Resolving and characterizing the incidence of millihertz EEG modulation in critically ill children.

OBJECTIVE: We analyze a slow electrographic pattern, Macroperiodic Oscillations (MOs), in the EEG from a cohort of young critical care patients (n = 43) with continuous EEG monitoring. We construct novel quantitative methods to quantify and understand MOs. METHODS: We applied a nonparametric bilevel spectral analysis to identify MOs, a millihertz (0.004-0.01 Hz) modulation of 5-15 Hz activity in two separate ICU patient cohorts (n = 195 total). We also developed a rigorous measure to quantify MOs strength and spatial expression, which was validated against surrogate noise data. RESULTS: Strong or spatially widespread MOs appear in both high clinical suspicion and a general ICU population. In the former, patients with strong or spatially widespread MOs tended to have worse clinical outcomes. Intracranial pressure and heart rate data from one patient provide insight into a potential broader physiological mechanism for MOs. CONCLUSIONS: We quantified millihertz EEG modulation (MOs) in cohorts of critically ill pediatric patients. We demonstrated high incidence in two patient populations. In a high suspicion cohort, MOs are associated with poor outcome, suggesting future potential as a diagnostic and prognostic aid. SIGNIFICANCE: These results support the existence of EEG dynamics across disparate time-scales and may provide insight into brain injury physiology in young children.

Macroperiodic Oscillations Are Associated With Seizures Following Acquired Brain Injury in Young Children.

PURPOSE: Seizures occur in 10% to 40% of critically ill children. We describe a phenomenon seen on color density spectral array but not raw EEG associated with seizures and acquired brain injury in pediatric patients. METHODS: We reviewed EEGs of 541 children admitted to an intensive care unit between October 2015 and August 2018. We identified 38 children (7%) with a periodic pattern on color density spectral array that oscillates every 2 to 5 minutes and was not apparent on the raw EEG tracing, termed macroperiodic oscillations (MOs). Internal validity measures and interrater agreement were assessed. We compared demographic and clinical data between those with and without MOs. RESULTS: Interrater reliability yielded a strong agreement for MOs identification (kappa: 0.778 [0.542-1.000]; P < 0.0001). There was a 76% overlap in the start and stop times of MOs among reviewers. All patients with MOs had seizures as opposed to 22.5% of the general intensive care unit monitoring population ( P < 0.0001). Macroperiodic oscillations occurred before or in the midst of recurrent seizures. Patients with MOs were younger (median of 8 vs. 208 days; P < 0.001), with indications for EEG monitoring more likely to be clinical seizures (42 vs. 16%; P < 0.001) or traumatic brain injury (16 vs. 5%, P < 0.01) and had fewer premorbid neurologic conditions (10.5 vs. 33%; P < 0.01). CONCLUSIONS: Macroperiodic oscillations are a slow periodic pattern occurring over a longer time scale than periodic discharges in pediatric intensive care unit patients. This pattern is associated with seizures in young patients with acquired brain injuries.

Poly (ε-Caprolactone)/Cellulose Nanofiber Blend Nanocomposites Containing ZrO2 Nanoparticles: A New Biocompatible Wound Dressing Bandage with Antimicrobial Activity.

Purpose: In the present study, the poly (ε-caprolactone)/cellulose nanofiber containing ZrO2 nanoparticles (PCL/CNF/ZrO2 ) nanocomposite was synthesized for wound dressing bandage with antimicrobial activity. Methods: PCL/CNF/ZrO2 nanocomposite was synthesized in three different zirconium dioxide amount (0.5, 1, 2%). Also the prepared nanocomposites were characterized by Infrared spectroscopy (FT-IR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). In addition, the morphology of the samples was observed by scanning electron microscopy (SEM). Results: Analysis of the XRD spectra showed a preserved structure for PCL semi-crystalline in nanocomposites and an increase in the concentrations of ZrO2 nanoparticles, the structure of nanocomposite was amorphous as well. The results of TGA, DTA, DSC showed thermal stability and strength properties for the nanocomposites which were more thermal stable and thermal integrate compared to PCL. The contact angles of the nanocomposites narrowed as the amount of ZrO2 in the structure increased. The evaluation of biological activities showed that the PCL/CNF/ZrO2 nanocomposite with various concentrations of ZrO2 nanoparticles exhibited moderate to good antimicrobial activity against all tested bacterial and fungal strains. Furthermore, cytocompatibility of the scaffolds was assessed by MTT assay and cell viability studies proved the non-toxic nature of the nanocomposites. Conclusion: The results show that the biodegradability of nanocomposite has advantages that can be used as wound dressing.

Intrinsic network reactivity differentiates levels of consciousness in comatose patients.

OBJECTIVE: We devise a data-driven framework to assess the level of consciousness in etiologically heterogeneous comatose patients using intrinsic dynamical changes of resting-state Electroencephalogram (EEG) signals. METHODS: EEG signals were collected from 54 comatose patients (GCS ⩽ 8) and 20 control patients (GCS > 8). We analyzed the EEG signals using a new technique, termed Intrinsic Network Reactivity Index (INRI), that aims to assess the overall lability of brain dynamics without the use of extrinsic stimulation. The proposed technique uses three sigma EEG events as a trigger for ensuing changes to the directional derivative of signals across the EEG montage. RESULTS: The INRI had a positive relationship with GCS and was significantly different between various levels of consciousness. In comparison, classical band-limited power analysis did not show any specific patterns correlated to GCS. CONCLUSIONS: These findings suggest that reaching low variance EEG activation patterns becomes progressively harder as the level of consciousness of patients deteriorate, and provide a quantitative index based on passive measurements that characterize this change. SIGNIFICANCE: Our results emphasize the role of intrinsic brain dynamics in assessing the level of consciousness in coma patients and the possibility of employing simple electrophysiological measures to recognize the severity of disorders of consciousness (DOC).

Adaptive Seizure Onset Detection Framework Using a Hybrid PCA-CSP Approach.

Epilepsy is one of the most common neurological disorders in the world. Prompt detection of seizure onset from electroencephalogram (EEG) signals can improve the treatment of epileptic patients. This paper presents a new adaptive patient-specific seizure onset detection framework that dynamically selects a feature from enhanced EEG signals to discriminate seizures from normal brain activity. The proposed framework employs principal component analysis and common spatial patterns to enhance the EEG signals and uses the extracted discriminative feature as an input for adaptive distance-based change point detector to identify the seizure onsets. Experimental results from the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset show the computational efficiency of the proposed method (analyzing EEG signals in a time window of 3 s within 0.1 s using a Core i7 PC) while providing comparable results to the existing methods in terms of average sensitivity, latency, and false detection rate. The proposed method is advantageous for real-time monitoring of epileptic patients and could be used to improve early diagnosis and treatment of patients suffering from recurrent seizures.

An improved synchronization likelihood method for quantifying neuronal synchrony.

Indirect quantification of the synchronization between two dynamical systems from measured experimental data has gained much attention in recent years, especially in the computational neuroscience community where the exact model of the neuronal dynamics is unknown. In this regard, one of the most promising methods for quantifying the interrelationship between nonlinear non-stationary systems is known as Synchronization Likelihood (SL), which is based on the likelihood of the auto-recurrence of embedding vectors (similar patterns) in multiple dynamical systems. However, synchronization likelihood method uses the Euclidean distance to determine the similarity of two patterns, which is known to be sensitive to outliers. In this study, we propose a discrete synchronization likelihood (DSL) method to overcome this limitation by using the Manhattan distance in the discrete domain (l1 norm on discretized signals) to identify the auto-recurrence of embedding vectors. The proposed method was tested using unidirectional and bidirectional identical/non-identical coupled Hénon Maps, a Watts-Strogatz small-world network with nonlinearly coupled nodes based on Kuramoto model and the real-world ADHD-200 fMRI benchmark dataset. According to the results, the proposed method shows comparable and in some cases better performance than the conventional SL method, especially when the underlying highly connected coupled dynamical system goes through subtle changes in the bivariate case or sudden shifts in the multivariate case.

Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury.

Recent studies suggest that disruptions in resting state functional connectivity - a measure of stationary statistical association between brain regions - can be used as an objective marker of brain injury. However, fewer characterizations have examined the disruption of intrinsic brain dynamics after brain injury. Here, we examine this issue using electroencephalographic (EEG) data from brain-injured patients, together with a control analysis wherein we quantify the effect of the injury on the ability of intrinsic event responses to traverse their respective state spaces. More specifically, the lability of intrinsically evoked brain activity was assessed by collapsing three sigma event responses in all channels of the obtained EEG signals into a low-dimensional space. The directional derivative of these responses was then used to assay the extent to which brain activity reaches low-variance subspaces. Our findings suggest that intrinsic dynamics extracted from resting state EEG signals can differentiate various levels of consciousness in severe cases of coma. More specifically the cost of moving from one state to another in the state-space trajectories of the underlying dynamics becomes lower as the level of consciousness of patients deteriorates.

Pattern discrimination of neuroelectric waveforms evoked by synchronized visuo-motor integration.

Today, there is a significant demand for fast, accurate, and automated methods for the discrimination of latent patterns in neuroelectric waveforms. One of the main challenges is the development of efficient feature extraction tools to utilize the rich spatio-temporal information inherent in large scale human electrocortical activity. In this paper, our aim is to isolate the most suitable feature extraction method for accurate classification of EEG data related to distinct modes of sensorimotor integration. Our results demonstrate that with some user-dependent input for feature space constraint, a simple classification framework can be developed to accurately distinguish between brain electrical activity patterns during two distinct conditions.