Static and Dynamic rs-fMRI for Focal Epilepsy Localization with Direct Comparison to IEEG
Poster No:
1658
Submission Type:
Abstract Submission
Authors:
Hadi Narimani1, elaine liang1, Brenda Wan1, Will Wilson1, Paolo Federico1, Pierre LeVan1
Institutions:
1University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
For patients with drug-resistant focal epilepsy, surgery is the most effective treatment when the epileptogenic zone, can be clearly identified and surgically resected (Jehi., 2022; Lamberink., 2017). This study investigates the efficacy of static and dynamic resting-state fMRI (rs-fMRI) techniques; Amplitude of Low-Frequency Fluctuations (ALFF), Fractional Amplitude of Low-Frequency Fluctuations (fALFF) (Zou., 2008), and Regional Homogeneity (ReHo)(Zang., 2004); for identifying the seizure onset zone (SOZ) in focal epilepsy and explores the influence of interictal epileptiform discharges (IEDs) on these imaging metrics using simultaneously acquired intracranial EEG.
Methods:
This study involved 41 patients with drug-resistant focal epilepsy who underwent simultaneous intracranial EEG-fMRI (iEEG-fMRI).
IEEG data was recorded at either 10 kHz or 20 kHz using a SynAmpsRT system and Scan 4.4 software.
MRI scans were performed with a 3T GE Discovery scanner with the following parameters: T1-weighted anatomical MRI (TE 3.8 ms, TR 9.3 ms, flip angle 12 degrees, field of view of 24 cm, matrix 320 x 256 x 64, and voxel size 0.47 mm x 0.47 mm x 2.00 mm) and EPI fMRI (TE 30 ms, TR 1500 ms, flip angle 65 degrees, field of view 24 cm, matrix 64 x 64, and voxel size 3.75 mm x 3.75 mm x 5.00 mm).
Image preprocessing included motion correction, slice timing, brain extraction, smoothing, filtering, and normalization, with independent component analysis (ICA) used to remove fMRI artifacts.
ALFF, fALFF, and ReHo were calculated 3x3x3 voxel ROIs around each electrode. Dynamic variability of ALFF, fALFF, and ReHo was also calculated as the standard deviation across 120-second sliding windows.
Static and dynamic values of ALFF, fALFF, and ReHo were then statistically compared between seizure onset zone (SOZ) and non-SOZ electrodes, as well as between electrodes showing interictal epileptiform discharges (IEDs) and non-IED electrodes using two-sample t-tests (p<0.05)
IEEG data was recorded at either 10 kHz or 20 kHz using a SynAmpsRT system and Scan 4.4 software.
MRI scans were performed with a 3T GE Discovery scanner with the following parameters: T1-weighted anatomical MRI (TE 3.8 ms, TR 9.3 ms, flip angle 12 degrees, field of view of 24 cm, matrix 320 x 256 x 64, and voxel size 0.47 mm x 0.47 mm x 2.00 mm) and EPI fMRI (TE 30 ms, TR 1500 ms, flip angle 65 degrees, field of view 24 cm, matrix 64 x 64, and voxel size 3.75 mm x 3.75 mm x 5.00 mm).
Image preprocessing included motion correction, slice timing, brain extraction, smoothing, filtering, and normalization, with independent component analysis (ICA) used to remove fMRI artifacts.
ALFF, fALFF, and ReHo were calculated 3x3x3 voxel ROIs around each electrode. Dynamic variability of ALFF, fALFF, and ReHo was also calculated as the standard deviation across 120-second sliding windows.
Static and dynamic values of ALFF, fALFF, and ReHo were then statistically compared between seizure onset zone (SOZ) and non-SOZ electrodes, as well as between electrodes showing interictal epileptiform discharges (IEDs) and non-IED electrodes using two-sample t-tests (p<0.05)
Results:
In the comparison between SOZ and non-SOZ, significant differences were seen in 7 patients for static ALFF (Fig. 1-A), eight patients for static fALFF(Fig. 1-C), and nine patients forstatic ReHo (Fig. 1-E). In the dynamic analyses, significant differences were observed in dALFF for eight patients (Fig. 1-B), in dfALFF in nine patients (Fig. 1-D), and in dReHo in nine patients (Fig. 1- F). However, patients were almost equally split between showing increased or decreased values in the SOZ compared to non-SOZ areas.
In the comparison between IED and non-IED areas, significant differences were seen in ten patients for static ALFF values, ten patients for static ReHo, and seven patients for static fALFF. In dynamic analyses, significant differences were observed in eight patients for dALFF, four patients for dfALFF, and seven patients for dReHo. Again, however, there was an almost even split of patients showing increased values or decreased values in IED areas, except for static ReHo, were 8 patients had significantly lower ReHo values in IED areas while only 2 patients had significantly higher ReHo.
Nevertheless, group analysis revealed a significantly lower dynamic ALFF in the SOZ (p = 0.03), and significantly lower static ReHo in areas with IEDs (p = 0.01).
Additionally, Pearson correlations were used to determine whether the various metrics were associated with IED rates (Fig. 2). Only dynamic fALFF values showed a significant correlation with IED rates (p = 0.03, Fig. 2-D).
In the comparison between IED and non-IED areas, significant differences were seen in ten patients for static ALFF values, ten patients for static ReHo, and seven patients for static fALFF. In dynamic analyses, significant differences were observed in eight patients for dALFF, four patients for dfALFF, and seven patients for dReHo. Again, however, there was an almost even split of patients showing increased values or decreased values in IED areas, except for static ReHo, were 8 patients had significantly lower ReHo values in IED areas while only 2 patients had significantly higher ReHo.
Nevertheless, group analysis revealed a significantly lower dynamic ALFF in the SOZ (p = 0.03), and significantly lower static ReHo in areas with IEDs (p = 0.01).
Additionally, Pearson correlations were used to determine whether the various metrics were associated with IED rates (Fig. 2). Only dynamic fALFF values showed a significant correlation with IED rates (p = 0.03, Fig. 2-D).
·Paired scatter plots and bar charts illustrating individual comparisons of static and dynamic rs-fMRI measures. X-axes is the number of patients with significant differences, Y-axes show is magnitude
·Group-level graph illustrating the relationship between IED rates and various measures (ALFF(A), dALFF(B), fALFF(C), dfALFF(D), ReHo(E), and dReHo(F)), analyzed using Pearson correlation
Conclusions:
The study demonstrates that while rsfMRI metrics are variable in individual focal epilepsy patients, dynamic features such as dALFF showed value in pinpointing the SOZ. Moreover, we could show a direct effect of IEDs on ReHo values, suggesting that rs-fMRI may be able to provide information about IED occurrence non-invasively. Further research will focus on the temporal fluctuations in IED occurrence and their relation to rsfMRI metrics.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 1
Keywords:
Electroencephaolography (EEG)
Epilepsy
FUNCTIONAL MRI
Other - Resting State fMRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Lamberink, H. J. (2017). Individualised prediction model of seizure recurrence and long-term outcomes after withdrawal of antiepileptic drugs in seizure-free patients: A systematic review and individual participant data meta-analysis. The Lancet Neurology, 16(7), 523–531.
Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22(1), 394–400.
Zou, Q.-H. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141.