichannel EEG in conjunction with advanced source modeling techniques is an area of active development and will allow a better noninvasive characterization of the irritative zone in presurgical epilepsy evaluation. Finally, additional information on epilepsy may be gathered by either MEG or EEG analysis of data beyond the usual bandwidths used in clinical practice, namely by analysis of activity at high frequencies and near-DC activity.

Brain↗