Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-rapid eye movement sleep (EE-SWAS), a sleep stage dominated by sleep spindles, brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, p≈0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (p≈0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 seconds after each thalamic spike (p<0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Spike ripples localize the epileptogenic zone best: an international intracranial study.

We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz) and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.

A General, Noise-Driven Mechanism for the 1/f-Like Behavior of Neural Field Spectra.

Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo without requiring a specific biological mechanism or collective critical behavior.

Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures.

In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks-in which edges indicate strong enough coupling between brain regions-are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.

Multiple sources of fast traveling waves during human seizures: resolving a controversy.

During human seizures organized waves of voltage activity rapidly sweep across the cortex. Two contradictory theories describe the source of these fast traveling waves: either a slowly advancing narrow region of multiunit activity (an ictal wavefront) or a fixed cortical location. Limited observations and different analyses prevent resolution of these incompatible theories. Here we address this disagreement by combining the methods and microelectrode array recordings (N=11 patients, 2 females, N=31 seizures) from previous human studies to analyze the traveling wave source. We find - inconsistent with both existing theories - a transient relationship between the ictal wavefront and traveling waves, and multiple stable directions of traveling waves in many seizures. Using a computational model that combines elements of both existing theories, we show that interactions between an ictal wavefront and fixed source reproduce the traveling wave dynamics observed in vivo We conclude that combining both existing theories can generate the diversity of ictal traveling waves.Significance StatementThe source of voltage discharges that propagate across cortex during human seizures remains unknown. Two candidate theories exist, each proposing a different discharge source. Support for each theory consists of observations from a small number of human subject recordings, analyzed with separately developed methods. How the different, limited data and different analysis methods impact the evidence for each theory is unclear. To resolve these differences, we combine the unique, human microelectrode array recordings collected separately for each theory and analyze these combined data with a unified approach. We show that neither existing theory adequately describes the data. We then propose a new theory that unifies existing proposals and successfully reproduces the voltage discharge dynamics observed in vivo.

Children with Rolandic epilepsy have micro- and macrostructural abnormalities in white matter constituting networks necessary for language function.

INTRODUCTION: Self-limited epilepsy with centrotemporal spikes is a transient developmental epilepsy with a seizure onset zone localized to the centrotemporal cortex that commonly impacts aspects of language function. To better understand the relationship between these anatomical findings and symptoms, we characterized the language profile and white matter microstructural and macrostructural features in a cohort of children with SeLECTS. METHODS: Children with active SeLECTS (n = 13), resolved SeLECTS (n = 12), and controls (n = 17) underwent high-resolution MRIs including diffusion tensor imaging sequences and multiple standardized neuropsychological measures of language function. We identified the superficial white matter abutting the inferior rolandic cortex and superior temporal gyrus using a cortical parcellation atlas and derived the arcuate fasciculus connecting them using probabilistic tractography. We compared white matter microstructural characteristics (axial, radial and mean diffusivity, and fractional anisotropy) between groups in each region, and tested for linear relationships between diffusivity metrics in these regions and language scores on neuropsychological testing. RESULTS: We found significant differences in several language modalities in children with SeLECTS compared to controls. Children with SeLECTS performed worse on assessments of phonological awareness (p = 0.045) and verbal comprehension (p = 0.050). Reduced performance was more pronounced in children with active SeLECTS compared to controls, namely, phonological awareness (p = 0.028), verbal comprehension (p = 0.028), and verbal category fluency (p = 0.031), with trends toward worse performance also observed in verbal letter fluency (p = 0.052), and the expressive one-word picture vocabulary test (p = 0.068). Children with active SeLECTS perform worse than children with SeLECTS in remission on tests of verbal category fluency (p = 0.009), verbal letter fluency (p = 0.006), and the expressive one-word picture vocabulary test (p = 0.045). We also found abnormal superficial white matter microstructure in centrotemporal ROIs in children with SeLECTS, characterized by increased diffusivity and fractional anisotropy compared to controls (AD p = 0.014, RD p = 0.028, MD p = 0.020, and FA p = 0.024). Structural connectivity of the arcuate fasciculus connecting perisylvian cortical regions was lower in children with SeLECTS (p = 0.045), and in the arcuate fasciculus children with SeLECTS had increased diffusivity (AD p = 0.007, RD p = 0.006, MD p = 0.016), with no difference in fractional anisotropy (p = 0.22). However, linear tests comparing white matter microstructure in areas constituting language networks and language performance did not withstand correction for multiple comparisons in this sample, although a trend was seen between FA in the arcuate fasciculus and verbal category fluency (p = 0.047) and the expressive one-word picture vocabulary test (p = 0.036). CONCLUSION: We found impaired language development in children with SeLECTS, particularly in those with active SeLECTS, as well as abnormalities in the superficial centrotemporal white matter as well as the fibers connecting these regions, the arcuate fasciculus. Although relationships between language performance and white matter abnormalities did not pass correction for multiple comparisons, taken together, these results provide evidence of atypical white matter maturation in fibers involved in language processing, which may contribute to the aspects of language function that are commonly affected by the disorder.

Focal Sleep Spindle Deficits Reveal Focal Thalamocortical Dysfunction and Predict Cognitive Deficits in Sleep Activated Developmental Epilepsy.

Childhood epilepsy with centrotemporal spikes (CECTS) is the most common focal epilepsy syndrome, yet the cause of this disease remains unknown. Now recognized as a mild epileptic encephalopathy, children exhibit sleep-activated focal epileptiform discharges and cognitive difficulties during the active phase of the disease. The association between the abnormal electrophysiology and sleep suggests disruption to thalamocortical circuits. Thalamocortical circuit dysfunction resulting in pathologic epileptiform activity could hinder the production of sleep spindles, a brain rhythm essential for memory processes. Despite this pathophysiologic connection, the relationship between spindles and cognitive symptoms in epileptic encephalopathies has not been previously evaluated. A significant challenge limiting such work has been the poor performance of available automated spindle detection methods in the setting of sharp activities, such as epileptic spikes. Here, we validate a robust new method to accurately measure sleep spindles in patients with epilepsy. We then apply this detector to a prospective cohort of male and female children with CECTS with combined high-density EEGs during sleep and cognitive testing at varying time points of disease. We show that: (1) children have a transient, focal deficit in spindles during the symptomatic phase of disease; (2) spindle rate anticorrelates with spike rate; and (3) spindle rate, but not spike rate, predicts performance on cognitive tasks. These findings demonstrate focal thalamocortical circuit dysfunction and provide a pathophysiological explanation for the shared seizures and cognitive symptoms in CECTS. Further, this work identifies sleep spindles as a potential treatment target of cognitive dysfunction in this common epileptic encephalopathy.SIGNIFICANCE STATEMENT Childhood epilepsy with centrotemporal spikes is the most common idiopathic focal epilepsy syndrome, characterized by self-limited focal seizures and cognitive symptoms. Here, we provide the first evidence that focal thalamocortical circuit dysfunction underlies the shared seizures and cognitive dysfunction observed. In doing so, we identify sleep spindles as a mechanistic biomarker, and potential treatment target, of cognitive dysfunction in this common developmental epilepsy and provide a novel method to reliably quantify spindles in brain recordings from patients with epilepsy.

Quantifying seizure termination patterns reveals limited pathways to seizure end.

OBJECTIVE: Despite their possible importance in the design of novel neuromodulatory approaches and in understanding status epilepticus, the dynamics and mechanisms of seizure termination are not well studied. We examined intracranial recordings from patients with epilepsy to differentiate seizure termination patterns and investigated whether these patterns are indicative of different underlying mechanisms. METHODS: Seizures were classified into one of two termination patterns: (a) those that end simultaneously across the brain (synchronous), and (b) those whose termination is piecemeal across the cortex (asynchronous). Both types ended with either a burst suppression pattern, or continuous seizure activity. These patterns were quantified and compared using burst suppression ratio, absolute energy, and network connectivity. RESULTS: Seizures with electrographic generalization showed burst suppression patterns in 90% of cases, compared with only 60% of seizures which remained focal. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous seizure activity were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous seizure activity compared to seizures with burst suppression. SIGNIFICANCE: Based on this spatiotemporal classification scheme, we suggest that there are a limited number of seizure termination patterns and dynamics. If this bears out, it would imply that the number of mechanisms underlying seizure termination is also constrained. Seizures with different termination patterns exhibit different dynamics even before their start. This may provide useful clues about how seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.

Persistent abnormalities in Rolandic thalamocortical white matter circuits in childhood epilepsy with centrotemporal spikes.

OBJECTIVE: Childhood epilepsy with centrotemporal spikes (CECTS) is a common, focal, transient, developmental epilepsy syndrome characterized by unilateral or bilateral, independent epileptiform spikes in the Rolandic regions of unknown etiology. Given that CECTS presents during a period of dramatic white matter maturation and thatspikes in CECTS are activated during non-rapid eye movement (REM) sleep, we hypothesized that children with CECTS would have aberrant development of white matter connectivity between the thalamus and the Rolandic cortex. We further tested whether Rolandic thalamocortical structural connectivity correlates with spike rate during non-REM sleep. METHODS: Twenty-three children with CECTS (age = 8-15 years) and 19 controls (age = 7-15 years) underwent 3-T structural and diffusion-weighted magnetic resonance imaging and 72-electrode electroencephalographic recordings. Thalamocortical structural connectivity to Rolandic and non-Rolandic cortices was quantified using probabilistic tractography. Developmental changes in connectivity were compared between groups using bootstrap analyses. Longitudinal analysis was performed in four subjects with 1-year follow-up data. Spike rate was quantified during non-REM sleep using manual and automated techniques and compared to Rolandic connectivity using regression analyses. RESULTS: Children with CECTS had aberrant development of thalamocortical connectivity to the Rolandic cortex compared to controls (P = .01), where the expected increase in connectivity with age was not observed in CECTS. There was no difference in the development of thalamocortical connectivity to non-Rolandic regions between CECTS subjects and controls (P = .19). Subjects with CECTS observed longitudinally had reductions in thalamocortical connectivity to the Rolandic cortex over time. No definite relationship was found between Rolandic connectivity and non-REM spike rate (P > .05). SIGNIFICANCE: These data provide evidence that abnormal maturation of thalamocortical white matter circuits to the Rolandic cortex is a feature of CECTS. Our data further suggest that the abnormalities in these tracts do not recover, but are increasingly dysmature over time, implicating a permanent but potentially compensatory process contributing to disease resolution.

Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes.

In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.

The natural history of seizures and neuropsychiatric symptoms in childhood epilepsy with centrotemporal spikes (CECTS).

OBJECTIVE: Childhood epilepsy with centrotemporal spikes (CECTS) (formally benign epilepsy with centrotemporal spikes, BECTS) is a common childhood epilepsy syndrome characterized by psychiatric, behavioral, and cognitive abnormalities and self-limited seizures. Although CECTS is one of the most well-characterized electroclinical epilepsy syndromes, the natural history of neuropsychiatric outcomes is poorly understood. We report the psychiatric, behavioral, and cognitive profiles over the course of disease from a large, prospectively-enrolled, longitudinal cohort of children with CECTS. We further characterize the detailed seizure course and test the relationship between several proposed risk factors and neuropsychiatric and seizure outcomes in these children. METHODS: Patients diagnosed with CECTS were enrolled as part of a community-based study and followed from diagnosis through disease resolution (16.0 ±â€¯3.1 years, N = 60). Twenty sibling controls were also recruited. We report the natural history of premorbid neuropsychiatric concerns, postmorbid neuropsychiatric diagnoses, long-term neuropsychological performance, seizure course, antiseizure medication (ASM) treatment response, and the relationship between duration seizure-free and remission. Age at onset and premorbid neuropsychiatric concerns were tested as predictors of seizure count, epilepsy duration, postmorbid neuropsychiatric diagnoses, and long-term neuropsychological performance. Antiseizure medication treatment duration, seizure count, and epilepsy duration were tested as predictors of postmorbid neuropsychiatric diagnoses and long-term neuropsychological performance. RESULTS: Children with CECTS had a high incidence of ADD/ADHD symptoms (18.3%) or learning difficulties (21.7%) before diagnosis. New or persistent ADHD (20%), mood disorders (23.6%), learning difficulties (14.5%), and behavioral disorders (7.3%) were common after CECTS diagnosis. At 9-year follow-up, performance on formal neuropsychological testing was comparable to population statistics and sibling controls. More than two-thirds of treated children experienced at least one seizure during treatment. Most children (61.7%) had entered terminal resolution after 12 months seizure-free. Among all children, for each month seizure-free, there was a 6-7% increase in the probability of achieving terminal remission (p < 1e-10). The presence of a premorbid neurodevelopmental concern predicted a longer epilepsy duration (p = 0.02), higher seizure count (p = 0.02), and a postmorbid psychiatric or neurodevelopmental diagnosis (p = 0.002). None of the tested features predicted long-term neuropsychological performance. SIGNIFICANCE: Children are at high risk of neuropsychiatric symptoms along the course of the disease in CECTS, however, long-term cognitive performance is favorable. The majority of children had a seizure while being treated with ASMs, suggesting that CECTS is not as pharmacoresponsive as assumed or that treatment approaches are not optimized. Among treated and untreated children, future seizure-risk can be estimated from duration seizure-free. The presence of a premorbid neuropsychiatric concern predicted a more severe disease course in CECTS.

Dynamic connectivity modulates local activity in the core regions of the default-mode network.

Segregation and integration are distinctive features of large-scale brain activity. Although neuroimaging studies have been unraveling their neural correlates, how integration takes place over segregated modules remains elusive. Central to this problem is the mechanism by which a brain region adjusts its activity according to the influence it receives from other regions. In this study, we explore how dynamic connectivity between two regions affects the neural activity within a participating region. Combining functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same group of subjects, we analyzed resting-state data from the core of the default-mode network. We observed directed influence from the posterior cingulate cortex (PCC) to the anterior cingulate cortex (ACC) in the 10-Hz range. This time-varying influence was associated with the power alteration in the ACC: strong influence corresponded with a decrease of power around 13-16 Hz and an increase of power in the lower (1-7 Hz) and higher (30-55 Hz) ends of the spectrum. We also found that the amplitude of the 30- to 55-Hz activity was coupled to the phase of the 3- to 4-Hz activity in the ACC. These results characterized the local spectral changes associated with network interactions. The specific spectral information both highlights the functional roles of PCC-ACC connectivity in the resting state and provides insights into the dynamic relationship between local activity and coupling dynamics of a network.

The probability of seizures during continuous EEG monitoring in high-risk neonates.

OBJECTIVE: We evaluated the impact of monitoring indication, early electroencephalography (EEG), and clinical features on seizure risk in all neonates undergoing continuous EEG (cEEG) monitoring following a standardized monitoring protocol. METHODS: All cEEGs from unique neonates 34-48 weeks postmenstrual age monitored from 1/2011-10/2017 (n = 291) were included. We evaluated the impact of cEEG monitoring indication (acute neonatal encephalopathy [ANE], suspicious clinical events [SCEs], or other high-risk conditions [OHRs]), age, medication status, and early EEG abnormalities (including the presence of epileptiform discharges and abnormal background continuity, amplitude, asymmetry, asynchrony, excessive sharp transients, and burst suppression) on time to first seizure and overall seizure risk using Kaplan-Meier survival curves and multivariable Cox proportional hazards models. RESULTS: Seizures occurred in 28% of high-risk neonates. Discontinuation of monitoring after 24 hours of seizure-freedom would have missed 8.5% of neonates with seizures. Overall seizure risk was lower in neonates monitored for ANE compared to OHR (P = .004) and trended lower compared to SCE (P = .097). The time course of seizure presentation varied by group, where the probability of future seizure was less than 1% after 17 hours of seizure-free monitoring in the SCE group, but required 42 hours in the OHR group, and 73 hours in the ANE group. The presence of early epileptiform discharges increased seizure risk in each group (ANE: adjusted hazard ratio [aHR] 4.32, 95% confidence interval [CI] 1.23-15.13, P = .022; SCE: aHR 10.95, 95% CI 4.77-25.14, P < 1e-07; OHR: aHR 56.90, 95% CI 10.32-313.72, P < 1e-05). SIGNIFICANCE: Neonates who undergo cEEG are at high risk for seizures, and risk varies by monitoring indication and early EEG findings. Seizures are captured in nearly all neonates undergoing monitoring for SCE within 24 hours of cEEG monitoring. Neonates monitored for OHR and ANE can present with delayed seizures and require longer durations of monitoring. Early epileptiform discharges are the best early EEG feature to predict seizure risk.

Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models.

To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train data with a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.

Ictal and preictal power changes outside of the seizure focus correlate with seizure generalization.

OBJECTIVE: The treatment of focal epilepsies is largely predicated on the concept that there is a "focus" from which the seizure emanates. Yet, the physiological context that determines if and how ictal activity starts and propagates remains poorly understood. To delineate these phenomena more completely, we studied activity outside the seizure-onset zone prior to and during seizure initiation. METHODS: Stereotactic depth electrodes were implanted in 17 patients with longstanding pharmacoresistant epilepsy for lateralization and localization of the seizure-onset zone. Only seizures with focal onset in mesial temporal structures were used for analysis. Spectral analyses were used to quantify changes in delta, theta, alpha, beta, gamma, and high gamma frequency power, in regions inside and outside the area of seizure onset during both preictal and seizure initiation periods. RESULTS: In the 78 seizures examined, an average of 9.26% of the electrode contacts outside of the seizure focus demonstrated changes in power at seizure onset. Of interest, seizures that were secondarily generalized, on average, showed power changes in a greater number of extrafocus electrode contacts at seizure onset (16.7%) compared to seizures that remained focal (3.8%). The majority of these extrafocus changes occupied the delta and theta bands in electrodes placed in the ipsilateral, lateral temporal lobe. Preictally, we observed extrafocal high-frequency power decrements, which also correlated with seizure spread. SIGNIFICANCE: This widespread activity at and prior to the seizure-onset time further extends the notion of the ictogenic focus and its relationship to seizure spread. Further understanding of these extrafocus, periictal changes might help identify the neuronal dynamics underlying the initiation of seizures and how therapies can be devised to control seizure activity.

Age-Related Changes in 1/f Neural Electrophysiological Noise.

Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15-53 years) and scalp EEG data from healthy younger (20-30 years) and older (60-70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often <-1 for electrophysiological data and has been shown to approach white noise (defined as χ = 0) with increasing task difficulty. We observed, in both electrophysiological datasets, that aging is associated with a flatter (more noisy) 1/f power spectral density, even at rest, and that visual cortical 1/f noise statistically mediates age-related impairments in visual working memory. These results provide electrophysiological support for the neural noise hypothesis of aging. Significance statement: Understanding the neurobiological origins of age-related cognitive decline is of critical scientific, medical, and public health importance, especially considering the rapid aging of the world's population. We find, in two separate human studies, that 1/f electrophysiological noise increases with aging. In addition, we observe that this age-related 1/f noise statistically mediates age-related working memory decline. These results significantly add to this understanding and contextualize a long-standing problem in cognition by encapsulating age-related cognitive decline within a neurocomputational model of 1/f noise-induced deficits in neural communication.

Slow Spatial Recruitment of Neocortex during Secondarily Generalized Seizures and Its Relation to Surgical Outcome.

Understanding the spatiotemporal dynamics of brain activity is crucial for inferring the underlying synaptic and nonsynaptic mechanisms of brain dysfunction. Focal seizures with secondary generalization are traditionally considered to begin in a limited spatial region and spread to connected areas, which can include both pathological and normal brain tissue. The mechanisms underlying this spread are important to our understanding of seizures and to improve therapies for surgical intervention. Here we study the properties of seizure recruitment-how electrical brain activity transitions to large voltage fluctuations characteristic of spike-and-wave seizures. We do so using invasive subdural electrode arrays from a population of 16 patients with pharmacoresistant epilepsy. We find an average delay of ∼30 s for a broad area of cortex (8 × 8 cm) to be recruited into the seizure, at an estimated speed of ∼4 mm/s. The spatiotemporal characteristics of recruitment reveal two categories of patients: one in which seizure recruitment of neighboring cortical regions follows a spatially organized pattern consistent from seizure to seizure, and a second group without consistent spatial organization of activity during recruitment. The consistent, organized recruitment correlates with a more regular, compared with small-world, connectivity pattern in simulation and successful surgical treatment of epilepsy. We propose that an improved understanding of how the seizure recruits brain regions into large amplitude voltage fluctuations provides novel information to improve surgical treatment of epilepsy and highlights the slow spread of massive local activity across a vast extent of cortex during seizure.

Amplitude-Modulated Bursting: A Novel Class of Bursting Rhythms.

We report on the discovery of a novel class of bursting rhythms, called amplitude-modulated bursting (AMB), in a model for intracellular calcium dynamics. We find that these rhythms are robust and exist on open parameter sets. We develop a new mathematical framework with broad applicability to detect, classify, and rigorously analyze AMB. Here we illustrate this framework in the context of AMB in a model of intracellular calcium dynamics. In the process, we discover a novel family of singularities, called toral folded singularities, which are the organizing centers for the amplitude modulation and exist generically in slow-fast systems with two or more slow variables.

A biologically constrained, mathematical model of cortical wave propagation preceding seizure termination.

Epilepsy--the condition of recurrent, unprovoked seizures--manifests in brain voltage activity with characteristic spatiotemporal patterns. These patterns include stereotyped semi-rhythmic activity produced by aggregate neuronal populations, and organized spatiotemporal phenomena, including waves. To assess these spatiotemporal patterns, we develop a mathematical model consistent with the observed neuronal population activity and determine analytically the parameter configurations that support traveling wave solutions. We then utilize high-density local field potential data recorded in vivo from human cortex preceding seizure termination from three patients to constrain the model parameters, and propose basic mechanisms that contribute to the observed traveling waves. We conclude that a relatively simple and abstract mathematical model consisting of localized interactions between excitatory cells with slow adaptation captures the quantitative features of wave propagation observed in the human local field potential preceding seizure termination.