Obstructive sleep apnea (OSA) is a common but underdiagnosed and undertreated sleep disorder among people with epilepsy (PWE). In PWE, this sleep disorder is often managed as a comorbid condition rather than a contributor to epilepsy outcomes. For many years, OSA has been associated with higher seizure burden and interictal epileptiform discharges. Emerging evidence links OSA to late onset epilepsy (LOE) and increased risk markers for sudden unexpected death in epilepsy (SUDEP). This evidence also suggests that treating OSA with continuous positive airway pressure may improve seizure control. This critical review of the literature posits that OSA should be viewed as a modifiable risk factor for PWE. We apply the Bradford Hill criteria for causation as a framework to appraise the evidence connecting OSA with (1) seizure severity, (2) incident LOE, and (3) SUDEP risk. OSA supports eight of nine Bradford Hill criteria to varying degrees: strength, consistency, temporality, biological gradient, plausibility, coherence, analogy, and experiment, but not specificity. Key limitations include confounding, selection and adherence biases, and limited randomized evidence. However, the evidence supports integrating systematic OSA screening and evidence-based treatment into epilepsy care. Future research should prioritize randomized trials to assess the impact of OSA treatment on epilepsy incidence, severity, and SUDEP risk.

Epilepsy is characterized by widespread structural brain alterations extending beyond the epileptic zone, involving both cortical and subcortical regions. Importantly, the clinical manifestation of epilepsy, including seizure types, psychiatric comorbidities, and treatment responses, has been shown to differ between sexes. However, sex differences in structural alterations in epilepsy have been seldomly reported in neuroimaging studies, partly due to limited sample sizes and single-center designs. Here, we systematically investigated sex differences in common epilepsies and their related clinical variables using structural neuroimaging biomarkers in an international multi-center cohort of 1,253 epilepsy patients and 1,077 healthy controls. We studied cortical thickness and subcortical volume in two types of epilepsy: temporal lobe epilepsy (TLE) and genetic generalized epilepsy (GGE). Both male and female patients with TLE showed widespread cortical and subcortical thinning compared with controls. In GGE, when compared separately to controls, male patients showed only subtle structural alterations, whereas female patients exhibited more widespread structural alterations. Sex-stratified analyses revealed some variation in the extent and distribution of cortical thickness and subcortical volume alterations between male and female patients in both epilepsy cohorts. Yet, we did not find significant sex-by-diagnosis interaction effects in TLE and GGE. Similarly, no significant interaction effects were observed between sex and age of onset or disease duration in either patient group. Overall, although we observed some differences in regional cortical thickness and subcortical volume between male and female patients with epilepsy, we did not find significant sex-by-diagnosis interactions. Our findings indicate that sex differences in behavioral and clinical outcomes of epilepsy may involve biological or functional processes that require further investigation.

Diagnostic MRI evaluation of temporal lobe epilepsy (TLE) depends on the subjective visual interpretation of MRI images. These interpretations could be enhanced by quantitative artificial intelligence (AI) support tools. Humans often make sequential and conditional decisions during their radiological interpretations, such as whether an abnormality is present and, if present, characterizing the abnormality. It is not known whether it is superior to train AI to treat every decision separately in a similar step-wise manner or to train a model holistically on all decisions simultaneously. Here, we analysed three large epilepsy MRI datasets [n = 3676, 2320 people with epilepsy and 1356 healthy controls (HC)] to perform two tasks: (i) establish the presence of a TLE pattern on MRI and (ii) determine TLE pattern lateralization. We compared Step-wise models that independently classify TLE versus HC and lateralize patients as left TLE (L-TLE) or right TLE (R-TLE), against a simultaneous model trained to distinguish all three classes in a single step. To do this, 3D volumetric T1-weighted images were input into an EfficientNetV2 model multiple times to ensure reproducibility of results. Class prediction, model classification confidence and saliency maps were output for interpretability. Step-wise models outperformed the Simultaneous model on both tasks (both Ps < 0.001), with an average ∼2.8% accuracy increase for discriminating HC from TLE and an average 12.7% accuracy increase for distinguishing L-TLE from R-TLE. For both the Step-wise and Simultaneous models, important features discriminating TLE from HC included the known TLE limbic pattern involving the hippocampus, parahippocampal cortical regions, cingulate cortex and lateral temporal regions. However, there was less concordance between the Step-wise and Simultaneous models for the L-TLE versus R-TLE task (all Fisher's Zs > 10.5, Ps < 0.001); the Step-wise model focused less on subcortical regions such as the thalamus and hippocampus and focused more on distributed cortical pathology. Across the two Step-wise models, 95.1% of TLE patients had accurate classifications in either HC versus TLE and/or L-TLE versus R-TLE tasks. These results included 69.6% of patients being both correctly labelled as TLE and lateralized, 13.9% being correctly labelled TLE but lateralized incorrectly and 11.6% being lateralized correctly but not detected as TLE. These findings provide evidence that diagnostic tasks with simpler, Step-wise AI models may enhance diagnostic performance and interpretability in clinical workflows. Future AI clinical support tools can leverage this step-wise approach in the early identification of TLE-related structural patterns, supporting timely diagnosis and treatment decisions.

Accurate phase tracking of deep-brain activity is critical for effective closed-loop and phase-locked neuromodulation therapies. However, direct access to deep neural phase through intracranial recordings remains clinically restrictive due to the invasiveness. Here we validate and clinically benchmark the Gabor-Nelson (GN) dipole estimation method for reconstructing deep-brain oscillatory phase from non-invasive scalp EEG. GN is a geometry-based, imaging-independent approach that offers computationally efficient dipole reconstruction and has rarely been applied to source-level phase estimation in human neuroscience. We compared GN with an established MRI-informed Inverse Solution (IS) method using a three-stage reconstruction pipeline consisting of dipole modeling, dimensionality reduction, and frequency-dependent phase-delay correction. Validation is performed using (i) cadaveric recordings, where known ground-truth seizure waveforms were replayed through implanted deep electrodes, and (ii) simultaneous scalp EEG and SEEG recordings in human patients, where pseudo-ground truth was approximated via the intracranial contacts. GN achieved phase accuracy and signal fidelity comparable to IS across both datasets despite requiring no anatomical imaging. In cadaver recordings, phase-corrected reconstruction correlations exceeded r > 0.91 and ΔΦ < 9° in mean phase error. In patient SEEG data, GN reached up to r ≈ 0.80 with phase offsets suitable for neuromodulatory timing. GN offers a viable, low-barrier, imaging-independent alternative to traditional inverse modeling for non-invasive seizure phase tracking. This framework opens pathways for scalable, phase-locked and closed-loop stimulation therapies in epilepsy and potentially other network-based brain disorders.

Purpose of Review:Alzheimer disease (AD) and epilepsy are major causes of neurologic disability and are reciprocally related: epileptiform discharges, subclinical seizures, and epilepsy are more prevalent in patients with AD compared with controls; progressive cognitive impairment commonly afflicts epilepsy patients; and late-onset epilepsy patients have higher rates of new-onset dementia. Recent Findings:Epidemiologic studies support shared risk factors (e.g., genetic variants, vascular disease, sleep disorders, microbiome) with notable divergences. AD and epilepsy have some overlapping anatomic (e.g., hippocampus, entorhinal, and association cortex), clinical (e.g., memory, attentional, and executive) impairments, and neuropathologic (e.g., amyloid, tau, neurofibrillary tangles) features. Shared clinical and translational challenges include underlying mechanisms (e.g., genetic variants, neuroinflammation, metabolic and mitochondrial dysfunction, excitatory/inhibitory imbalance, microbiome, and sociodemographic factors) and identifying valid and reliable biomarkers (e.g., total tau and phosphorylated tau (p-tau), amyloid deposition, Aβ42/Aβ40 ratio) to assess disease progression, predict outcomes, and assess potentially disease-modifying interventions. Summary:Identifying convergences and divergences between epilepsy and AD may inform our understanding. The clinical, neurophysiologic, neuropathologic, and molecular pathologic changes in AD and epilepsy may reveal pathophysiologic insights and therapeutic opportunities.

Our brains dynamically adapt to a multisensory world by orchestrating diverse inputs across sensory streams. This process engages multiple brain regions, but it remains unclear how audiovisual stimuli are represented and evolve over time, especially in naturalistic scenarios. Here, we employed a movie-viewing paradigm to explore this question. We recorded intracranial electrocorticography (iEEG) to measure brain activity in 19 participants watching a short multilingual movie. Using unsupervised clustering and supervised encoding models, we identified a robust modality-specific gradient in the frontal cortex, wherein the ventral division primarily processes auditory information and the dorsal division processes visual inputs. Further, we found that this cortical organization dynamically changed, adapting to different movie contexts. This result potentially reflects flexible audiovisual-resource assignment to construct a coherent percept of the movie. Leveraging behavioral ratings, we found that the frontal cortex is the primary site in this modality assignment process. Together, our findings shed new light on the functional architecture of the frontal cortex underlying flexible multisensory representation and integration in natural contexts.

Extensive neuroimaging research in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) has identified brain atrophy as a disease phenotype. While it is also related to a complex genetic architecture, the transition from genetic risk factors to brain vulnerabilities remains unclear. Using a population-based approach, we examined the associations between epilepsy-related polygenic risk for HS (PRS-HS) and brain structure in healthy developing children, assessed their relation to brain network architecture, and evaluated its correspondence with case-control findings in TLE-HS diagnosed patients relative to healthy individuals. We used genome-wide genotyping and structural T1-weighted MRI of 3826 neurotypical children from the Adolescent Brain Cognitive Development (ABCD) study. Surface-based linear models related PRS-HS to cortical thickness measures, and subsequently contextualized findings with structural and functional network architecture based on epicentre mapping approaches. Imaging-genetic associations were then correlated to atrophy and disease epicentres in 785 patients with TLE-HS relative to 1512 healthy controls aggregated across multiple sites. Higher PRS-HS was associated with decreases in cortical thickness across temporo-parietal as well as fronto-central regions of neurotypical children. These imaging-genetic effects were anchored to the connectivity profiles of distinct functional and structural epicentres. Compared with disease-related alterations from a separate epilepsy cohort, regional and network correlates of PRS-HS strongly mirrored cortical atrophy and disease epicentres observed in patients with TLE-HS and were highly replicable across different studies. Findings were consistent when using statistical models controlling for spatial autocorrelations and robust to variations in analytic methods. Capitalizing on recent imaging-genetic initiatives, our study provides novel insights into the genetic underpinnings of structural alterations in TLE-HS, revealing common morphological and network pathways between genetic vulnerability and disease mechanisms. These signatures offer a foundation for early risk stratification and personalized interventions targeting genetic profiles in epilepsy.

OBJECTIVES:Distinguishing Idiopathic Generalized Epilepsy (IGE) from focal epilepsy (FE) can be challenging when IGE cases present with asymmetric clinical or electroencephalographic (i.e., atypical) features. We aimed to identify factors that lead to discordance in IGE diagnoses among epileptologists. METHODS:41 patients with IGE and 6 patients with FE or mixed focal and generalized epilepsy followed at the NYU Comprehensive Epilepsy Center for ≥5 years were identified. IGE cases included typical (n = 18) and atypical (n = 23) presentations. Anonymized summaries of patients at initial presentation and 5-year follow-up were presented in random order to epileptologists who categorized them as generalized epilepsy (GE), FE, or other. Factors leading to discordance were identified. RESULTS:Inter-rater agreement was moderate for atypical IGE cases at initial presentation (AC1 = 0.54, 95% CI 0.31-0.77, p < 0.01) and 5-year follow-up (AC1 = 0.48, 95% CI 0.25-0.72, p < 0.01). For atypical cases, asymmetric epileptiform activity was most commonly associated with increased discordance. DISCUSSION:Epileptologists may underdiagnose IGE when patients have asymmetric EEG or clinical features. Review of one time point (i.e., vignettes) may mask the diversity of features that may support a localized epilepsy when serial reviews reveal a generalized epilepsy.

Speech is a defining human behavior, and this ability depends critically on speech motor cortex. While the ventral precentral and postcentral gyri are classically regarded as chiefly articulatory and somatosensory regions, a growing body of literature challenges this simplification. Most prior research, however, has examined cued or structured speech production tasks, neglecting the automatic, overlearned speech commonly utilized in clinical assessment. Consequently, the neural dynamics and precise timing of cortical recruitment during automatic speech remain poorly understood. Here, we present intracranial electrocorticography (ECoG) recordings from the left perisylvian cortex in participants performing automatic speech such as counting and recitation of overlearned sequences. We investigate neural dynamics using encoding (multivariate temporal response function) and decoding (deep neural network speech synthesis) models. We show that automatic speech engages a distributed network across superior temporal, precentral, and post-central cortices, characterized by attenuated pre-articulatory activity and weaker frontal encoding. Furthermore, two complementary decoding strategies reveal that speech motor cortex represents a mixture of feedforward and feedback signals, with a subset of sites exhibiting exclusively feed-forward dynamics. These results delineate the spatiotemporal cortical organization of automatic speech and establish that the speech motor cortex supports more complex dynamics than purely feedforward control.

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants) and intracranial electroencephalography (iEEG, 2 participants) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time. Significance statement:How the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

In the phase 3 randomized controlled trial (RCT; NCT03355209) of fenfluramine in Lennox-Gastaut syndrome (LGS), patients in fenfluramine treatment groups (0.2 mg/kg/day, 0.7 mg/kg/day) experienced greater reduction from baseline in frequency of seizures associated with a fall versus placebo, which was sustained in the open-label extension (OLE) study (NCT03355209). In this post hoc analysis, trajectories of fenfluramine effectiveness and safety, along with dose changes over time, are described for patients with LGS randomized to placebo in RCT who switched to fenfluramine in OLE (PBO-FFA) and those who received fenfluramine in both RCT/OLE (FFA-FFA). Among patients who completed 12 months in OLE (N = 151), numerical improvements in effectiveness outcomes were seen in the PBO-FFA group (n = 59) after initiating fenfluramine and were similar to those in the FFA-FFA group (n = 92). Regression to the mean was not observed in the PBO-FFA group, suggesting that changes were due to fenfluramine. Incidence of the most commonly reported treatment-emergent adverse events increased in the PBO-FFA group after fenfluramine initiation but decreased in the FFA-FFA group in OLE. These data demonstrate rapid improvement in seizure frequency and global functioning in both groups with continued clinical improvement as the mean fenfluramine dose was increased. These results confirm that sustained fenfluramine treatment is effective and tolerable. PLAIN LANGUAGE SUMMARY: This study assessed the change over time in the number of seizures, overall improvement, and side effects in patients with LGS receiving placebo (no active medicine) or fenfluramine in a 14-week study; all patients later received fenfluramine in the extension study. Overall, the number of seizures (associated with a fall) decreased once patients initially receiving placebo changed to fenfluramine (optimal effect around Month 4 while receiving a higher dose), but as expected, common side effects were reported more frequently once patients began fenfluramine treatment. Patients, parents, and doctors should be aware of this time course to allow fenfluramine enough time to work.

BACKGROUND AND OBJECTIVES:Direct electrocortical stimulation (DES) is the gold standard for mapping eloquent cortex, yet existing functional atlases are limited by sampling biases and density-based methods that obscure a region's true functional probability. Consequently, interpatient variability and the functional contributions of nontraditional language areas, such as the middle frontal gyrus, remain poorly characterized, particularly in epilepsy populations where functional reorganization is common. We therefore developed a probabilistic functional atlas of extraoperative DES and applied data-driven methods to characterize the functional organization of language, motor, and sensory cortex. METHODS:This was a retrospective observational study of patients undergoing intracranial monitoring for drug-resistant epilepsy (2008-2023). Electrical stimulation was delivered to intracranial electrodes during language tasks, and language, motor, and sensory findings were recorded. Positive and negative stimulation sites were analyzed in standard patient space and using the Human Connectome Project parcellation atlas. A multilevel statistical framework, including probability mapping, bootstrapped region-of-interest analyses, and kernel density estimation, defined structure-function relationships. Generalized linear mixed-effects models assessed the influence of clinical variables on language disruption. RESULTS:We analyzed 2,124 trials from 125 patients (mean age 30 years; 47% female). Instead of eloquent functions clustering into discrete canonical regions, they were distributed throughout the cortex along probabilistic continua. Language disruption demonstrated high interpatient variability with the middle frontal gyrus emerging as a high probability area for naming and speech arrest. Motor responses were not confined to the precentral gyrus but frequently extended into parietal association cortex. Clinically, early seizure onset (p = 0.006) and the presence of a temporal lobe lesion (p = 0.003) independently predicted a lower probability of language disruption in the temporal lobe. DISCUSSION:This extraoperative DES atlas provides a comprehensive benchmark for understanding functional cortical organization in epilepsy. We add evidence to the growing literature that language and motor systems are more distributed and variable than classically described. Substantial interpatient variability underscores the necessity of individualized mapping to guide safe neurosurgical planning. Limitations include the retrospective design and sampling bias inherent to electrode placement.

Objectives Temporal lobe epilepsy (TLE) impacts multiple brain networks. Aberrant functional connectivity has been demonstrated in resting-state networks (RSNs) that mediate higher brain functions in TLE. This study aimed to identify the reproducible patterns of altered functional connectivity in TLE in a large, international cohort through ENIGMA-Epilepsy.Methods Resting-state functional MRI datasets from nine centers across North America, South America, Europe and South Africa, including 442 people with TLE and 387 healthy adults, were analyzed. We examined group differences in whole-brain connectivity in patients compared to controls in seven major RSNs. We also investigated whole-brain connectivity maps for key nodes within the default mode network (DMN). Furthermore, the associations between connectivity patterns and clinical variables were assessed.Results We found lower within-network connectivity scores (13.6% on average) and higher between-network connectivity scores (129% on average) in non-limbic RSN in TLE. This pattern was reproducible across all seven sites and most robust for DMN and visual networks. Patterns of connectivity were not associated with age of seizure onset or disease duration and were mostly similar in patients with left and right TLE with a few exceptions; isolated regions of high connectivity in left TLE and lower connectivity in right TLE compared to controls.Significance We show strong evidence of lower connectivity within most RSNs and higher connectivity outside of these networks that was highly consistent across geographically diverse sites, demonstrating the robustness and generalizability of our findings. The findings demonstrate a consistent disruption of network organization in TLE that may underlie cognitive co-morbidities and seizure propagation patterns observed in this patient population.Plain Language Summary In this international ENIGMA-Epilepsy study, resting-state fMRI data from 442 individuals with TLE showed reduced connectivity within major resting-state networks (about 14% lower) and markedly increased connectivity between networks (about 129% higher), compared to 387 healthy controls. These patterns were highly reproducible across sites. Connectivity alterations were not related to age of onset or disease duration and were largely similar across left and right TLE, aside from small, region-specific differences. Overall, the study demonstrates a robust, widespread reorganization of brain network connectivity in TLE, which may help explain associated cognitive difficulties and seizure spread.

Prediction of future events is essential for guiding effective actions in dynamic environments. Studies on the neural mechanisms for time-forward predictions have typically used stimuli with relatively simple statistical regularities. Here, we investigated time-forward predictions using stimuli containing statistical regularities similar to those found in natural auditory stimuli. Using intracranial EEG recordings in neurosurgical patients, we found that prediction signals were primarily carried by low-frequency activity across widespread cortical regions, including sensory, parietal, and frontal areas. Prediction-error (PE) signals were found in both low- and high-frequency activity, with high-frequency components localized mainly to sensory areas. Contrary to previous hypotheses, prediction and PE signals did not show a clear spatial or spectral segregation. Directed connectivity between brain regions decreased over the course of the stimulus sequence as predictability increased, except for pathways originating from the parietal cortex. These results reveal integrative and distributed predictive processing and highlight the dorsal auditory pathway in time-forward prediction.

OBJECTIVE:CDKL5 deficiency disorder (CDD) is a rare X-linked developmental and epileptic encephalopathy caused by loss-of-function variants in the CDKL5 gene. Preclinical experiments using enzyme replacement or gene therapies show promise and could be transformative therapies. This precompetitive consortium sought to harmonize nonseizure clinical endpoint selection for efficacy trials. Clinical Assessment of Neurodevelopmental Measures in CDD (CANDID) is an ongoing study evaluating the feasibility and suitability of neurocognitive tests and functioning scales in CDD patients. METHODS:CANDID is a 3-year, longitudinal, noninterventional global study involving children and adults with CDD. On-site and remote visits include clinical, behavioral, developmental, and quality of life assessments. RESULTS:We enrolled 112 patients (111 included in analyses); mean age = 8.3 years (range <1-28); 93% female; 10 participants were ≥18 years old. In the first 28 days, 82% had >16 seizures; six were seizure-free. Median seizure onset was at 1.5 months (range = 0-66). Patients used an average of 2.6 antiseizure medications at baseline. The most frequent comorbidities included gastrointestinal hypomotility, muscle tone abnormalities, and sleep disorders. Gross Motor Function Measure-88 (GMFM-88) scores indicated a floor effect in crawling, standing, and walking across all ages. Vineland-3 and Bayley-4 scores could be derived in most, with receptive language, interpersonal relationships, and fine and gross motor scores increasing with age. Bruni sleep questionnaire identified sleep initiation, sleep-awake transition, and excessive somnolence as the most disrupted components across all age groups. The mean Quality of Life Inventory-Disability total scores ranged from 53% to 64%, the independence domain being the most impacted. SIGNIFICANCE:The scales in the CANDID study capture disease-related deficits and phenotype variability in CDD. Floor effects in subdomains aligned with disease severity. The GMFM-88 lacks granularity, and its operational limitations make it unsuitable for CDD trials. Baseline analyses demonstrate the feasibility and potential value of most selected scales, supporting their use in optimizing trial design and endpoint selection for future CDD clinical trials.

Deoxyhypusine synthase (DHPS) syndrome is a rare, autosomal recessive neurodevelopmental disorder caused by biallelic pathogenic variants in the DHPS gene, which encodes deoxyhypusine synthase. This enzyme is essential for the post-translational hypusination of eukaryotic translation initiation factor 5A (eIF5A), a modification crucial for cell viability, protein synthesis, and neuronal development. Patients with DHPS deficiency typically present with global developmental delays, intellectual disabilities, speech and motor impairments, seizures, and various dysmorphic features. Molecular studies show that DHPS mutations disrupt eIF5A hypusination, impairing translation elongation and cellular homeostasis. Animal and cellular models have confirmed the neurotoxic effects of impaired hypusination. Although no targeted therapy is available, advances in understanding the molecular basis of the disorder have enabled translational research, including modulation of polyamine metabolism. Here, we describe the development of a gene therapy strategy to deliver DHPS cDNA to mutant human brain cells in cortical organoids derived from patient stem cells, successfully restoring hypusination. This approach also improved survival in a mouse model of DHPS deficiency, highlighting the potential of rescuing DHPS expression as a treatment.

BACKGROUND AND OBJECTIVES:Severe hypoxemia after generalized convulsive seizures (GCSs) can trigger neural injury and is a potential biomarker for sudden unexpected death in epilepsy (SUDEP). Some degree of variability in interbreath interval is normal, but increased variability may suggest dysfunctional breathing control and may be associated with severe postictal hypoxemia. We evaluated the relationship between interictal breathing variability and severity and duration of hypoxemia after GCS. METHODS:We prospectively collected video-EEG, respiratory flow and effort, pulse oximetry (SpO2), and ECG from people with epilepsy (PWE). Measures of interictal interbreath interval variability (coefficient of variation, root mean square of successive differences [RMSSD], and long-term [SD-2] variability from Poincaré plots) from interictal asleep and awake periods and other relevant variables were evaluated as covariates for primary outcomes: (1) hypoxemia duration (length of time SpO2 <90%) and (2) severity of hypoxemia (SpO2 nadir), and secondary outcome: occurrence of combined prolonged and pronounced hypoxemia. Univariable and multivariable models were created for primary outcomes, but only univariable analyses were performed for the secondary outcome. RESULTS:Of 2,506 participants enrolled, 257 (141 [∼54%] female; mean age = 37.9 years) had ≥1 GCS, but only 152 GCS in 123 had evaluable respiratory data. Multivariable model for hypoxemia duration showed that SpO2 nadir (mean ratio [MR] = 0.88, 95% CI 0.81-0.96, p = 0.002) and SD-2 of the awake interbreath interval (MR = 1.06, 95% CI 1.01-1.13, p = 0.04) were significantly associated. RMSSD of the non-REM interbreath interval (mean difference = -5.01, 95% CI -8.10 to -1.93, p = 0.002) was the only variable significantly associated with hypoxemia severity after controlling for duration of postictal generalized EEG suppression, SD-2 of the awake interbreath interval, and body mass index. Univariable analyses for combined prolonged and pronounced hypoxemia showed SD-2 of the awake interbreath interval, temporal lobe epilepsy, ictal central apnea, and a shorter tonic phase duration were significantly associated. DISCUSSION:Measures of interictal respiratory variability are associated with severe and prolonged hypoxemia after GCS. Increased interictal respiratory variability suggests baseline respiratory dysregulation in some PWE and may be a surrogate for SUDEP risk.