Improved Epileptic Discharge Localization based on HRF parameters in Intracranial EEG-fMRI
Poster No:
1042
Submission Type:
Abstract Submission
Authors:
Yosra Jamiliyan1, Will Wilson1, Paolo Federico1, Pierre LeVan1
Institutions:
1University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
Epilepsy affects 1% of the global population, with 20% experiencing drug-resistant epilepsy. For these patients, epilepsy surgery offers a potential cure by removing the epileptogenic zone (Gelžinienė et al., 2008). Simultaneous intracranial EEG and fMRI (iEEG-fMRI) has shown promise to localize this zone by detecting BOLD responses to interictal epileptiform discharges (IED). In iEEG-fMRI, the abundance of IEDs results in a high sensitivity, with activation maps often having multiple widespread activation clusters that are not all clinically relevant (Wilson et al. 2024). This study aims to address this challenge by analyzing the shape (delay, dispersion) of the hemodynamic response function (HRF) within the identified IED-related activation clusters to improve the precision of seizure onset localization, with the potential to enhance outcomes for epilepsy surgery (Cunningham et al., 2012; de Bertoldi et al., 2015).This approach was based on the expectation that concordant clusters would exhibit lower deviations in delay and dispersion from the canonical HRF, reflecting more reliable physiological activation compared to discordant clusters (Lemieux et al. 2008).
Methods:
Simultaneous intracranial EEG (iEEG) and fMRI were analyzed from 5 focal epilepsy patients, where epileptologists identified epileptic events from the iEEG data. Each patient was scanned for one to three 20-minute runs. EEG was recorded using SynAmpsRT with a sampling rate of 10–20 kHz. fMRI was acquired using 3T GE Discovery scanner with echo planar imaging (TE = 30 ms, TR = 1500 ms, flip angle = 65°, 24 slices, 3.75 × 3.75 × 5 mm resolution). Anatomical imaging included 2D multi-slice and 3D T1-weighted imaging. A standard General Linear Model (GLM) using the canonical HRF was applied to the fMRI data for each run to identify activation clusters associated with IEDs (p<0.05 corrected). Electrode locations where the IEDs occurred on iEEG were used as references to categorize these activation clusters as either "concordant" if within 2 cm of the electrode contact, or "discordant." otherwise. For each cluster, the BOLD time series was averaged across voxels, from which delay and dispersion values of the HRF were derived by performing a new regression on this time series that included delay and dispersion derivatives. The distributions of these values were compared between concordant and discordant clusters and thresholded using the Interquartile Range (IQR) method to determine the potential of this approach to eliminate discordant clusters.
Results:
An example of a subject with both concordant and discordant clusters is shown in Figure 1. Histograms of the HRF delays and dispersions of all 83 discordant clusters and 11 concordant clusters from the 5 subjects are shown in Figure 2 . Discordant clusters exhibited a broader spread in delay values (Figure 2a) and greater variability in dispersion values, with prominent outliers (Figure 2b). IQR thresholds could be used to discard 11 discordant clusters in these subjects without affecting any concordant clusters. So, this approach could improve the specificity of the analysis without sacrificing sensitivity.
Conclusions:
By analyzing HRF delay and dispersion in concordant and discordant IED-related activation clusters, this approach showed that discordant clusters had IED-related HRFs with higher deviations from the canonical HRF, even though the GLM used to generate the activation maps used the canonical HRF. This allowed for the identification and elimination of some discordant clusters; concordant clusters exhibited lower variability, reflecting true epileptic activation, while discordant clusters could be due to artifacts. Removing these discordant clusters improved the specificity of the activation maps, resulting in more accurate and reliable mapping for surgical planning.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Methods Development
Task-Independent and Resting-State Analysis 2
Keywords:
Data analysis
Electroencephaolography (EEG)
Epilepsy
FUNCTIONAL MRI
Other - iEEG
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
de Bertoldi, F. (2015). Improving the reliability of single-subject fMRI by weighting intra-run variability. NeuroImage, 114, 287-293.
Gelžinienė, G. (2008). Presurgical evaluation of epilepsy patients. Medicina, 44(8), 585.
Lemieux, L. (2008). Noncanonical spike‐related BOLD responses in focal epilepsy. Human brain mapping, 29(3), 329-345.
Wilson, W. (2024). Mapping interictal discharges using intracranial EEG-fMRI to predict postsurgical outcomes. Brain, 147(12), 4157-4168.