Psychomotor and Eeg-derived Measures of Vigilance in Patients with Epilepsy
Abstract number :
3.201
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2022
Submission ID :
2205070
Source :
www.aesnet.org
Presentation date :
12/5/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:27 AM
Authors :
Caroline Martin, B.S. – Vanderbilt University; catie Chang, PhD – Vanderbilt University; Dario Englot, PhD, MD – Vanderbilt University Medical Center; Eric Feng, G.E. – Vanderbilt University; Jasmine Jiang, B.S. – Vanderbilt University Medical Center; Victoria Morgan, PhD – Vanderbilt University; Haatef Pourmotabbed, M.S. – Vanderbilt University; Sean Tuttle, G.E. – Vanderbilt University; Shiyu Wang, B.S. – Vanderbilt University; Kristin Wills, B.S. – Vanderbilt University Medical Center
Rationale: Patients with temporal lobe epilepsy (TLE) tend to suffer from broad neurocognitive deficits extending beyond the seizure’s focal region. Over time, recurrent seizures may impair subcortical-to-cortical brain connections, contributing to global cognitive deficits. Subcortical vigilance structures may be especially relevant in epilepsy, as TLE patients often experience sleep-wake disturbances and attention deficits. Here, vigilance refers to sustained attention or tonic alertness and can be estimated using the psychomotor vigilance task (PVT) or electroencephalography (EEG). Using resting-state EEG-fMRI data and a 5-min computerized PVT, we explored vigilance differences between TLE patients and controls.
Methods: We studied 39 participants (9 patients, 30 controls). Prior to EEG-fMRI, all subjects completed PVT and the Epworth Sleepiness Scale (ESS; self-report questionnaire). For PVT, the outcome measures of interest were response speed (1/reaction time or RT), slowest 10% of the response times (10% largest values for 1/RT), and fastest 10% of the response times. In the MRI, subjects rested with their eyes closed for two 20-minute scans. We used an MR-compatible amplifier to record EEG with 32 channels, including electrooculogram (EOG) to capture eye movements. We evaluated: (1) EEG alpha-theta, or the ratio of alpha (8-12 Hz) over theta (4-8 Hz) spectral potential. Higher ratio equates to higher alertness. (2) VIGALL classification, which quantitatively stages EEG and EOG into discrete vigilance states. Higher VIGALL stages correspond to higher alertness: 1 (sleep), 2-3 (drowsy), 4-6 (alert).
Results: In Figure 1A, patients show lower PVT scores (slower response speed) compared to controls. Though non-significant after a Bonferroni correction, the trend suggests a group difference in vigilance, especially using the 10% slowest RT (p(uncorr) = 0.02). Similarly, patients show higher, yet non-significant, ESS scores (greater self-reported drowsiness). In Figure 1B, we demonstrate EEG vigilance measures. Alpha-theta shows a trend towards significance during scan02 (p(uncorr)=0.04), suggesting greater vigilance differences over time. VIGALL results are consistent with this effect and even outperform alpha-theta (p(uncorr) = 0.02). In Figure 2, we plotted the average EEG time course for patients and controls, separated by scan number. Again, we see greater differences (less overlap) between the time courses for patients and controls, especially during scan02.
Conclusions: Here, we explored vigilance differences between TLE patients and controls. Consistent with prior reports, PVT and EEG results showed a group difference. For EEG, this difference increased over time. This work is part of an ongoing study that aims to define the relationships between connectivity, vigilance, and cognition in epilepsy.
Funding: This work was supported by NIH grant NS112252.
Neurophysiology