Source localization of ictal epileptic activity investigated by high resolution EEG and validated by SEEG

Koessler, Laurent; Bénar, Christian; Maillard, Louis; Badier, Jean‐Michel; Vignal, Jean Pierre; Bartolomei, Fabricē; Chauvel, Patrick; Gavaret, Martine

doi:10.1016/j.neuroimage.2010.02.067

Cited by 110 publications

(119 citation statements)

References 53 publications

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“…In drug-resistant epilepsy investigations, it is a suitable method to localize the epileptogenic zone that is currently delineated by invasive recordings (stereoelectroencephalography or electrocorticography). 16,22 The accuracy of brain anatomical localizations depends on the number and the accuracy of localization of scalp-surface sensors. 25 Brain sources imaging is obtained by the co-registration of multimodal data: both functional (electroencephalography-EEG, magnetoencephalography-MEG, near-infrared spectroscopy-NIRS…) and anatomical (magnetic resonance imaging-MRI, CT scan…).…”

Section: Introductionmentioning

confidence: 99%

EEG–MRI Co-registration and Sensor Labeling Using a 3D Laser Scanner

Koessler

Cecchin²,

Caspary³

et al. 2010

Ann Biomed Eng

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This paper deals with the co-registration of an MRI scan with EEG sensors. We set out to evaluate the effectiveness of a 3D handheld laser scanner, a device that is not widely used for co-registration, applying a semi-automatic procedure that also labels EEG sensors. The scanner acquired the sensors' positions and the face shape, and the scalp mesh was obtained from the MRI scan. A pre-alignment step, using the position of three fiducial landmarks, provided an initial value for co-registration, and the sensors were automatically labeled. Co-registration was then performed using an iterative closest point algorithm applied to the face shape. The procedure was conducted on five subjects with two scans of EEG sensors and one MRI scan each. The mean time for the digitization of the 64 sensors and three landmarks was 53 s. The average scanning time for the face shape was 2 min 6 s for an average number of 5,263 points. The mean residual error of the sensors co-registration was 2.11 mm. These results suggest that the laser scanner associated with an efficient co-registration and sensor labeling algorithm is sufficiently accurate, fast and user-friendly for longitudinal and retrospective brain sources imaging studies.

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Section: Introductionmentioning

confidence: 99%

EEG–MRI Co-registration and Sensor Labeling Using a 3D Laser Scanner

Koessler

Cecchin²,

Caspary³

et al. 2010

Ann Biomed Eng

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“…1. Input MRI and brain surface to the software , they can be conducted using other open softwares 16,17 and commercial products 18,19 . Also, besides sLORETA 13 , other inverse solutions such as multiple dipole models and Beamformer can be applied 4 .…”

mentioning

confidence: 99%

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Bae

Deshmukh

Song

et al. 2015

JoVE

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Electroencephalogram (EEG) has been traditionally used to determine which brain regions are the most likely candidates for resection in patients with focal epilepsy. This methodology relies on the assumption that seizures originate from the same regions of the brain from which interictal epileptiform discharges (IEDs) emerge. Preclinical models are very useful to find correlates between IED locations and the actual regions underlying seizure initiation in focal epilepsy. Rats have been commonly used in preclinical studies of epilepsy 1 ; hence, there exist a large variety of models for focal epilepsy in this particular species. However, it is challenging to record multichannel EEG and to perform brain source imaging in such a small animal. To overcome this issue, we combine a patented-technology to obtain 32-channel EEG recordings from rodents 2 and an MRI probabilistic atlas for brain anatomical structures in Wistar rats to perform brain source imaging. In this video, we introduce the procedures to acquire multichannel EEG from Wistar rats with focal cortical dysplasia, and describe the steps both to define the volume conductor model from the MRI atlas and to uniquely determine the IEDs. Finally, we validate the whole methodology by obtaining brain source images of IEDs and compare them with those obtained at different time frames during the seizure onset.

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“…Für die meisten epileptologischen Fragestellungen sind diskrete Dipolmodelle ausreichend, wobei überlap-pende Aktivitäten aufgrund von Propagationen berücksichtigt werden sollten (multiple Quellenanalyse). Bei einer vergleichenden Studie zur iktalen Quellenanalyse in EEG und Stereo-EEG wurden verschiedene Modelle eingesetzt, und es zeigten sich für einfache Dipollösungen bessere Ergebnisse, als für MUSIC, LO-RETA und sLORETA [21]. Eine Kombination verschiedener Verfahren mit gemeinsamer Interpretation der Ergebnisse ist generell zu empfehlen.…”

Section: Inverses Problemunclassified

“…Die Ableitung von Anfällen gelingt im MEG im Gegensatz zum Langzeit-Video-EEG nur vereinzelt [47]. Dipolanalysen früher iktaler Muster im Oberflächen-EEG erbrachten vielversprechende Ergebnisse, auf die hier nicht im Detail eingegangen werden soll [4,21]. Meist wurden dabei Dipolanalysen oder verteilte Quellenmodelle für gemittelte rhythmische Anfallsmuster berechnet und mit invasiver Diagnostik oder Outcome nach Operation verglichen.…”

Section: Interiktale Und Iktale Quellenanalyseunclassified

Quellenanalyse epileptischer Aktivität

Bast

2011

Z. Epileptol.

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Source localization of ictal epileptic activity investigated by high resolution EEG and validated by SEEG

Cited by 110 publications

References 53 publications

EEG–MRI Co-registration and Sensor Labeling Using a 3D Laser Scanner

EEG–MRI Co-registration and Sensor Labeling Using a 3D Laser Scanner

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Quellenanalyse epileptischer Aktivität

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