MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.
Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3572141?pdf=render |
| _version_ | 1818562303769968640 |
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| author | Rasheda Arman Chowdhury Jean Marc Lina Eliane Kobayashi Christophe Grova |
| author_facet | Rasheda Arman Chowdhury Jean Marc Lina Eliane Kobayashi Christophe Grova |
| author_sort | Rasheda Arman Chowdhury |
| collection | DOAJ |
| description | Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered. |
| first_indexed | 2024-12-14T01:01:56Z |
| format | Article |
| id | doaj.art-01774f691ec14de7be418dfe519dc80f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T01:01:56Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-01774f691ec14de7be418dfe519dc80f2022-12-21T23:23:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0182e5596910.1371/journal.pone.0055969MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.Rasheda Arman ChowdhuryJean Marc LinaEliane KobayashiChristophe GrovaLocalizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.http://europepmc.org/articles/PMC3572141?pdf=render |
| spellingShingle | Rasheda Arman Chowdhury Jean Marc Lina Eliane Kobayashi Christophe Grova MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches. PLoS ONE |
| title | MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches. |
| title_full | MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches. |
| title_fullStr | MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches. |
| title_full_unstemmed | MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches. |
| title_short | MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches. |
| title_sort | meg source localization of spatially extended generators of epileptic activity comparing entropic and hierarchical bayesian approaches |
| url | http://europepmc.org/articles/PMC3572141?pdf=render |
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