Conventional Electroencephalography Research Articles

Electrographic monitoring for seizure detection in the neonatal unit: current status and future direction.

Neonatal neurocritical intensive care is dedicated to safeguarding the newborn brain by prioritising clinical practices that promote early identification, diagnosis and treatment of brain injuries. The most common newborn neurological emergency is neonatal seizures, which may also be the initial clinical indication of neurological disease. A high seizure burden in the newborn period independently contributes to increased mortality and morbidity. The majority of seizures in newborns are subclinical (without clinical presentation), and hence identification may be difficult. Neuromonitoring techniques most frequently used to monitor brain wave activity include conventional electroencephalography (cEEG) or amplitude-integrated EEG (aEEG). cEEG with video is the gold standard for diagnosing and treating seizures. Many neonatal units do not have access to cEEG, and frequently those that do, have little access to real-time interpretation of monitoring. IMPACT: EEG monitoring is of no benefit to an infant without expert interpretation. Whilst EEG is a reliable cot-side tool and of diagnostic and prognostic use, both conventional EEG and amplitude-integrated EEG have strengths and limitations, including sensitivity to seizure activity and ease of interpretation. Automated seizure detection requires a sensitive and specific algorithm that can interpret EEG in real-time and identify seizures, including their intensity and duration.

Ictal Direct Current Shifts Preceded Much Earlier Than High Frequency Oscillations After Status: Is It the Effect of Status or Antiseizure Medication?

While spikes and sharp waves are considered as markers of epilepsy in conventional electroencephalography, ictal direct current (DC) shifts and high-frequency oscillations (HFOs) appear to be useful biomarkers for epileptogenicity. We analyzed how ictal DC shifts and HFOs were affected by focal status epilepticus and antiseizure medications (ASMs). A 20-year-old female patient who underwent long-term intracranial electrode implantation for epilepsy surgery presented with 72 habitual seizures and a focal status epilepticus episode lasting for 4 h. Ten, 3, and 10 consecutive habitual seizures were analyzed before the status, after the status, and after ASM (valproate) loading, respectively. Before and immediately after the status, ictal DC shifts remained the same in terms of the amplitude, duration, and slope of DC shifts. High-frequency oscillations also remained the same in terms of the duration, frequency, and power except for the power of the lower frequency band. After ASM loading, the duration, amplitude, and slope of the ictal DC shift were significantly attenuated. The duration, frequency, and power of the HFOs were significantly attenuated. Furthermore, the interval between the DC onset and HFO onset was significantly longer and the interval between the HFO onset and ictal DC shift peak was significantly shorter. The attenuation of ictal DC shifts and HFOs after ASM loading implies that astrocyte and neuronal activity may be both attenuated by ASMs. This finding may help with our understanding of the pathophysiology of epilepsy and can aid with the discovery of new approaches for epilepsy management.

Tripolar concentric ring electrodes for capturing localised electroencephalography signals during sleep.

By design, tripolar concentric ring electrodes (TCRE) provide more focal brain activity signals than conventional electroencephalography (EEG) electrodes placed further apart. This study compared spectral characteristics and rates of data loss to noisy epochs with TCRE versus conventional EEG signals recorded during sleep. A total of 20 healthy sleepers (12 females; mean [standard deviation] age 27.8 [9.6] years) underwent a 9-h sleep study. Participants were set up for polysomnography recording with TCRE to assess brain activity from 18 sites and conventional electrodes for EEG, eyes, and muscle movement. A fast Fourier transform using multitaper-based estimation was applied in 5-s epochs to scored sleep. Odds ratios with Bonferroni-adjusted 95% confidence intervals were calculated to determine the proportional differences in the number of noisy epochs between electrode types. Relative power was compared in frequency bands throughout sleep. Linear mixed models showed significant main effects of signal type (p < 0.001) and sleep stage (p < 0.001) on relative spectral power in each power band, with lower relative spectral power across all stages in TCRE versus EEG in alpha, beta, sigma, and theta activity, and greater delta power in all stages. Scalp topography plots showed distinct beta activation in the right parietal lobe with TCRE versus EEG. EEG showed higher rates of noisy epochs compared to TCRE (1.3% versus 0.8%, p < 0.001). TCRE signals showed marked differences in brain activity compared to EEG, consistent with more focal measurements and region-specific differences during sleep. TCRE may be useful for evaluating regional differences in brain activity with reduced muscle artefact compared to conventional EEG.

O010 Capturing Localised Electroencephalography Signals During Sleep using Tripolar Concentric Ring Electrodes

Abstract Introduction Tri -concentric ring electrodes (TCRE) evaluate the current density underlying each electrode and provide improved signal-to-noise compared to conventional electroencephalography (EEG) electrodes. This pilot study used TCRE for the first time to compare TCRE versus more conventional EEG signals during sleep . Materials and Method Twenty healthy sleepers (8 males, mean±SD age 27.8±9.6 y) completed a 9-hr sleep opportunity. Eighteen TCRE electrodes were placed based on the 10-20 system, along with more conventional EEG recorded from the outer rings of paired TCRE electrodes (emulated EEG; eEEG). A Fast Fourier Transform using multitaper-based estimation was applied in 5 second epochs to calculate absolute and relative powers in delta, alpha, theta, sigma and beta frequency bands for eEEG and TCRE signals throughout conventionally scored sleep stages. Results At the Cz position, TCRE showed reduced relative powers in beta frequency bands across sleep stages compared to eEEG. Further, TCRE demonstrated lower relative beta activity during wake (mean difference [95% confidence lower, upper]; -14%, [-15.9%, -12.1%]), N1 sleep (-5.7%, [-7.6%, -3.7%]), and REM sleep (-4.4%, [-6.3%, -2.5%]). TCRE also recorded higher relative delta power across all stages of sleep and wake. Conclusions Lower signal power in high frequency bands and higher power in lower frequency bands supports more favourable signal-to-noise ratio with TCRE compared to more conventional EEG. Thus, TCRE shows promise for evaluating sleep related brain activity. The more focal nature of TCRE recordings may be an additional advantage, that warrants further analysis.

A preliminary study on a new method for evaluating brain maturation in preterm infants

To establish a new method for evaluating the brain maturation of preterm infants based on the features of electroencephalographic activity. A prospective study was conducted on the video electroencephalography (vEEG) and amplitude-integrated electroencephalography (aEEG) recordings within 7 days after birth of preterm infants who had a postmenstrual age (PMA) of 25-36 weeks and met the inclusion criteria. The background activity of aEEG+conventional electroencephalography (cEEG) was scored according to the features of brain maturation as a new evaluation system and was compared with the aEEG evaluation system. The correlations of the evaluation results of the two methods with gestational age (GA), PMA, and head circumference were evaluated. The intervals of the total scores of aEEG+cEEG and aEEG were calculated for preterm infants with different PMAs and were compared between groups. The consistency of the new scoring system was evaluated among different raters. A total of 52 preterm infants were included. The total scores of aEEG+cEEG and aEEG were positively correlated with GA, PMA, and head circumference (P<0.05), and the correlation coefficient between the total scores of the two systems and PMA and GA was >0.9. The normal score intervals for aEEG+cEEG and aEEG scoring systems were determined in preterm infants with different PMAs as follows: infants with a PMA of less than 28 weeks had scores of 13.0 (11.0, 14.0) points for aEEG+cEEG and 6.0 (4.0, 7.0) points for aEEG; infants with a PMA between 28 and 29+6 weeks had scores of 16.0 (14.5, 17.0) points for aEEG+cEEG and 8.0 (6.0, 8.0) points for aEEG; infants with a PMA between 30 and 31+6 weeks had scores of 18.0 (17.0, 21.0) points for aEEG+cEEG and 9.0 (8.0, 10.0) points for aEEG; infants with between 32 and 33+6 weeks had scores of 22.0 (20.0, 24.5) points for aEEG+cEEG and 10.0 (10.0, 10.8) points for aEEG; infants with a PMA between 34 and 36 weeks had scores of 26.0 (24.5, 27.5) points for aEEG+cEEG and 11.0 (10.0, 12.0) points for aEEG. There were significant differences in the total scores of aEEG+cEEG and aEEG among the different PMA groups (P<0.05). There was a high consistency between different raters when using the scoring system to evaluate the brain maturation of preterm infants (κ=0.86). The aEEG+cEEG scoring system established in this study can quantitatively reflect the brain maturation of preterm infants, with a good discriminatory ability between preterm infants with different PMAs and high consistency between different raters.

Amplitude-Integrated Electroencephalography Evolution and Magnetic Resonance Imaging Injury in Mild and Moderate to Severe Neonatal Encephalopathy.

This study aimed to describe the evolution of amplitude-integrated electroencephalography (aEEG) in neonatal encephalopathy (NE) during therapeutic hypothermia (TH) and evaluate the association between aEEG parameters and magnetic resonance imaging (MRI) injury. aEEG data of infants who underwent TH were reviewed for background, sleep wake cycling (SWC), and seizures. Conventional electroencephalography (cEEG) background was assessed from the reports. Discordance of background on aEEG and cEEG was defined if there was a difference in the severity of the background. MRI injury (total score ≥ 5) was assessed by using the Weeke scoring system. A total of 46 infants were included; 23 (50%) with mild NE and 23 (50%) with moderate to severe NE. Comparing mild NE with moderate to severe NE, the initial aEEG background differed with more mild being continuous (70 vs. 52%), with fewer being discontinuous (0 vs. 22%) and flat tracing (0 vs. 4%), whereas burst suppression (4 vs. 4%) and low voltage (26 vs. 18%) did not differ. There was a notably common discordance between the background assessment on cEEG with aEEG in 82% with continuous and 40% low voltage aEEG background. MRI abnormalities were identified in four infants with mild NE and seven infants with moderate to severe NE. MRI injury was associated with aEEG seizures in infants with moderate to severe NE. aEEG seizures are useful to predict MRI injury in moderate to severe NE infants. There is a large discrepancy between aEEG, cEEG, and MRI in neonates treated by TH. · MRI injury was identified in 29% of moderate NE infants and in 50% of severe NE infants.. · aEEG seizures were associated with MRI injury in the moderate to severe NE infants.. · MRI injury was identified in 16% infants with mild NE.. · Mild NE infants with normal aEEG were unlikely to have MRI injury.. · There was a large discrepancy between aEEG, cEEG, and MRI in infants treated by TH..

Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp.

Electroencephalography has garnered interest for applications in mobile healthcare, human-machine interfaces, and IoT. Conventional electroencephalography relies on wet and dry electrodes. Despite favourable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes and makes them unsuited for long-term mobile recording. In contrast, dry electrodes are better suited for long-term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non-adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Here, we report a conical microstructure array (CMSA) based sensor made of carbon nanotube-polydimethylsiloxane composite. The CMSA sensor is fabricated using our innovative, cost-effective, and scalable method of viscosity-controlled dip-pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical-grade wet gel electrodes. This article is protected by copyright. All rights reserved.

Predicting the Neurodevelopmental Outcome in Extremely Preterm Newborns Using a Multimodal Prognostic Model Including Brain Function Information

Early assessment of the prognosis of preterm newborns is crucial for accurately informing parents and making treatment decisions. The currently available prognostic models rarely incorporate functional brain information from conventional electroencephalography (cEEG). To examine the performance of a multimodal model combining (1) brain function information with (2) brain structure information (cranial ultrasonography), and (3) perinatal and (4) postnatal risk factors for the prediction of death or neurodevelopmental impairment (NDI) in extremely preterm infants. Preterm newborns (23-28 weeks' gestational age) admitted to the neonatal intensive care unit at Amiens-Picardie University Hospital were retrospectively included (January 1, 2013, to January 1, 2018). Risk factors from the 4 categories were collected during the first 2 weeks post delivery. Neurodevelopmental impairment was assessed at age 2 years with the Denver Developmental Screening Test II. No or moderate NDI was considered a favorable outcome. Death or severe NDI was considered an adverse outcome. Data analysis was performed from August 26, 2021, to March 31, 2022. After the selection of variables significantly associated with outcome, 4 unimodal prognostic models (considering each category of variable independently) and 1 multimodal model (considering all variables simultaneously) were developed. After a multivariate analysis for models built with several variables, decision-tree algorithms were run on each model. The areas under the curve for decision-tree classifications of adverse vs favorable outcomes were determined for each model, compared using bootstrap tests, and corrected for type I errors. A total of 109 newborns (58 [53.2% male]) born at a mean (SD) gestational age of 26.3 (1.1) weeks were included. Among them, 52 (47.7%) had a favorable outcome at age 2 years. The multimodal model area under the curve (91.7%; 95% CI, 86.4%-97.0%) was significantly higher than those of the unimodal models (P < .003): perinatal model (80.6%; 95% CI, 72.5%-88.7%), postnatal model (81.0%; 95% CI, 72.6%-89.4%), brain structure model (cranial ultrasonography) (76.6%; 95% CI, 67.8%-85.3%), and brain function model (cEEG) (78.8%; 95% CI, 69.9%-87.7%). In this prognostic study of preterm newborns, the inclusion of brain information in a multimodal model was associated with significant improvement in the outcome prediction, which may have resulted from the complementarity of the risk factors and reflected the complexity of the mechanisms that interfered with brain maturation and led to death or NDI.

Hyperperfusion in the cerebellum lobule VIIb in patients with epileptic seizures

BackgroundThe cerebellum plays an important role in motor control, however, its involvement in epilepsy has not been fully understood. Arterial spin labelling perfusion magnetic resonance image (ASL) is a noninvasive method to evaluate cerebral and cerebellar blood flow. We investigated cerebellar perfusion in patients with epileptic seizures using ASL.MethodsAdult patients with epileptic seizures who underwent ASL in three post labeling delay (PLD) conditions (1525, 1800, and 2500 msec) and conventional electroencephalography (EEG) on the same day were investigated. Clinical and EEG characteristics of them were retrospectively analyzed.ResultsSix patients (6 women, age; 36.2 ± 17.9 years (mean ± SD)) showed hyperperfusion in selective areas in the cerebellar paravermis of lobule VIIb. One patient with generalized epilepsy (tentative diagnosis of juvenile myoclonic epilepsy or epilepsy with myoclonic absences) showed unilateral hypoperfusion in PLD 1525 msec and hyperperfusion in PLD 1800 and 2500 msec at the area while EEG showed generalized spike-wave complexes. After successful treatment, these perfusion abnormalities disappeared. In two patients with focal epilepsy manifesting with asymmetrical motor symptoms, cerebellar hyperperfusion was found on the opposite side to the seizure focus estimated by seizure semiology. Besides hyperperfusion of the VIIb lobule, hypoperfusion at the same area was detected in shorter PLD condition in four patients and in longer PLD condition in one patient.ConclusionThe cerebellar paravermis of lobule VIIb can be a component of motor circuit and participate in epileptic network in humans. Cerebellar perfusion abnormalities can be associated with neurovascular coupling via capillary bed.

High-resolution hippocampal diffusion tensor imaging of mesial temporal sclerosis in refractory epilepsy.

We explore the possibility of using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to discern microstructural abnormalities in the hippocampus indicative of mesial temporal sclerosis (MTS) at the subfield level. We analyzed data from 57 patients with refractory epilepsy who previously underwent 3.0-T magnetic resonance imaging (MRI) including DTI as a standard part of presurgical workup. We collected information about each subject's seizure semiology, conventional electroencephalography (EEG), high-density EEG, positron emission tomography reports, surgical outcome, and available histopathological findings to assign a final diagnostic category. We also reviewed the radiology MRI report to determine the radiographic category. DTI- and NODDI-based metrics were obtained in the hippocampal subfields. By examining diffusion characteristics among subfields in the final diagnostic categories, we found lower orientation dispersion indices and elevated axial diffusivity in the dentate gyrus in MTS compared to no MTS. By similarly examining among subfields in the different radiographic categories, we found all diffusion metrics were abnormal in the dentate gyrus and CA1. We finally examined whether diffusion imaging would better inform a radiographic diagnosis with respect to the final diagnosis, and found that dentate diffusivity suggested subtle changes that may help confirm a positive radiologic diagnosis. The results suggest that diffusion metric analysis at the subfield level, especially in dentate gyrus and CA1, maybe useful for clinical confirmation of MTS.

Neonatal seizures: stepping outside the comfort zone.

Seizures are the most common neurological disorders in newborns. Managing neonatal seizures is challenging, especially for neurologists who are not neonatal specialists. Acute brain injury during ischemic insult is a key component of seizure occurrence, while genetic and metabolic disorders play less prevalent but more severe roles. The diagnosis of neonatal seizure is ambiguous, as the subjective differentiation between seizure and nonepileptic events is difficult; therefore, electrographic recording is the gold standard for diagnosis. The detection of electrographic seizures by neonatologists is currently facilitated by amplitude-integrated electroencephalography availability in many neonatal intensive care units. Although it is less sensitive than conventional electroencephalography, it is better to record all risky neonates to filter the abnormal events as early as possible to enable the initiation of dedicated therapy at proper dose and time and facilitate the initial response to antiepileptic drugs. This, in turn, helps maintain the balance between unnecessary drug use and their neurotoxic effects. Moreover, the early treatment of electrographic seizures plays a vital role in the suppression of subsequent abnormal brain electricity (status epilepticus) and shortening the hospital stay. An explicit understanding of seizure etiology and pathophysiology should direct attention to the proper prescription of short- and long-term antiepileptic medications to solve the challenging issue of whether neonatal seizures progress to postneonatal epilepsy and long-term cognitive deficits. This review addresses recent updates in different aspects of neonatal seizures, particularly electrographic discharge, including their definition, etiology, classification, diagnosis, management, and neurodevelopmental outcomes.

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG.

AEEG vs cEEG's sensivity for seizure detection in the setting of neonatal intensive care units: A systematic review and meta-analysis.

Amplitude-integrated electroencephalography (aEEG)'s accuracy compared to conventional electroencephalography (cEEG) has not been fully established. The aim of our study was to conduct a systematic review on the sensitivity of the aEEG for neonatal seizure detection. Studies from PubMed and Google Scholar databases comparing recordings of cEEG and aEEG in newborns were included according to the PRISMA method. A quality assessment using the QUADAS-2 tool was provided. A random-effect model was used to account for different sources of variations among studies. Publication biases were represented by a funnel plot, and funnel plot symmetry was assessed. Fourteen studies were reported; sensitivity of each diagnostic tool used (single-channel aEEG, two-channel aEEG, two-channel aEEG plus raw trace EEG) was compared to that of the gold-standard cEEG and to those of the other methods used. Overall sensitivity of the aEEG ranged from 31.25% to 90%. Our study provides evidence that sensitivity of aEEG varies significantly and that seizure detection rate is lower than that of cEEG. The two-channel aEEG with raw trace EEG shows a high sensitivity and might represent a valid alternative to the cEEG in the setting of neonatal intensive care units (NICUs).

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features.

How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery-Based Brain-Machine Interfaces.

Motor imagery offers an excellent opportunity as a stimulus‐free paradigm for brain–machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real‐time, continuous classification of motor imagery brain signals is introduced. This low‐profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network‐machine learning provides a real‐time, continuous motor imagery‐based brain–machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real‐time control of a virtual reality game.

Acute symptomatic seizures and COVID-19: Hospital-based study

Background and purposePost COVID-19 seizures are relatively rare. The aim of the present study was to estimate the frequency of acute symptomatic seizures among patients with COVID-19 and to discuss possible pathophysiological mechanisms. Material and methodsOut of 439 cases with COVID-19 that were admitted to Assiut and Aswan University hospitals during the period from 1 June to 10 August 2020, 19 patients (4.3 %) presented with acute symptomatic seizures. Each patient underwent computed tomography (CT) or magnetic resonance imaging (MRI) of the brain and conventional electroencephalography (EEG). Laboratory investigations included: blood gases, complete blood picture, serum D-Dimer, Ferritin, C-reactive protein, renal and liver functions, and coagulation profile. ResultsOf the 19 patients, 3 had new onset seizures without underlying pathology (0.68 % out of the total 439 patients); 2 others (0.46 %) had previously diagnosed controlled epilepsy with breakthrough seizures. The majority of cases (14 patients, 3.19 %) had primary pathology that could explain the occurrence of seizures: 5 suffered a post COVID-19 stroke (3 ischemic and 2 hemorrhagic stroke); 6 patients had COVID-related encephalitis; 2 patients were old ischemic stroke patients; 1 patient had a brain tumor and developed seizures post COVID-19. Conclusionacute symptomatic seizure is not a rare complication of post COVID-19 infection. Both new onset seizures and seizures secondary to primary brain insult (post COVID encephalitis or recent stroke) were observed.

Generalized Rhythmic Delta Activity Frontally Predominant Differentiates Dementia with Lewy Bodies From Alzheimer's Disease and Parkinson's Disease Dementia: A Conventional Electroencephalography Visual Analysis.

Introduction. An easily accessible and inexpensive neurophysiological technique such as conventional electroencephalography may provide an accurate and generally applicable biomarker capable of differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and Parkinson's disease-associated dementia (PDD). Method. We carried out a retrospective visual analysis of resting-state electroencephalography (EEG) recording of 22 patients with a clinical diagnosis of 19 probable and 3 possible DLB, 22 patients with probable AD and 21 with PDD, matched for age, duration, and severity of cognitive impairment. Results. By using the grand total EEG scoring method, the total score and generalized rhythmic delta activity frontally predominant (GRDAfp) alone or, even better, coupled with a slowing of frequency of background activity (FBA) and its reduced reactivity differentiated DLB from AD at an individual level with an high accuracy similar to that obtained with quantitative EEG (qEEG). GRDAfp alone could also differentiate DLB from PDD with a similar level of diagnostic accuracy. AD differed from PDD only for a slowing of FBA. The duration and severity of cognitive impairment did not differ between DLB patients with and without GRDAfp, indicating that this abnormal EEG pattern should not be regarded as a disease progression marker. Conclusions. The findings of this investigation revalorize the role of conventional EEG in the diagnostic workup of degenerative dementias suggesting the potential inclusion of GRDAfp alone or better coupled with the slowing of FBA and its reduced reactivity, in the list of supportive diagnostic biomarkers of DLB.

Intracranial recording in patients with aphasia using nanomaterial-based flexible electronics: promises and challenges.

In recent years, researchers have studied how nanotechnology could enhance neuroimaging techniques. The application of nanomaterial-based flexible electronics has the potential to advance conventional intracranial electroencephalography (iEEG) by utilising brain-compatible soft nanomaterials. The resultant technique has significantly high spatial and temporal resolution, both of which enhance the localisation of brain functions and the mapping of dynamic language processing. This review presents findings on aphasia, an impairment in language and communication, and discusses how different brain imaging techniques, including positron emission tomography, magnetic resonance imaging, and iEEG, have advanced our understanding of the neural networks underlying language and reading processing. We then outline the strengths and weaknesses of iEEG in studying human cognition and the development of intracranial recordings that use brain-compatible flexible electrodes. We close by discussing the potential advantages and challenges of future investigations adopting nanomaterial-based flexible electronics for intracranial recording in patients with aphasia.

Bioamplificador de Alta Resolución para Electroencefalografía de Amplio Espectro

En este artículo se presenta el desarrollo de la etapa de hardware de un bioamplificador multicanal, de alta resolución y bajo ruido, diseñado para adquirir 8 canales de las señales de biopotencial emitidas por el cerebro humano. Básicamente es un electroencefalógrafo (EEG) de 8 derivaciones, pero diseñado para captar las señales con ancho de banda ampliado, en comparación con los EEG comunes. Este bioamplificador se realiza con el propósito de adquirir señales de sectores específicos del cerebro, para estudiar patrones de frecuencia superiores a los rangos comúnmente analizados en la electroencefalografía convencional. El producto obtenido está formado por 8 circuitos idénticos acoplados para captar todas las señales con respecto a un electrodo central. Cada circuito consiste en un amplificador diferencial con circuitos de realimentación del voltaje en modo común, que permiten mejorar la relación de rechazo al ruido. Se utilizan circuitos de acoplamiento AC de alta eficiencia para reducción del corrimiento de línea de base sin alterar la señal. Cada derivación tiene ganancia de 10000, para obtener niveles de amplitud en el orden de Volts a partir de las señales captadas en el orden de los micro-Volts. Los resultados preliminares muestran que el sistema funciona eficientemente dentro de un rango de 0.5 a 300 Hz, con niveles de error en la medición de amplitud inferiores al 0.14%.

KCNQ2 Encephalopathy Showing a Distinct Ictal Amplitude-Integrated Electroencephalographic Pattern

KCNQ2 mutations induce a neonatal-onset epileptic encephalopathy of widely varying severity, ranging from benign familial neonatal epilepsy to severe refractory epileptic encephalopathy. Refractory seizures with KCNQ2 mutations have a positive response to sodium-channel blockers. Recently, a distinctive ictal pattern has been reported during amplitude-integrated electroencephalographic (aEEG) monitoring in infants with KCNQ2 encephalopathy. Herein, we describe a case of KCNQ2 encephalopathy with this distinctive ictal aEEG pattern, which was confirmed using conventional electroencephalography (EEG). A 3-day-old female infant presented with neonatal seizures accompanied by cyanosis and desaturation. Her seizure semiology was tonic and focal clonic. Her ictal aEEG demonstrated a sudden rise in amplitude followed by a suppressed background pattern. This pattern was also confirmed on conventional EEG. Her seizures were refractory despite the administration of multiple conventional antiepileptic drugs. Finally, c.794C>T; p. (Ala265Val) mutation was observed in the KCNQ2 gene on genetic testing, and she was diagnosed with KCNQ2 encephalopathy. Identifying this distinctive ictal pattern on aEEG monitoring facilitates the early detection of KCNQ2 encephalopathy and timely targeted treatment in patients with refractory seizures. Keywords: KCNQ2; Mutation; Infant; Seizures; Epilepsy; Electroencephalography