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Neuroimage. 2014 Oct 15;100:715-24. doi: 10.1016/j.neuroimage.2014.06.076. Epub 2014 Jul 8.

Multiple sparse volumetric priors for distributed EEG source reconstruction.

Author information

1
Ghent University - iMinds, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB floor 5, 9000, Ghent, Belgium; iMinds Medical IT Department, Belgium. Electronic address: gregor.strobbe@ugent.be.
2
Ghent University - iMinds, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB floor 5, 9000, Ghent, Belgium; iMinds Medical IT Department, Belgium. Electronic address: pieter.vanmierlo@ugent.be.
3
University of Oldenburg, Methods in Neurocognitive Psychology, Department of Psychology, 26111 Oldenburg, Germany; University of Oldenburg, Research Center Neurosensory Science, 26111 Oldenburg, Germany; University of Oldenburg, Cluster of Excellence Hearing4all, 26111 Oldenburg, Germany. Electronic address: maarten.de.vos@uni-oldenburg.de.
4
KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium; iMinds Medical IT Department, Belgium. Electronic address: bogdan.mijovic@esat.kuleuven.be.
5
Catholic University College of Bruges-Ostend, Faculty of Engineering Technology, Electronics/ICT, Zeedijk 101, 8400, Ostend, Belgium. Electronic address: hans.hallez@kuleuven.be.
6
KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium; iMinds Medical IT Department, Belgium. Electronic address: sabine.vanhuffel@esat.kuleuven.be.
7
SISTEMIC, Department of Electronic Engineering, Universidad de Antioquia UDEA, Calle 70 No. 52-21, Medellín, Colombia. Electronic address: josedavid@udea.edu.co.
8
Ghent University - iMinds, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB floor 5, 9000, Ghent, Belgium. Electronic address: stefaan.vandenberghe@ugent.be.

Abstract

We revisit the multiple sparse priors (MSP) algorithm implemented in the statistical parametric mapping software (SPM) for distributed EEG source reconstruction (Friston et al., 2008). In the present implementation, multiple cortical patches are introduced as source priors based on a dipole source space restricted to a cortical surface mesh. In this note, we present a technique to construct volumetric cortical regions to introduce as source priors by restricting the dipole source space to a segmented gray matter layer and using a region growing approach. This extension allows to reconstruct brain structures besides the cortical surface and facilitates the use of more realistic volumetric head models including more layers, such as cerebrospinal fluid (CSF), compared to the standard 3-layered scalp-skull-brain head models. We illustrated the technique with ERP data and anatomical MR images in 12 subjects. Based on the segmented gray matter for each of the subjects, cortical regions were created and introduced as source priors for MSP-inversion assuming two types of head models. The standard 3-layered scalp-skull-brain head models and extended 4-layered head models including CSF. We compared these models with the current implementation by assessing the free energy corresponding with each of the reconstructions using Bayesian model selection for group studies. Strong evidence was found in favor of the volumetric MSP approach compared to the MSP approach based on cortical patches for both types of head models. Overall, the strongest evidence was found in favor of the volumetric MSP reconstructions based on the extended head models including CSF. These results were verified by comparing the reconstructed activity. The use of volumetric cortical regions as source priors is a useful complement to the present implementation as it allows to introduce more complex head models and volumetric source priors in future studies.

KEYWORDS:

Bayesian model comparison; Electroencephalography; Multiple sparse priors; Statistical Parametric Mapping; Volumetric sparse priors

[Indexed for MEDLINE]

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