sLORETA allows reliable distributed source reconstruction based on subdural strip and grid recordings

Dümpelmann, Matthias; Ball, Tonio; Schulze‐Bonhage, Andreas

doi:10.1002/hbm.21276

Cited by 63 publications

(48 citation statements)

References 35 publications

(55 reference statements)

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“…S12 (supplementary materials) shows the IRES solutions in this data set. Comparing these results with the clinical findings reported in (Dümpelmann et al, 2012), the obtained results are in well accordance with such findings. Additionally the source localization is performed using the cMEM algorithm (Grova et al, 2006) on another Brainstorm tutorial (http://neuroimage.usc.edu/brainstorm/Tutorials/EpilepsyBest?highlight=%28cMEM%29).…”

Section: Resultssupporting

confidence: 92%

“…This tutorial includes the anonymous data of a patient suffering from focal fronto-parietal epilepsy, who underwent surgery and is seizure-free within a 5 year follow-up duration (http://neuroimage.usc.edu/brainstorm/Tutorials/Epilepsy). The data in this tutorial were originally analyzed and published in a paper by Dümpelmann et al (Dümpelmann et al, 2012). The patient underwent iEEG recording prior to surgery.…”

Section: Resultsmentioning

confidence: 99%

“…The patient underwent iEEG recording prior to surgery. The iEEG study results as well as the post-operational MRI are not available in the data set but are presented in the published paper (Dümpelmann et al, 2012). The procedure outlined in the Brainstorm tutorial was followed to get the average spikes and the head model, with the exception of the head model conductivity (ratio) for which we used the conductivities we have used so far, throughout the paper.…”

Section: Resultsmentioning

confidence: 99%

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Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Sohrabpour

Worrell

et al. 2016

NeuroImage

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Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source.

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Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Sohrabpour

Worrell

et al. 2016

NeuroImage

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show abstract

“…It is assumed that the ECoG allows a much better analysis of the sources than the EEG, because of the elimination of the smearing effect of the skull bones and because of a much higher signal to noise ratio (SNR). In the recent literature, rather few source localization attempts based on ECoG analysis were published (Gharib et al, 1995;Zhang et al, 2008;Dümpelmann et al, 2009Dümpelmann et al, , 2012Ramantani et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluating dipolar source localization feasibility from intracerebral SEEG recordings

Caune

Ranta²,

Cam³

et al. 2014

NeuroImage

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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 approach 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 sources investigation and might be used as a complement to routine visual interpretations.

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“…Auch die Analyse von Propagationsphänomenen könnte so verbessert werden. Erste Ansätze der Modellierung der Daten aus invasiven Elektroden mit "standardized low-resolution electromagnetic tomography" (sLORE-TA, [16]) oder im Modell eines komplexen neuronalen Netzes sind vielversprechend ( [13,14]). …”

Section: Kombination Mit Fmrt Und Anwendung Im Tiefen-eegunclassified

Klinischer Stellenwert der Quellenanalyse epileptischer Aktivität

Bast

2012

Z. Epileptol.

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Die Quellenanalysen interiktaler und iktaler epilepsietypischer Aktivität in MEG und EEG sind nichtinvasive Verfahren, die zunehmend in der prächir urgischen Epilepsiediagnostik eingesetzt werden. Sie bedienen sich verschiedener inverser Modelle, auf die in der Zeitschrift für Epileptologie in einem separaten Beitrag eingegangen wurde [4]. Gegenüber allen anderen nichtinvasiven Verfahren der Epilepsiediagnostik haben MEG und EEG den Vorteil einer sonst unerreicht hohen zeitlichen bei einer befriedigenden räumlichen Auflösung. Quellenanalyse im MEG Diagnostischer EinsatzDie MEG-Quellenanalyse ist mittlerweile als ergänzende nichtinvasive Methode im Rahmen der prächirurgischen Epilepsiediagnostik etabliert und wurde 1997 durch die amerikanische Gesundheitsbehörde Food and Drug Administration (FDA) als klinische Methode in Kombination mit struktureller Bildgebung anerkannt. Im Jahr 2003 erhielt sie in den USA eine Codenummer der "current procedural terminology" (CPT) für Epilepsielokalisation und prächirurgisches "brain mapping". Viele Epilepsiezentren können das MEG allerdings nicht nutzen, da seine Verfügbarkeit nicht zuletzt wegen der hohen Kosten beschränkt ist.Zahlreiche Studien konnten die diagnostische Wertigkeit des MEG belegen [7,9,18,30,31,42,43,46,48,50,52,57,59]. Das MEG kann helfen, bei tuberöser Sklerose epileptogene Tuber zu identifizieren ([26, 27, 64]; . Abb. 1). Mit dem MEG konnte die intrinsische Epileptogenität von fokalen kortikalen Dysplasien ([7, 24, 45, 63]; . Abb. 2) und Polymikrogyrien [8,11] gezeigt werden. Die MEGQuellenanalyse wurde bei Kavernomen [58], arteriovenösen Malformationen [44] und Gliomen [49] angewendet. Lokalisationsgenauigkeit Stefan et al. [57] berichteten über die bislang größte Gruppe von 455 Patienten, die systematisch mithilfe des MEG in der prä-chirurgischen Diagnostik abgeklärt wurden. In 70% der Fälle zeigte sich eine epilepsietypische Aktivität. Bei 35% der Patienten erbrachte das MEG zusätzliche Befunde, die bei 10% aller Patienten für die spätere Operationsentscheidung maß-gebend waren. Dieselbe Arbeitsgruppe untersuchte prospektiv den Nutzen des MEG zusätzlich zum Routine-Langzeit-EEG [50]. Das MEG lokalisierte in 72% der Fälle in den korrekten Lappen und war damit dem iktalen EEG vergleichbar, während das interiktale EEG nur in 60% der Fälle korrekt lokalisierte. Allerdings wurde für das EEG lediglich eine visuelle Auswertung vorgenommen. Bei 11 von 25 Patienten mit nichtkonklusiven EEGBefunden fand sich eine MEG-Dipol-Lokalisation im später resezierten Lappen; zehn dieser Patienten wurden nach Resektion anfallsfrei. In einer Studie von Pataraia et al. [48], die 82 Patienten umfasste, erbrachte das MEG in 40% eine zusätz-liche lokalisatorische Information. Das interiktale MEG war dabei mit einer Lokalisationsrate von 70% innerhalb des spä-ter resezierten Areals besser als das Langzeit-EEG, das nur in 40% der Fälle konkordant war. Knowlton et al. [31] analysierten bei 72 Patienten die Lokalisationsgenauigkeit verschiedener nichtinvasiver Verfahren [iktale Single...

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sLORETA allows reliable distributed source reconstruction based on subdural strip and grid recordings

Cited by 63 publications

References 35 publications

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Evaluating dipolar source localization feasibility from intracerebral SEEG recordings

Klinischer Stellenwert der Quellenanalyse epileptischer Aktivität

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