Back

Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity

Holden, M. M.; Premoli, I.; Clark, S. R.; Richardson, M. P.; Goldsworthy, M. R.; Rogasch, N. C.

2025-11-04 neuroscience
10.1101/2025.11.02.686141 bioRxiv
Show abstract

Electroencephalography (EEG) signals comprise both oscillatory (periodic) and non-oscillatory (aperiodic) components. Aperiodic activity forms the 1/f-like background of the EEG power spectrum and can be characterised by parameters describing the offset and slope (1/f exponent). Evidence from computational modelling and GABAergic drug studies suggests that increased inhibitory input steepens this slope. This study examined the effects of two antiseizure medications (ASMs) that reduce cortical excitability through different mechanism of action on aperiodic EEG activity. Resting EEG was recorded with eyes open and closed from 13 healthy male volunteers at baseline and two hours after administration of lamotrigine (300 mg), levetiracetam (3000 mg), or placebo. Power spectra were computed using Welchs method and aperiodic parameters estimated with the specparam algorithm. In the eyes-open condition, lamotrigine significantly reduced the aperiodic offset from pre- to post-dose (p = 0.022) and relative to placebo (p = 0.0032), indicating lower broadband power at low frequencies. No significant changes in slope were found under either condition during eyes-closed, and levetiracetam produced no measurable effects on any parameter. These findings indicate that aperiodic metrics may capture specific neurophysiological mechanisms, with lamotrigine producing a broadband reduction in EEG power consistent with its suppression of cortical excitability via sodium channel blockade.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.