Analysis of mesial temporal seizure onset and propagation using the directed transfer function method

Franaszczuk, Piotr J.; Bergey, Gregory K.; Kamiński, Maciej

doi:10.1016/0013-4694(94)90163-5

Cited by 129 publications

(104 citation statements)

References 16 publications

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“…While it is difficult to keep all signals stationary during the evolvement of spontaneous EEGs, we select an ictal onset data segment by avoiding rapid activity pattern transitions in scalp EEG waveforms in order to maintain quasi-stationarity. A similar concept has been used effectively in ECoG studies by other researchers (Franaszczuk et al, 1994;Franaszczuk & Bergey, 1998). The present results also indicate that the system formed by epileptic sources can be efficiently and accurately modeled by MVAR.…”

Section: Mvar Modelingmentioning

confidence: 99%

“…The build-up process of the seizure occurrence can be observed after the ictal onset. In order for MVAR to sufficiently model ictal sources, the selected ictal onset segment must be quasi-stationary (Franaszczuk et al, 1994). The channel waveforms and TFR drawn in Fig.…”

Section: Ictal Source Analysis Protocolmentioning

confidence: 99%

“…The DTF technique estimates the causal interaction through multivariate autoregressive (MVAR) modeling (Franaszczuk et al, 1985). While the Granger causality has been successfully applied to data from intracranial recordings recently (Brovelli et al, 2004), the DTF technique has also been used to decide the onset and propagation of seizure activities with ECoG signals (Franaszczuk et al, 1994;Franaszczuk & Bergey, 1998). An advantage of performing DTF on intracranial recordings is that such signals are measured at near field locations close to the sources whereas EEG and MEG are measured at relatively far fields, which complicates the estimation of causal interactions with the volume conductor effect.…”

Section: Introductionmentioning

confidence: 99%

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Ictal source analysis: Localization and imaging of causal interactions in humans

et al. 2007

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We propose a new integrative approach to characterize the structure of seizures in the space, time, and frequency domains. Such characterization leads to a new technical development for ictal source analysis for the presurgical evaluation of epilepsy patients. The present new ictal source analysis method consists of three parts. First, a three-dimensional source scanning procedure is performed by a spatio-temporal FINE source localization method to locate the multiple sources responsible for the time evolving ictal rhythms at their onsets. Next, the dynamic behavior of the sources is modeled by a multivariate autoregressive process (MVAR). Lastly, the causal interaction patterns among the sources as a function of frequency are estimated from the MVAR modeling of the source temporal dynamics. The causal interaction patterns indicate the dynamic communications between sources, which are useful to distinguish the primary sources responsible for the ictal onset from the secondary sources caused by the ictal propagation. The present ictal analysis strategy has been applied to a number of seizures from five epilepsy patients, and their results are consistent with observations from either MRI lesions or SPECT scans, which indicate its effectiveness. Each step of the ictal source analysis is statistically evaluated in order to guarantee the confidence in the results.

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Section: Mvar Modelingmentioning

confidence: 99%

Section: Ictal Source Analysis Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ictal source analysis: Localization and imaging of causal interactions in humans

et al. 2007

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“…Several methods divide EEG electrodes [15], [16], MEG SQUIDS [3], or fMRI voxels [9] into disjoint hypothesisdriven ROIs and study coherences within or between ROIs. Other methods set out ROIs representing EEG electrodes [10], [17], MEG SQUIDS [5], or fMRI-ROIs [6] along rows and columns, thus obtaining a square contingency table. By arranging ROIs along rows and columns of a matrix, the spatial relations are lost.…”

mentioning

confidence: 99%

Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Caat

Maurits

Roerdink

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-A typical data-driven visualization of electroencephalography (EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, we define a functional unit (FU) as a data-driven region of interest (ROI). An FU is a spatially connected set of electrodes recording pairwise significantly coherent signals, represented in the coherence graph by a spatially connected clique. Earlier we presented two methods to detect FUs, a maximal clique based (MCB) method (time complexity O(3 n/3 ), with n the number of vertices) and a more efficient watershed based (WB) method (time complexity O(n 2 log n)). To reduce the potential over-segmentation of the WB method, we introduce an improved watershed based (IWB) method (time complexity O(n 2 log n)). The IWB method merges basins representing FUs during the segmentation if they are spatially connected and if their union is a clique. The WB and IWB method both are up to a factor of 100,000 faster than the MCB method for a typical multichannel setting with 128 EEG channels, thus making interactive visualization of multichannel EEG coherence possible. Results show that, considering the MCB method as the gold standard, the difference between IWB and MCB FU maps is smaller than between WB and MCB FU maps. We also introduce two novel group maps for data-driven group analysis as extensions of the IWB method. First, the group mean coherence map preserves dominant features from a collection of individual FU maps. Second, the group FU size map visualizes the average FU size per electrode across a collection of individual FU maps. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data-driven group analysis. Results, in accordance with conventional findings, indicate differences in EEG coherence between younger and older adults. However, they also suggest that an initial selection of hypothesis-driven ROIs could be extended with additional datadriven ROIs.

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“…Autoregressive modeling (Gath et al, 1992;Franaszczuk et al, 1994), time-frequency analysis (Franaszczuk et al, 1998;Osorio et al, 1998), wavelet analysis (for a review see Unser and Aldroubi, 1996), and methods related to non-linear dynamics (Pijn et al, 1991;Bullmore et al, 1994;Lehnertz and Elger, 1995) have been used for the analysis of seizure EEG in intracranial, surface, or combined intracranial and surface recordings.…”

Section: Introductionmentioning

confidence: 99%

Space-oriented segmentation and 3-dimensional source reconstruction of ictal EEG patterns

Lantz

Michel

Seeck

et al. 2001

Clinical Neurophysiology

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Objectives: Characterization of the EEG pattern during the early phase of a seizure is crucial for identifying the epileptic focus. The purpose of the present investigation was to evaluate a method that divides ictal EEG activity into segments of relatively constant surface voltage distribution, and to provide a 3-dimensional localization of the activity during the different segments.Methods: For each timepoint the electrical voltage distribution on the scalp (the voltage map) was determined from the digitized EEG recording. Through a spatial cluster analysis time sequences where the maps did not change much (segments) were identi®ed, and a 3-dimensional source reconstruction of the activity corresponding to the different mean maps was performed using a distributed linear inverse solution algorithm.Results: Segments dominating early in seizure development were identi®ed, and source reconstruction of the EEG activity corresponding to the maps of these segments yielded results which were consistent with the results from invasive recordings. In some cases a sequence of consecutive segments was obtained, which might re¯ect ictal propagation.Conclusions: Segmentation of ictal EEG with subsequent 3-dimensional source reconstruction is a useful method to non-invasively determine the initiation and perhaps also the spread of epileptiform activity in patients with epileptic seizures. q

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Analysis of mesial temporal seizure onset and propagation using the directed transfer function method

Cited by 129 publications

References 16 publications

Ictal source analysis: Localization and imaging of causal interactions in humans

Ictal source analysis: Localization and imaging of causal interactions in humans

Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Space-oriented segmentation and 3-dimensional source reconstruction of ictal EEG patterns

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