# Sleep-related epilepsy through the lens of stereo-EEG: Clinical and research update

Lino Nobili, Steve Alex Gibbs, Gaia Burlando, Gaia Patrone, Giuseppe de Venuto, Sheng H Wang, Gabriele Arnulfo, Michele Terzaghi, Stefano Francione, Ivana Sartori

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Lino Nobili, Steve Alex Gibbs, Gaia Burlando, Gaia Patrone, Giuseppe de Venuto, et al. Sleep-related epilepsy through the lens of stereo-EEG: Clinical and research update. Clinical Neurophysiology, 2025, 177, pp.2110811. 10.1016/j.clinph.2025.2110811. hal-05283865

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# Sleep-related epilepsy through the lens of stereo-EEG: Clinical and research update

Lino Nobili<sup>a,b,*</sup>, Steve Alex Gibbs<sup>c</sup>, Gaia Burlando<sup>d,e</sup>, Gaia Patrone<sup>a</sup>, Giuseppe De Venuto<sup>d,e</sup>, Sheng H. Wang<sup>e,f,g,h,i</sup>, Gabriele Arnulfo<sup>b,d</sup>, Michele Terzaghi<sup>j,k</sup>, Stefano Francione<sup>b</sup>, Ivana Sartori<sup>l</sup>

## ARTICLE INFO

**Keywords:**

Stereo-electroencephalography

Sleep-related hypermotor epilepsy

## ABSTRACT

The complex interactions between sleep and epilepsy have drawn increasing attention, and stereo-electroencephalography (SEEG) has become a pivotal tool for investigating their underlying pathophysiological mechanisms. This review highlights key contributions from SEEG studies over the past two decades, with a focus on Sleep-Related Hypermotor Epilepsy (SHE). Considered a disorder of frontal lobe origin, SHE is now recognized as a network-based epilepsy with a broader involvement of cortical regions. Sleep instability in Non-Rapid Eye Movement (NREM) sleep, indexed by the cyclic alternating pattern (CAP), and increased bistability, emerge as critical facilitators of epileptiform discharges. In contrast, rapid eye movement (REM) sleep, particularly its phasic substate, exerts a strong suppressive effect on epileptic activity. SEEG has been instrumental in characterizing these mechanisms and identifying novel biomarkers, including cross-frequency coupling and network-level measures of cortical instability. These findings have implications not only for diagnosis and surgical targeting but also for the development of neuromodulatory and state-based therapeutic approaches. Looking forward, the integration of SEEG with advanced computational tools offers new avenues for real-time brain-state mapping and seizure risk stratification. By bridging clinical neurophysiology with systems neuroscience, SEEG provides a unique platform for advancing the understanding of epilepsy within the dynamic context of sleep.

**Abbreviations:** CAP, Cyclic Alternating Pattern; EZ, Epileptogenic Zone; IED, Interictal Epileptiform Discharge; REM, Rapid Eye Movement; SOZ, Seizure Onset Zone; SHE, Sleep-Related Hypermotor Epilepsy; SEEG, Stereo-electroencephalography.

* Corresponding author.

**E-mail addresses:** lino.nobili@unige.it (L. Nobili), steve.gibbs@umontreal.ca (S.A. Gibbs), gaia.burlando@edu.unige.it (G. Burlando), gaia.patrone@edu.unige.it (G. Patrone), giuseppe.devenuto@edu.unige.it (G. De Venuto), sheng.wang@helsinki.fi (S.H. Wang), gabriele.arnulfo@unige.it (G. Arnulfo), michele.terzaghi@mondino.it (M. Terzaghi), stefanofrancione@gaslini.org (S. Francione), ivana.sartori@ospedaleniguarda.it (I. Sartori).

<footnote><sup>a</sup> Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, Largo Paolo Daneo 3, 16132 Genoa, Italy</footnote>

<footnote><sup>b</sup> Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Full Member of the ERN EpiCARE, Via Gerolamo Gaslini 5, 16147 Genoa, Italy</footnote>

<footnote><sup>c</sup> Department of Neurosciences, Center for Advanced Research in Sleep Medicine, Sacred Heart Hospital, University of Montréal, 5400 West Gouin Boulevard, Montréal, QC H4J 1C5, Canada</footnote>

<footnote><sup>d</sup> Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via all'Opera Pia 13, 16145 Genoa, Italy</footnote>

<footnote><sup>e</sup> Department of Neuroscience and Biomedical Engineering, Aalto University, Rakentajanaukio 2, 02150 Espoo, Finland</footnote>

<footnote><sup>f</sup> Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 8, 00290 Helsinki, Finland</footnote>

<footnote><sup>g</sup> CEA, NeuroSpin, Université Paris-Saclay, Bat 145 - Point Courrier 156, 91191 Gif-sur-Yvette, France</footnote>

<footnote><sup>h</sup> MIND Team, Inria, 1 rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France</footnote>

<footnote><sup>i</sup> Jack H. Miller Magnetoencephalography Center, Helen DeVos Children's Hospital, 100 Michigan St NE, 49503 Grand Rapids, MI, USA</footnote>

<footnote><sup>j</sup> Department of Brain and Behavioral Sciences, Viale Golgi 19, University of Pavia 27100 Pavia, Italy</footnote>

<footnote><sup>k</sup> Unit Sleep Medicine, IRCCS Mondino Foundation, Via Mondino 2, 27100 Pavia, Italy</footnote>

<footnote><sup>l</sup> "Claudio Munari" Center for Epilepsy Surgery, ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, 20162 Milan, Italy</footnote>
# 1. Introduction

Stereo-electroencephalography (SEEG) is an invasive neurophysiological technique developed to precisely identify the epileptogenic zone (EZ) in patients with drug-resistant epilepsy, particularly when standard scalp EEG and imaging techniques fail to provide clear localizing information. Introduced by Bancaud and Talairach (1965), SEEG involves the stereotactonic implantation of multiple intracerebral electrodes that directly record electrical activity from cortical and deep brain regions with remarkable spatial resolution and accuracy (Cardinale et al., 2019). Initially employed as a diagnostic tool for surgical planning, SEEG has since become the gold standard for mapping seizure onset and propagation networks, especially in cases with normal or inconclusive MRI findings or complex semiologies. Unlike subdural grid electrodes, SEEG electrodes can be strategically implanted in both superficial and deep cortical areas, offering minimal invasiveness with maximal spatial sampling. This allows for a comprehensive evaluation of epileptic networks, including structures that participate in both ictal onset and secondary propagation. SEEG recordings are uniquely valuable for studying seizures with focal onset, including those occurring predominantly during sleep.

## 1.1. SEEG in sleep-related epilepsy

Sleep profoundly modulates seizure dynamics, altering both threshold and propagation, yet standard scalp EEG during sleep is limited by insufficient spatial resolution and poor sensitivity to deep generators. SEEG circumvents these limitations by enabling high-fidelity recordings from a wide range of cortical and subcortical structures across all sleep stages. This makes it a uniquely powerful tool for investigating how sleep-related oscillatory activity and brain-state transitions modulate seizure generation. One of SEEG's defining strengths is its ability to capture subtle interictal epileptiform discharges (IEDs) and brief ictal patterns that often elude scalp EEG. In recent years, SEEG recordings from epilepsy patients who also presented with parasomnias or other sleep disorders have provided critical insights into how epileptic and non-epileptic motor phenomena may interact or overlap. These studies have also helped clarify the distinct networks involved in epileptic versus non-epileptic behaviors during sleep, improving our ability to differentiate between the two and reducing the risk of misdiagnosis (Nobili et al., 2006; Gibbs et al., 2016). These recordings have clarified how epileptic networks can modulate, or be modulated by, arousal systems and sleep microstructure, uncovering mechanistic links between seizures, physiological arousals, and sleep instability that are otherwise inaccessible with non-invasive methods.

## 1.2. SEEG in Sleep-related Hypermotor Epilepsy (SHE)

Crucially, SEEG studies have also redefined the nosology of sleep-related epilepsies. SHE, once presumed to originate solely from the frontal lobes, has been shown through SEEG to involve a broader set of cortical areas, including insular, temporal, parietal, and occipital regions, highlighting the network-based nature of this condition (Nobili et al., 2004; Ryvlin et al., 2006; Proserpio et al., 2011; Gibbs et al., 2015, 2019; Wadi et al., 2024). In addition to its clinical applications, SEEG has emerged as a central tool in epilepsy research. The ability to record local field potentials from distributed brain regions has enabled detailed analysis of interictal and ictal patterns in relation to sleep oscillations during Non-Rapid Eye Movement (NREM) sleep, such as delta waves, spindles, and arousal instability in the framework of Cyclic Alternating Pattern (CAP) phases. These recordings have led to important discoveries on the dynamic regulation of epileptic activity by sleep-related neuronal processes, including delta-band spatial-temporal instability (Zubler et al., 2017), and cross-frequency coupling between delta-phase (0.5-4 Hz) and beta-gamma (15-200 Hz) band bistability (Burlando et al., 2025). This manuscript provides an overview of recent SEEG-

based contributions to the understanding of sleep-related epilepsies, with a particular focus on mechanisms uncovered through invasive recordings. In doing so, we highlight a body of work largely derived from our own investigative efforts over the past two decades. While enriched by collaborations and external findings, the majority of the studies summarized here reflect a sustained research program dedicated to understanding the complex interplay between sleep physiology and epileptogenic networks. Through this lens, we aim to illustrate how SEEG not only informs clinical decision-making but also serves as a powerful scientific tool for revealing the hidden dynamics of sleep and epilepsy.

# 2. SHE: A SEEG-based network definition

SHE provides a compelling example of how SEEG has advanced diagnostic clarity and deepened our understanding of epileptic networks. Historically, nocturnal paroxysmal motor events, now recognized as manifestations of SHE (Tinuper et al., 2016), were misdiagnosed as non-epileptic phenomena, often categorized as parasomnias or movement disorders and referred to as “nocturnal paroxysmal dystonia” (Lugaresi et al., 1986). Early clinical reports described bizarre motor behaviors without clear epileptic correlates on scalp EEG, leading to diagnostic uncertainty. Over time, accumulating clinical and video-polysomnographic evidence began to challenge these assumptions, shifting the diagnostic perspective toward an epileptic etiology. These nocturnal events were subsequently reclassified as “nocturnal frontal lobe epilepsy” (NFLE) (Provini et al., 1999; Tinuper et al., 1990), based on the stereotyped semiology and the limited EEG findings available at the time. However, this frontal-centric view eventually proved too narrow, failing to capture the full anatomical and electro-physiological diversity of the condition. SEEG has played a pivotal role in reshaping these frameworks. Contrary to earlier assumptions, SEEG studies revealed that hypermotor seizures during sleep could originate from a range of cortical regions beyond the frontal lobe, including temporal, insular, parietal, and even occipital lobes (Nobili et al., 2004; Ryvlin et al., 2006; Proserpio et al., 2011; Gibbs et al., 2018, 2019; Wadi et al., 2024). For example, SEEG recordings identified temporal lobe origins for seizures with clinical features very similar to those typically associated with frontal lobe epilepsy (Nobili et al., 2004), emphasizing that semiology alone may be insufficient for anatomical localization. Proserpio et al. (2011) further demonstrated that hypermotor seizures can also arise from the insula, parietal cortex, and even the occipital lobe, a finding recently confirmed by Wadi et al. (2024). In fact, the occipital origin reported by Wadi et al. (2024) echoed a similar case described earlier in Proserpio's study (Proserpio et al., 2011), under-scoring how SEEG can uncover unexpected epileptogenic zones. These observations support the view of SHE as a network disorder rather than a syndrome confined to a single lobe. SEEG has also proven invaluable in distinguishing frontal from extrafrontal SHE through specific electro-clinical features, especially regarding the temporal evolution of seizures. Gibbs et al. (2018) demonstrated that extrafrontal seizures typically exhibit a longer delay (median ~10 s) between the onset of subtle clinical signs, such as eye opening or small postural changes, and the emergence of overt hypermotor activity. In contrast, frontal seizures are usually associated with very short latencies (median ~1.6 s), where hyperkinetic behavior follows almost immediately after the initial sign. A latency greater than five seconds strongly suggests an extrafrontal origin, a distinction that can guide diagnosis even in the absence of intracranial EEG. This delay, often visible in clinical video recordings, provides a practical diagnostic clue: when the hyperkinetic phase is preceded by a longer pause after the first motor sign, clinicians should consider extrafrontal seizure onset. Furthermore, extrafrontal seizures, particularly those with temporal origins, tend to be longer in duration, both electrically and behaviorally, compared to their frontal counterparts. Therefore, SEEG allows the identification of the seizure onset zone (SOZ) in frontal and extrafrontal lobes and clearly distinguishes the SOZ
from the symptomatogenic zone, at the roots of clinical hypermotor manifestations.

The precision offered by SEEG extends beyond diagnosis; it has a direct impact on surgical outcomes. Accurate localization of the epileptogenic zone allows for tailored resections that are often smaller and more targeted, improving the likelihood of seizure freedom while minimizing unnecessary tissue removal (Gibbs et al., 2019). This represents a paradigm shift in the surgical approach to SHE, emphasizing individualized network mapping over lobar-based assumptions.

In summary, SEEG has profoundly reshaped our conceptualization of SHE, evolving from an initial misdiagnosis as a parasomnia (Lugaresi and Cirignotta, 1981), through a frontal-lobe-centric model (Tinuper et al., 1990), to a modern understanding of SHE as a network-based epilepsy (Gibbs et al., 2016). By uncovering the diversity of cortical regions involved, clarifying the temporal features of seizure evolution, analyzing the propagation of the critical discharge and the cortical connectivity, SEEG provides an essential tool for accurate diagnosis, clinical decision-making, and optimized surgical planning.

### 3. Epileptic discharges, minor motor events, and arousal instability: The contribution of SEEG

SEEG has provided crucial insight into the continuum of motor manifestations in nocturnal frontal lobe epilepsy (NFLE), revealing a nuanced relationship between epileptic discharges (EDs), minor motor events (MMEs), arousals, and sleep instability. This continuum, ranging from subtle motor fragments to fully developed hyperkinetic seizures, had previously eluded detection using scalp EEG due to the brief duration and deep origin of many discharges. In an early study using intracerebral recordings, Nobili et al. (2003) demonstrated that paroxysmal motor events of increasing complexity, ranging from MME (such as head deviation and brief limb movements) to paroxysmal arousals and major seizures, could all be linked to epileptic discharges arising from the supplementary motor area (SMA) and cingulate cortex. The increasing behavioral complexity reflected longer and more widespread propagation of the ictal discharge, thus confirming the hypothesis that seizure semiology mirrors the spatiotemporal dynamics of epileptic activity. However, subsequent SEEG investigations clarified that not all EDs during sleep correspond to overt seizures. In their systematic analysis, Terzaghi et al. (2007) found that MMEs could occur either with or without an associated ED. While more than two-thirds of MMEs were temporally related to EDs, these motor events varied significantly in type and intensity, and lacked stereotypical features that would define them as seizures. Importantly, some discharges coincided with motor fragments, while others occurred silently or merely preceded physiological movements, such as repositioning during sleep. SEEG recordings in these studies (Terzaghi et al., 2008) revealed that the coupling of EDs and MMEs is frequently mediated by arousal instability, as indexed by the CAP. This physiological marker of sleep fragmentation (Terzano et al., 1985) was tightly linked to the emergence of both EDs and MMEs. MMEs and EDs were significantly more likely to occur during CAP phase A, a period of transient arousal and heightened cortical excitability. This suggests that EDs may act as internal perturbations that, by increasing arousal level, indirectly facilitate the expression of motor behaviors. However, given the close temporal association and the lack of demonstrated causality, it is equally plausible that transient arousal instability might favor the emergence of EDs. SEEG data underscore the complexity of this relationship, which likely reflects a dynamic, bidirectional interaction between epileptic activity and sleep-state transitions. Rather than acting as direct triggers of MMEs, EDs in this framework are seen as modulators of sleep stability. The MMEs may thus be conceptualized not as true epileptic events but as motor outputs mediated by state transitions in cortical excitability. This distinction is crucial in differentiating epileptic seizures from benign sleep-related movements, especially in the setting of refractory SHE, where MME may outnumber seizures and contribute to diagnostic confusion. Recent studies have further

highlighted the possible overlap between sleep-related hypermotor epilepsy and disorders of arousal, particularly in terms of NREM fragmentation and hypermotor behaviors. At the same time, they underscored the diagnostic value of analyzing sleep EEG macro- and microstructure, as well as the distribution of sleep stages and the timing of events, in differentiating these conditions (Proserpio et al., 2019; Mutti et al., 2020). This intricate relationship is further illustrated by the case of a patient described by Nobili et al. (2006), in whom intracerebral recordings revealed a high frequency of periodic leg movements (PLMs) that occurred during CAP phase A and were frequently preceded by epileptic discharges, despite an absence of overt EEG correlates on scalp recordings. Here, SEEG disclosed a novel pathophysiological interaction: epileptic discharges can facilitate the occurrence of PLMs and contribute to excessive daytime sleepiness, not solely through seizures but by disrupting sleep microstructure. Importantly, after surgical removal of the cortical area generating the discharges, the patient became seizure-free and exhibited a marked reduction in both CAP rate and PLM index. This finding supports the hypothesis that focal epileptic discharges can act as triggers for physiological sleep disorders, further blurring the line between epileptic and non-epileptic sleep-related phenomena.

### 4. Temporal oscillations, spatial instability, and the modulation of epileptic activity: Insights from SEEG

The classical view of NREM sleep as a globally synchronized state that promotes epileptic activity has been significantly refined over the years. Scalp EEG studies had already identified key oscillatory mechanisms such as CAP (Terzano et al., 1991; Parrino et al., 2000) as physiological markers of sleep instability associated with increased vulnerability to interictal epileptic discharges and seizures. However, SEEG, with its ability to record from both deep and superficial cortical structures, combined with advanced analytical techniques, has enabled a much more detailed, systems-level understanding of how these oscillatory instabilities contribute to epileptogenicity. These techniques include assessments of large-scale phase synchrony (Arnulfo et al., 2020), artificial connection corrected cross-frequency phase-phase synchrony (CFS) and phase-amplitude coupling (PAC) (Siebenhühner et al., 2020), along with local criticality (Fuscà et al., 2023) and bistability (Wang et al., 2023). Rather than contradicting the CAP-based framework, SEEG has confirmed and expanded it, revealing local dynamics that are invisible to scalp EEG and emphasizing the role of spatial fragmentation in the interaction between sleep and epilepsy. In the study by Zubler et al. (2017), the relationship between interictal spikes (IS) and the temporal and spatial dynamics of sleep was systematically examined. In ten patients with drug-resistant SHE and histologically confirmed focal cortical dysplasia type 2 (FCD2), the authors analyzed SEEG and scalp EEG recordings across full NREM cycles. They investigated the relationship between IS frequency and delta and sigma power, and introduced two novel indices, temporal instability (fluctuations in delta power over time) and spatial instability (variance in delta power across cortical regions at a given time point). Contrary to traditional assumptions, delta power was negatively correlated with IS frequency in most subjects. IS were reduced during deep, stable delta activity and were more frequent during lighter NREM stages or transitional periods characterized by low or heterogeneously distributed delta activity (Fig. 1). In contrast, sigma power, associated with sleep spindles, showed a positive correlation with IS, particularly in diffusion zones surrounding the seizure-onset area.

These results challenged the belief that deep sleep invariably promotes epileptic discharges. Instead, they suggested that cortical instability, especially during lighter, transitional stages, creates permissive conditions for IEDs. One of the study's insights was that deep sleep is not achieved uniformly across the cortex. They showed that while some regions, especially outside epileptogenic zones, may rapidly enter delta-dominated states, other areas, particularly within or near the seizure-
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Spatial and temporal instability during sleep (modified from Zubler et al. (2017)) (A) Relative delta power in spike-free channels represented as a matrix (channel × time); black rectangles (1) and (2) correspond to the segments presented in B and C, respectively. (B) Relative delta power (RDP) computed on 5-s bins, represented as a matrix (channels × bins) during unstable sleep. On the right, temporal average of RDP (i.e., averaged over all bins for each channel independently), the SD of which defines the spatial instability; (on the bottom), spatial average (over all channels), the SD of which defines the temporal instability. (C) Same as B but for stable sleep. (D) EEG markers and interictal spikes during one non-rapid-eye-movement cycle, average over all patients (each variable is represented as mean ± SEM). From top to bottom: Absolute delta (blue) and sigma (black) power; Spatial (blue) and temporal (black) instability; Interictal spike rate in the seizure-onset zone (blue) and the diffusion zone (black). (E) Relative importance of the four EEG markers in predicting the IS rate in a two-step linear model (partial least squares). In the seizure-onset zone, the relative contribution of sigma power over all patients was the most significantly different from a distribution with zero-mean (positive correlation); the other marker with a statistically significant contribution was temporal instability. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

onset zone (SOZ), may remain in a wake-like, more desynchronized state for prolonged periods. To further quantify these relationships, the authors built a linear model incorporating sigma power, delta power, and the two instability metrics. This model explained over 50 % of the variance in IED rate across patients and identified temporal instability and sigma power as the most robust predictors. These findings introduced a new framework: instead of a global stage-based classification, a dynamic, spatially heterogeneous model of sleep is more relevant for understanding epileptic vulnerability.

To build upon these findings, a more recent study by Burlando et al. (2025) incorporated additional physiological markers, including measures of bistability, a dynamic property of neuronal networks that reflects the tendency of brain regions to alternate between high and low excitability states. In the context of epilepsy, bistability captures the instability of local cortical circuits, where small perturbations (such as oscillatory fluctuations) can push the system toward a hyper-excitable state more prone to epileptiform discharges (Prince and Connors, 1986; Badawy et al., 2013; Meisel et al., 2015; Fuscà et al., 2023). High bistability is thought to reflect increased susceptibility to spike generation and seizure initiation, particularly in structurally or functionally altered tissue (Wang et al., 2024). This study focused on how slow delta oscillatory dynamics modulate higher-frequency epileptic biomarkers, such as β- and γ-band activity. The authors simultaneously employed several advanced metrics, including first and second-order phase synchrony, bistability indices, and phase-amplitude coupling (PAC), to explore how delta-phase fluctuations temporally frame epileptic vulnerability in seizure-onset regions. Burlando et al. distinguished between two types of PAC: inward-PAC, where delta-phase from large-scale networks modulates local β-γ amplitude, reflecting a region's receptivity to widespread oscillatory input; outward-PAC, where local delta-phase modulates amplitude elsewhere, indicating a region's influence on broader network excitability. They showed that inward-PAC was strongly correlated with local bistability, particularly in seizure-onset zones, while outward-PAC tracked with delta-phase synchrony

across regions, with both patterns showing positive correlations to epileptiform spikes. These corroborating results from categorically different metrics suggest that large-scale delta-phase instability facilitates the entrainment of localized high-frequency epileptic dynamics, making cortical regions more likely to generate IEDs. Notably, this relationship was most prominent during NREM stage 2, a phase characterized by light sleep, transient arousals, and higher sleep instability. This directly supports and extends Zubler's observations, linking instability not only to discharge probability but also to network-wide susceptibility to pathological synchronization. In both Zubler and Burlando studies, SEEG proved essential for uncovering the fine spatial and temporal features of sleep that promote epileptic activity, features that would be invisible or averaged out on scalp EEG. Together, they corroborate and highlight a unifying principle: epileptic discharges are more likely to emerge in dynamically unstable states, where local desynchronization, fragmented delta activity, and permissive low-frequency dynamics align to shape cortical vulnerability. These findings carry both conceptual and clinical implications. First, they suggest that not all NREM sleep is equal in terms of seizure risk, as sleep fragmentation, temporal delta variability, and local desynchronization may be more important markers than global stage classification. Second, they reinforce the idea that therapeutic interventions should consider sleep dynamics as a modifiable contributor to epileptogenesis. Interventions aimed at stabilizing local sleep architecture or disrupting pathological PAC patterns could represent novel strategies for seizure suppression. The identification of advanced biomarkers such as δ-β/γ PAC and bistability introduces new tools for future SEEG-based analysis. These metrics may help characterize not only the seizure-onset zone but also functionally involved network nodes, contributing to more refined diagnostic and prognostic models, not only in SHE but potentially across other focal epilepsy syndromes as well. Finally, the results of these metrics could be integrated into multimodal connectivity analyses, including resting-state fMRI, diffusion tensor imaging, and cortico-cortical evoked potentials, to more precisely define the networks
involved in seizure generation and modulation. Once specific networks and their shared characteristics are identified, this approach could pave the way for neurochemical studies aimed at investigating whether re-ceptor- or neurotransmitter-specific mechanisms contribute to their heightened susceptibility under conditions of sleep instability.

# 5. REM sleep as a natural antiepileptic state: Insights from SEEG

Among the various stages of sleep, rapid eye movement (REM) sleep stands out for its paradoxical electrophysiological characteristics: despite exhibiting a wake-like EEG pattern, it is associated with the lowest frequency of epileptic events, particularly in focal epilepsies and even in patients with SHE (Lanigar and Bandyopadhyay, 2017; Pro-serpio et al., 2019). SEEG studies have played a central role in charac-terizing this phenomenon, elucidating the mechanisms through which REM, and especially phasic REM, exerts its robust inhibitory effect on epileptic discharges. Early investigations using intracranial EEG already showed that epileptic spikes diminish significantly during REM sleep (Rossi et al., 1984). However, SEEG allowed for a much more detailed view by examining not only global rates of interictal epileptiform dis-charges (IEDs) but also their distribution across specific REM substates, and their behavior within different brain regions, including seizure-onset zones (SOZ) and physiologically normal areas.

In a key SEEG study, Frauscher et al. (2016) systematically compared phasic REM (characterized by rapid eye movements and EEG desynch-ronization) and tonic REM (a quieter phase with less motor activation) in 12 patients undergoing presurgical evaluation for drug-resistant epilepsy. The study revealed that phasic REM sleep was associated with significantly lower rates of IEDs, ripples (>80 Hz), and fast ripples (>250 Hz) compared to tonic REM. Specifically, only 39 % of spikes and 18 % of fast ripples occurred during phasic REM sleep, while tonic REM accounted for the majority (61 % and 82 %, respectively). This effect was strongest in epileptogenic zones, where pathological activity was most suppressed during phasic REM. Interestingly, while pathological high-frequency oscillations were suppressed, physiological ripples increased during phasic REM, especially in non-epileptogenic areas. This dual behavior suggests that REM sleep suppresses pathological syn-chronization while possibly preserving or even enhancing physiological processes such as memory consolidation, which are known to be active during REM. These findings were independently replicated and expanded by Campana et al. (2017) in a SEEG study of nine patients with focal epilepsy (including those with focal cortical dysplasia and hippo-campal sclerosis). Their data confirmed that spike rates in seizure-onset and propagation zones were significantly lower during phasic REM than during tonic REM or NREM sleep, regardless of histopathological sub-strate or lobar localization. The authors also directly assessed local and global synchronization, finding higher synchronization during tonic REM and greater desynchronization during phasic REM, suggesting a mechanistic link between EEG desynchronization and suppression of epileptic activity. The broader implications of these findings are well summarized in a recent review (Nobili et al., 2025), which frames phasic REM sleep as a “natural antiepileptic state.” This phase is marked by increased cholinergic tone, cortical desynchronization, and distinctive phasic phenomena such as ponto-geniculo-occipital waves, which may all contribute to disrupting the pathological synchronization necessary for seizure initiation and spread. Moreover, SEEG studies demonstrate that the suppressive effects of REM sleep are state- and region-specific, with reductions in IED amplitude and propagation outside the seizure-onset zone, reinforcing the idea that phasic REM actively interrupts epileptic network connectivity (Campana et al., 2017). The rarity of seizures during REM sleep, even in conditions such as SHE, where sei-zures predominantly occur during sleep, highlights the protective po-tential of this brain state. This has led to interest in therapeutic strategies that could mimic or enhance the neurophysiological properties of phasic REM, such as pharmacological agents targeting cholinergic systems or neuromodulatory approaches promoting cortical desynchronization.

# 6. Future research directions: SEEG as a platform for decoding sleep-epilepsy dynamics

As SEEG continues to refine our understanding of epileptogenic networks, future research is increasingly focused on the dynamic role of sleep in shaping seizure vulnerability. Sleep is not merely a passive context for epilepsy, it actively modulates excitability, synchrony, and circuit stability. SEEG, with its capacity to record high-resolution ac-tivity across both deep and superficial structures during all stages of sleep, is uniquely suited to explore these interactions. One critical area of investigation is the identification of network-level biomarkers of epileptogenicity that are sensitive to sleep-related changes in brain dy-namics. As shown in recent studies, including our work, the vulnera-bility to interictal discharges during NREM sleep is shaped not only by structural abnormalities but by oscillatory mechanisms such as delta instability, cross-frequency coupling, and spatially fragmented syn-chronization. We summarized how these features, especially bistability and phase-amplitude coupling (PAC), can help localize epileptogenic activity. Moving forward, the key question is how such findings can be leveraged to map brain-state transitions and guide therapeutic strate-gies. An emerging frontier is the application of these analytic frame-works to REM sleep, particularly phasic REM, which consistently suppresses epileptic activity. While this suppressive effect is well established, its mechanisms remain unclear. Our ongoing SEEG-based studies aim to apply advanced data-science approaches to assess PAC, phase-locking value (PLV), and bistability to contrast phasic to tonic REM epochs, with the goal of identifying the circuit-level properties that make REM sleep uniquely protective. These efforts may ultimately lead to REM-mimicking interventions, for example, pharmacological or neurostimulation-based approaches that reproduce the desynchronized, low-bistability environment of REM. In parallel, novel computational frameworks are being developed to represent SEEG-derived features in more interpretable and integrative ways. In our current work (Wang et al., 2025), we are exploring low-dimensional latent space models that embed multivariate biomarkers, such as delta instability, synchrony, and PAC, into continuous manifolds reflecting seizure risk. Rather than classifying discrete zones, this approach maps state-dependent vulner-ability over time, offering a new direction for seizure forecasting and personalized monitoring. Such tools could transform SEEG from a purely localizing modality into a state-space mapping system, capable of tracking cortical excitability across the sleep-wake cycle. In this framework, seizure onset is not simply the result of focal pathology, but of the brain escaping the physiological critical regime, entering a super-critical state defined by high instability, hyper-synchrony, and hyper-excitability, properties that evolve dynamically with sleep.

To advance this vision, future studies should: (a) further characterize stage-specific markers of epileptogenicity (b) explore how transitions between sleep stages modulate excitability and network behavior (c) develop real-time analytic tools to track vulnerability in clinical SEEG recordings (d) translate physiological insights into interventional tar-gets, aiming to stabilize or steer sleep-related network dynamics. In conclusion, the next frontier for SEEG in epilepsy research lies in decoding the temporal evolution of epileptogenic risk during sleep. By integrating physiological models, advanced analytics, and sleep neuro-biology, SEEG can become a platform not just for surgical planning but for understanding how brain states shape seizure risk and how those states might be harnessed for prevention.

# Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Acknowledgements

This work was supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006)—A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). Also, this work was supported by the Italian Ministry of Health, 5 × 1000 project 2017, “SOLAR: Sleep disorders in children: an innovative clinical research perspective”.

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