Full-band EEG (FbEEG): an emerging standard in electroencephalography

Signatures of migraine aura in high-density-EEG

OBJECTIVE

Cortical spreading depolarization is highly conserved among the species. It is easily detectable in direct cortical surface recordings and has been recorded in the cortex of humans with severe neurological disease. It is considered the pathophysiological correlate of human migraine aura, but direct electrophysiological evidence is still missing. As signatures of cortical spreading depolarization have been recognized in scalp EEG, we investigated typical spontaneous migraine aura, using full band high-density EEG (HD-EEG).

METHODS

In this prospective study, patients with migraine with aura were investigated during spontaneous migraine aura and interictally. Time compressed HD-EEG were analyzed for the presence of cortical spreading depolarization characterized by (a) slow potential changes below 0.05 Hz, (b) suppression of faster activity from 0.5 Hz - 45 Hz (c) spreading of these changes to neighboring regions during the aura phase. Further, topographical changes in alpha-power spectral density (8-14 Hz) during aura were analyzed.

RESULTS

In total, 26 HD-EEGs were recorded in patients with migraine with aura, thereof 10 HD-EEGs during aura. Eight HD-EEGs were recorded in the same subject. During aura, no slow potentials were recorded, but alpha-power was significantly decreased in parieto-occipito-temporal location on the hemisphere contralateral to visual aura, lasting into the headache phase. Interictal alpha-power in patients with migraine with aura did not differ significantly from age- and sex-matched healthy controls.

CONCLUSIONS

Unequivocal signatures of spreading depolarization were not recorded with EEG on the intact scalp in migraine. The decrease in alpha-power contralateral to predominant visual symptoms is consistent with focal depression of spontaneous brain activity as a consequence of cortical spreading depolarization but is not specific thereof.

SIGNIFICANCE

Cortical spreading depolarization is relevant in migraine, other paroxysmal neurological disorders and neurointensive care.

Quantitative analysis of high-frequency activity in neonatal EEG

OBJECTIVE

To determine the presence and potential utility of independent high-frequency activity recorded from scalp electrodes in the electroencephalogram (EEG) of newborns.

METHODS

We compare interburst intervals and continuous activity at different frequencies for EEGs retrospectively recorded at 256 Hz from 4 newborn groups: 1) 36 preterms (<32 weeks' gestational age, GA); 2) 12 preterms (32-37 weeks' GA); 3) 91 healthy full terms; 4) 15 full terms with hypoxic-ischemic encephalopathy (HIE). At 4 standard frequency bands (delta, 0.5-3 Hz; theta, 3-8 Hz; alpha, 8-15 Hz; beta, 15-30 Hz) and 3 higher-frequency bands (gamma1, 30-48 Hz; gamma2, 52-99 Hz; gamma3, 107-127 Hz), we compared power spectral densities (PSDs), quantitative features, and machine learning model performance. Feature selection and further machine learning methods were performed on one cohort.

RESULTS

We found significant (P < 0.01) differences in PSDs, quantitative analysis, and machine learning modelling at the higher-frequency bands. Machine learning models using only high-frequency features performed best in preterm groups 1 and 2 with a median (95% confidence interval, CI) Matthews correlation coefficient (MCC) of 0.71 (0.12-0.88) and 0.66 (0.36-0.76) respectively. Interburst interval-detector models using both high- and standard-bandwidths produced the highest median MCCs in all four groups. High-frequency features were largely independent of standard-bandwidth features, with only 11/84 (13.1%) of correlations statistically significant. Feature selection methods produced 7 to 9 high-frequency features in the top 20 feature set.

CONCLUSIONS

This is the first study to identify independent high-frequency activity in newborn EEG using in-depth quantitative analysis. Expanding the EEG bandwidths of analysis has the potential to improve both quantitative and machine-learning analysis, particularly in preterm EEG.

Noninvasive modulation of human corticostriatal activity

Corticostriatal activity is an appealing target for nonpharmacological treatments of brain disorders. In humans, corticostriatal activity may be modulated with noninvasive brain stimulation (NIBS). However, a NIBS protocol with a sound neuroimaging measure demonstrating a change in corticostriatal activity is currently lacking. Here, we combine transcranial static magnetic field stimulation (tSMS) with resting-state functional MRI (fMRI). We first present and validate the ISAAC analysis, a well-principled framework that disambiguates functional connectivity between regions from local activity within regions. All measures of the framework suggested that the region along the medial cortex displaying greater functional connectivity with the striatum is the supplementary motor area (SMA), where we applied tSMS. We then use a data-driven version of the framework to show that tSMS of the SMA modulates the local activity in the SMA proper, in the adjacent sensorimotor cortex, and in the motor striatum. We finally use a model-driven version of the framework to clarify that the tSMS-induced modulation of striatal activity can be primarily explained by a change in the shared activity between the modulated motor cortical areas and the motor striatum. These results suggest that corticostriatal activity can be targeted, monitored, and modulated noninvasively in humans.

Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy

Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.

The enigma of infra-slow fluctuations in the human EEG

Spontaneous Infra-Slow Fluctuations (ISFs) of the human EEG (EEG-ISFs) were discovered 60 years ago when appropriate amplifiers for their recordings were designed. To avoid skin-related artifacts the recording of EEG-ISFs required puncturing the skin under the electrode. In the beginning of the 21st century the interest in EEG-ISFs was renewed with the appearance of commercially available DC-coupled amplified and by observation of ISFs of the blood oxygen level–dependent (BOLD) functional magnetic resonance imaging signal at a similar frequency. The independent components of irregular EEG-ISFs were shown to correlate with BOLD signals which in turn were driven by changes in arousal level measured by galvanic skin response (GSR), pupil size and HRV. There is no consensus regarding the temporal organization of EEG-ISFs: some studies emphasize the absence of peaks on EEG-ISFs spectra, some studies report prominent oscillations with frequency around 0.1 or 0.02 Hz, while some emphasize multiple discrete infraslow oscillations. No studies used parameters of EEG-ISFs as neuromarkers to discriminate psychiatric patients from healthy controls. Finally, a set of working hypotheses is suggested that must be tested in future research to solve the enigma of EEG-ISFs.

Source localized infraslow neurofeedback training in people with chronic painful knee osteoarthritis: A randomized, double-blind, sham-controlled feasibility clinical trial

Persistent pain is a key symptom in people living with knee osteoarthritis (KOA). Infra-slow Neurofeedback (ISF-NF) training is a recent development focusing on modulating cortical slow-wave activity to improve pain outcomes. A parallel, two-armed double-blinded, randomized sham-controlled, feasibility clinical trial aimed to determine the feasibility and safety of a novel electroencephalography-based infraslow fluctuation neurofeedback (EEG ISF-NF) training in people with KOA and determine the variability of clinical outcomes and EEG changes following NF training. Eligible participants attended nine 30-min ISF-NF training sessions involving three cortical regions linked to pain. Feasibility measures were monitored during the trial period. Pain and functional outcomes were measured at baseline, post-intervention, and follow-up after 2 weeks. Resting-state EEG was recorded at baseline and immediate post-intervention. Participants were middle-aged (61.7 ± 7.6 years), New Zealand European (90.5%), and mostly females (62%) with an average knee pain duration of 4 ± 3.4 years. The study achieved a retention rate of 91%, with 20/22 participants completing all the sessions. Participants rated high levels of acceptance and “moderate to high levels of perceived effectiveness of the training.” No serious adverse events were reported during the trial. Mean difference (95% CI) for clinical pain and function measures are as follows for pain severity [active: 0.89 ± 1.7 (−0.27 to 2.0); sham: 0.98 ± 1.1 (0.22–1.7)], pain interference [active: 0.75 ± 2.3 (−0.82 to 2.3); Sham: 0.89 ± 2.1 (−0.60 to 2.4)], pain unpleasantness [active: 2.6 ± 3.7 (0.17–5.1); sham: 2.8 ± 3 (0.62–5.0)] and physical function [active: 6.2 ± 13 (−2.6 to 15); sham: 1.6 ± 12 (−6.8 to 10)]. EEG sources demonstrated frequency-specific neuronal activity, functional connectivity, and ISF ratio changes following NF training. The findings of the study indicated that the ISF-NF training is a feasible, safe, and acceptable intervention for pain management in people with KOA, with high levels of perceived effectiveness. The study also reports the variability in clinical, brain activity, and connectivity changes following training.

Slow wave synchronization and sleep state transitions

Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.

Full-Band EEG Recordings Using Hybrid AC/DC-Divider Filters

Full-band DC recordings enable recording of slow electrical brain signals that are severely compromised during conventional AC recordings. However, full-band DC recordings may be limited by the amplifier's dynamic input range and the loss of small amplitude high-frequency signals. Recently, Neuralynx has proposed full-band recordings with inverse filtering for signal reconstruction based on hybrid AC/DC-divider RRC filters that enable only partial suppression of DC signals. However, the quality of signal reconstruction for biological signals has not yet been assessed. Here, we propose a novel digital inverse filter based on a mathematical model describing RRC filter properties, which provides high computational accuracy and versatility. Second, we propose procedures for the evaluation of the inverse filter coefficients, adapted for each recording channel to minimize the error caused by the deviation of the real values of the RRC filter elements from their nominal values. We demonstrate that this approach enables near 99% reconstruction quality of high-potassium-induced cortical spreading depolarizations (SDs), endothelin-induced ischemic negative ultraslow potentials (NUPs), and whole-cell recordings of membrane potential using RRC filters. The quality of the reconstruction was significantly higher than with the existing inverse filtering procedures. Thus, RRC filters with inverse filtering are optimal for full-band EEG recordings in various applications.

Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI

Mental stress is a major individual and societal burden and one of the main contributing factors that lead to pathologies such as depression, anxiety disorders, heart attacks, and strokes. Given that anxiety disorders are one of the most common comorbidities in youth with autism spectrum disorder (ASD), this population is particularly vulnerable to mental stress, severely limiting overall quality of life. To prevent this, early stress quantification with machine learning (ML) and effective anxiety mitigation with non-pharmacological interventions are essential. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals for stress assessment by comparing several ML classifiers, namely support vector machine (SVM) and deep learning methods. We trained a total of eleven subject-dependent models-four with conventional brain-computer interface (BCI) methods and seven with deep learning approaches-on the EEG of neurotypical (n=5) and ASD (n=8) participants performing alternating blocks of mental arithmetic stress induction, guided and unguided breathing. Our results show that a multiclass two-layer LSTM RNN deep learning classifier is capable of identifying mental stress from ongoing EEG with an overall accuracy of 93.27%. Our study is the first to successfully apply an LSTM RNN classifier to identify stress states from EEG in both ASD and neurotypical adolescents, and offers promise for an EEG-based BCI for the real-time assessment and mitigation of mental stress through a closed-loop adaptation of respiration entrainment.

Asymmetry of Interhemispheric Connectivity during Rapid Movements of Right and Left Hands: A TMS-EEG Study

The interhemispheric signal propagation (ISP) obtained by electroencephalography during transcranial magnetic stimulation (TMS) allows for the assessment of the interhemispheric connectivity involved in inhibitory processes. To investigate the functional asymmetry of hemispheres during rapid movement, we compared ISP in the left and right hemispheres during rapid hand movements. In 11 healthy right-handed adults, we delivered TMS to the M1 and recorded ISP from the M1 to the contralateral hemisphere. We found that ISP from the left to right hemisphere during right-hand rapid movement was higher than ISP from the right to left hemisphere during the left-hand rapid movement. These results indicate that the left M1 strongly inhibits the right M1, and that the left hemisphere is dominant for rapid movements as well as sequential movements.

A set of composite, non-redundant EEG measures of NREM sleep based on the power law scaling of the Fourier spectrum

Features of sleep were shown to reflect aging, typical sex differences and cognitive abilities of humans. However, these measures are characterized by redundancy and arbitrariness. Our present approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is based on the power law scaling of the Fourier spectra with the additional consideration of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles. Measures derived are the spectral intercept and slope, as well as the maximal spectral peak amplitude and frequency in the sleep spindle range, effectively reducing 191 spectral measures to 4, which were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

The Effect of Massage Stimulation on the General Movements Quality in Breastfed Preterm Infant

General Movements (GMs) assessment is one of the modalities to detect abnormal infant development early, the examination of preterm infant development is very important. Preterm infant has a long-term health impact and the risk of neonatal morbidity and mortality, need breast milk as the best nutritionand also need stimulation, one of which is massage. This study aimto analyze massage stimulation effect on the general movements in breastfed preterm infant in Dr. Soetomo Hospital Surabaya.A randomize control group pre test post test design was conducted on preterm infant, 39 babies were enrolled. Massage stimulation was done for 10 consecutive days 3 times in the neonatal ward while observing the condition.The quality assessment of GMs is according to the standardization of the Prechtl method, through two stages, taking video recording and editing GMs, and analysis the video recordings of GMs.Statistical by comparison test.The quality of preterm GMs in massage group (85.7%) and control group(69.6%) with p=0.287. The quality of GMs writhing in the massage group was dominated by abnormal GMs (65%), in control group were dominated by normal (63.2%) with p=0,150. In the fidgety GMs, the massage group (75%) and not massage (78.9%) were dominated by normal GMs, with p=1,000.There were no differences in the quality of preterm GMs, writhingGMs and fidgetyGMs for preterm infants in both group.

Graphene active sensor arrays for long-term and wireless mapping of wide frequency band epicortical brain activity

Graphene active sensors have demonstrated promising capabilities for the detection of electrophysiological signals in the brain. Their functional properties, together with their flexibility as well as their expected stability and biocompatibility have raised them as a promising building block for large-scale sensing neural interfaces. However, in order to provide reliable tools for neuroscience and biomedical engineering applications, the maturity of this technology must be thoroughly studied. Here, we evaluate the performance of 64-channel graphene sensor arrays in terms of homogeneity, sensitivity and stability using a wireless, quasi-commercial headstage and demonstrate the biocompatibility of epicortical graphene chronic implants. Furthermore, to illustrate the potential of the technology to detect cortical signals from infra-slow to high-gamma frequency bands, we perform proof-of-concept long-term wireless recording in a freely behaving rodent. Our work demonstrates the maturity of the graphene-based technology, which represents a promising candidate for chronic, wide frequency band neural sensing interfaces.

The temporal evolution of pre-stimulus slow cortical potentials is associated with an upcoming stimulus' access to visual consciousness

Slow cortical potentials (SCPs) have been proposed to be essential for the formation of conscious experience. To examine their temporal characteristics, we recorded electroencephalography during a visual backward-masking task, which required participants to localize the missing part of a target stimulus. A subsequent confidence rating was used as a proxy for the target's access to consciousness. Event-related potentials (ERPs) of all correct trials were determined relative to a brief period immediately before the target and then compared among consciousness levels. In an interval ranging from 2 s prior to target presentation up to this period, a negative relationship between slowly fluctuating ERP values and the level of consciousness became evident. After target presentation, high conscious awareness was characterized by an enhanced visual awareness negativity, an increased P3 component, and associated positive SCPs. Together, these findings add new evidence to the proposed role of SCPs in the emergence of visual consciousness.

Recording Electrical Brain Activity with Novel Stretchable Electrodes Based on Supersonic Cluster Beam Implantation Nanotechnology on Conformable Polymers

Background

Multielectrodes are implanted in central and peripheral nervous systems for rehabilitation and diagnostic purposes. The physical resistance of intracranial devices to mechanical stress is critical and fractures or electrode displacement may occur. We describe here a new recording device with stretchable properties based on Supersonic Cluster Beam Implantation (SCBI) technology with high mechanical adaptability to displacement and movement.

Results

The capability of SCBI-based multichannel electrodes to record brain electrical activity was compared to glass/silicon microelectrodes in acute in vitro experiments on the isolated guinea pig brain preparation. Field potentials and power frequency analysis demonstrated equal recording features for SCBI and standard electrodes. Chronic in vivo epidural implantation of the SCBI electrodes confirmed excellent long-term recording properties in comparison to standard EEG metal electrodes. Tissue biocompatibility was demonstrated by neuropathological evaluation of the brain tissue 2 months after the implantation of the devices in the subarachnoid space.

Conclusion

We confirm the biocompatibility of novel SCBI-based stretchable electrode devices and demonstrate their suitability for recording electrical brain activity in pre-clinical settings.

Filters: When, Why, and How (Not) to Use Them

Filters are commonly used to reduce noise and improve data quality. Filter theory is part of a scientist's training, yet the impact of filters on interpreting data is not always fully appreciated. This paper reviews the issue and explains what a filter is, what problems are to be expected when using them, how to choose the right filter, and how to avoid filtering by using alternative tools. Time-frequency analysis shares some of the same problems that filters have, particularly in the case of wavelet transforms. We recommend reporting filter characteristics with sufficient details, including a plot of the impulse or step response as an inset.

Multi-feature localization of epileptic foci from interictal, intracranial EEG

OBJECTIVE

When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data.

METHODS

We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording.

RESULTS

The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95.

CONCLUSION

SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects.

SIGNIFICANCE

In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.

Automatic epileptic seizure classification in multichannel EEG time series with linear discriminant analysis

BACKGROUND

An electroencephalogram (EEG) is the most dominant method for detecting epileptic seizures. However, the existing techniques use single-channel EEGs from public databases and the sample size is small.

OBJECTIVE

This study proposes a strategy to distinguish multichannel EEGs for health control, particularly the interictal and ictal multichannel EEGs of epileptic patients.

METHOD

We calculated five features (variance, Pearson correlation coefficient, Hoeffding's D measure, Shannon entropy, inter-quartile range), which are based on maximal overlap discrete wavelet transform. These features were then fed into linear discriminant analysis for classification purposes. Finally, the proposed method was tested on data on 34 healthy people, 21 interictal patients and 30 ictal patients taken from a hospital.

RESULTS

Our experimental results show that the accuracy between healthy and epileptic seizures was 96.88% and the area under the curve (AUC) is 1. The accuracy between interictal and epileptic seizures was 94.12% and the AUC was 0.97. We also obtained an accuracy and AUC equal to 1 for discrimination of interictal EEGs from normal. Finally, we obtained an AUC of 0.83 and an accuracy of 85.88% for discrimination in these three classes. Therefore, our study achieves sufficient performance.

CONCLUSIONS

Our proposed method can serve as an auxiliary tool for clinicians who wish to make clinical decisions and reduces the burden of detecting epileptic seizures.

High-resolution mapping of infraslow cortical brain activity enabled by graphene microtransistors

Recording infraslow brain signals (<0.1 Hz) with microelectrodes is severely hampered by current microelectrode materials, primarily due to limitations resulting from voltage drift and high electrode impedance. Hence, most recording systems include high-pass filters that solve saturation issues but come hand in hand with loss of physiological and pathological information. In this work, we use flexible epicortical and intracortical arrays of graphene solution-gated field-effect transistors (gSGFETs) to map cortical spreading depression in rats and demonstrate that gSGFETs are able to record, with high fidelity, infraslow signals together with signals in the typical local field potential bandwidth. The wide recording bandwidth results from the direct field-effect coupling of the active transistor, in contrast to standard passive electrodes, as well as from the electrochemical inertness of graphene. Taking advantage of such functionality, we envision broad applications of gSGFET technology for monitoring infraslow brain activity both in research and in the clinic.

Short "Infraslow" Activity (SISA) With Burst Suppression in Acute Anoxic Encephalopathy: A Rare, Specific Ominous Sign With Acute Posthypoxic Myoclonus or Acute Symptomatic Seizures

OBJECTIVE

Slow wave with frequency <0.5 Hz are recorded in various situations such as normal sleep, epileptic seizures. However, its clinical significance has not been fully clarified. Although infra-slow activity was recently defined as activity between 0.01 and 0.1 Hz, we focus on the activity recorded with time constant of 2 seconds for practical usage. We defined short "infraslow" activity (SISA) less than 0.5 Hz recorded with time constant of 2 seconds and investigated the occurrence and clinical significance of SISA in acute anoxic encephalopathy.

METHODS

This study evaluated the findings of electroencephalography in consecutive 98 comatose patients with acute anoxic encephalopathy after cardiac arrest. We first classified electroencephalography findings conventionally, then investigated SISA by time constant of 2 second and a high-cut filter of 120 Hz, to clarify the relationship between SISA and clinical profiles, especially of clinical outcomes and occurrence of acute posthypoxic myoclonus or acute symptomatic seizures.

RESULTS

Short infra-slow activity was found in six patients (6.2%), superimposed on the burst phase of the burst-suppression pattern. All six patients showed acute posthypoxic myoclonus or acute symptomatic seizures (generalized tonic-clonic seizures) and its prognosis was poor. This 100% occurrence of acute posthypoxic myoclonus or acute symptomatic seizures was significantly higher than that in patients without SISA (39.1%; P < 0.05).

CONCLUSIONS

Short infra-slow activity in acute anoxic encephalopathy could be associated with acute posthypoxic myoclonus and acute symptomatic seizures. Short infra-slow activity could be a practically feasible biomarker for myoclonus or seizures and poor prognosis in acute anoxic encephalopathy, if it occurs with burst suppression.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data

Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data.

•

Preprocessing is essential for EEG and MEG data analysis.

•

Robust methods for data preprocessing are not affected by glitches and artifacts.

•

Methods include robust detrending, rereferencing, inpainting and step removal.

•

These methods are effective and complementary with standard techniques such as ICA.

A simple encoding method for Sigma-Delta ADC based biopotential acquisition systems

Sigma Delta analogue-to-digital converters allow acquiring the full dynamic range of biomedical signals at the electrodes, resulting in less complex hardware and increased measurement robustness. However, the increased data size per sample (typically 24 bits) demands the transmission of extremely large volumes of data across the isolation barrier, thus increasing power consumption on the patient side. This problem is accentuated when a large number of channels is used as in current 128-256 electrodes biopotential acquisition systems, that usually opt for an optic fibre link to the computer. An analogous problem occurs for simpler low-power acquisition platforms that transmit data through a wireless link to a computing platform. In this paper, a low-complexity encoding method is presented to decrease sample data size without losses, while preserving the full DC-coupled signal. The method achieved a 2.3 average compression ratio evaluated over an ECG and EMG signal bank acquired with equipment based on Sigma-Delta converters. It demands a very low processing load: a C language implementation is presented that resulted in an 110 clock cycles average execution on an 8-bit microcontroller.

Neural and metabolic basis of dynamic resting state fMRI

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.

Direct current electrocorticography for clinical neuromonitoring of spreading depolarizations

Spreading depolarizations cause cortical electrical potential changes over a wide spectral range that includes slow potentials approaching the direct current (or 0 Hz) level. The negative direct current shift (<0.05 Hz) is an important identifier of cortical depolarization and its duration is a measure of potential tissue injury associated with longer lasting depolarizations. To determine the feasibility of monitoring the full signal bandwidth of spreading depolarizations in patients, we performed subdural electrocorticography using platinum electrode strips and direct current-coupled amplifiers in 27 patients with acute brain injury at two neurosurgical centers. While large baseline direct current offsets developed, loss of data due to amplifier saturation was minimal and rates of baseline drift throughout recordings were generally low. Transient negative direct current shifts of spreading depolarizations were easily recognized and in 306/551 (56%) cases had stereotyped, measurable characteristics. Following a standardized training session, novice scorers achieved a high degree of accuracy and interobserver reliability in identifying depolarizations, suggesting that direct current-coupled recordings can facilitate bedside diagnosis for future trials or clinical decision-making. We conclude that intracranial monitoring of slow potentials can be achieved with platinum electrodes and that unfiltered, direct current-coupled recordings are advantageous for identifying and assessing the impact of spreading depolarizations.

Real-time monitoring of human blood-brain barrier disruption

Chemotherapy aided by opening of the blood-brain barrier with intra-arterial infusion of hyperosmolar mannitol improves the outcome in primary central nervous system lymphoma. Proper opening of the blood-brain barrier is crucial for the treatment, yet there are no means available for its real-time monitoring. The intact blood-brain barrier maintains a mV-level electrical potential difference between blood and brain tissue, giving rise to a measurable electrical signal at the scalp. Therefore, we used direct-current electroencephalography (DC-EEG) to characterize the spatiotemporal behavior of scalp-recorded slow electrical signals during blood-brain barrier opening. Nine anesthetized patients receiving chemotherapy were monitored continuously during 47 blood-brain barrier openings induced by carotid or vertebral artery mannitol infusion. Left or right carotid artery mannitol infusion generated a strongly lateralized DC-EEG response that began with a 2 min negative shift of up to 2000 μV followed by a positive shift lasting up to 20 min above the infused carotid artery territory, whereas contralateral responses were of opposite polarity. Vertebral artery mannitol infusion gave rise to a minimally lateralized and more uniformly distributed slow negative response with a posterior-frontal gradient. Simultaneously performed near-infrared spectroscopy detected a multiphasic response beginning with mannitol-bolus induced dilution of blood and ending in a prolonged increase in the oxy/deoxyhemoglobin ratio. The pronounced DC-EEG shifts are readily accounted for by opening and sealing of the blood-brain barrier. These data show that DC-EEG is a promising real-time monitoring tool for blood-brain barrier disruption augmented drug delivery.

DC-EEG recordings of mindfulness

OBJECTIVE

To assess the frequency spectrum of the normal waking human eyes-closed EEG while concentrating on a mental task.

METHODS

Ten adult normal volunteers listened to a CD encouraging mindfulness for one hour and five minutes while their EEG was recorded on a 128 channel DC based ANT system. The software package BESA Research version 6.1 was used for data analysis. The data were subjected to topographic display, frequency as well as independent component analysis.

RESULTS

Near-DC activity that extended beyond one hour, as well as rhythmic wave durations ranging from about 10 to 35min, was observed in all subjects. For this task the major topographic distribution was mainly in frontal near midline areas and the inferior portions of the hemispheres.

CONCLUSIONS

The study demonstrated that rhythms below the infraslow band, as well as a near-DC component, exist in the normal human EEG. Their significance for health and disease now needs to be explored.

SIGNIFICANCE

Since DC-based EEG/MEG systems are already in use by some laboratories, investigators are encouraged to include the exploration of these ultra-slow waves in the review of their data.

Smart Helmet: Wearable Multichannel ECG and EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.

The suppression of scale-free fMRI brain dynamics across three different sources of effort: aging, task novelty and task difficulty

There is growing evidence that fluctuations in brain activity may exhibit scale-free (“fractal”) dynamics. Scale-free signals follow a spectral-power curve of the form P(f ) ∝ f^−β, where spectral power decreases in a power-law fashion with increasing frequency. In this study, we demonstrated that fractal scaling of BOLD fMRI signal is consistently suppressed for different sources of cognitive effort. Decreases in the Hurst exponent (H), which quantifies scale-free signal, was related to three different sources of cognitive effort/task engagement: 1) task difficulty, 2) task novelty, and 3) aging effects. These results were consistently observed across multiple datasets and task paradigms. We also demonstrated that estimates of H are robust across a range of time-window sizes. H was also compared to alternative metrics of BOLD variability (SD_BOLD) and global connectivity (Gconn), with effort-related decreases in H producing similar decreases in SD_BOLD and Gconn. These results indicate a potential global brain phenomenon that unites research from different fields and indicates that fractal scaling may be a highly sensitive metric for indexing cognitive effort/task engagement.

Ultraflexible organic amplifier with biocompatible gel electrodes

In vivo electronic monitoring systems are promising technology to obtain biosignals with high spatiotemporal resolution and sensitivity. Here we demonstrate the fabrication of a biocompatible highly conductive gel composite comprising multi-walled carbon nanotube-dispersed sheet with an aqueous hydrogel. This gel composite exhibits admittance of 100 mS cm⁻² and maintains high admittance even in a low-frequency range. On implantation into a living hypodermal tissue for 4 weeks, it showed a small foreign-body reaction compared with widely used metal electrodes. Capitalizing on the multi-functional gel composite, we fabricated an ultrathin and mechanically flexible organic active matrix amplifier on a 1.2-μm-thick polyethylene-naphthalate film to amplify (amplification factor: ∼200) weak biosignals. The composite was integrated to the amplifier to realize a direct lead epicardial electrocardiography that is easily spread over an uneven heart tissue.

Flexible electronics promise the opportunity to monitor biological activity via implanted devices. Here, the authors develop a biocompatible conductive carbon nanotube/gel composite and couple it with an ultrathin flexible amplifier, enabling in vivo measurement of epicardial electrocardiogram signals.

Infraslow status epilepticus: A new form of subclinical status epilepticus recorded in a child with Sturge-Weber syndrome

BACKGROUND

Analysis of infraslow EEG activity (ISA) has shown potential in the evaluation of patients with epilepsy and in the differentiation between focal and generalized epilepsies. Infraslow EEG activity analysis may also provide insights into the pathophysiology of refractory clinical and subclinical status epilepticus. The purpose of this report is to describe a girl with Sturge-Weber syndrome (SWS) who presented with a 96-h refractory encephalopathy and nonischemic hemiparesis and who was identified to have infraslow status epilepticus (ISSE), which successfully resolved after midazolam administration.

METHODS

The continuous EEG recording of a 5-year-old girl with known structural epilepsy due to Sturge-Weber syndrome is presented. The patient presented to the ED with acute confusion, eye deviation, and right hemiparesis similar to two previous admissions. Despite administration of lorazepam, fosphenytoin, phenobarbital, and valproic loads, the patient showed no improvement in the clinical condition after 48 h. The continuous video-EEG monitoring (VEM) showed continuous severe diffuse nonrhythmic asymmetric slowing but no apparent ictal activity on continuous conventional EEG recording settings. As brain CT, CTA, CTV, and complete MRI scans including DWI obtained within 72 h of presentation failed to demonstrate any ischemic changes, analysis of the EEG infraslow (ISA) activity was undertaken using LFF: 0.01 Hz and HFF: of 0.1 Hz, respectively.

RESULTS

Continuous subclinical unilateral rhythmic ictal ISA was identified. This was only evident on the left hemisphere which correlated with the structural changes due to SWS. A trial of continuous 120 to 240 μg/kg/h of IV midazolam resulted in immediate resolution of the contralateral hemiparesis and encephalopathy.

CONCLUSION

Continuous prolonged rhythmic ictal infraslow activity (ISA) can cause super-refractory subclinical focal status epilepticus. This has not been previously reported, and we propose that this be called infraslow status epilepticus (ISSE). Infraslow EEG activity analysis should be performed in all patients with unexplained subclinical status epilepticus. This article is part of a Special Issue entitled "Status Epilepticus".

Looking for a precursor of spontaneous Sleep Slow Oscillations in human sleep: The role of the sigma activity

Sleep Slow Oscillations (SSOs), paradigmatic EEG markers of cortical bistability (alternation between cellular downstates and upstates), and sleep spindles, paradigmatic EEG markers of thalamic rhythm, are two hallmarks of sleeping brain. Selective thalamic lesions are reportedly associated to reductions of spindle activity and its spectrum ~14 Hz (sigma), and to alterations of SSO features. This apparent, parallel behavior suggests that thalamo-cortical entrainment favors cortical bistability. Here we investigate temporally-causal associations between thalamic sigma activity and shape, topology, and dynamics of SSOs. We recorded sleep EEG and studied whether spatio-temporal variability of SSO amplitude, negative slope (synchronization in downstate falling) and detection rate are driven by cortical-sigma-activity expression (12-18Hz), in 3 consecutive 1s-EEG-epochs preceding each SSO event (Baselines). We analyzed: (i) spatial variability, comparing maps of baseline sigma power and of SSO features, averaged over the first sleep cycle; (ii) event-by-event shape variability, computing for each electrode correlations between baseline sigma power and amplitude/slope of related SSOs; (iii) event-by-event spreading variability, comparing baseline sigma power in electrodes showing an SSO event with the homologous ones, spared by the event. The scalp distribution of baseline sigma power mirrored those of SSO amplitude and slope; event-by-event variability in baseline sigma power was associated with that in SSO amplitude in fronto-central areas; within each SSO event, electrodes involved in cortical bistability presented higher baseline sigma activity than those free of SSO. In conclusion, spatio-temporal variability of thalamocortical entrainment, measured by background sigma activity, is a reliable estimate of the cortical proneness to bistability.

Infraslow EEG activity modulates cortical excitability in postanoxic encephalopathy

Infraslow activity represents an important component of physiological and pathological brain function. We study infraslow activity (<0.1 Hz) in 41 patients with postanoxic coma after cardiac arrest, including the relationship between infraslow activity and EEG power in the 3-30 Hz range, using continuous full-band scalp EEG. In all patients, infraslow activity (0.015-0.06 Hz) was present, irrespective of neurological outcome or EEG activity in the conventional frequency bands. In two patients, low-amplitude (10-30 μV) infraslow activity was present while the EEG showed no rhythmic activity above 0.5 Hz. In 13/15 patients with a good outcome and 20/26 patients with a poor one, EEG power in the 3-30 Hz frequency range was correlated with the phase of infraslow activity, quantified by the modulation index. In 9/14 patients with burst-suppression with identical bursts, bursts appeared in clusters, phase-locked to the infraslow oscillations. This is substantiated by a simulation of burst-suppression in a minimal computational model. Infraslow activity is preserved in postanoxic encephalopathy and modulates cortical excitability. The strongest modulation is observed in patients with severe postanoxic encephalopathy and burst-suppression with identical bursts.

Spontaneous activity in the waiting brain: a marker of impulsive choice in attention-deficit/hyperactivity disorder?

•

In controls very low frequency (VLF) EEG attenuated during task and waiting periods.

•

In ADHD there was less attenuation during tasks and none at all during waiting.

•

Degree of waiting attenuation correlated with parent's ratings of impulsive choice.

•

Aberrant waiting VLF EEG may be a neural marker for impulsive choice in ADHD.

Background

Spontaneous very low frequency oscillations (VLFO), seen in the resting brain, are attenuated when individuals are working on attention demanding tasks or waiting for rewards (Hsu et al., 2013). Individuals with attention-deficit/hyperactivity disorder (ADHD) display excess VLFO when working on attention tasks. They also have difficulty waiting for rewards. Here we examined the waiting brain signature in ADHD and its association with impulsive choice.

Methods

DC-EEG from 21 children with ADHD and 21 controls (9–15 years) were collected under four conditions: (i) resting; (ii) choosing to wait; (iii) being “forced” to wait; and (iv) working on a reaction time task. A questionnaire measured two components of impulsive choice.

Results

Significant VLFO reductions were observed in controls within anterior brain regions in both working and waiting conditions. Individuals with ADHD showed VLFO attenuation while working but to a reduced level and none at all when waiting. A closer inspection revealed an increase of VLFO activity in temporal regions during waiting. Excess VLFO activity during waiting was associated with parents’ ratings of temporal discounting and delay aversion.

Conclusions

The results highlight the potential role for waiting-related spontaneous neural activity in the pathophysiology of impulsive decision-making of ADHD.

Spreading depression in continuous electroencephalography of brain trauma

OBJECTIVE

Cortical spreading depolarizations are a pathophysiological mechanism and candidate target for advanced monitoring in acute brain injury. Here we investigated manifestations of spreading depolarization in continuous electroencephalography (EEG) as a broadly applicable, noninvasive method for neuromonitoring.

METHODS

Eighteen patients requiring surgical treatment of traumatic brain injury were monitored by invasive electrocorticography (ECoG; subdural electrodes) and noninvasive scalp EEG during intensive care. Spreading depolarizations were first identified in subdural recordings, and EEG was then examined visually and quantitatively to identify correlates.

RESULTS

A total of 455 spreading depolarizations occurred during 65.9 days of simultaneous ECoG/EEG monitoring. For 179 of 455 events (39%), depolarizations caused temporally isolated, transient depressions of spontaneous EEG amplitudes to 57% (median) of baseline power. Depressions lasted 21 minutes (median) and occurred as suppressions of high-amplitude delta activity present as a baseline pattern in the injured hemisphere. For 62 of 179 (35%) events, isolated depressions showed a clear spread of depression between EEG channels with delays of 17 minutes (median), sometimes spanning the entire hemisphere. A further 188 of 455 (41%) depolarizations were associated with continuous EEG depression that lasted hours to days due to ongoing depolarizations. Depolarizations were also evidenced in EEG as shifts in direct current potentials.

INTERPRETATION

Leão's spreading depression can be observed in clinically standard, continuous scalp EEG, and underlying depolarizations can spread widely across the injured cerebral hemisphere. These results open the possibility of monitoring noninvasively a neuronal pathophysiological mechanism in a wide range of disorders including ischemic stroke, subarachnoid hemorrhage, and brain trauma, and suggest a novel application for continuous EEG.

Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents

Flexible communication within the brain, which relies on oscillatory activity, is not confined to adult neuronal networks. Experimental evidence has documented the presence of discontinuous patterns of oscillatory activity already during early development. Their highly variable spatial and time-frequency organization has been related to region specificity. However, it might be equally due to the absence of unitary criteria for classifying the early activity patterns, since they have been mainly characterized by visual inspection. Therefore, robust and unbiased methods for categorizing these discontinuous oscillations are needed for increasingly complex data sets from different labs. Here, we introduce an unsupervised detection and classification algorithm for the discontinuous activity patterns of rodents during early development. For this, in a first step time windows with discontinuous oscillations vs. epochs of network “silence” were identified. In a second step, the major features of detected events were identified and processed by principal component analysis for deciding on their contribution to the classification of different oscillatory patterns. Finally, these patterns were categorized using an unsupervised cluster algorithm. The results were validated on manually characterized neonatal spindle bursts (SB), which ubiquitously entrain neocortical areas of rats and mice, and prelimbic nested gamma spindle bursts (NG). Moreover, the algorithm led to satisfactory results for oscillatory events that, due to increased similarity of their features, were more difficult to classify, e.g., during the pre-juvenile developmental period. Based on a linear classification, the optimal number of features to consider increased with the difficulty of detection. This algorithm allows the comparison of neonatal and pre-juvenile oscillatory patterns in their spatial and temporal organization. It might represent a first step for the unbiased elucidation of activity patterns during development.

High frequency oscillations and infraslow activity in epilepsy

In pre-surgical evaluation of epilepsy, there has been an increased interest in the study of electroencephalogram (EEG) activity outside the 1-70 Hz band of conventional frequency activity (CFA). Research over the last couple of decades has shown that EEG activity in the 70-600 Hz range, termed high frequency oscillations (HFOs), can be recorded intracranially from all brain regions both interictally and at seizure onset. In patients with epilepsy, HFOs are now considered as pathologic regardless of their frequency band although it may be difficult to distinguish them from the physiologic HFOs, which occur in a similar frequency range. Interictal HFOs are likely to be confined mostly to the seizure onset zone, thus providing a new measure for localizing it. More importantly, several studies have linked HFOs to underlying epileptogenicity, suggesting that HFOs can serve as potential biomarkers for the illness. Along with HFOs, analysis of ictal baseline shifts (IBS; or direct current shifts) and infraslow activity (ISA) (ISA: <0.1 Hz) has also attracted attention. Studies have shown that: IBSs can be recorded using the routine AC amplifiers with long time constants; IBSs occur at the time of conventional EEG onset, but in a restricted spatial distribution compared with conventional frequencies; and inclusion of IBS contacts in the resection can be associated with favorable seizure outcome. Only a handful of studies have evaluated all the EEG frequencies together in the same patient group. The latter studies suggest that the seizure onset is best localized by the ictal HFOs, the IBSs tend to provide a broader localization and the conventional frequencies could be non-localizing. However, small number of patients included in these studies precludes definitive conclusions regarding post-operative seizure outcome based on selective or combined resection of HFO, IBS and CFA contacts. Large, preferably prospective, studies are needed to further evaluate the implications of different EEG frequencies in epilepsy.

Electrophysiological biomarkers of epilepsy

In patients being evaluated for epilepsy and in animal models of epilepsy, electrophysiological recordings are carried to capture seizures to determine the existence of epilepsy. Electroencephalography recordings from the scalp, or sometimes directly from the brain, are also used to locate brain areas where seizure begins, and in surgical treatment help plan the area for resection. As seizures are unpredictable and can occur infrequently, ictal recordings are not ideal in terms of time, cost, or risk when, for example, determining the efficacy of existing or new anti-seizure drugs, evaluating potential anti-epileptogenic interventions, or for prolonged intracerebral electrode studies. Thus, there is a need to identify and validate other electrophysiological biomarkers of epilepsy that could be used to diagnose, treat, cure, and prevent epilepsy. Electroencephalography recordings in the epileptic brain contain other interictal electrophysiological disturbances that can occur more frequently than seizures, such as interictal spikes (IIS) and sharp waves, and from invasive studies using wide bandwidth recording and small diameter electrodes, the discovery of pathological high-frequency oscillations (HFOs) and microseizures. Of IIS, HFOs, and microseizures, a significant amount of recent research has focused on HFOs in the pathophysiology of epilepsy. Results from studies in animals with epilepsy and presurgical patients have consistently found a strong association between HFOs and epileptogenic brain tissue that suggest HFOs could be a potential biomarker of epileptogenicity and epileptogenesis. Here, we discuss several aspects of HFOs, as well as IIS and microseizures, and the evidence that supports their role as biomarkers of epilepsy.

Biomarkers of epileptogenic zone defined by quantified stereo-EEG analysis

OBJECTIVE

In one third of patients with a diagnosis of pharmacoresistant focal epilepsy who are candidates for therapeutic surgery, cerebral areas responsible for seizure generation can be defined exclusively with invasive intracranial recordings. A correct presurgical identification of the epileptogenic zone (EZ) with intracranial electrodes has a direct impact on postsurgical outcome. We aimed at identifying biomarkers of the EZ based on computer-assisted inspection of intracranial electroencephalography (EEG).

METHODS

Computer-driven intracranial EEG analysis in the domains of time, frequency, and space was retrospectively applied to a population of 10 patients with focal epilepsy to detect EZ electrophysiologic markers. Next, a prospective study was performed on 14 surgery candidate patients. The stereo-EEG computer-assisted analysis of EZ boundaries performed blind from patients data was compared to that defined with the traditional visual inspection completed by neurophysiologists.

RESULTS

In the retrospective study, the EZ was characterized by the combined detection of three biomarkers observed at seizure onset: (1) fast activity at 80-120 Hz associated with (2) very slow transient polarizing shift and (3) voltage depression (flattening). Correlations between these indexes were calculated for each seizure. In the prospective study, the quantified analysis based on the three biomarkers confirmed a complete overlap between leads within the EZ identified by expert clinicians. In 2 of 14 patients the proposed biomarkers partially identified the EZ.

SIGNIFICANCE

Our findings demonstrate and validate with a prospective unbiased study the use of three neurophysiologic intracranial EEG parameters as excellent biomarkers of ictogenesis and as reliable indicators of EZ boundaries.

Infralow frequencies and ultradian rhythms

Our brain activity demonstrates amazing stability across multiple time frames ranging from a few milliseconds to several hours. The longer cycles are commonly called ultradian rhythms and they correspond to infralow frequencies (ILFs) in the milli-Hz range (0.001 Hz). Ultradian rhythms between 90 minutes and 2 hours or longer are readily observed in our electroencephalogram, and they reflect periods of activity and rest, cycles of cortical excitability and plasticity followed by relative inactivity. Our nightly sleep is organized into similar stages (rapid eye movement and non-rapid eye movement sleep) as is our daily behavior (ie, the basic rest-activity cycle). Astrocytes often exhibit milli-Hz ILFs, and they play a major role in shaping neuronal plasticity and activity, and thus may organize or influence the basic rhythms of sleep and waking. The nature and importance of astrocytes in human brain functioning is subsequently reviewed.

Safety of EEG-fMRI recordings in newborn infants at 3T: a study using a baby-size phantom

OBJECTIVE

We aimed to study EEG electrode temperatures during MRI recordings using a neonatal-size phantom to establish the safety of neonatal EEG-MRI.

METHODS

We constructed a phantom set-up for co-registration of EEG and MRI measurements with newborn size configurations. The set-up consisted of a spherical glass phantom fitted with a customised MRI-compatible 64-channel EEG cap and EEG amplifier. Temperatures were recorded during and after five different scanning sequences (two T2∗ sensitised BOLD fMRI, one T1-weighted and two T2-weighted spin echo) in five electrode locations using a thermistor that was integrated into the electrode housing. A temperature increase >4°C was defined a priori as the safety limit.

RESULTS

During fMRI and T1 sequences, none of the electrodes showed meaningful temperature changes. Only one MRI sequence (T2 with Max turbo factor 25; SAR 89%) caused temperature increase in one electrode (Fpz; +4.1°C) that slightly exceeded our predefined safety limit, while the conventional T2 sequence was within safety limits (up to +1.7°C).

CONCLUSIONS

Co-registration of EEG and fMRI can be considered safe in babies with respect to electrode heating, which is the primary safety concern.

SIGNIFICANCE

The present findings open up a possibility to commence studies where EEG and MRI/fMRI are co-registered in human babies. Such studies hold significant promise of a better understanding of the early development of brain function and neurovascular coupling.

Interictal infraslow activity in patients with epilepsy

OBJECTIVE

To evaluate if interictal infraslow activity (ISA), as obtained from a conventional EEG system, can contribute information about the epileptogenic process.

METHODS

The entire long-term intracranial monitoring sessions of 12 consecutive patients were evaluated on an XLTEK system for ISA. Three additional patients had long-term scalp recordings.

RESULTS

In intracranial as well as scalp recordings, the ISA background was consistently higher in the waking state than during sleep. From this background emerged intermittently focal changes, which could achieve in intracranial recordings millivolt amplitudes, while they remained in the microvolt range in scalp recordings. Although they were mainly contiguous between adjacent channels, this was not necessarily the case and intermittent build-up could be seen distant from the epileptogenic zone or radiographic lesion.

CONCLUSIONS

Interictal ISA can be detected in routine intracranial and scalp recordings, without the need for DC amplifiers, and can provide additional information.

SIGNIFICANCE

Since ISA is a separate element of the electromagnetic spectrum, apparently non-neuronal in origin, its assessment should be included not only in the pre-surgical evaluation of epilepsy patients but also in patients with other neurologic disorders and normal volunteers.