Directional connectivity in the EEG is able to discriminate wakefulness from NREM sleep

EEG Signal Processing Techniques and Applications

Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity [...].

Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

A framework to quantify controlled directed interactions in network physiology applied to cognitive function assessment

The complex nature of physiological systems where multiple organs interact to form a network is complicated by direct and indirect interactions, with varying strength and direction of influence. This study proposes a novel framework which quantifies directional and pairwise couplings, while controlling for the effect of indirect interactions. Simulation results confirm the superiority of this framework in uncovering directional primary links compared to previous published methods. In a practical application of cognitive attention and alertness tasks, the method was used to assess controlled directed interactions between the cardiac, respiratory and brain activities (prefrontal cortex). It revealed increased interactions during the alertness task between brain wave activity on the left side of the brain with heart rate and respiration compared to resting phases. During the attention task, an increased number of right brain wave interactions involving respiration was also observed compared to rest, in addition to left brain wave activity with heart rate. The proposed framework potentially assesses directional interactions in complex network physiology and may detect cognitive dysfunctions associated with altered network physiology.

Validation of a new approach for distinguishing anesthetized from awake state in patients using directed transfer function applied to raw EEG

We test whether a measure based on the directed transfer function (DTF) calculated from short segments of electroencephalography (EEG) time-series can be used to monitor the state of the patients also during sevoflurane anesthesia as it can for patients undergoing propofol anesthesia. We collected and analyzed 25-channel EEG from 7 patients (3 females, ages 41–56 years) undergoing surgical anesthesia with sevoflurane, and quantified the sensor space directed connectivity for every 1-s epoch using DTF. The resulting connectivity parameters were compared to corresponding parameters from our previous study (n = 8, patients anesthetized with propofol and remifentanil, but otherwise using a similar protocol). Statistical comparisons between and within studies were done using permutation statistics, a data driven algorithm based on the DTF-parameters was employed to classify the epochs as coming from awake or anesthetized state. According to results of the permutation tests, DTF-parameter topographies were significantly different between the awake and anesthesia state at the group level. However, the topographies were not significantly different when comparing results computed from sevoflurane and propofol data, neither in the awake nor in anesthetized state. Optimizing the algorithm for simultaneously having high sensitivity and specificity in classification yielded an accuracy of 95.1% (SE = 0.96%), with sensitivity of 98.4% (SE = 0.80%) and specificity of 94.8% (SE = 0.10%). These findings indicate that the DTF changes in a similar manner when humans undergo general anesthesia caused by two distinct anesthetic agents with different molecular mechanisms of action.

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This article proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.

Measuring depth of anaesthesia using changes in directional connectivity: a comparison with auditory middle latency response and estimated bispectral index during propofol anaesthesia

General anaesthesia is associated with changes in connectivity between different regions of the brain, the assessment of which has the potential to provide a novel marker of anaesthetic effect. We propose an index that quantifies the strength and direction of information flow in electroencephalographic signals collected across the scalp, assess its performance in discriminating 'wakefulness' from 'anaesthesia', and compare it with estimated bispectral index and the auditory middle latency response. We used a step-wise slow induction of anaesthesia in 10 patients to assess graded changes in electroencephalographic directional connectivity at propofol effect-site concentrations of 2 μg.ml^-1 , 3 μg.ml^-1 and 4 μg.ml^-1 . For each stable effect-site concentration, connectivity was estimated from multichannel electroencephalograms using directed coherence, together with middle latency response and estimated bispectral index. We used a linear support vector machine classifier to compare the performance of the different electroencephalographic features in discriminating wakefulness from anaesthesia. We found a significant reduction in the strength of long-range connectivity (interelectrode distance > 10 cm) (p < 0.008), and a reversal of information flow from markedly postero-frontal to fronto-posterior (p < 0.006) between wakefulness and a propofol effect-site concentration of 2 μg.ml^-1 . This then remained relatively constant as effect-site concentration increased, consistent with a step change in directed coherence with anaesthesia. This contrasted with the gradual change with increasing anaesthetic dose observed for estimated bispectral index and middle latency response. Directed coherence performed best in discriminating wakefulness from anaesthesia with an accuracy of 95%, indicating the potential of this new method (on its own or combined with others) for monitoring adequacy of anaesthesia.