
Proceedings Paper
Neural mass model parameter identification for MEG/EEGFormat | Member Price | Non-Member Price |
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Paper Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) have excellent time resolution. However,
the poor spatial resolution and small number of sensors do not permit to reconstruct a general spatial activation
pattern. Moreover, the low signal to noise ratio (SNR) makes accurate reconstruction of a time course also
challenging. We therefore propose to use constrained reconstruction, modeling the relevant part of the brain
using a neural mass model: There is a small number of zones that are considered as entities, neurons within a zone
are assumed to be activated simultaneously. The location and spatial extend of the zones as well as the interzonal
connection pattern can be determined from functional MRI (fMRI), diffusion tensor MRI (DTMRI), and
other anatomical and brain mapping observation techniques. The observation model is linear, its deterministic
part is known from EEG/MEG forward modeling, the statistics of the stochastic part can be estimated. The
dynamics of the neural model is described by a moderate number of parameters that can be estimated from the
recorded EEG/MEG data. We explicitly model the long-distance communication delays. Our parameters have
physiological meaning and their plausible range is known. Since the problem is highly nonlinear, a quasi-Newton
optimization method with random sampling and automatic success evaluation is used. The actual connection
topology can be identified from several possibilities. The method was tested on synthetic data as well as on true
MEG somatosensory-evoked field (SEF) data.
Paper Details
Date Published: 29 March 2007
PDF: 9 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65110F (29 March 2007); doi: 10.1117/12.709146
Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)
PDF: 9 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65110F (29 March 2007); doi: 10.1117/12.709146
Show Author Affiliations
Jan Kybic, Czech Technical Univ. (Czech Republic)
Olivier Faugeras, Odyssée Lab.-ENPC/ENS/INRIA (France)
Olivier Faugeras, Odyssée Lab.-ENPC/ENS/INRIA (France)
Maureen Clerc, Odyssée Lab.-ENPC/ENS/INRIA (France)
Théo Papadopoulo, Odyssée Lab.-ENPC/ENS/INRIA (France)
Théo Papadopoulo, Odyssée Lab.-ENPC/ENS/INRIA (France)
Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)
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