Simultaneous intracranial EEG and fMRI of interictal epileptic discharges in humans

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

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Corrigendum to Simultaneous intracranial EEG fMRI of interictal epileptic discharges in humans [NeuroImage 54/1 (2011) 182–190]

NeuroImage ◽

10.1016/j.neuroimage.2010.12.061 ◽

2011 ◽

Vol 55 (2) ◽

pp. 844

Author(s):

Serge Vulliemoz ◽

David W. Carmichael ◽

Karin Rosenkranz ◽

Beate Diehl ◽

Roman Rodionov ◽

...

Keyword(s):

Intracranial Eeg ◽

Epileptic Discharges

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The spike onset zone

Neurology ◽

10.1212/wnl.0000000000005998 ◽

2018 ◽

Vol 91 (7) ◽

pp. e666-e674 ◽

Cited By ~ 16

Author(s):

Hui Ming Khoo ◽

Nicolás von Ellenrieder ◽

Natalja Zazubovits ◽

Daniel He ◽

François Dubeau ◽

...

Keyword(s):

Gray Matter ◽

Hemodynamic Response ◽

Main Channel ◽

Noninvasive Method ◽

Seizure Onset ◽

Median Latency ◽

Intracranial Eeg ◽

Remote Locations ◽

Seizure Onset Zone ◽

Epileptic Discharges

ObjectiveTo determine whether the maximum hemodynamic response to scalp interictal epileptic discharges (IEDs) corresponds to the region where IEDs originate and from where they propagate.MethodsWe studied 19 patients who underwent first an EEG-fMRI showing responses in the gray matter, and then intracranial EEG (iEEG). We coregistered the hemodynamic responses to the iEEG electrode contacts and analyzed IEDs in the iEEG channel adjacent to a maximum response (labeled the main channel), in relation to IEDs in other channels during a widespread intracranial IED event. IEDs in the main channel were aligned at their peak, and IEDs in each channel were averaged time-locked to these instants. The beginning and peak of IEDs in the averaged trace were identified, blinded to the identity of the main channel. The latency of IEDs was computed between the earliest and all other channels.ResultsThe median latency of IEDs in the main channel was significantly smaller than in other channels for either the peak (15.5 vs 67.5 milliseconds, p = 0.00037) or the beginning (46.5 vs 118.4 milliseconds, p = 0.000048). The latency of IED was significantly correlated to the distance from the maximum hemodynamic response (p < 0.0001 for either the peak or the beginning).ConclusionIED adjacent to a maximum hemodynamic response, which often corresponds to the seizure onset zone, is more likely to precede IEDs in remote locations during a widespread intracranial discharge. Thus, EEG-fMRI is a unique noninvasive method to reveal the origin of IEDs, which we propose to label the spike onset zone.

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Deep Neural Architectures for Mapping Scalp to Intracranial EEG

International Journal of Neural Systems ◽

10.1142/s0129065718500090 ◽

2018 ◽

Vol 28 (08) ◽

pp. 1850009 ◽

Cited By ~ 35

Author(s):

Andreas Antoniades ◽

Loukianos Spyrou ◽

David Martin-Lopez ◽

Antonio Valentin ◽

Gonzalo Alarcon ◽

...

Keyword(s):

Deep Learning ◽

Brain Activity ◽

Statistical Tests ◽

Automated Classification ◽

Intracranial Eeg ◽

Scalp Eeg ◽

Eeg Data ◽

Epileptic Discharges ◽

Highly Correlated ◽

Intracranial Recording

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

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