Local tissue and hemodynamic properties reflect pathological discharges in the epileptic brain
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M1 & M2 (Mezzanine Level)
Poster No:
257
Submission Type:
Abstract Submission
Authors:
Yigu Zhou1, Nicolás von Ellenrieder1, Thaera Arafat2, Jessica Royer3, Jordan DeKraker4, Ke Xie3, Alexander Ngo4, Ella Sahlas4, Casey Paquola5, Raluca Pana6, Jean Gotman1, Birgit Frauscher7, Boris Bernhardt4
Institutions:
1Montréal Neurological Institute, Montréal, Québec, 2Montreal Neurological Institute, Montrea, Quebec, 3McGill University, Montreal, QC, 4McGill University, Montreal, Quebec, 52Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich, Jülich, North Rhine-Westphalia, 6Montreal Neurological Institute, Montreal, Quebec, 7Duke University School of Medicine, Durhan, NC
First Author:
Co-Author(s):
Casey Paquola
2Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich
Jülich, North Rhine-Westphalia
2Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich
Jülich, North Rhine-Westphalia
Introduction:
Epilepsy is one of the most common neurological conditions worldwide. Although many epileptic patients improve with anti-seizure medication, up to 40% of them are drug-resistant (Engel, 2016). For these patients, the most successful treatment is epilepsy surgery, whereby the region giving rise to seizures is removed. Non-invasive techniques such as magnetic resonance imaging (MRI) are key to identifying the surgical target and ensuring a seizure-free future in drug-resistant patients. Where conventional MRI is inconclusive, patients need to undergo intracranial electroencephalography (iEEG), an invasive procedure not without risk of complication, that offers restricted spatial sampling. While whole-brain structural and functional alterations have been widely studied in the epileptic brain using a tandem iEEG-MRI approach, finer-scale local alterations have yet to be assessed.
Methods:
Twenty patients diagnosed with drug-resistant epilepsy (33.9±9 years, 65% female) were implanted with bipolar depth electrodes for presurgical iEEG assessment. Sleep recordings from 1979 channels were retained for automated detection of ripple events. Channels were labeled pathological if per-minute ripple rate (RR) exceeded the 90th percentile of region-specific RR (Frauscher et al., 2018a; Frauscher et al., 2018b; von Ellenrieder et al., 2020) based on pre-established cut-off (Zweiphenning et al., 2022). Patients underwent multimodal MRI at 3Tesla following an established protocol (Royer et al., 2022), assessing microstructure with quantitative T1 relaxometry (qT1: .8mm isotropic), water diffusion with diffusion-weighted imaging (DWI: 1.6mm isotropic; TR=3.5s; b-values=300, 700, 2000s/mm2; diffusion directions/shell=10, 40, and 90), and hemodynamics with resting-state functional MRI (rs-fMRI: TR=0.6s; TE=30ms; 3mm isotropic, 7mins) sequences. Multimodal MRI data were processed and co-registered using micapipe v0.2.2 (Cruces et al., 2022). To assess tissue properties, we computed the first three statistical moments of depth-sampled qT1 intensities (laminar density mean, variance and skewness), and DWI measures of diffusivity (ADC) and anisotropy (FA). To assess hemodynamics, we computed a condensed multidisciplinary library of 22 timeseries features on rs-fMRI timeseries (Lubba et al., 2019; Fulcher et al., 2017). Multimodal metrics in patients were standardized against 75 age- and sex-matched healthy controls, then sampled from surface vertices within 5mm from individual channels based on the fsLR-5k surface template. Mean-centered partial least squares (PLS) and mediation analysis investigated associations between tissue properties, local functional dynamics, and electrophysiological abnormalities.
Results:
MRI measures were uncorrelated with RR (|r|<0.1). Mean-centered PLS identified laminar variance, ADC and FA, as well as 12 hemodynamic timeseries features significantly distinguishing pathological from non-pathological channels. These features contributed to a multivariate composite score for tissue properties and hemodynamics, entering simple linear regression and mediation analysis to explore a pathway from tissue alterations and hemodynamic changes to pathological ripple formation (see Figure 2 for statistics). For the first time, we integrated iEEG and multimodal MRI to show that local tissue properties, without mediation by local functional hemodynamics, could reflect pathological electrophysiological activity.
Conclusions:
Combining optimal access to brain dynamics via iEEG with advanced whole-brain MRI, our data highlighted that local tissue properties reflected ripple production, without mediation by local hemodynamics. This supports further development of non-invasive structural imaging techniques for minimizing risk in epilepsy patient care.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
ELECTROPHYSIOLOGY
Epilepsy
MRI
Multivariate
1|2Indicates the priority used for review
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Benkarim, O., Park, B. Y., Degré-Pelletier, J., Nelson, M. C., DeKraker, J., Leppert, I. R., Tardif, C., Poline, J. B., Concha, L., & Bernhardt, B. C. (2022). Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. NeuroImage, 263, 119612.
Engel J., Jr (2016). What can we do for people with drug-resistant epilepsy? The
2016 Wartenberg Lecture.
Frauscher, B., von Ellenrieder, N., Zelmann, R., Doležalová, I., Minotti, L., Olivier,
A., . . . Gotman, J. (2018a). Atlas of the normal intracranial electroencephalogram: neurophysiological awake activity in different cortical areas. Brain, 141(4), 1130-1144.
Frauscher, B., von Ellenrieder, N., Zelmann, R., Rogers, C., Nguyen, D. K., Kahane,
P., Dubeau, F., & Gotman, J. (2018b). High-Frequency Oscillations in the Normal Human Brain. Annals of neurology, 84(3), 374–385.
Fulcher, B. D., & Jones, N. S. (2017). hctsa: A Computational Framework for
Automated Time-Series Phenotyping Using Massive Feature Extraction. Cell systems, 5(5), 527–531.e3.
Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., & Jones, N. S.
(2019). catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery, 33(6), 1821-1852.
Royer, J., Rodríguez-Cruces, R., Tavakol, S., Larivière, S., Herholz, P., Li, Q., . . .
Bernhardt, B. C. (2022). An Open MRI Dataset For Multiscale Neuroscience. Scientific Data, 9(1), 569.
von Ellenrieder, N., Gotman, J., Zelmann, R., Rogers, C., Nguyen, D. K., Kahane,
P., . . . Frauscher, B. (2020). How the Human Brain Sleeps: Direct Cortical Recordings of Normal Brain Activity. Annals of Neurology, 87(2), 289-301.
Zweiphenning, W. J. E. M., von Ellenrieder, N., Dubeau, F., Martineau, L., Minotti, L.,
Hall, J. A., Chabardes, S., Dudley, R., Kahane, P., Gotman, J., & Frauscher, B. (2022). Correcting for physiological ripples improves epileptic focus identification and outcome prediction. Epilepsia, 63(2), 483–496.