2025/10/29 by Massimo Rizzi, Peter J. West, Annamaria Vezzani +2 · 1 voice
Medicine · Neuroscience · #EEG and Brain-Computer Interfaces #Epilepsy research and treatment #Functional Brain Connectivity Studies
paper · pdf · doi:10.1111/epi.18708
openalex created_date 2025/10/29 · openalex publication_date 2025/10/29 · openalex updated_date 2026/07/15
The assessment of antiseizure medication (ASM) efficacy still predominantly relies on changes in seizure frequency (SF). However, in people with epilepsy (PWE), intrinsic variability in seizure timing and patterns significantly influences this assessment, often leading to potential false-negative or false-positive results. A low seizure rate particularly increases false-positive risk, as average seizure-free periods can be statistically comparable to therapy duration. This impacts clinical practice, necessitating extended monitoring and delaying appropriate treatments. For new drug development, PWE with low SFs are typically excluded from clinical trials, hindering patient enrollment and resulting in cohorts that may not adequately represent the general PWE population. To address the well-known limitations of SF as a therapeutic marker, in our recent study published in Epilepsia (doi: https://doi.org/10.1111/epi.18397), we utilized a widely used mouse model of acquired epilepsy that exhibits similar SF variability to human patients. Using recurrence quantification analysis (RQA), a robust method for analyzing short, noisy, and nonstationary time series like the electroencephalogram (EEG), we demonstrated that electroencephalographic signal dimension serves as a reliable, SF-independent measure of both seizure susceptibility and therapeutic response to ASMs. The electroencephalographic signal dimension (DIM), computed from RQA, is an inverse index of the average degree of correlation among brain cells. As we demonstrated in our paper (doi: https://doi.org/10.1111/epi.18397), DIM shows peculiar features to overcome the abovementioned limitations. DIM leverages the network disease characteristic of epilepsy, which arises from excessive correlation among firing neurons, thus acting as an (inverse) proxy of epileptic tissue internal correlation directly underpinning seizure susceptibility and modulating the EEG activity. In our study, DIM provided a stable and consistent biomarker for the risk of developing a seizure (i.e., seizure susceptibility), regardless of whether a seizure actually occurs. This makes DIM a superior measure to SF, which is affected by the inherent stochastic nature of seizure occurrence, which is modulated by the system's susceptibility but is not a direct and continuous measure of it. This makes SF often inconsistent, especially in subjects with low seizure rates. These findings highlighted that DIM offers immediate benefit in preclinical studies and may hold promise for clinical application. We now present new, compelling evidence that further strengthens and extends our initial findings, consolidating the relevance and reliability of electroencephalographic signal dimension as a biomarker for seizure susceptibility compared to seizure rate. We used the same mice and methodology as published, basing our reasoning on the fact that no evidence supports any effect on seizure susceptibility of drug vehicles (NaCl .9% or methylcellulose .5%, administered intraperitoneally). Therefore, for each mouse, both SF and DIM measured during baseline should be statistically equivalent to those measured during vehicle administration. This implies both metrics must satisfy two related statistical relationships: (1) baseline and vehicle measurements should be significantly correlated; and (2) they can be modeled by a regression line with a slope statistically equal to 1, with minor fluctuations around unity. Accordingly, we first established the existence of statistically significant correlations between baseline and vehicle measurements. The DIM measurements were always significantly correlated across all groups of mice, whereas only a minority of SF values showed significant correlation, revealing inconsistencies among mice treated with the same vehicle (Figure 1, top left table). Such differences in correlation statistics are rooted in the respective frequency distributions. Whereas DIM showed a Gaussian-like distribution consistent with a sensible measure of individual system's susceptibility, SF gave rise to exponential frequency rate distribution reminiscent of a Poisson/rare-event-like stochastic phenomenon (Figure 1, top right histograms), due to the inherent stochastic nature of seizure occurrence. To confirm the ability of both DIM and SF to meet the baseline–vehicle equivalence constraints, we adopted an ordinary linear regression approach to evaluate how close slope values were to unity, in the presence of possible systematic biases (e.g., circadian rhythms, manipulation or environmental stress, equipment noise; Figure 1A–F). We found that the slopes of DIM regression lines were all statistically significant and distributed, on average, around unity (Figure 1A–E; mean ± SEM = .92 ± .19). In contrast, SF regression statistics were poor (Figure 1A–F, right graphs) and even inconsistent with their respective correlation analysis, as in the case of the phenytoin–vehicle group, which showed no significant regression line despite a significant correlation. Finally, to exclude that the slopes close to unity in the DIM regressions were spurious, because they were induced by the systematic biases rather than driven by the actual equivalence between baseline and vehicle measurements, we ran a second regression analysis setting the intercepts to zero. The closeness of DIM slope values to unity was consistently confirmed and strengthened for each group of mice (Figure 2A–F, left graphs). In contrast, SF measurements continued to show poor regression statistics, confirming that their intrinsic high variability prevents a stable relationship among measurements (Figure 2A–F, right graphs). The sequence of our analyses provides clear biologically and statistically sound evidence of DIM robustness and stability compared to SF. Such a stable relationship between baseline and vehicle is a necessary requirement for an unbiased evaluation of any ASM efficacy. This critical requirement is poorly satisfied by SF but is perfectly met by DIM, highlighting the importance of shifting the focus from seizure rate to the susceptibility to undergo a seizure. Therefore, our findings further confirm the limitations of SF as a biomarker of seizure susceptibility and support DIM as a valid and robust alternative to SF in preclinical testing of experimental therapeutics with translational potential in clinical settings. If validated for human use, DIM's ability to measure seizure risk could pave the way for a paradigm shift from continuous to on-demand or intermittent therapy to rapidly decrease the risk of a seizure. This targeted approach could also reduce the side effects associated with chronic medication. The successful implementation of DIM's use will rely upon the development of an algorithm for real-time DIM calculation, integrated into advanced electroencephalographic closed loop systems. The authors wish to thank Kyle Thomson for his helpful technical support. The work presented has been funded in whole or in part with federal funds from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services, under contract No. HHS 75N95022C00007. We acknowledge the CINECA award under the ISCRA initiative for the availability of high-performance computing resources and support. Open access funding provided by BIBLIOSAN. None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. The reference data are available in Rizzi M, West PJ, Vezzani A, Wilcox KS, Giuliani A. EEG signal dimension is an index of seizure propensity and antiseizure medication effects in a mouse model of acquired epilepsy. Epilepsia. 2025;66:3035–3047. https://doi.org/10.1111/epi.18397.