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Igor Podgorny

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YNIMG Journal 2007 Journal Article

A novel integrated MEG and EEG analysis method for dipolar sources

  • Ming-Xiong Huang
  • Tao Song
  • Donald J. Hagler
  • Igor Podgorny
  • Veikko Jousmaki
  • Li Cui
  • Kathleen Gaa
  • Deborah L. Harrington

The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEG's insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.

YNIMG Journal 2006 Journal Article

Vector-based spatial–temporal minimum L1-norm solution for MEG

  • Ming-Xiong Huang
  • Anders M. Dale
  • Tao Song
  • Eric Halgren
  • Deborah L. Harrington
  • Igor Podgorny
  • Jose M. Canive
  • Stephen Lewis

Minimum L1-norm solutions have been used by many investigators to analyze MEG responses because they provide high spatial resolution images. However, conventional minimum L1-norm approaches suffer from instability in spatial construction, and poor smoothness of the reconstructed source time-courses. Activity commonly “jumps” from one grid point to (usually) the neighboring grid points. Equivalently, the time-course of one specific grid point can show substantial “spiky-looking” discontinuity. In the present study, we present a new vector-based spatial–temporal analysis using a L1-minimum-norm (VESTAL). This approach is based on a principle of MEG physics: the magnetic waveforms in sensor-space are linear functions of the source time-courses in the imaging-space. Our computer simulations showed that VESTAL provides good reconstruction of the source amplitude and orientation, with high stability and resolution in both the spatial and temporal domains. “Spiky-looking” discontinuity was not observed in the source time-courses. Importantly, the simulations also showed that VESTAL can resolve sources that are 100% correlated. We then examined the performance of VESTAL in the analysis of human median-nerve MEG responses. The results demonstrated that this method easily distinguishes sources very spatially close to each other, including individual primary somatosensory areas (BA 1, 2, 3b), primary motor area (BA 4), and other regions in the somatosensory system (e. g. , BA 5, 7, SII, SMA, and temporal–parietal junction) with high temporal stability and resolution. VESTAL's potential for obtaining information on source extent was also examined.

v2026.09.13