A Comparison of Automatically Extracted Quantitative EEG Features for Seizure Risk Stratification in Neonatal Encephalopathy.

PURPOSE: Seizures occur in up to 40% of neonates with neonatal encephalopathy. Earlier identification of seizures leads to more successful seizure treatment, but is often delayed because of limited availability of continuous EEG monitoring. Clinical variables poorly stratify seizure risk, and EEG use to stratify seizure risk has previously been limited by need for manual review and artifact exclusion. The goal of this study is to compare the utility of automatically extracted quantitative EEG (qEEG) features for seizure risk stratification. METHODS: We conducted a retrospective analysis of neonates with moderate-to-severe neonatal encephalopathy who underwent therapeutic hypothermia at a single center. The first 24 hours of EEG underwent automated artifact removal and qEEG analysis, comparing qEEG features for seizure risk stratification. RESULTS: The study included 150 neonates and compared the 36 (23%) with seizures with those without. Absolute spectral power best stratified seizure risk with area under the curve ranging from 63% to 71%, followed by range EEG lower and upper margin, median and SD of the range EEG lower margin. No features were significantly more predictive in the hour before seizure onset. Clinical examination was not associated with seizure risk. CONCLUSIONS: Automatically extracted qEEG features were more predictive than clinical examination in stratifying neonatal seizure risk during therapeutic hypothermia. qEEG represents a potential practical bedside tool to individualize intensity and duration of EEG monitoring and decrease time to seizure recognition. Future work is needed to refine and combine qEEG features to improve risk stratification.

Macroperiodic Oscillations: A Potential Novel Biomarker of Outcome in Neonatal Encephalopathy.

PURPOSE: Neonatal encephalopathy (NE) is a common cause of neurodevelopmental morbidity. Tools to accurately predict outcomes after therapeutic hypothermia remain limited. We evaluated a novel EEG biomarker, macroperiodic oscillations (MOs), to predict neurodevelopmental outcomes. METHODS: We conducted a secondary analysis of a randomized controlled trial of neonates with moderate-to-severe NE who underwent standardized clinical examination, magnetic resonance (MR) scoring, video EEG, and neurodevelopmental assessment with Bayley III evaluation at 18 to 24 months. A non-NE cohort of neonates was also assessed for the presence of MOs. The relationship between clinical examination, MR score, MOs, and neurodevelopmental assessment was analyzed. RESULTS: The study included 37 neonates with 24 of whom survived and underwent neurodevelopmental assessment (70%). The strength of MOs correlated with severity of clinical encephalopathy. MO strength and spread significantly correlated with Bayley III cognitive percentile (P = 0.017 and 0.046). MO strength outperformed MR score in predicting a combined adverse outcome of death or disability (P = 0.019, sensitivity 100%, specificity 77% vs. P = 0.079, sensitivity 100%, specificity 59%). CONCLUSIONS: MOs are an EEG-derived, quantitative biomarker of neurodevelopmental outcome that outperformed a comprehensive validated MRI injury score and a detailed systematic discharge examination in this small cohort. Future work is needed to validate MOs in a larger cohort and elucidate the underlying pathophysiology of MOs.

Variable Association of Physiologic Changes With Electrographic Seizure-Like Events in Infants Born Preterm.

OBJECTIVES: To determine the incidence of seizure-like events in a cohort of infants born preterm as well as the prevalence of associated vital sign changes (heart rate [HR], respiratory rate, and pulse oximetry [SpO2]). STUDY DESIGN: We performed prospective conventional video electroencephalogram monitoring on infants born at 23-30 weeks of gestational age during the first 4 postnatal days. For detected seizure-like events, simultaneously captured vital sign data were analyzed during the pre-event baseline and during the event. Significant vital sign changes were defined as HR or respiratory rate >±2 SD from the infant's own baseline physiologic mean, derived from a 10-minute interval before the seizure-like event. Significant change in SpO2 was defined as oxygen desaturation during the event with a mean SpO2 <88%. RESULTS: Our sample included 48 infants with median gestational age of 28 weeks (IQR 26-29) and birth weight of 1125 g (IQR 963-1265). Twelve (25%) infants had seizure-like discharges with a total of 201 events; 83% (10/12) of infants had vital sign changes during these events, and 50% (6/12) had significant vital sign changes during the majority of the seizure-like events. Concurrent HR changes occurred the most frequently. CONCLUSIONS: Individual infant variability was observed in the prevalence of concurrent vital sign changes with electroencephalographic seizure-like events. Physiologic changes associated with preterm electrographic seizure-like events should be investigated further as a potential biomarker to assess the clinical significance of such events in the preterm population.

The Role of SPECT and PET in Epilepsy.

OBJECTIVE. The purpose of this article is to summarize the role of molecular imaging of the brain by use of SPECT, FDG PET, and non-FDG PET radiotracers in epilepsy. CONCLUSION. Quantitative image analysis with PET and SPECT has increased the diagnostic utility of these modalities in localizing epileptogenic onset zones. A multi-modal platform approach integrating the functional imaging of PET and SPECT with the morphologic information from MRI in presurgical evaluation of epilepsy can greatly improve outcomes.

A comparison of resting state functional magnetic resonance imaging to invasive electrocortical stimulation for sensorimotor mapping in pediatric patients.

Localizing neurologic function within the brain remains a significant challenge in clinical neurosurgery. Invasive mapping with direct electrocortical stimulation currently is the clinical gold standard but is impractical in young or cognitively delayed patients who are unable to reliably perform tasks. Resting state functional magnetic resonance imaging non-invasively identifies resting state networks without the need for task performance, hence, is well suited to pediatric patients. We compared sensorimotor network localization by resting state fMRI to cortical stimulation sensory and motor mapping in 16 pediatric patients aged 3.1 to 18.6 years. All had medically refractory epilepsy that required invasive electrographic monitoring and stimulation mapping. The resting state fMRI data were analyzed using a previously trained machine learning classifier that has previously been evaluated in adults. We report comparable functional localization by resting state fMRI compared to stimulation mapping. These results provide strong evidence for the utility of resting state functional imaging in the localization of sensorimotor cortex across a wide range of pediatric patients.

Long term electroencephalography in preterm neonates: Safety and quality of electrode types.

OBJECTIVES: The objective of this study was to compare gold cup and hydrogel electrodes for frequency of electrode replacement, longevity of the original electrodes after initial placement, recording quality, and skin safety issues in long-term EEG studies in preterm neonates. METHODS: We performed a prospective trial with newborns born at ≥23 weeks and ≤30 weeks of gestational age (GA). Two mirror image EEG electrode arrays were utilized on consecutive subjects, where gold cup electrodes alternated with hydrogel electrodes. RESULTS: Our sample included 50 neonates with mean GA of 27 (±1) weeks. The mean recording time was 84 (±15) hours. No difference was present in the frequency of replacement of either type across the total recording time (p = 0.8). We collected the time at which electrodes were first replaced, and found that hydrogel electrodes showed a longer uninterrupted recording time of 28(±2) hours vs. 20(±2) hours for gold cup electrodes (p = 0.01). Recording quality was similar in either type (p = 0.2). None of the patients experienced significant skin irritation from a discrete electrode. CONCLUSION: Long-term EEG studies can be performed with either gold cup or hydrogel electrodes, validating the safety and quality of both electrode types. SIGNIFICANCE: Hydrogel electrodes are a reasonable alternative for use in long-term EEG studies in preterm neonates.

Slow wave sleep disruption increases cerebrospinal fluid amyloid-ß levels.

See Mander et al. (doi:10.1093/awx174) for a scientific commentary on this article.Sleep deprivation increases amyloid-ß, suggesting that chronically disrupted sleep may promote amyloid plaques and other downstream Alzheimer's disease pathologies including tauopathy or inflammation. To date, studies have not examined which aspect of sleep modulates amyloid-ß or other Alzheimer's disease biomarkers. Seventeen healthy adults (age 35-65 years) without sleep disorders underwent 5-14 days of actigraphy, followed by slow wave activity disruption during polysomnogram, and cerebrospinal fluid collection the following morning for measurement of amyloid-ß, tau, total protein, YKL-40, and hypocretin. Data were compared to an identical protocol, with a sham condition during polysomnogram. Specific disruption of slow wave activity correlated with an increase in amyloid-ß40 (r = 0.610, P = 0.009). This effect was specific for slow wave activity, and not for sleep duration or efficiency. This effect was also specific to amyloid-ß, and not total protein, tau, YKL-40, or hypocretin. Additionally, worse home sleep quality, as measured by sleep efficiency by actigraphy in the six nights preceding lumbar punctures, was associated with higher tau (r = 0.543, P = 0.045). Slow wave activity disruption increases amyloid-ß levels acutely, and poorer sleep quality over several days increases tau. These effects are specific to neuronally-derived proteins, which suggests they are likely driven by changes in neuronal activity during disrupted sleep.

Automated selective disruption of slow wave sleep.

BACKGROUND: Slow wave sleep (SWS) plays an important role in neurophysiologic restoration. Experimentally testing the effect of SWS disruption previously required highly time-intensive and subjective methods. Our goal was to develop an automated and objective protocol to reduce SWS without affecting sleep architecture. NEW METHOD: We developed a custom Matlab™ protocol to calculate electroencephalogram spectral power every 10s live during a polysomnogram, exclude artifact, and, if measurements met criteria for SWS, deliver increasingly louder tones through earphones. Middle-aged healthy volunteers (n=10) each underwent 2 polysomnograms, one with the SWS disruption protocol and one with sham condition. RESULTS: The SWS disruption protocol reduced SWS compared to sham condition, as measured by spectral power in the delta (0.5-4Hz) band, particularly in the 0.5-2Hz range (mean 20% decrease). A compensatory increase in the proportion of total spectral power in the theta (4-8Hz) and alpha (8-12Hz) bands was seen, but otherwise normal sleep features were preserved. N3 sleep decreased from 20±34 to 3±6min, otherwise there were no significant changes in total sleep time, sleep efficiency, or other macrostructural sleep characteristics. COMPARISON WITH EXISTING METHOD: This novel SWS disruption protocol produces specific reductions in delta band power similar to existing methods, but has the advantage of being automated, such that SWS disruption can be performed easily in a highly standardized and operator-independent manner. CONCLUSION: This automated SWS disruption protocol effectively reduces SWS without impacting overall sleep architecture.

Prediction of Neonatal Seizures in Hypoxic-Ischemic Encephalopathy Using Electroencephalograph Power Analyses.

BACKGROUND: The severity of the initial encephalopathy in neonatal hypoxic-ischemic encephalopathy correlates with seizure burden. Early electroencephalograph (EEG) background activity reflects the severity of encephalopathy. Thus, we hypothesized that early EEG background would be predictive of subsequent seizures in neonatal hypoxic-ischemic encephalopathy. METHODS: This study included infants undergoing therapeutic hypothermia at St. Louis Children's Hospital between January 2009 and April 2013. Two pediatric epilepsy specialists independently characterized EEG background qualitatively using amplitude-integrated EEG trends. Total EEG power in the 1-20 Hz frequency band was calculated for quantitative EEG background assessment. Seizures were identified on conventional full montage EEG. Statistical analysis was performed using logistic regression. RESULTS: Seventy-eight of the 93 eligible infants had artifact-free EEG data; 23 of 78 infants (29%) developed seizures, and of these, 11 developed status epilepticus. The best predictors of subsequent seizures during the first hour of EEG recording were a flat tracing pattern on amplitude-integrated EEG (sensitivity 26%, specificity 98%, likelihood ratio 13, positive predictive value 85%) and the total EEG power less than 10 µV2 (sensitivity 52%, specificity 98%, likelihood ratio 30, positive predictive value 92%). CONCLUSIONS: Early EEG biomarkers predict subsequent seizures in infants with hypoxic-ischemic encephalopathy. Compared with the qualitative amplitude-integrated EEG background, total EEG power improves our ability to identify high-risk infants from the first hour of EEG recording. Infants with a total EEG power of less than 10 µV2 have a 90% risk of subsequent seizures. Quantitative EEG measures could stratify cohorts while evaluating novel neuroprotective strategies in neonatal hypoxic-ischemic encephalopathy.

Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance.

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

Automating the analysis of EEG recordings from prematurely-born infants: a Bayesian approach.

OBJECTIVE: To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results. METHODS: Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5-1.5Hz, >100µV), delta brushes (delta portion: 0.5-1.5Hz, >100µV; "brush" portion: 8-22Hz, <75µV), and interburst intervals (<10µV), though the approach taken can be generalized to identify other EEG features of interest. RESULTS: When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or "brush") and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm's true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability. CONCLUSION: Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants. SIGNIFICANCE: The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.

Characterization of scale-free properties of human electrocorticography in awake and slow wave sleep States.

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2-200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies.

Determination of neural state classification metrics from the power spectrum of human ECoG.

Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject. An adaptive piecewise linear fitting solution was developed to extract features for classification of brain state. Performance was evaluated by comparison to an a posteriori based feature search method. This analysis, using the 1/ƒ characteristics of the human ECoG signal, demonstrates utility in advancing the ability to perform automated brain state discrimination.

The temporal structures and functional significance of scale-free brain activity.

Scale-free dynamics, with a power spectrum following P proportional to f(-beta), are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. The functional significance of scale-free brain activity is indicated by task performance modulation and regional variation, with beta being larger in default network and visual cortex and smaller in hippocampus and cerebellum. The precise patterns of nested frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic waves and stock market fluctuations, suggesting system-specific generative mechanisms. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications.

Cortical network functional connectivity in the descent to sleep.

Descent into sleep is accompanied by disengagement of the conscious brain from the external world. It follows that this process should be associated with reduced neural activity in regions of the brain known to mediate interaction with the environment. We examined blood oxygen dependent (BOLD) signal functional connectivity using conventional seed-based analyses in 3 primary sensory and 3 association networks as normal young adults transitioned from wakefulness to light sleep while lying immobile in the bore of a magnetic resonance imaging scanner. Functional connectivity was maintained in each network throughout all examined states of arousal. Indeed, correlations within the dorsal attention network modestly but significantly increased during light sleep compared to wakefulness. Moreover, our data suggest that neuronally mediated BOLD signal variance generally increases in light sleep. These results do not support the view that ongoing BOLD fluctuations primarily reflect unconstrained cognition. Rather, accumulating evidence supports the hypothesis that spontaneous BOLD fluctuations reflect processes that maintain the integrity of functional systems in the brain.

Electrophysiological correlates of the brain's intrinsic large-scale functional architecture.

Spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signals demonstrate consistent temporal correlations within large-scale brain networks associated with different functions. The neurophysiological correlates of this phenomenon remain elusive. Here, we show in humans that the slow cortical potentials recorded by electrocorticography demonstrate a correlation structure similar to that of spontaneous BOLD fluctuations across wakefulness, slow-wave sleep, and rapid-eye-movement sleep. Gamma frequency power also showed a similar correlation structure but only during wakefulness and rapid-eye-movement sleep. Our results provide an important bridge between the large-scale brain networks readily revealed by spontaneous BOLD signals and their underlying neurophysiology.

Loss of resting interhemispheric functional connectivity after complete section of the corpus callosum.

Slow (<0.1 Hz), spontaneous fluctuations in the functional magnetic resonance imaging blood oxygen level-dependent (BOLD) signal have been shown to exhibit phase coherence within functionally related areas of the brain. Surprisingly, this phenomenon appears to transcend levels of consciousness. The genesis of coherent BOLD fluctuations remains to be fully explained. We present a resting state functional connectivity study of a 6-year-old child with a radiologically normal brain imaged both before and after complete section of the corpus callosum for the treatment of intractable epilepsy. Postoperatively, there was a striking loss of interhemispheric BOLD correlations with preserved intrahemispheric correlations. These unique data provide important insights into the relationship between connectional anatomy and functional organization of the human brain. Such observations have the potential to increase our understanding of large-scale brain systems in health and disease as well as improve the treatment of neurologic disorders.

Moving GLM ballistocardiogram artifact reduction for EEG acquired simultaneously with fMRI.

OBJECTIVE: Simultaneous acquisition of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) enables studies of brain activity at both high temporal and high spatial resolution. However, EEG acquired in a magnetic field is contaminated by ballistocardiogram (BKG) artifact. The most commonly used method of BKG artifact reduction, averaged artifact subtraction (AAS), was not designed to account for overlapping BKG waveforms generated by adjacent beats. We describe a new method based on a moving general linear model (mGLM) that accounts for overlapping BKG waveforms. METHODS: Simultaneous EEG-fMRI at 3 Tesla was performed in nine normal human subjects (8-11 runs/subject, 5.52 min/run). Gradient switching artifact was effectively reduced using commercially supplied procedures. Cardiac beats were detected using a novel correlation detector algorithm applied to the EKG trace. BKG artifact was reduced using both mGLM and AAS. RESULTS: mGLM recovered BKG waveforms outlasting the median inter-beat interval. mGLM more effectively than AAS removed variance in the EEG attributable to BKG artifact. CONCLUSIONS: mGLM offers advantages over AAS especially in the presence of variable heart rate. SIGNIFICANCE: The BKG artifact reduction procedure described herein improves the technique of simultaneous EEG-fMRI. Potential applications include basic investigations of the relationship between scalp potentials and functional imaging signals as well as clinical localization of epileptic foci.