Poster No:
1678
Submission Type:
Abstract Submission
Authors:
Daniel Pittman1, William Wilson2, Perry Dykens2, Victoria Mosher2, Laura Gill3, Paolo Federico2
Institutions:
1University of Calgary, Calgary, AL, 2University of Calgary, Calgary, Alberta, 3Univeristy of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
Electroencephalography (EEG) has been the dominant diagnostic modality for the clinical evaluation of patients with epilepsy. Recordings acquired during the inter-ictal period, from either scalp or intracranially implanted electrode arrays (iEEG), are visually appraised by epileptologists, to identify characteristic morphological events. The most common are inter-ictal epileptiform discharges (IEDs), recognized as markers of the seizure onset zone (SOZ), where epileptic seizures arise. More recently, alternate markers have been employed, comprised of brief periods of activity of low-amplitude high-frequency oscillations (HFOs). These may be superior SOZ markers, but there has been debate surrounding whether they are more indicative of true pathology, or rather normal physiological brain behaviors.
EEG can be recorded simultaneously during functional magnetic resonance imaging (fMRI), which measures oxygenation level changes within the brain over time, reflective of local neural metabolism, and thus neural activity levels. The EEG event timings are included to produce 3D brain volumes whose locations are scored according to the statistical significance of the synchronicity of the brain's oxygenation with the EEG events. This localization process has been used to identify brain regions systematically related to the EEG events, which are considered a surrogate marker of the SOZ.
The low amplitude HFOs are difficult to detect on scalp EEG, but the combination of using more sensitive iEEG, simultaneously with functional MR (fMRI), greatly improves their utility. Even so, a challenge frequently arising is that HFOs may occur very often or almost continually, which does not produce a sequence of events with discriminatory value as an input for fMRI analyses. It is suggested that physiological HFOs occur independently and individually, whereas pathological HFOs occur simultaneously across several contacts of an iEEG electrode. Consolidating such co-occurring HFOs (cHFOs), and removing independent HFOs (iHFOs), would reduce the analysis to presumed pathological events, and improve localization of the SOZ.
Methods:
Eleven epilepsy patients undergoing EEG monitoring for pre-surgical evaluation were recruited to undergo a 60-minute simultaneous iEEG/fMRI exam. The EEG data was cleaned of MRI signal contamination, filtered (80-125Hz) and converted to within-electrode bipolar montages. The channels of the electrode identified as being implanted closest to the suspected SOZ underwent processing to algorithmically identify HFOs (6 contiguous half-cycles whose absolute amplitude exceeded 3.1 standard deviations from the mean). An additional process was applied wherein the HFOs of an electrode were identified as being temporally overlapping for >50% of their duration, and as being more than 90% in phase. Such qualifying clusters of HFOs constituted a consolidated HFO (cHFO), spanning the interval across its constituents, and non-qualified events classified as iHFOs
fMRI analyses were performed using as inputs the timings of a) all iHFOs on the SOZ electrode, and b) just the cHFOs. The resultant statistical volumes were compared by simple mathematical analysis of all supra-threshold voxels, evaluated according to their degree of overlap and their average distance to the SOZ electrode.
Results:
The cHFO fMRI results had overall fewer supra-threshold voxels than the iHFO results. They also had shorter average distance to the SOZ electrode contacts. Consolidation of the iHFOs resulted in an average event count reduction of 80%.
Conclusions:
Consolidated HFOs produced better localization than iHFOs, indicating their greater likelihood of being pathological HFOs. This was likely due to the greater statistical design efficiency of the significantly fewer cHFO events.. The substantial reduction of events also significantly reduces the workload for visual review.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Methods Development 2
Keywords:
Data analysis
Design and Analysis
Electroencephaolography (EEG)
Epilepsy
FUNCTIONAL MRI
MRI
Source Localization
Other - HFO, SOZ
1|2Indicates the priority used for review
Provide references using author date format
Engel Jr, J., Bragin, A., Staba, R., & Mody, I. (2009). 'High‐frequency oscillations: What is normal and what is not?', Epilepsia, 50(4), 598–604.
Jacobs, J., LeVan, P., Chander, R., Hall, J., Dubeau, F., & Gotman, J. (2008). 'Interictal high‐frequency oscillations (80–500 Hz) are an indicator of seizure onset areas independent of spikes in the human epileptic brain', Epilepsia, 49(11), 1893–1907.