A simulation study of the error in dipole source localization for EEG spikes with a realistic head model

Clin Neurophysiol. 2003 Jun;114(6):1069-78. doi: 10.1016/s1388-2457(03)00064-6.

Abstract

Objective: We tried to determine the error range of dipole modeling for EEG spikes originating from various clinically important sources by a simulation study employing a realistic head model. The computed error range was also compared with the degree of disturbance of dipole modeling caused by adding background activity to the spike.

Methods: The scalp fields generated by temporal, frontal and rolandic epileptic sources with spatial extent were simulated, and the corresponding 3-dimensional maps of residual variance (RV) were built by computing the RV for a single dipole at each point on a fine imaginary grid in the brain. Single dipole modeling was also performed for the simulated scalp fields after adding real background activity.

Results: The brain volume corresponding to a small RV was compact for the frontal sources and the lateral and baso-mesial temporal sources, and large for the anterior and baso-lateral temporal sources. The distribution of dipoles estimated for spikes contaminated with background corresponded to that of the volume of small RV and to spike-amplitude. Estimates were improved by employing inferior temporal electrodes.

Conclusions: When evaluating dipole models of epileptic spikes, error ranges can be estimated and they vary considerably from region to region.

Significance: This study illustrates the variability of the error in dipole modeling of epileptic spikes. This variability is important when considering the clinical interpretation of modeling results.

Publication types

  • Comparative Study

MeSH terms

  • Brain Mapping*
  • Cerebral Cortex / anatomy & histology
  • Cerebral Cortex / physiology*
  • Computer Simulation*
  • Electrodes, Implanted
  • Electroencephalography*
  • Evoked Potentials / physiology
  • Humans
  • Magnetic Resonance Imaging
  • Models, Neurological*
  • Reproducibility of Results
  • Scalp
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Stochastic Processes