EEG Spectral Exponents and Visual Chirp Responses Mirror Anti-Seizure Medication Load in Refractory Focal Epilepsy

Abstract

Objective: Quantitative markers of cortical excitability may help identify responders to anti-seizure medications (ASMs). We studied the relationship between ASM load and two electroencephalography (EEG) markers of cortical excitability in people with refractory epilepsy.

Methods: We included individuals with refractory focal epilepsy undergoing presurgical evaluation, involving ASM tapering and sleep deprivation. We obtained daily resting state EEG and EEG responses to visual stimulation at linearly increasing flash frequency (10-40 Hz chirp). We extracted the aperiodic exponent from resting state EEG power spectra and analysed chirp response at driving and second-order harmonic frequencies. We modelled ASM load, which we related to the EEG markers using linear mixed-effects regression.

Results: Forty-eight subjects (median age 34 years, age range 16-62 years, 19 females) participated. The spectral exponent became less negative with ASM load reduction (p = 0.02), mainly attributable to reduced low-frequency power. Lowering ASM load increased the harmonic response to chirp stimulation (p = 0.004), also after accounting for sleep deprivation (p = 0.02), but did not affect the driving response. ASM tapering specifically increased harmonic responses to high stimulation frequencies (27-40 Hz, p = 0.01).

Interpretation: Resting state EEG spectral exponents and visual chirp responses reflect ASM load in refractory epilepsy. Low-frequency spectral changes in resting state EEG may only mirror ASM-induced spectral slowing. Visual chirp stimulation reveals enhanced harmonic EEG responses during low ASM loads, likely due to both increased high gamma activity and increased response to visual perturbations. Implementation of the markers would need normative values to reduce the delay to individually optimised treatment regimens.

Keywords: anti‐seizure medications; electroencephalography; photic stimulation; resting state EEG; spectral analysis.

FIGURE 1

Extraction of quantitative markers from resting state electroencephalographic (EEG) data and EEG responses to visual chirp stimulation. (A) Power spectral density of an example central channel's resting state EEG (left), and the fit of the 1/f (aperiodic) component, demonstrated in the log–log space (right). The slope of the fitted aperiodic component in the log–log space equals the spectral exponent fit in the original space. (B) Time‐frequency representation of a representative example EEG response to visual chirp stimulation, averaged across 12 chirp trials. Each trial, lasting 5.7 s, presents flashes of linearly increasing frequency, with four flashes at each integer frequency between 10 and 40 Hz (upper black vertical lines). The average chirp response is baseline‐corrected using the average 1.5‐s interval preceding the stimulus onset from the participant's first available recording session. The responses at stimulation frequencies (dashed outline) and doubles of the stimulation frequencies (dotted outline) are averaged to arrive at a single driving and second‐order harmonic response, respectively. The driving and harmonic responses are further decomposed into a response at low, medium, and high stimulation frequencies.

FIGURE 2

Simulation of anti‐seizure medication (ASM) load for an example participant. Per ASM type, doses were normalised to the defined daily dose (DDD) and their elimination was modelled by first‐order kinetics, rendering hourly estimates of ASM load. The ASM load at 2 p.m. of each admission day, summed across medication types, was expressed relative to the summed value at 2 p.m. of the last day with unchanged home medication (the third day before admission in this example, labelled as ‘reference’). We associated the reduction of the normalised, summed ASM load with the electroencephalographic (EEG) markers. In the plot, (0) indicates 2 p.m. on the reference day, (1) to (4) indicate the 2 p.m. measurement time points at the first four admission days, and (5) indicates the 10 a.m. measurement point on day 5, when the measurement was advanced to the morning to anticipate discharge of the participant around noon. Time is given in hours relative to midnight on the first day of admission. AU, arbitrary units.

FIGURE 3

Flowchart illustrating the study participant and electroencephalography (EEG) recording selection procedure. fbTCS, focal to bilateral tonic–clonic seizure; FIAS, focal impaired awareness seizure.

FIGURE 4

Illustration of effects of anti‐seizure medication (ASM) load reduction on the spectral exponent of resting state electroencephalography (EEG) and the visual chirp response. Results are shown for low ASM loads (> 75% reduction compared to pre‐tapering values) and high ASM loads (< 25% reduction compared to pre‐tapering values). (A) Average resting state EEG power spectra (left) and aperiodic fits (right), with their 95% confidence intervals. ASM tapering yielded a less negative spectral exponent, which is equivalent to a flatter slope of the aperiodic fit in the log–log space. This result was confirmed (p < 0.05) by mixed linear regression analysis. The shift of the exponent mediated by ASM tapering seems primarily driven by a power reduction in the EEG power across the delta‐to‐alpha frequency range. (B) Average EEG responses at driving (left) and harmonic (right) frequencies assessed per chirp stimulation frequency, with their 95% confidence intervals. ASM tapering specifically increased the harmonic response for the high (i.e., 26–40 Hz) stimulation frequencies. This result was confirmed (p < 0.05) by linear mixed‐effects regression analysis.