Neural mass model parameter identification for MEG/EEG

Jan Kybic; Olivier Faugeras; Maureen Clerc; Théo Papadopoulo

doi:10.1117/12.709146

29 March 2007 Neural mass model parameter identification for MEG/EEG

Jan Kybic, Olivier Faugeras, Maureen Clerc, Théo Papadopoulo

Proceedings Volume 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images; 65110F (2007) https://doi.org/10.1117/12.709146
Event: Medical Imaging, 2007, San Diego, CA, United States

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.

Citation Download Citation

Jan Kybic, Olivier Faugeras, Maureen Clerc, and Théo Papadopoulo "Neural mass model parameter identification for MEG/EEG", Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65110F (29 March 2007); https://doi.org/10.1117/12.709146

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KEYWORDS

Data modeling

Magnetoencephalography

Magnetic resonance imaging

Sensors

Electroencephalography

Brain

Functional magnetic resonance imaging

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