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

Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity Marissa M. Holden , Isabella Premoli , Scott R. Clark , View ORCID Profile Mark P. Richardson , Mitchell R. Goldsworthy , Nigel C. Rogasch doi: https://doi.org/10.1101/2025.11.02.686141 Marissa M. Holden 1 School of Pharmacy and Biomedical Sciences, College of Health, Adelaide University , Australia 2 Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isabella Premoli 3 Saniona, Glostrup , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Scott R. Clark 4 School of Medicine, College of Health, Adelaide University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark P. Richardson 5 Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark P. Richardson Mitchell R. Goldsworthy 1 School of Pharmacy and Biomedical Sciences, College of Health, Adelaide University , Australia 2 Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute , Australia 6 School of Psychology, College of Education, Behavioural and Social Sciences, Adelaide University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nigel C. Rogasch 1 School of Pharmacy and Biomedical Sciences, College of Health, Adelaide University , Australia 2 Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute , Australia 7 Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Nigel.rogasch{at}adelaide.edu.au Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Objectiv 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). This study examined the effects of two antiseizure medications (ASMs), which reduce cortical excitability through different mechanisms of action, on aperiodic and periodic EEG activity. Methods 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 Welch’s method. Aperiodic parameters were estimated using the specparam algorithm, and periodic activity was quantified after subtraction of the aperiodic component. Results In the eyes-open condition, lamotrigine significantly reduced the aperiodic offset from pre- to post-dose and relative to placebo, and reduced the exponent relative to placebo, indicating a flattening of the spectra. No significant changes in slope or offset were observed during eyes-closed, and levetiracetam produced no significant effects on any aperiodic parameter. Lamotrigine reduced theta power in both conditions, reduced alpha power, and increased gamma power during eyes open. Levetiracetam increased beta power in both conditions. Conclusions Lamotrigine altered both aperiodic and periodic EEG activity, with aperiodic effects restricted to the eyes-open condition, whereas levetiracetam influenced periodic activity without altering aperiodic parameters. Significance Aperiodic EEG measures may provide a non-invasive approach for assessing ASM-related neurophysiological effects and improve understanding of how distinct pharmacological mechanisms influence large-scale cortical activity. 1. Introduction Electroencephalography (EEG) is a widely used neuroimaging method which captures the summation of the brain’s electrical activity using electrodes placed on the scalp ( Biasiucci et al., 2019 ). EEG signals are comprised of a mix of both periodic (i.e., oscillatory) and aperiodic (i.e., non-oscillatory) neural activity (Donoghue and others, 2020). Early EEG studies highlight the behavioural significance of periodic activity, for example the clear change in the power of oscillations between 8-12 Hz (alpha-band activity) during eyes open and eyes closed resting state EEG recordings ( Berger, 1929 ). Subsequent research defined delta (0.5 – 4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12 – 30 Hz) and gamma (30 – 100 Hz) oscillatory bands, each with distinguishable behavioural and cognitive correlates( Jasper and Andrews, 1938 ). In addition to periodic oscillatory activity, EEG signals also contain aperiodic non-oscillatory activity, which is represented by the broadband 1/f β background shape of the power spectra (Donoghue and others, 2020; He, 2014 ). Recent studies have shown that features of the aperiodic 1/f β background, such as the slope steepness (measured by the β exponent) and background offset, can change with different brain states ( Lendner et al., 2020 ), during tasks ( Voytek et al., 2015 ), across the lifespan (Merkin and others, 2023) and in brain disorders ( Peterson et al., 2023 ). These findings have triggered a renewed interest in understanding the neural origins of aperiodic activity in EEG signals. While the role of synchronous firing of large populations of neurons is well established in generating EEG oscillations, the physiological underpinnings of aperiodic EEG activity are less well understood. Biophysical models of neural activity have posited that the steepness of the spectral slope results from the balance of excitatory and inhibitory postsynaptic potentials mediated by glutamatergic AMPA receptors and GABA A receptors respectively ( Gao et al., 2017 ). In one model, increasing the ratio of excitatory to inhibitory input decreased the spectral exponent (i.e., flattens the slope), whereas increasing inhibitory input increases the exponent (i.e., steepens the slope) ( Gao et al., 2017 ). However, in another model, altering other postsynaptic features such as the decay time of inhibitory postsynaptic current and the leakage current reversal potential also impacted spectral exponents, sometimes in non-linear ways ( Brake et al., 2024 ). In contrast, a simplified single neuron model showed that changes in firing rate resulted in broadband shifts in the offset of the power spectra without impacting the steepness of the slope, particularly over higher frequencies ( Miller et al., 2009 ). Together, predictions from these models suggest that various different neural mechanisms including changes in the ratio of excitation and inhibition could contribute to features of aperiodic EEG activity. Another method for investigating the neurophysiology of aperiodic EEG activity is to measure changes in EEG power spectra following administration of a drug with known molecular mechanisms of action. In line with predictions from biophysical modelling, drugs that increase inhibition, including GABA A receptor agonists diazepam (Salvatore and others, 2023), pentobarbital (Salvatore and others, 2023), allopregnanolone (Salvatore and others, 2023), propofol ( Colombo et al., 2019 ), and the GABA reuptake inhibitor tiagabine ( Muthukumaraswamy and Liley, 2018 ), increase the spectral exponent (steepen the slope). Furthermore, drugs that decrease excitation, such as perampanel ( Muthukumaraswamy and Liley, 2018 ), a glutamatergic AMPA receptor antagonist, and xenon ( Colombo et al., 2019 ), a glutamatergic NMDA receptor antagonist, also increase the spectral exponent (steepen the slope). Ketamine, another NMDA receptor antagonist which paradoxically increases excitability, possibly via reduced excitatory input to inhibitory interneurons, either has no effect (Salvatore and others, 2023) or decreases the spectral exponent (flattens the slope) (Waschke and others, 2021). However, not all drugs directly support the relationship between excitation/inhibition ratio and spectral slope. For instance, picrotoxin, a GABA A receptor antagonist which decreases inhibition, increases the spectral exponent (steepens the slope) ( Salvatore et al., 2024 ). Furthermore, little is known about how drugs that target other mechanisms which alter neural excitability, such as ion channel blockers or drugs that limit presynaptic neurotransmitter release, impact aperiodic features of EEG activity. The aim of this study was to investigate how anti-seizure medications (ASMs) which typically reduce neural excitability influence aperiodic EEG activity. We extracted the aperiodic component from resting-state EEG (eyes open and closed) recorded before and after administration of acute doses of lamotrigine (LTG) and levetiracetam (LEV). LTG is a voltage-gated sodium channel blocker which reduces theta oscillations (4-7 Hz), whilst LEV primarily targets synaptic vesicle protein 2A (SV2A) to modulate excitatory neurotransmission which increases beta and gamma oscilllations. Beyond these primary mechanisms, LTG also binds nicotinic acetylcholine and 5-HT3 receptors ( Vallés et al., 2008 ), and LEV has been linked to effects on AMPA receptors, monoaminergic and adenosinergic systems, GABAergic transmission, calcium homeostasis, and intracellular pH regulation ( Contreras-García et al., 2022 ). We quantified changes in both the spectral exponent (i.e., slope) and offset of the background of the EEG spectra after accounting for oscillatory peaks using established methods ( specparam ; formerly known as FOOOF) (Donoghue and others, 2020). Given that both drugs are thought to reduce neural excitability, two non-exclusive biologically plausible scenarios were hypothesised: 1) reduced excitability shifts the E/I balance towards inhibition, steepening the slope (i.e., increased exponent); and/or 2) reduced excitability decreases firing rates reducing the broadband offset. We also calculated the spectra after subtracting the aperiodic background to assess whether previously reported oscillatory changes occurred in addition to aperiodic changes following drug intake. 2. Methods 2.1 Participants This study utilized data from a previous experiment ( Biondi et al., 2022 ; Premoli et al., 2017 ). The original study recruited fifteen healthy male volunteers (mean age 25.2 ± 4.62), all of whom provided written informed consent before participating in any experimental procedures. Participants received LTG, LEV and a placebo (PBO). Thirteen of this sample were made available for the current study. All participants were confirmed to be right-handed according to the Edinburgh Handedness Inventory (laterality score ≥ 75%). Exclusion criteria included the intake of CNS-active medications, recent use of any drugs (including nicotine and alcohol), and the presence of neurological or psychiatric disorders. Additionally, participants were excluded if they had any contraindications to the medications used in the study (LEV/LTG). The study protocol was approved by the King’s College London Research Ethics Committee (CREC) and conducted in accordance with relevant guidelines and regulations. All participants provided written informed consent to participate in the study. 2.2 EEG Data Acquisition Participants were seated and instructed to remain silent and still, refraining from any cognitive or mental tasks. EEG data were acquired using a TMS-compatible EEG system (BrainAmp MRplus; Brain Products). The EEG signal was recorded with a 64-electrode EasyCap (EasyCap 64Ch; Brain Products). Electrodes were arranged according to the International 10–20 system, with channel AFz as ground and FCz as reference. The signals were hardware-filtered between DC and 1000 Hz and digitized at a sampling rate of 5 kHz. Conductive gel was applied to each electrode using a blunt-needle syringe to reduce impedance below 10 kΩ, which was maintained throughout the recording sessions. 2.3 Experimental Design and Procedure A single oral dose of either LTG (300 mg), a voltage-sensitive sodium channel blocker, LEV (3000 mg), a specific binder of synaptic vesicle protein 2A, or PBO was administered to participants on separate occasions, each spaced one week apart. This design ensured a sufficient washout period between each condition to prevent any carryover effects. Participants were asked to keep their eyes open and closed in separate conditions to assess different states of brain activity. Resting EEG recordings for both eyes-open and eyes-closed conditions were collected for 3 minutes each and performed at baseline (pre-drug) and 2 hours after drug intake. Concurrent single pulse transcranial magnetic stimulation and EEG conditions were also collected pre and post drug intake separately from the resting EEG recordings but were not analysed in this study and are published elsewhere( Biondi et al., 2022 ; Premoli et al., 2017 ). Participants were monitored closely throughout the study to ensure adherence to the protocol and to record any adverse effects. Drug administration and the sequence of eyes-open and eyes-closed conditions varied across sessions to minimize potential biases. 2.4 EEG Pre-processing The raw EEG data files from eyes-open and eyes-closed recordings, both pre- and post-drug administration, were transferred from King’s College London. These files were imported into MATLAB 2022b to undergo a customized pre-processing pipeline using EEGLAB ( Delorme and Makeig, 2004 ). A spatial map of the channel locations was uploaded to the recordings. The data were then down-sampled to 1000 Hz, bandpass filtered between 0.1 and 100 Hz, and notch filtered between 48-52 Hz. Subsequently, the data were segmented into 2-second epochs. Each epoch was visually inspected, and those contaminated with excessive eyeblink or muscle artifacts were removed. Additionally, any disconnected channels (i.e., with flat lines) were identified during this inspection and were excluded. To further refine the data, independent component analysis (ICA) was performed using FastICA. Components associated with residual eye movement and ongoing muscle artifacts were identified and removed following the default criteria of the TESA component selection function ( Rogasch et al., 2017 ). Although some of TESA’s component selection criteria are specific for event-related transcranial magnetic stimulation data (e.g., TMS-evoked muscle activity), the eye blink/movement criteria used here are equally applicable to resting-state data, and the criteria for detecting ongoing muscle activity were developed on resting-state data. Missing channels were interpolated, and the data were re-referenced to the average of all electrodes. 2.5 Spectral Analysis Power spectra were calculated using Welch’s method via the PWELCH function in MATLAB. Each channel’s signal was divided into non-overlapping 2-second segments, with the power spectral density (PSD) computed using a Hamming window of the same length. Periodograms were calculated for each segment and averaged to produce the final PSD estimate with a frequency resolution of 0.48 Hz. The computed PSDs and their corresponding frequency values were then saved as JSON files for further analysis. 2.6 Spectral parameterisation The specparam analysis (formerly FOOOF) (Donoghue et al., 2020), was conducted in Python and executed in Spyder3 (Anaconda3). The algorithm first estimates the aperiodic component, using the power at the first frequency and the slope between the first and last points of the spectrum to capture the general 1/f power decline. A multi-Gaussian fit then removes oscillatory peaks, isolating the aperiodic component and enhancing the accuracy of the initial fit. The aperiodic exponent and offset were extracted across the frequency range (1-45 Hz) with peak width limits set between 1 and 8, in fixed mode (without a knee). We used the fixed mode parameterisation because the resting-state EEG spectra over the analysed frequency range does not consistently demonstrate a clear bend or knee, as is often evident in invasive recordings. Estimating the knee parameter over a relatively narrow frequency range can reduce the parameter stability and increase the risk of overfitting. Periodic oscillatory activity was assessed by subtracting the aperiodic component from the original spectra, and then averaging across five frequency bands: delta (2-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz) and gamma (30-45 Hz). The goodness-of-fit metric, R², was also extracted to assess the explained variance of the model and thus allowing for the assessment of model performance. 2.7 Statistical Analysis To assess changes in aperiodic parameters exponent and offset, periodic frequency bands and goodness of fit, cluster-based permutation tests were conducted using the FieldTrip toolbox in MATLAB ( Oostenveld et al., 2011 ). A within-subjects design was employed, with each participant treated as a factor in the statistical model. Clusters were identified by grouping at least two neighbouring channels, with an alpha level of 0.05 set for initial cluster inclusion. The cluster statistic was computed using the maximum sum method, and Monte Carlo simulations with 5000 randomizations were applied to correct for multiple comparisons. Dependent samples t-tests were used to compare pre- and post-drug conditions, with two-tailed testing to detect both positive and negative changes. 3. Results 3.1 The Effect of ASMs on Aperiodic EEG Activity with Eyes Open Offset First, we examined the effect of ASMs on aperiodic offset within the eyes-open condition ( figure 1 ). We could not find evidence for changes in offset values following PBO and LEV intake (no significant clusters). However, LTG intake led to a significant decrease in offset values which was largest over left frontal, central and right parietal electrodes (maximum summed t = -75.1, p = 0.018; figure 2 A & B ). To further investigate the influence of drug effects on aperiodic offset, we directly compared the pre- and post-drug offset values between conditions. Consistent with the post vs pre analysis, the post-LTG spectra had significantly smaller offset values compared to the post-PBO spectra (maximum summed t = -83.8, p < 0.001; figure 2D ). We could not find evidence for differences when comparing pre-drug offset values between conditions (no significant clusters), indicating that the offset of the power spectra is reproducible across sessions ( figure 2C ). Furthermore, we could not find evidence for differences in the post-drug spectra when comparing LEV and PBO (no significant clusters). Download figure Open in new tab Figure 1: Drug related changes in EEG power spectra and aperiodic fit (eyes open). The top row illustrates the uncorrected power spectral density (PSD) during eyes-open EEG in log-log space for pre- and post-drug conditions. The bottom row shows the corresponding aperiodic model fits. PSD (A-C) and aperiodic model fits (D-F) are averaged across 63 channels, while D and H represents the average across the channels in the significant cluster for LTG. Download figure Open in new tab Figure 2: Drug-related changes in aperiodic offset (eyes open). Offset values averaged across participants during the three-minute eyes-open resting state EEG recording. Pre- and post-drug values are shown in (A) and differences between post-and pre-drug offsets presented as t-statistics in (B). The right columns illustrate baseline comparisons of PBO vs. LEV and PBO vs. LTG (C), and the corresponding post drug comparisons are shown in (D). * shows channels contributing to significant clusters (p<0.05). Exponent Next, we assessed the impact of different ASMs on the aperiodic exponent. For the eyes-open condition, we could not find evidence for changes in the aperiodic exponent between pre- and post-drug administration for either LEV or PBO (no significant clusters) ( figure 3 ). Two negative clusters emerged following LTG intake which can be seen in the central electrodes in figure 3B indicating a reduction in exponent; although, neither cluster reached the threshold for statistical significance (cluster 1: maximum summed t = -10.4, p = 0.102; cluster 2: maximum summed t = -9.8, p = 0.104). When comparing post drug spectra, a negative cluster emerged indicating smaller exponent values following LTG compared to PBO (maximum summed t = -32.7, p = 0.040). Comparison of pre-drug exponent values across conditions revealed no significant clusters for any condition, ( Figure 3C ) and we could not find evidence for differences in the post-drug aperiodic exponent when comparing LEV and PBO (no significant clusters) ( Figure 3D ). Taken together, these findings suggest that LTG flattens the EEG spectra by reducing low-frequency and increasing high-frequency aperiodic power (e.g., reduced offset, reduced exponent) during an eyes-open resting state condition. Download figure Open in new tab Figure 3: Drug-related changes in aperiodic exponent (eyes open). Aperiodic exponent values during the three-minute eyes-open resting-state EEG recording, averaged across participants. (A) Pre- and post-drug values. (B) Differences between post- and pre-drug exponents, represented as t-statistics. (C) Baseline (pre-drug) comparisons between PBO vs. LEV and PBO vs. LTG. (D) Corresponding post-drug comparisons between PBO vs. LEV and PBO vs. LTG. * shows channels contributing to significant clusters (p<0.05). 3.2 The Effect of ASMs on Aperiodic EEG Activity with Eyes Closed Offset Figure 4 shows the power spectra before and after drug intake in the eyes closed condition. We could not find evidence of offset value changes from pre- to post-drug intake for LEV, LTG or PBO (no significant clusters; figure 5 ). Pre-drug offset values did not differ between conditions, with no significant clusters identified ( Figure 5C ). Comparing post-LTG to post-PBO revealed a negative cluster (maximum summed t = -13.7, p = 0.102) indicating a smaller offset following LTG, though this did not meet the alpha threshold for significance ( figure 5D ). We could not find evidence for a difference in post-LEV and post-PBO offset values (no significant clusters). Download figure Open in new tab Figure 4: Drug related changes in EEG power spectra and aperiodic fit (eyes closed). The top row illustrates the uncorrected power spectra for pre- and post-drug conditions during the eyes-closed EEG recording, shown in log-log space. The bottom row displays the corresponding aperiodic model fits. Both spectra and aperiodic model fits have been averaged across all 63 channels for each drug condition. Download figure Open in new tab Figure 5: Drug-related changes in aperiodic offset (eyes closed). Offset values averaged across participants during the three-minute eyes-closed resting state EEG recording. Pre- and post-drug values are shown in (A) and differences between post-and pre-drug offsets presented as t-statistics in (B). The right columns illustrate baseline comparisons of PBO vs. LEV and PBO vs. LTG (C), and the corresponding post drug comparisons are shown in (D). Exponent We could not find evidence for changes in the aperiodic exponent following either LEV, LTG or PBO intake (no significant clusters) ( Figure 6 ). We could not find evidence for changes in the exponent when comparing post-LTG and post-LEV against post-PBO (no significant clusters). These findings indicate that the flattening of the EEG spectra was specific to the eyes-open condition and not observed during the eyes-closed condition. Download figure Open in new tab Figure 6: Drug-related changes in Aperiodic Exponent (eyes closed). Aperiodic exponent values during the three-minute eyes-closed resting-state EEG recording, averaged across participants. (A) Pre- and post-drug values. (B) Differences between post- and pre-drug exponents, represented as t-statistics. (C) Baseline (pre-drug) comparisons between PBO vs. LEV and PBO vs. LTG. (D) Corresponding post-drug comparisons between PBO vs. LEV and PBO vs. LTG. 3.3 The Effect of ASMs on Periodic EEG Activity Eyes open We next assessed changes in periodic activity following ASM intake after correcting for aperiodic activity. In the eyes open condition, LEV increased beta oscillatory power (maximum summed t = 187.2, p < 0.001). In contrast, LTG reduced theta (maximum summed t = −243.9, p < 0.001) and alpha oscillatory power (maximum summed t = −238.3, p = 0.001), while increasing gamma power (maximum summed t = 19.9, p = 0.012; figure 7 ). No significant changes were observed in the remaining oscillatory bands for either drug. Download figure Open in new tab Figure 7: Drug related changes in corrected EEG power spectra (eyes open). Plots illustrate the corrected power spectral density (PSD) after subtraction of the 1/f aperiodic component during eyes-open EEG in semi-log space for pre- and post-drug conditions. Lines show the average of channels contributing to significant clusters. Inset topographies show t-values (post vs pre) and significant channels contributing to clusters (*, p<0.05). Shaded grey bars represent the frequency range of significant cluster. Eyes closed In the eyes closed condition, LEV again increased beta oscillatory power (maximum summed t = 230.0, p = 0.001), whereas LTG reduced theta power (maximum summed t = −186.2, p = 0.004; figure 8 ). No significant differences were identified in the remaining oscillatory bands for either drug. Overall, these findings indicate that changes in periodic oscillatory power following LEV and LTG intake occur in addition to alterations in aperiodic activity, with the strongest effects observed in the eyes open condition. Download figure Open in new tab Figure 8: Drug related changes in corrected EEG power spectra (eyes closed). Plots illustrate the corrected power spectral density (PSD) after subtraction of the 1/f aperiodic component during eyes-open EEG in semi-log space for pre- and post-drug conditions. Lines show the average of channels contributing to significant clusters. Inset topographies show t-values (post vs pre) and significant channels contributing to clusters (*, p<0.05). Shaded grey bars represent the frequency range of significant cluster. 3.4 The effect of drugs on model fit quality Eyes open Finally, to assess changes in model fit quality over time, we extracted the goodness-of-fit parameter (R²) from the specparam algorithm. No significant changes were observed following PBO intake (no significant clusters; mean R² ± SD: pre = 0.97 ± 0.05, post = 0.99 ± 0.02, averaged across electrodes) or LTG intake (no significant clusters; pre = 0.98 ± 0.02, post = 0.97 ± 0.06). In contrast, model fit quality significantly declined after LEV intake (max. summed t = -8.9, p = 0.029; mean R² ± SD: pre = 0.98 ± 0.04, post = 0.98 ± 0.05, Figure 9 ), although these changes were numerically small. Download figure Open in new tab Figure 9. Changes in model fit quality following drug intake (eyes open). R² values during a three-minute resting-state recording, averaged across participants, for eyes-open across all drug conditions. The left and middle column show pre- and post-drug R² values, while the right column displays the difference between post-and pre-drug R² values as t-statistics. * shows channels contributing to significant clusters (p<0.05). Eyes closed During the eyes closed condition, we could not find evidence for a change in model fit quality following LTG intake (mean R² ± SD; pre = 0.99 ± 0.01, post = 0.98 ± 0.03), LEV intake (mean R² ± SD; pre = 0.99 ± 0.01, post = 0.99 ± 0.01), or PBO intake (mean R² ± SD; pre = 0.99 ± 0.01, post = 0.99 ± 0.01; figure 10 ). Together, these findings suggest that changes in model fit quality did not impact changes in aperiodic parameters following drug intake. Download figure Open in new tab Figure 10. Changes in model fit quality following drug intake (eyes open). R² values during a three-minute resting-state recording, averaged across participants, for eyes-open across all drug conditions. The left and middle column show pre- and post-drug R² values, while the right column displays the difference between post-and pre-drug R² values as t-statistics. 4. Discussion The aim of the current study was to assess how ASMs influence aperiodic EEG activity. Using two drugs with distinct mechanisms of action, we observed a significant reduction in aperiodic offset during eyes-open recordings following LTG intake. This effect was evident in both pre–post and post-placebo comparisons and was largely confined to lower frequencies. We also observed a reduction in aperiodic exponent following LTG, including in the post-LTG versus post-placebo comparison, suggesting a flattening of the power spectrum after LTG administration. In addition to these aperiodic changes, LTG reduced periodic theta and alpha power while increasing gamma power in the eyes-open condition, whereas only theta reductions were observed during eyes-closed recordings. In contrast, LEV did not alter aperiodic exponent or offset in either condition, despite producing increases in periodic beta power in both eyes-open and eyes-closed recordings. Overall, these findings suggest that aperiodic EEG parameters may be particularly sensitive to VGSC-mediated changes in neural excitability, with these effects appearing to be specific to the eyes-open condition, whereas alterations in presynaptic neurotransmitter release may preferentially influence periodic oscillatory activity. Lamotrigine (LTG) reduces neuronal excitability by blocking VGSCs and interacting with nicotinic ACh and 5-HT3 receptors ( Cunningham and Jones, 2000 ), effects that likely contribute to its suppression of rapid, sustained firing in response to current injection, as observed in early in vitro studies ( Calabresi et al., 1999 ; Helen et al., 1992 ; Salvati et al., 1999 ).Recent research has also demonstrated a notable decrease in the number of action potentials per cell over time following LTG treatment ( Kazmierska-Grebowska et al., 2021 ). In humans, spike rates measured from intracranial electrodes in patients with epilepsy are positively correlated with broadband spectral shifts (e.g., reductions in offset and no change in exponent) ( Manning et al., 2009 ). However, we found a reduction in both the offset and slope in the eyes open condition, suggesting a flattening of the EEG power spectra following LTG. This was accompanied by reductions in the theta oscillatory power in both the eyes open and closed conditions, and alpha oscillatory power decreases and gamma power increases specifically in the eyes open condition. Broadband reductions in spectral power following acute LTG administration align with previous EEG studies assessing longer-term treatment. After a 7-week titration period, Smith et al ., (2006) reported significant reductions in EEG power between 2-12 Hz, with the most pronounced effects in the 6-12 Hz alpha band. Clemens et al ( Clemens et al., 2006 ) expanded upon these findings in a medication-naive epileptic population, reporting reductions in EEG power across the 2-30 Hz range, along with an increase in alpha mean frequency (AMF). Using the same data from the current study, Biondi et al ( Biondi et al., 2022 ) reported a more targeted effect of LTG, with a selective reduction in theta band activity during eyes-closed resting-state EEG and no effects in other frequency bands, similar to our current findings taking into account aperiodic activity. Together, these findings suggest that both aperiodic and periodic activity are altered by LTG with a general reduction in power <12 Hz, especially when eyes are open. The reasons why LTG-induced changes in aperiodic and periodic activity were more pronounced during the eyes-open relative to the eyes-closed condition remain unclear. One possibility is that the findings reflect residual eye-movement-related activity that was not removed using ICA. At high doses, LTG has been associated with abnormal slowing of eye movements and drowsiness, and reductions in eye movements can decrease both the aperiodic offset and exponent of resting EEG spectra ( Tröndle and Langer, 2026 ). However, eye-movement-related effects are typically maximal over frontal and occipital regions ( Tröndle and Langer, 2026 ), which differs from the predominantly centro-parietal distribution observed in the present study. Moreover, abnormal eye movements are generally associated with higher and prolonged LTG dosing, contrasting with the acute single dose administered here. An alternative explanation is that the neurophysiological mechanisms underlying LTG-induced spectral alterations are more detectable during the eyes-open state, which is characterised by greater sensory input and cortical desynchronisation. Consistent with this possibility, state-dependent changes in EEG power spectra have also been reported for other pharmacological agents, including caffeine( Siepmann and Kirch, 2002 ), suggesting that stronger effects during eyes-open resting state may not be unique to LTG. Nonetheless, residual ocular or myogenic contributions cannot be fully excluded and should be considered when interpreting the state-dependent nature of these findings Reductions in low-frequency power are not consistently observed across ASMs that share a primary VGSC mechanism. Carbamazepine (CBZ) acts primarily on VGSCs, with secondary actions at calcium channels and adenosine receptors ( Mantegazza et al., 2010 ), yet has been associated with increased low-frequency power and a reduction in AMF after several weeks of dosing ( Marciani et al., 1992 ). Smith et al ., (2006) further highlighted these differences by showing that LTG increased AMF, while CBZ did not. Following a single dose of CBZ in healthy individuals, Darmani et al ( Darmani et al., 2019 ) also observed an increase in EEG power across multiple bands including theta, alpha and beta frequencies. Following chronic dosing, the increase in power and AMF slowing are thought to result from the neurotoxic impacts of CBZ and may reflect a neurophysiological correlate of impaired cognition( Höller et al., 2018 ). Together, these findings highlight that drugs with shared molecular mechanisms can result in distinct changes to EEG spectral features, suggesting that multiple mechanisms may contribute to the observed shifts in the EEG spectrum. Rather than altering VGSC properties ( Zona et al., 2001 ), LEV primarily modulates neurotransmitter release by binding to SV2A ( Poulain and Margineanu, 2002 ). Additional targets such as AMPA, noradrenaline, adenosine, and serotonin receptors, along with mechanisms involving calcium homeostasis, GABAergic transmission, and intracellular pH regulation, have also been reported to contribute to the effects of LEV ( Contreras-García et al., 2022 ). Such molecular actions may underlie findings from single-cell models, which show reduced spike variability and amplitude following LEV administration, likely due to dampened synaptic release ( Arato et al., 2021 ). Reduced spike rates were also observed following LEV administration in another study ( Cavitt and Privitera, 2004 ), which yielded stable offset values, suggesting an alternative underlying mechanism. The lack of change in the aperiodic features of the EEG spectra following LEV in the current study suggest aperiodic activity is not strongly influenced by presynaptic neurotransmitter release. In healthy individuals, LEV appears to exert minimal influence on EEG power spectra. For example, Mecarelli et al ., (2004) reported no significant changes in absolute power across any frequency bands during eyes-closed resting-state EEG recordings. However, Salinsky et al ., (2011) found that in healthy volunteers, LEV administration led to a decrease in lower frequency power, accompanied by increases in alpha and beta bands. Changes have also been observed in patients with epilepsy, although these tend to be limited to higher frequency bands ( Park and Kwon, 2009 ). Using the same data as this study, Biondi et al ., (2022) observed increases in beta, and gamma power in the eyes-closed condition. We found increases in beta power, but not gamma power after correcting for aperiodic activity in both eyes-open and eyes-closed conditions, suggesting that these alterations reflect genuine oscillatory changes rather than shifts in the aperiodic component. Overall, our findings suggest LEV increases periodic beta oscillatory power without altering aperiodic activity. Several limitations should be considered when interpreting these findings. First, the sample size was modest and consisted exclusively of healthy male participants, which may have limited sensitivity to detect subtle drug-induced changes and reduces the generalisability of the findings. Second, both drugs were administered as a single acute dose without serum concentration measurements, and therefore the observed effects may differ from those associated with chronic therapeutic administration in epilepsy populations. Third, although extensive preprocessing and ICA-based artifact rejection were applied, residual ocular or myogenic activity cannot be fully excluded, particularly given that LTG-related effects were observed primarily during the eyes-open condition. Finally, aperiodic parameter estimation is influenced by methodological choices including fitting range, parameterisation approach, and the contribution of oscillatory activity to the spectra. Accordingly, the present findings should be interpreted cautiously until replicated using larger samples and complementary analytical approaches. 5. Conclusions In summary, we found that LTG, but not LEV, altered aperiodic neural activity measured in EEG power spectra. Our findings inform the neurophysiological origins of EEG dynamics by demonstrating how molecular mechanisms influence large-scale neural activity. The potential sensitivity of the spectra to such small-scale effects emphasizes the value of integrating ASMs with EEG to enhance our understanding of the underlying neurophysiological signals involved. Furthermore, our findings showcase the precision of EEG measures, like resting state power spectra, as a powerful tool for exploring and clinically evaluating intricate neurophysiological processes in conscious humans. 7. Funding NCR was supported by the Australian Research Council, Australia [FT210100694]. 8. Author contributions Marissa M. Holden: Formal analysis, Writing - Original Draft, Visualization. Isabella Premoli: Methodology, Investigation, Data Curation, Writing - Review & Editing. Scott R. Clark: Writing - Review & Editing, Supervision. Mark P. Richardson: Investigation, Writing - Review & Editing. Mitchell R. Goldsworthy: Conceptualization, Writing - Review & Editing, Supervision, Project administration. Nigel C. Rogasch: Conceptualization, Writing - Review & Editing, Supervision, Project administration 9. Competing Interest Statement In the past 5 years, NCR has received: grant research funding from the Australian Research Council (ARC), and the Medical Research Future Fund (MRFF); contract research funding from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and CMAX Clinical Research PTY LTD; and consultancy fees from OVID Therapeutics Inc. SRC has received research funding from the Wellcome Trust, NIMH, NHMRC, Lundbeck-Otsuka, and Janssen-Cilag; has received consulting fees from Insight Timer; has received honoraria for lectures or manuscript writing for Lundbeck-Otsuka and Servier; has served on advisory boards for Lundbeck-Otsuka and Servier; and is a board member of the Mental Health Foundation of Australia. MPR has received grant research funding from: UK Medical Research Council, UK National Institute of Health Research, Epilepsy Research UK, European Commission H2020, Canadian Institutes of Health Research, Maudsley Charity; contract research funding from Autifony Therapeutics, Xenon Pharma GW Pharma; has provided consultancy to Lundbeck and UNEEG Medical. and is a founder of NeuralPulse Ltd. 10. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the authors used ChatGPT in order to assist with language editing, improving clarity and structure of the manuscript, and refining the interpretation and discussion of the findings. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. 11. Data availability The dataset analysed during the current study is available from the corresponding author on reasonable request and with permission. Funder Information Declared Australian Research Council , FT210100694 Footnotes ↵ # Co-senior author - Changed the fitting range from 1-40 to 1-45 Hz. - Emphasised the change in aperiodic activity following LTG in relation to flattening of spectra (as opposed to broadband offset reduction) - Included analysis of periodic activity in the corrected spectra (e.g., after subtracting the 1/f aperiodic component). 6. References ↵ Arato J , Sensi SL , Cozzi L . Real-Time Monitoring of Levetiracetam Effect on Human iPSC-Derived Neurons by Microelectrode Array: An In Vitro Model to Evaluate Antiepileptic Drugs . Biosensors 2021 ; 11 : 450 . doi: 10.3390/bios11110450 . OpenUrl CrossRef PubMed ↵ Berger H . On the Electroencephalogram of Man . Archives of Psychiatry and Neurological Diseases 1929 ; 87 : 527 – 70 . doi: 10.1007/BF01797193 . OpenUrl CrossRef PubMed ↵ Biasiucci A , Franceschiello B , Murray MM. Electroencephalography . Current Biology 2019 ; 29 . doi: 10.1016/j.cub.2018.11.052 . OpenUrl CrossRef PubMed ↵ Biondi A , Rocchi L , Santoro V , Rossini PG , Beatch GN , Richardson MP , et al. Spontaneous and TMS-related EEG changes as new biomarkers to measure anti-epileptic drug effects . Scientific Reports 2022 ; 12 : 1919 . OpenUrl PubMed ↵ Brake N , Duc F , Rokos A , Arseneau F , Shahiri S , Khadra A , et al. A neurophysiological basis for aperiodic EEG and the background spectral trend . Nature Communications 2024 ; 15 : 1514 . doi: 10.1038/s41467-024-45922-8 . OpenUrl CrossRef PubMed ↵ Calabresi P , Centonze D , Marfia GA , Pisani A , Bernardi G . An in vitro electrophysiological study on the effects of phenytoin, lamotrigine and gabapentin on striatal neurons . British J Pharmacology 1999 ; 126 : 689 – 96 . doi: 10.1038/sj.bjp.0702361 . OpenUrl CrossRef PubMed Web of Science ↵ Cavitt J , Privitera M . Levetiracetam Induces a Rapid and Sustained Reduction of Generalized Spike-Wave and Clinical Absence . Archives of Neurology 2004 ; 61 : 1604 – 7 . doi: 10.1001/archneur.61.10.1604 . OpenUrl CrossRef PubMed ↵ Clemens B , Janszky J , Szucs A , Clemens Z , Bessenyei M , Halasz P . Lamotrigine reduces EEG synchronization in patients with epilepsy . Clinical Neurophysiology 2006 ; 117 : 1186 – 91 . doi: 10.1016/j.clinph.2006.02.012 . OpenUrl CrossRef ↵ Colombo MA , Napolitani M , Boly M , Gosseries O , Casarotto S , Rosanova M , et al. Unveiling age-independent spectral markers of propofol-induced loss of consciousness by decomposing the electroencephalographic spectrum into its periodic and aperiodic components . Frontiers in Neuroscience 2019 . ↵ Contreras-García IJ , Cárdenas-Rodríguez N , Romo-Mancillas A , Bandala C , Zamudio SR , Gómez-Manzo S , et al. Levetiracetam Mechanisms of Action: From Molecules to Systems . Pharmaceuticals 2022 ; 15 : 475 . doi: 10.3390/ph15040475 . OpenUrl CrossRef ↵ Cunningham MO , Jones RS . The anticonvulsant, lamotrigine decreases spontaneous glutamate release but increases spontaneous GABA release in the rat entorhinal cortex in vitro . Neuropharmacology 2000 ; 39 : 2139 – 46 . OpenUrl CrossRef PubMed Web of Science ↵ Darmani G , Bergmann TO , Zipser C , Baur D , Müller-Dahlhaus F , Ziemann U . Effects of antiepileptic drugs on cortical excitability in humans: A TMS-EMG and TMS-EEG study . Human Brain Mapping 2019 ; 40 : 1276 – 89 . doi: 10.1002/hbm.24448 . OpenUrl CrossRef PubMed ↵ Delorme A , Makeig S . EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics . Journal of Neuroscience Methods 2004 ; 134 : 9 – 21 . doi: 10.1016/j.jneumeth.2003.10.009 . OpenUrl CrossRef PubMed Web of Science Donoghue T , others. Parameterizing neural power spectra into periodic and aperiodic components . Nature Neuroscience 2020 ; 23 : 1655 – 65 . doi: 10.1038/s41593-020-00744-x . OpenUrl CrossRef PubMed ↵ Gao R , Peterson EJ , Voytek B . Inferring synaptic excitation/inhibition balance from field potentials . NeuroImage 2017 ; 158 : 70 – 8 . doi: 10.1016/j.neuroimage.2017.06.078 . OpenUrl CrossRef PubMed ↵ He BJ. Scale-free brain activity: past, present, and future . Trends in Cognitive Sciences 2014 ; 18 : 480 – 7 . doi: 10.1016/j.tics.2014.04.003 . OpenUrl CrossRef PubMed Web of Science ↵ Helen C , Kamp D , Harris E . An in vitro investigation of the action of lamotrigine on neuronal voltage-activated sodium channels . Epilepsy Research 1992 ; 13 : 107 – 12 . doi: 10.1016/0920-1211(92)90065-2 . OpenUrl CrossRef PubMed Web of Science ↵ Höller Y , Helmstaedter C , Lehnertz K . Quantitative pharmaco-electroencephalography in antiepileptic drug research . CNS Drugs 2018 ; 32 : 839 – 48 . doi: 10.1007/s40263-018-0557-x . OpenUrl CrossRef PubMed ↵ Jasper HH , Andrews HL . Normal differentiation of occipital and precentral regions in man . Archives of Neurology & Psychiatry 1938 ; 39 : 96 – 115 . OpenUrl ↵ Kazmierska-Grebowska P , Siwiec M , Sowa JE , Caban B , Kowalczyk T , Bocian R , et al. Lamotrigine Attenuates Neuronal Excitability, Depresses GABA Synaptic Inhibition, and Modulates Theta Rhythms in Rat Hippocampus . International Journal of Molecular Sciences 2021 ; 22 : 13604 . doi: 10.3390/ijms222413604 . OpenUrl CrossRef ↵ Lendner JD , Helfrich RF , Mander BA , Romundstad L , Lin JJ , Walker MP , et al. An electrophysiological marker of arousal level in humans . eLife 2020 ; 9 : e55092 . doi: 10.7554/eLife.55092 . OpenUrl CrossRef PubMed ↵ Manning JR , Jacobs J , Fried I , Kahana MJ . Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans . Journal of Neuroscience 2009 ; 29 : 13613 – 20 . doi: 10.1523/JNEUROSCI.2041-09.2009 . OpenUrl Abstract / FREE Full Text ↵ Mantegazza M , Curia G , Biagini G , Ragsdale DS , Avoli M . Voltage-gated sodium channels as therapeutic targets in epilepsy and other neurological disorders . The Lancet Neurology 2010 ; 9 : 413 – 24 . doi: 10.1016/S1474-4422(10)70059-4 . OpenUrl CrossRef PubMed Web of Science ↵ Marciani MG , Salvolini U , Di Girolamo S . Carbamazepine effects on background EEG activity in patients with partial epilepsy . Electroencephalography and Clinical Neurophysiology 1992 ; 82 : 441 – 5 . doi: 10.1016/0013-4694(92)90055-B . OpenUrl CrossRef ↵ Mecarelli O , Pro S , Rychlicki F , Pulitano P , Tambasco N , Di Perri R . Clinical, cognitive and neurophysiologic correlates of short-term treatment with carbamazepine, oxcarbazepine and levetiracetam in newly diagnosed partial epilepsy: a randomized study . Epilepsia 2004 ; 45 : 1009 – 16 . doi: 10.1111/j.0013-9580.2004.63003.x . OpenUrl CrossRef PubMed Merkin A , others. Do age-related differences in aperiodic neural activity explain differences in resting EEG alpha? Neurobiology of Aging 2023 ; 121 : 78 – 87 . doi: 10.1016/j.neurobiolaging.2022.09.003 . OpenUrl CrossRef PubMed ↵ Miller KJ , Sorensen LB , Ojemann JG , den Nijs M. Power-Law Scaling in the Brain Surface Electric Potential . PLoS Computational Biology 2009 ; 5 : e1000609 . doi: 10.1371/journal.pcbi.1000609 . OpenUrl CrossRef PubMed ↵ Muthukumaraswamy SD , Liley DTJ . 1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes . NeuroImage 2018 ; 179 : 582 – 95 . OpenUrl CrossRef PubMed ↵ Oostenveld R , Fries P , Maris E , Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data . Computational Intelligence and Neuroscience 2011 :Article ID 156869. doi: 10.1155/2011/156869 . OpenUrl CrossRef PubMed ↵ Park SP , Kwon OY. Increased EEG Current-Source Density in the High Beta Frequency Band Induced by Levetiracetam Adjunctive Therapy in Refractory Partial Epilepsy . Journal of Clinical Neurology 2009 ; 5 : 178 – 85 . doi: 10.3988/jcn.2009.5.4.178 . OpenUrl CrossRef PubMed ↵ Peterson EJ , Rosen BQ , Campbell AM , others. Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations . Journal of Clinical Neurophysiology 2023 ; 54 . doi: 10.1177/15500594231165589 . OpenUrl CrossRef PubMed ↵ Poulain P , Margineanu DG . Levetiracetam opposes the action of GABAA antagonists in hypothalamic neurones . Neuropharmacology 2002 ; 42 : 346 – 52 . doi: 10.1016/S0028-3908(01)00185-X . OpenUrl CrossRef PubMed ↵ Premoli I , Biondi A , Carlesso S , Rivolta D , Richardson MP . Lamotrigine and levetiracetam exert a similar modulation of TMS-evoked EEG potentials . Epilepsia 2017 ; 58 : 42 – 50 . doi: 10.1111/epi.13599 . OpenUrl CrossRef PubMed ↵ Rogasch NC , Sullivan C , Thomson RH , Rose NS , Bailey NW , Fitzgerald PB , et al. Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software . NeuroImage 2017 ; 147 : 934 – 51 . OpenUrl PubMed ↵ Salinsky MC , Oken BS , Storzbach D , Dodrill CB. Effects of levetiracetam on EEG and cognition in healthy volunteers . Clinical Neurophysiology 2011 ; 122 : 1605 – 10 . doi: 10.1016/j.clinph.2011.09.012 . OpenUrl CrossRef ↵ Salvati P , Maj R , Caccia C , Cervini MA , Fornaretto MG , Lamberti E , et al. Biochemical and Electrophysiological Studies on the Mechanism of Action of PNU-151774E, A Novel Antiepileptic Compound . The Journal of Pharmacology and Experimental Therapeutics 1999 ; 288 : 1151 – 9 . doi: 10.1016/S0022-3565(24)38068-1 . OpenUrl Abstract / FREE Full Text ↵ Salvatore SV , Lambert PM , Benz A , Rensing NR , Wong M , Zorumski CF , et al. Periodic and aperiodic changes to cortical EEG in response to pharmacological manipulation . J Neurophysiol 2024 ; 131 : 529 – 40 . doi: 10.1152/jn.00445.2023 . OpenUrl CrossRef PubMed Salvatore SV , others. Periodic and aperiodic changes to cortical EEG in response to pharmacological manipulation . Journal of Neurophysiology 2023 ; 131 : 529 – 40 . doi: 10.1152/jn.00445.2023 . OpenUrl CrossRef ↵ Siepmann M , Kirch W . Effects of Caffeine on Topographic Quantitative EEG . Neuropsychobiology 2002 ; 45 : 161 – 6 . doi: 10.1159/000054958 . OpenUrl CrossRef PubMed ↵ Smith ME , Gevins A , Brown H , Karnik A , Du R . Distinct cognitive neurophysiologic profiles for lamotrigine and topiramate: electrophysiologic measures of drug action in the human brain . Epilepsia 2006 ; 47 : 695 – 703 . doi: 10.1111/j.1528-1167.2006.00594.x . OpenUrl CrossRef PubMed ↵ Tröndle M , Langer N. Non-Neural Sources Systematically Impact Aperiodic EEG Activity 2026 :2026.01.29.702285. doi: 10.64898/2026.01.29.702285 . OpenUrl Abstract / FREE Full Text ↵ Vallés AS , Garbus I , Antollini SS , Barrantes FJ . A novel agonist effect on the nicotinic acetylcholine receptor exerted by the anticonvulsive drug Lamotrigine . Biochimica et Biophysica Acta (BBA) - Biomembranes 2008 ; 1778 : 2395 – 404 . doi: 10.1016/j.bbamem.2008.06.012 . OpenUrl CrossRef PubMed ↵ Voytek B , Kramer MA , Case J , Lepage KQ , Tempesta ZR , Knight RT , et al. Age-Related Changes in 1/f Neural Electrophysiological Noise . Journal of Neuroscience 2015 ; 35 : 13257 – 65 . OpenUrl Abstract / FREE Full Text Waschke L , others. Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent . eLife 2021 ; 10 . doi: 10.7554/eLife.70068 . OpenUrl CrossRef PubMed ↵ Zona C , Niespodziany I , Marchetti C , Klitgaard H , Bernardi G , Margineanu DG . Levetiracetam does not modulate neuronal voltage-gated Na + and T-type Ca 2+ currents . Seizure 2001 ; 10 : 279 – 86 . doi: 10.1053/seiz.2001.0520 . OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted May 12, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity Marissa M. Holden , Isabella Premoli , Scott R. Clark , Mark P. Richardson , Mitchell R. Goldsworthy , Nigel C. Rogasch bioRxiv 2025.11.02.686141; doi: https://doi.org/10.1101/2025.11.02.686141 Share This Article: Copy Citation Tools Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity Marissa M. Holden , Isabella Premoli , Scott R. Clark , Mark P. Richardson , Mitchell R. Goldsworthy , Nigel C. Rogasch bioRxiv 2025.11.02.686141; doi: https://doi.org/10.1101/2025.11.02.686141 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13895) Bioinformatics (41951) Biophysics (21456) Cancer Biology (18594) Cell Biology (25520) Clinical Trials (138) Developmental Biology (13381) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22510) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88622) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)