Impaired sleep-dependent memory consolidation predicted by reduced sleep spindles in Rolandic epilepsy.

Background and Objectives: Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in Rolandic epilepsy and related sleep-activated epileptic encephalopathies. We investigate the relationship between sleep spindle deficits and deficient sleep dependent memory consolidation in children with Rolandic epilepsy. Methods: In this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using a validated automated detector. Results: Twenty-three children with Rolandic epilepsy (14 with resolved disease), and 19 age- and sex-matched controls were enrolled. Children with active Rolandic epilepsy had decreased memory consolidation compared to control children (p=0.001, mean percentage reduction: 25.7%, 95% CI [10.3, 41.2]%) and compared to children with resolved Rolandic epilepsy (p=0.007, mean percentage reduction: 21.9%, 95% CI [6.2, 37.6]%). Children with active Rolandic epilepsy had decreased sleep spindle rates in the centrotemporal region compared to controls (p=0.008, mean decrease 2.5 spindles/min, 95% CI [0.7, 4.4] spindles/min). Spindle rate positively predicted sleep-dependent memory consolidation (p=0.004, mean MST improvement of 3.9%, 95% CI [1.3, 6.4]%, for each unit increase in spindles per minute). Discussion: Children with Rolandic epilepsy have a sleep spindle deficit during the active period of disease which predicts deficits in sleep dependent memory consolidation. This finding provides a mechanism and noninvasive biomarker to aid diagnosis and therapeutic discovery for cognitive dysfunction in Rolandic epilepsy and related sleep activated epilepsy syndromes.

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.

Photothrombosis induced cortical stroke produces electrographic epileptic biomarkers in mice.

Objective: Interictal epileptiform spikes, high-frequency ripple oscillations, and their co-occurrence (spike ripples) in human scalp or intracranial voltage recordings are well-established epileptic biomarkers. While clinically significant, the neural mechanisms generating these electrographic biomarkers remain unclear. To reduce this knowledge gap, we introduce a novel photothrombotic stroke model in mice that reproduces focal interictal electrographic biomarkers observed in human epilepsy. Methods: We induced a stroke in the motor cortex of C57BL/6 mice unilaterally (N=7) using a photothrombotic procedure previously established in rats. We then implanted intracranial electrodes (2 ipsilateral and 2 contralateral) and obtained intermittent local field potential (LFP) recordings over several weeks in awake, behaving mice. We evaluated the LFP for focal slowing and epileptic biomarkers - spikes, ripples, and spike ripples - using both automated and semi-automated procedures. Results: Delta power (1-4 Hz) was higher in the stroke hemisphere than the non-stroke hemisphere in all mice ( p <0.001). Automated detection procedures indicated that compared to the non-stroke hemisphere, the stroke hemisphere had an increased spike ripple ( p =0.006) and spike rates ( p =0.039), but no change in ripple rate ( p =0.98). Expert validation confirmed the observation of elevated spike ripple rates ( p =0.008) and a trend of elevated spike rate ( p =0.055) in the stroke hemisphere. Interestingly, the validated ripple rate in the stroke hemisphere was higher than the non-stroke hemisphere ( p =0.031), highlighting the difficulty of automatically detecting ripples. Finally, using optimal performance thresholds, automatically detected spike ripples classified the stroke hemisphere with the best accuracy (sensitivity 0.94, specificity 0.94). Significance: Cortical photothrombosis-induced stroke in commonly used C57BL/6 mice produces electrographic biomarkers as observed in human epilepsy. This model represents a new translational cortical epilepsy model with a defined irritative zone, which can be broadly applied in transgenic mice for cell type specific analysis of the cellular and circuit mechanisms of pathologic interictal activity. Key Points: Cortical photothrombosis in mice produces stroke with characteristic intermittent focal delta slowing.Cortical photothrombosis stroke in mice produces the epileptic biomarkers spikes, ripples, and spike ripples.All biomarkers share morphological features with the corresponding human correlate.Spike ripples better lateralize to the lesional cortex than spikes or ripples.This cortical model can be applied in transgenic mice for mechanistic studies.

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 (EZ) compared to other leading interictal biomarkers in a multicenter, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centers who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection (ILAE 1 outcome) 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. 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, P < 0.001). Among ILAE 1 subjects, the mean spike ripple rate was higher in the RV (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 to 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, multicenter cohort, when surgical resection was successful, the majority of spike ripples were removed. Further, automatically detected spike ripples have improved specificity for epileptogenic tissue compared to spikes, spike-gamma, wideband HFOs, ripples, and fast ripples.

Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings.

Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.

The autism spectrum disorder risk gene NEXMIF over-synchronizes hippocampal CA1 network and alters neuronal coding.

Mutations in autism spectrum disorder (ASD) risk genes disrupt neural network dynamics that ultimately lead to abnormal behavior. To understand how ASD-risk genes influence neural circuit computation during behavior, we analyzed the hippocampal network by performing large-scale cellular calcium imaging from hundreds of individual CA1 neurons simultaneously in transgenic mice with total knockout of the X-linked ASD-risk gene NEXMIF (neurite extension and migration factor). As NEXMIF knockout in mice led to profound learning and memory deficits, we examined the CA1 network during voluntary locomotion, a fundamental component of spatial memory. We found that NEXMIF knockout does not alter the overall excitability of individual neurons but exaggerates movement-related neuronal responses. To quantify network functional connectivity changes, we applied closeness centrality analysis from graph theory to our large-scale calcium imaging datasets, in addition to using the conventional pairwise correlation analysis. Closeness centrality analysis considers both the number of connections and the connection strength between neurons within a network. We found that in wild-type mice the CA1 network desynchronizes during locomotion, consistent with increased network information coding during active behavior. Upon NEXMIF knockout, CA1 network is over-synchronized regardless of behavioral state and fails to desynchronize during locomotion, highlighting how perturbations in ASD-implicated genes create abnormal network synchronization that could contribute to ASD-related behaviors.

Different Methods to Estimate the Phase of Neural Rhythms Agree But Only During Times of Low Uncertainty.

Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically nonsinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty.

Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically non-sinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.

Decreased thalamocortical connectivity in resolved Rolandic epilepsy.

OBJECTIVE: Median nerve somatosensory evoked fields (SEFs) conduction times reflect the integrity of neural transmission across the thalamocortical circuit. We hypothesized median nerve SEF conduction time would be abnormal in children with Rolandic epilepsy (RE). METHODS: 22 children with RE (10 active; 12 resolved) and 13 age-matched controls underwent structural and diffusion MRI and median nerve and visual stimulation during magnetoencephalography (MEG). N20 SEF responses were identified in contralateral somatosensory cortices. P100 were identified in contralateral occipital cortices as controls. Conduction times were compared between groups in linear models controlling for height. N20 conduction time was also compared to thalamic volume and Rolandic thalamocortical structural connectivity inferred using probabilistic tractography. RESULTS: The RE group had slower N20 conduction compared to controls (p = 0.042, effect size 0.6 ms) and this difference was driven by the resolved RE group (p = 0.046). There was no difference in P100 conduction time between groups (p = 0.83). Ventral thalamic volume positively correlated with N20 conduction time (p = 0.014). CONCLUSIONS: Children with resolved RE have focally decreased Rolandic thalamocortical connectivity. SIGNIFICANCE: These results identify a persistent focal thalamocortical circuit abnormality in resolved RE and suggest that decreased Rolandic thalamocortical connectivity may support symptom resolution in this self-limited epilepsy.

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.

Preliminary evidence of a relationship between sleep spindles and treatment response in epileptic encephalopathy.

OBJECTIVE: Epileptic encephalopathy with spike-wave activation in sleep (EE-SWAS) is a challenging neurodevelopmental disease characterized by abundant epileptiform spikes during non-rapid eye movement (NREM) sleep accompanied by cognitive dysfunction. The mechanism of cognitive dysfunction is unknown, but treatment with high-dose diazepam may improve symptoms. Spike rate does not predict treatment response, but spikes may disrupt sleep spindles. We hypothesized that in patients with EE-SWAS: (1) spikes and spindles would be anti-correlated, (2) high-dose diazepam would increase spindles and decrease spikes, and (3) spindle response would be greater in those with cognitive improvement. METHODS: Consecutive EE-SWAS patients treated with high-dose diazepam that met the criteria were included. Using a validated automated spindle detector, spindle rate, duration, and percentage were computed in pre- and post-treatment NREM sleep. Spikes were quantified using a validated automated spike detector. The cognitive response was determined from a chart review. RESULTS: Spindle rate was anti-correlated with the spike rate in the channel with the maximal spike rate (p = 0.002) and averaged across all channels (p = 0.0005). Spindle rate, duration, and percentage each increased, and spike rate decreased, after high-dose diazepam treatment (p ≤ 2e-5, all tests). Spindle rate, duration, and percentage (p ≤ 0.004, all tests) were increased in patients with cognitive improvement after treatment, but not those without. Changes in spindle rate but not changes in spike rate distinguished between groups. INTERPRETATION: These findings confirm thalamocortical disruption in EE-SWAS, identify a mechanism through which benzodiazepines may support cognitive recovery, and introduce sleep spindles as a promising mechanistic biomarker to detect treatment response in severe epileptic encephalopathies.

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.

Preliminary evidence of a relationship between sleep spindles and treatment response in epileptic encephalopathy.

Objective: Epileptic encephalopathy with spike wave activation in sleep (EE-SWAS) is a challenging neurodevelopmental disease characterized by abundant epileptiform spikes during non-rapid eye movement (NREM) sleep accompanied by cognitive dysfunction. The mechanism of cognitive dysfunction is unknown, but treatment with high-dose diazepam may improve symptoms. Spike rate does not predict treatment response, but spikes may disrupt sleep spindles. We hypothesized that in patients with EE-SWAS: 1) spikes and spindles would be anticorrelated, 2) high-dose diazepam would increase spindles and decrease spikes, and 3) spindle response would be greater in those with cognitive improvement. Methods: Consecutive EE-SWAS patients treated with high-dose diazepam that met criteria were included. Using a validated automated spindle detector, spindle rate, duration, and percentage were computed in pre- and post-treatment NREM sleep. Spikes were quantified using a validated automated spike detector. Cognitive response was determined from chart review. Results: Spindle rate was anticorrelated with spike rate in the channel with the maximal spike rate ( p =0.002) and averaged across all channels ( p =0.0005). Spindle rate, duration, and percentage each increased, and spike rate decreased, after high-dose diazepam treatment ( p≤ 2e-5, all tests). Spindle rate, duration, and percentage ( p ≤0.004, all tests) were increased in patients with cognitive improvement after treatment, but not those without. Changes in spike rate did not distinguish between groups. Interpretation: These findings confirm thalamocortical disruption in EE-SWAS, identify a mechanism through which benzodiazepines may support cognitive recovery, and introduce sleep spindles as a promising mechanistic biomarker to detect treatment response in severe epileptic encephalopathies.

The 1/f-like behavior of neural field spectra are a natural consequence of noise driven brain dynamics.

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.

Interoperability with multiple Fast Healthcare Interoperability Resources (FHIR®) profiles and versions.

Objective: To provide a method for evaluating the interoperability of Fast Healthcare Interoperability Resources (FHIR®) clients and servers supporting different FHIR resources, profiles, and versions, and to determine the feasibility of FHIR servers supporting multiple FHIR Implementation Guides (IGs). Materials and Methods: A method of analysis, the FHIR Interoperability Table (FHIT), is proposed. The FHIT involves the concept of a "catchment," the type or category of data that a profile is intended to represent. The solution first aligns sender and/or receiver profiles according to their catchments, then determines the relationship between the admittances of those profiles, and finally interprets the relationship in terms of the feasibility of data exchange. Results: The FHIT method is demonstrated by analyzing the FHIR-based exchange between the US Core IG and the International Patient Summary IG. Discussion: The last few years have witnessed a significant growth in Fast Healthcare Interoperability Resources (FHIR), resulting in several major versions of FHIR, hundreds of IGs, and thousands of FHIR profiles. Previous work and available tools have not fully addressed the problem of interoperability between clients and servers that support different FHIR resources, profiles, and versions. Conclusion: Application of the proposed methodology allows interoperability problems in FHIR networks to be identified. In some cases, new profiles that resolve those conflicts can be derived, using intersections of the original profiles. There is a need for additional tools that implement the proposed method, as well as structured methods for expressing catchments in FHIR profiles.

Sleep spindles in the healthy brain from birth through 18 years.

STUDY OBJECTIVE: Sleep spindles are present from birth and reflect cognitive functions across the lifespan, but normative values for this cognitive biomarker across development are lacking. This study aims to establish normative spindle features over development. METHODS: All available normal 19-channel electroencephalograms from developmentally normal children between February 2002 and June 2021 in the MGH EEG lab were analyzed. Approximately, 20 000 spindles were hand-marked to train and validate an automated spindle detector across ages. Normative values for spindle rate, duration, frequency, refractory period, and interhemispheric lag are provided for each channel and each age. RESULTS: Sleep EEGs from 567 developmentally normal children (range 0 days to 18 years) were included. The detector had excellent performance (F1 = 0.47). Maximal spindle activity is seen over central regions during infancy and adolescence and frontopolar regions during childhood. Spindle rate and duration increase nonlinearly, with the most rapid changes during the first 4 months of life and between ages 3 and 14 years. Peak spindle frequency follows a U-shaped curve and discrete frontal slow and central fast spindles are evident by 18 months. Spindle refractory periods decrease between ages 1 and 14 years while interhemispheric asynchrony decreases over the first 3 months of life and between ages 1 and 14 years. CONCLUSIONS: These data provide age- and region-specific normative values for sleep spindles across development, where measures that deviate from these values can be considered pathological. As spindles provide a noninvasive biomarker for cognitive function across the lifespan, these normative measures can accelerate the discovery and diagnosis in neurodevelopmental disorders.

Transient, developmental functional and structural connectivity abnormalities in the thalamocortical motor network in Rolandic epilepsy.

Rolandic epilepsy (RE) is the most common focal, idiopathic, developmental epilepsy, characterized by a transient period of sleep-potentiated seizures and epileptiform discharges in the inferior Rolandic cortex during childhood. The cause of RE remains unknown but converging evidence has identified abnormalities in the Rolandic thalamocortical circuit. To better localize this transient disease, we evaluated Rolandic thalamocortical functional and structural connectivity in the sensory and motor circuits separately during the symptomatic and asymptomatic phases of this disease. We collected high resolution structural, diffusion, and resting state functional MRI data in a prospective cohort of children with active RE (n = 17), resolved RE (n = 21), and controls (n = 33). We then computed the functional and structural connectivity between the inferior Rolandic cortex and the ventrolateral (VL) nucleus of the thalamus (efferent pathway) and the ventroposterolateral (VPL) nucleus of the thalamus (afferent pathway) across development in children with active, resolved RE and controls. We compared connectivity with age in each group using linear mixed-effects models. We found that children with active RE have increasing thalamocortical functional connectivity between the VL thalamus and inferior motor cortex with age (p = 0.022) that is not observed in controls or resolved RE. In contrast, children with resolved RE have increasing thalamocortical structural connectivity between the VL nucleus and the inferior motor cortex with age (p = 0.025) that is not observed in controls or active RE. No relationships were identified between VPL nuclei and the inferior sensory cortex with age in any group. These findings localize the functional and structural thalamocortical circuit disruption in RE to the efferent thalamocortical motor pathway. Further work is required to determine how these circuit abnormalities contribute to the emergence and resolution of symptoms in this developmental disease.

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.