Seizure-related Impedance Changes in Humans with Drug-resistant Temporal Lobe Epilepsy
Abstract number :
3.215
Submission category :
2. Translational Research / 2C. Biomarkers
Year :
2024
Submission ID :
896
Source :
www.aesnet.org
Presentation date :
12/9/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Jie Cui, PhD – Mayo Clinic
Filip Mivalt, MS – Mayo Clinic
Vlad Sladky, BS – Department of Neurology, Mayo Clinic, Rochester MN USA
Long-Jun Wu, PhD – University of Texas Health Science Center at Houston
Jamie Van Gompel, MD – Mayo Clinic
Nicholas Gregg, MD – Mayo Clinic
Jiwon Kim, BEng – Mayo Clinic
Thomas Richner, PhD – Mayo Clinic
Brian Lundstrom, MD, PhD – Mayo Clinic
Hai-Long Wang, PhD – Mayo Clinic
Kai Miller, MD, PhD – Mayo Clinic
Timothy Denison, Ph.D. – University of Oxford
Benjamin Brinkmann, PhD – Department of Neurology, Mayo Clinic, Rochester MN USA
Vaclav Kremen, PhD, MS, EMBA – Department of Neurology, Mayo Clinic, Rochester MN USA
Gregory Worrell, MD, PhD – Mayo Clinic
Rationale: Electrical impedance plays a critical role in various brain phenomena. Previous animal studies have demonstrated the correlation between impedance and the brain’s extracellular space (ECS), which is crucial in influencing neuronal excitability and seizures. Recently, our research has revealed multiscale impedance dynamics in humans [1,2], leading us to hypothesize the ECS’s role in seizures. Animal models have shown fast impedance changes on millisecond timescales, linked to the activation of ion channels, and slow impedance change over seconds to minutes, attributed to cell swelling. However, very slow impedance changes spanning minutes to hours in people living in their natural environment remain largely unexplored. An understanding of impedance characteristics from human limbic circuitry across multiple timescales could provide insight into seizure generation mechanism and potentially contribute to seizure forecasting.
Methods: Impedance was sampled periodically (5-15 min/sample) over multiple months in anterior thalamus (THL), amygdala-hippocampus (AMG-HPC) and posterior HPC (post-HPC) using the investigational Medtronic Summit RC+S™ sensing-stimulation device in five patients with drug-resistant temporal lobe epilepsy. Ictal and peri-ictal impedance measured were compared to non-seizure baselines. Time-domain analyses were conducted using cross-correlation between impedance and seizures in ±5 min. (slow changes) and ±12 hours (very slow changes) windows around seizure onsets. The power spectra of impedance time series were estimated using the non-uniform fast Fourier transform.
Results: Our findings revealed that brain impedance increases during seizures (ictal) in the HPC (both AMG-HPC and post-HPC, one-way ANOVA p < 0.05) when compared to the non-seizure baselines. In the time domain, we observed a significant elevation of slow impedance changes ( >3 SEM) around seizure onsets (±5 min) in the HPC. Interestingly, we also noticed increases in the rate of impedance before seizure onset, which could indicate the acceleration of cell swelling. Our data suggest an increase in the level of very-slow impedance changes around seizure onset (about 1 hr before and 4 hours after). In the frequency domain, the impedance power spectra before seizure were significantly higher ( > 3 SEM) than those after the onset in the frequency range of Basic Rest-Activity Cycle (BRAC, 60 – 120 min/cycle) across all three areas. This might imply that seizures disturb the normal BRAC rhythms.
Conclusions: Our study found significant increases in impedance during seizures in human amygdala and hippocampus structures, consistent with animal studies showing cell swelling and concomitant ECS decrease during seizures. Additionally, our findings indicate that cell swelling may accelerate prior to the onset of electrographic seizure discharges. The cause of the prolonged elevation of impedance (±12 hours around seizure onset) remains unclear but suggesting that periods of increased seizure likelihood may extend for hours prior to seizures, beneficial to seizure forecasting.
[1] F. Mivalt, et al., J. Neurosci 43(39), 6653-6666, 2023. [2] J. Cui, et al., J. Neural Eng. 21, 2024.
Funding: NIH UH3-NS095495
Translational Research