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Neuroimage ; 98: 118-33, 2014 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-24795155

RESUMO

Stereo-electroencephalography (SEEG) is considered as the golden standard for exploring targeted structures during pre-surgical evaluation in drug-resistant partial epilepsy. The depth electrodes, inserted in the brain, consist of several collinear measuring contacts (sensors). Clinical routine analysis of SEEG signals is performed on bipolar montage, providing a focal view of the explored structures, thus eliminating activities of distant sources that propagate through the brain volume. We propose in this paper to exploit the common reference SEEG signals. In this case, the volume propagation information is preserved and electrical source localization (ESL) approaches can be proposed. Current ESL approaches used to localize and estimate the activity of the neural generators are mainly based on surface EEG/MEG signals, but very few studies exist on real SEEG recordings, and the case of equivalent current dipole source localization has not been explored yet in this context. In this study, we investigate the influence of volume conduction model, spatial configuration of SEEG sensors and level of noise on the ESL accuracy, using a realistic simulation setup. Localizations on real SEEG signals recorded during intracerebral electrical stimulations (ICS, known sources) as well as on epileptic interictal spikes are carried out. Our results show that, under certain conditions, a straightforward approach based on an equivalent current dipole model for the source and on simple analytical volume conduction models yields sufficiently precise solutions (below 10mm) of the localization problem. Thus, electrical source imaging using SEEG signals is a promising tool for distant brain source investigation and might be used as a complement to routine visual interpretations.


Assuntos
Córtex Cerebral/fisiologia , Eletroencefalografia/métodos , Adulto , Córtex Cerebral/fisiopatologia , Estimulação Elétrica , Fenômenos Eletrofisiológicos , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Modelos Neurológicos , Convulsões/fisiopatologia
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