title: The 3D topography of MEG source localization accuracy: effects of conductor model and noise.
abstract: OBJECTIVE
  To evaluate the effect that different head conductor models have on the source estimation accuracy of magnetoencephalography (MEG) under realistic conditions.
  
  
  METHODS
  Magnetic fields evoked by current dipoles were simulated using a highly refined 3-layer realistically shaped conductor model. Noise from a real MEG measurement was added to the simulated fields. Source parameters (location, strength, orientation) were estimated from the noisy signals using 3 spherically symmetric models and several one- and 3-layer realistically shaped boundary-element models. The effect of different measurement sensors (gradiometers, magnetometers) was also tested.
  
  
  RESULTS
  The noise typically present in brain signals masked the errors due to the different conductor models so that in most situations the models gave comparable results. Active cortical areas around the vertex and in the temporal, frontoparietal, and occipital regions were typically found with 2-4 mm accuracy, whereas source localization in several anterior frontal lobe and deep brain structures yielded errors exceeding 2 cm. Localization in anterior frontal regions may benefit most from the use of realistically shaped models.
  
  
  CONCLUSIONS
  The traditionally used sphere model is an adequate model for most research purposes. Any means that increase the signal-to-noise ratio are of highest importance in attempting to improve the source estimation accuracy.
mesh_terms: D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D003198:Computer Simulation; D004553:Electric Conductivity; D004574:Electromagnetic Fields; D005071:Evoked Potentials / Q000502:physiology*; D006257:Head; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D009949:Orientation; D015203:Reproducibility of Results; D012535:Scalp / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D012886:Skull / Q000502:physiology