Seizure Cycles under Pharmacotherapy.

OBJECTIVE: This study was undertaken to determine the effects of antiseizure medications (ASMs) on multidien (multiday) cycles of interictal epileptiform activity (IEA) and seizures and evaluate their potential clinical significance. METHODS: We retrospectively analyzed up to 10 years of data from 88 of the 256 total adults with pharmacoresistant focal epilepsy who participated in the clinical trials of the RNS System, an intracranial device that keeps records of IEA counts. Following adjunctive ASM trials, we evaluated changes over months in (1) rates of self-reported disabling seizures and (2) multidien IEA cycle strength (spectral power for periodicity between 4 and 40 days). We used a survival analysis and the receiver operating characteristics to assess changes in IEA as a predictor of seizure control. RESULTS: Among 56 (33.3%) of the 168 adjunctive ASM trials suitable for analysis, ASM introduction was followed by an average 50 to 70% decrease in multidien IEA cycle strength and a concomitant 50 to 70% decrease in relative seizure rate for up to 12 months. Individuals with a ≥50% decrease in IEA cycle strength in the first 3 months of an ASM trial had a higher probability of remaining seizure responders (≥50% seizure rate reduction, p < 10-7) or super-responders (≥90%, p < 10-8) over the next 12 months. INTERPRETATION: In this large cohort, a decrease in multidien IEA cycle strength following initiation of an adjunctive ASM correlated with seizure control for up to 12 months, suggesting that fluctuations in IEA mirror "disease activity" in pharmacoresistant focal epilepsy and may have clinical utility as a biomarker to predict treatment response. ANN NEUROL 2024;95:743-753.

Intrinsic Neural Timescales in the Temporal Lobe Support an Auditory Processing Hierarchy.

During rest, intrinsic neural dynamics manifest at multiple timescales, which progressively increase along visual and somatosensory hierarchies. Theoretically, intrinsic timescales are thought to facilitate processing of external stimuli at multiple stages. However, direct links between timescales at rest and sensory processing, as well as translation to the auditory system are lacking. Here, we measured intracranial EEG in 11 human patients with epilepsy (4 women), while listening to pure tones. We show that, in the auditory network, intrinsic neural timescales progressively increase, while the spectral exponent flattens, from temporal to entorhinal cortex, hippocampus, and amygdala. Within the neocortex, intrinsic timescales exhibit spatial gradients that follow the temporal lobe anatomy. Crucially, intrinsic timescales at baseline can explain the latency of auditory responses: as intrinsic timescales increase, so do the single-electrode response onset and peak latencies. Our results suggest that the human auditory network exhibits a repertoire of intrinsic neural dynamics, which manifest in cortical gradients with millimeter resolution and may provide a variety of temporal windows to support auditory processing.SIGNIFICANCE STATEMENT Endogenous neural dynamics are often characterized by their intrinsic timescales. These are thought to facilitate processing of external stimuli. However, a direct link between intrinsic timing at rest and sensory processing is missing. Here, with intracranial EEG, we show that intrinsic timescales progressively increase from temporal to entorhinal cortex, hippocampus, and amygdala. Intrinsic timescales at baseline can explain the variability in the timing of intracranial EEG responses to sounds: cortical electrodes with fast timescales also show fast- and short-lasting responses to auditory stimuli, which progressively increase in the hippocampus and amygdala. Our results suggest that a hierarchy of neural dynamics in the temporal lobe manifests across cortical and limbic structures and can explain the temporal richness of auditory responses.

Stimulation to probe, excite, and inhibit the epileptic brain.

Direct cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug-resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long-term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker-informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.

Passive and active markers of cortical excitability in epilepsy.

Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

Learning to generalize seizure forecasts.

OBJECTIVE: Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years-long electroencephalographic (EEG) recordings, we previously found that patient-reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal-ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. METHODS: We used retrospective long-term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24-h horizon. RESULTS: With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) = .70 and .69 and median Brier skill score (BSS) = .07 and .08. In direct comparison, individualized models had similar median performance (AUC = .67, BSS = .08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. SIGNIFICANCE: Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient-reported vs. electrographic) or IEA (icEEG vs. sqEEG).

Seizure forecasting: Bifurcations in the long and winding road.

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

Cues for seizure timing.

The cyclical organization of seizures in epilepsy has been described since antiquity. However, historical explanations for seizure cycles-based on celestial, hormonal, and environmental factors-have only recently become testable with the advent of chronic electroencephalography (cEEG) and modern statistical techniques. Here, factors purported over millennia to influence seizure timing are viewed through a contemporary lens. We discuss the emerging concept that seizures are organized over multiple timescales, each involving differential influences of external and endogenous rhythm generators. Leveraging large cEEG datasets and circular statistics appropriate for cyclical phenomena, we present new evidence for circadian (day-night), multidien (multi-day), and circannual (about-yearly) variation in seizure activity. Modulation of seizure timing by multiscale temporal variables has implications for diagnosis and therapy in clinical epilepsy. Uncovering the mechanistic basis for seizure cycles, particularly the factors that govern multidien periodicity, will be a major focus of future work.

Accuracy of omni-planar and surface casting of epileptiform activity for intracranial seizure localization.

OBJECTIVE: Intracranial electroencephalography (ICEEG) recordings are performed for seizure localization in medically refractory epilepsy. Signal quantifications such as frequency power can be projected as heatmaps on personalized three-dimensional (3D) reconstructed cortical surfaces to distill these complex recordings into intuitive cinematic visualizations. However, simultaneously reconciling deep recording locations and reliably tracking evolving ictal patterns remain significant challenges. METHODS: We fused oblique magnetic resonance imaging (MRI) slices along depth probe trajectories with cortical surface reconstructions and projected dynamic heatmaps using a simple mathematical metric of epileptiform activity (line-length). This omni-planar and surface casting of epileptiform activity approach (OPSCEA) thus illustrated seizure onset and spread among both deep and superficial locations simultaneously with minimal need for signal processing supervision. We utilized the approach on 41 patients at our center implanted with grid, strip, and/or depth electrodes for localizing medically refractory seizures. Peri-ictal data were converted into OPSCEA videos with multiple 3D brain views illustrating all electrode locations. Five people of varying expertise in epilepsy (medical student through epilepsy attending level) attempted to localize the seizure-onset zones. RESULTS: We retrospectively compared this approach with the original ICEEG study reports for validation. Accuracy ranged from 73.2% to 97.6% for complete or overlapping onset lobe(s), respectively, and ~56.1% to 95.1% for the specific focus (or foci). Higher answer certainty for a given case predicted better accuracy, and scorers had similar accuracy across different training levels. SIGNIFICANCE: In an era of increasing stereo-EEG use, cinematic visualizations fusing omni-planar and surface functional projections appear to provide a useful adjunct for interpreting complex intracranial recordings and subsequent surgery planning.

Measuring synchrony in bio-medical timeseries.

Paroxysms are sudden, unpredictable, short-lived events that abound in physiological processes and pathological disorders, from cellular functions (e.g., hormone secretion and neuronal firing) to life-threatening attacks (e.g., cardiac arrhythmia, epileptic seizures, and diabetic ketoacidosis). With the increasing use of personal chronic monitoring (e.g., electrocardiography, electroencephalography, and glucose monitors), the discovery of cycles in health and disease, and the emerging possibility of forecasting paroxysms, the need for suitable methods to evaluate synchrony-or phase-clustering-between events and related underlying physiological fluctuations is pressing. Here, based on examples in epilepsy, where seizures occur preferentially in certain brain states, we characterize different methods that evaluate synchrony in a controlled timeseries simulation framework. First, we compare two methods for extracting the phase of event occurrence and deriving the phase-locking value, a measure of synchrony: (M1) fitting cycles of fixed period-length vs (M2) deriving continuous cycles from a biomarker. In our simulations, M2 provides stronger evidence for cycles. Second, by systematically testing the sensitivity of both methods to non-stationarity in the underlying cycle, we show that M2 is more robust. Third, we characterize errors in circular statistics applied to timeseries with different degrees of temporal clustering and tested with different strategies: Rayleigh test, Poisson simulations, and surrogate timeseries. Using epilepsy data from 21 human subjects, we show the superiority of testing against surrogate time-series to minimize false positives and false negatives, especially when used in combination with M1. In conclusion, we show that only time frequency analysis of continuous recordings of a related bio-marker reveals the full extent of cyclical behavior in events. Identifying and forecasting cycles in biomedical timeseries will benefit from recordings using emerging wearable and implantable devices, so long as conclusions are based on conservative statistical testing.

Chance and risk in epilepsy.

PURPOSE OF REVIEW: Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. RECENT FINDINGS: Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. SUMMARY: The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.

Sleep fragmentation alters brain energy metabolism without modifying hippocampal electrophysiological response to novelty exposure.

Sleep is viewed as a fundamental restorative function of the brain, but its specific role in neural energy budget remains poorly understood. Sleep deprivation dampens brain energy metabolism and impairs cognitive functions. Intriguingly, sleep fragmentation, despite normal total sleep duration, has a similar cognitive impact, and in this paper we ask the question of whether it may also impair brain energy metabolism. To this end, we used a recently developed mouse model of 2 weeks of sleep fragmentation and measured 2-deoxy-glucose uptake and glycogen, glucose and lactate concentration in different brain regions. In order to homogenize mice behaviour during metabolic measurements, we exposed them to a novel environment for 1 h. Using an intra-hippocampal electrode, we first showed that hippocampal electroencephalograph (EEG) response to exploration was unaltered by 1 or 14 days of sleep fragmentation. However, after 14 days, sleep fragmented mice exhibited a lower uptake of 2-deoxy-glucose in cortex and hippocampus and lower cortical lactate levels than control mice. Our results suggest that long-term sleep fragmentation impaired brain metabolism to a similar extent as total sleep deprivation without affecting the neuronal responsiveness of hippocampus to a novel environment.

Recurrent secondary generalization in frontal lobe epilepsy: Predictors and a potential link to surgical outcome?

OBJECTIVE: Frontal lobe epilepsy (FLE) frequently leads to secondary generalized tonic-clonic seizures (SGTCS). However, little is known about the clinical, electrophysiologic, and radiologic correlates of SGTCS and whether these could influence diagnosis and treatment. METHODS: A cohort of 48 patients with confirmed FLE was retrospectively identified and dichotomized into a group with and a group without SGTCS defined by history (≥1/year) or video-electroencephalography (vEEG). Demographics, seizure semiology, vEEG, neuroimaging data, and estimated seizure-onset zone were tabulated, and their association with the occurrence of SGTCS was evaluated with use of a chi-square test. Independent predictors of SGTCS were confirmed using a stepwise multivariate analysis. Similarly, these predictors as well as a history of SGTCS were tested as multivariate predictors of the postoperative International League Against Epilepsy (ILAE) score in the surgical subgroup (n = 25). RESULTS: We identified three independent predictors of a history of SGTCS in FLE, including loss of responsiveness at seizure onset (corrected p = 0.04), a semiology involving early elementary motor signs (corrected p = 0.01), and multifocal spikes on EEG (corrected p = 0.02). A seizure-free outcome occurred in 57% of surgical cases and was more likely in the group without SGTCS (100%, p = 0.001). When considering only SGTCS occurring during video-EEG monitoring, the association with semiology and surgical outcome vanished, but the association with preserved awareness and a multifocal EEG persisted. SIGNIFICANCE: A history of SGTCS is related to a specific ictal semiology and interictal EEG, and may have a role in surgical risk stratification.

Hippocampal network activity forecasts epileptic seizures.

Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.

Designing next-generation subscalp devices for seizure monitoring: A systematic review and meta-analysis of established extracranial hardware.

Implantable brain recording and stimulation devices apply to a broad spectrum of conditions, such as epilepsy, movement disorders and depression. For long-term monitoring and neuromodulation in epilepsy patients, future extracranial subscalp implants may offer a promising, less-invasive alternative to intracranial neurotechnologies. To inform the design and assess the safety profile of such next-generation devices, we estimated extracranial complication rates of deep brain stimulation (DBS), cranial peripheral nerve stimulation (PNS), responsive neurostimulation (RNS) and existing subscalp EEG devices (sqEEG), as proxy for future implants. Pubmed was searched systematically for DBS, PNS, RNS and sqEEG studies from 2000 to February 2024 (48 publications, 7329 patients). We identified seven categories of extracranial adverse events: infection, non-infectious cutaneous complications, lead migration, lead fracture, hardware malfunction, pain and hemato-seroma. We used cohort sizes, demographics and industry funding as metrics to assess risks of bias. An inverse variance heterogeneity model was used for pooled and subgroup meta-analysis. The pooled incidence of extracranial complications reached 14.0%, with infections (4.6%, CI 95% [3.2 - 6.2]), surgical site pain (3.2%, [0.6 - 6.4]) and lead migration (2.6%, [1.0 - 4.4]) as leading causes. Subgroup analysis showed a particularly high incidence of persisting pain following PNS (12.0%, [6.8 - 17.9]) and sqEEG (23.9%, [12.7 - 37.2]) implantation. High rates of lead migration (12.4%, [6.4 - 19.3]) were also identified in the PNS subgroup. Complication analysis of DBS, PNS, RNS and sqEEG studies provides a significant opportunity to optimize the safety profile of future implantable subscalp devices for chronic EEG monitoring. Developing such promising technologies must address the risks of infection, surgical site pain, lead migration and skin erosion. A thin and robust design, coupled to a lead-anchoring system, shall enhance the durability and utility of next-generation subscalp implants for long-term EEG monitoring and neuromodulation.

Triggered Seizures for Ictal SPECT Imaging: A Case Series and Feasibility Study.

Ictal SPECT is an informative seizure imaging technique to tailor epilepsy surgery. However, capturing the onset of unpredictable seizures is a medical and logistic challenge. Here, we sought to image planned seizures triggered by direct stimulation of epileptic networks via stereotactic electroencephalography (sEEG) electrodes. Methods: In this case series of 3 adult participants with left temporal epilepsy, we identified and stimulated sEEG contacts able to trigger patient-typical seizures. We administered 99mTc-HMPAO within 12 s of ictal onset and acquired SPECT images within 40 min without any adverse events. Results: Ictal hyperperfusion maps partially overlapped concomitant sEEG seizure activity. In both participants known for periictal aphasia, SPECT imaging revealed hyperperfusion in the speech cortex lacking sEEG coverage. Conclusion: Triggering of seizures for ictal SPECT complements discrete sEEG sampling with spatially complete images of early seizure propagation. This readily implementable method revives interest in seizure imaging to guide resective epilepsy surgery.

Feasibility, Safety, and Performance of Full-Head Subscalp EEG Using Minimally Invasive Electrode Implantation.

BACKGROUND AND OBJECTIVES: Current practice in clinical neurophysiology is limited to short recordings with conventional EEG (days) that fail to capture a range of brain (dys)functions at longer timescales (months). The future ability to optimally manage chronic brain disorders, such as epilepsy, hinges upon finding methods to monitor electrical brain activity in daily life. We developed a device for full-head subscalp EEG (Epios) and tested here the feasibility to safely insert the electrode leads beneath the scalp by a minimally invasive technique (primary outcome). As secondary outcome, we verified the noninferiority of subscalp EEG in measuring physiologic brain oscillations and pathologic discharges compared with scalp EEG, the established standard of care. METHODS: Eight participants with pharmacoresistant epilepsy undergoing intracranial EEG received in the same surgery subscalp electrodes tunneled between the scalp and the skull with custom-made tools. Postoperative safety was monitored on an inpatient ward for up to 9 days. Sleep-wake, ictal, and interictal EEG signals from subscalp, scalp, and intracranial electrodes were compared quantitatively using windowed multitaper transforms and spectral coherence. Noninferiority was tested for pairs of neighboring subscalp and scalp electrodes with a Bland-Altman analysis for measurement bias and calculation of the interclass correlation coefficient (ICC). RESULTS: As primary outcome, up to 28 subscalp electrodes could be safely placed over the entire head through 1-cm scalp incisions in a ∼1-hour procedure. Five of 10 observed perioperative adverse events were linked to the investigational procedure, but none were serious, and all resolved. As a secondary outcome, subscalp electrodes advantageously recorded EEG percutaneously without requiring any maintenance and were noninferior to scalp electrodes for measuring (1) variably strong, stage-specific brain oscillations (alpha in wake, delta, sigma, and beta in sleep) and (2) interictal spikes peak-potentials and ictal signals coherent with seizure propagation in different brain regions (ICC >0.8 and absence of bias). DISCUSSION: Recording full-head subscalp EEG for localization and monitoring purposes is feasible up to 9 days in humans using minimally invasive techniques and noninferior to the current standard of care. A longer prospective ambulatory study of the full system will be necessary to establish the safety and utility of this innovative approach. TRIAL REGISTRATION INFORMATION: clinicaltrials.gov/study/NCT04796597.

Sustained sleep fragmentation affects brain temperature, food intake and glucose tolerance in mice.

Sleep fragmentation is present in numerous sleep pathologies and constitutes a major feature of patients with obstructive sleep apnea. A prevalence of metabolic syndrome, diabetes and obesity has been shown to be associated to obstructive sleep apnea. While sleep fragmentation has been shown to impact sleep homeostasis, its specific effects on metabolic variables are only beginning to emerge. In this context, it is important to develop realistic animal models that would account for chronic metabolic effects of sleep fragmentation. We developed a 14-day model of instrumental sleep fragmentation in mice, and show an impact on both brain-specific and general metabolism. We first report that sleep fragmentation increases food intake without affecting body weight. This imbalance was accompanied by the inability to adequately decrease brain temperature during fragmented sleep. In addition, we report that sleep-fragmented mice develop glucose intolerance. We also observe that sleep fragmentation slightly increases the circadian peak level of glucocorticoids, a factor that may be involved in the observed metabolic effects. Our results confirm that poor-quality sleep with sustained sleep fragmentation has similar effects on general metabolism as actual sleep loss. Altogether, these results strongly suggest that sleep fragmentation is an aggravating factor for the development of metabolic dysfunctions that may be relevant for sleep disorders such as obstructive sleep apnea.

Focal seizures unfold variably over time.

This scientific commentary refers to 'Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states' by Schroeder et al. (https://doi.org/10.1093/braincomms/fcad205).