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

Lioi, Giulia; Bell, Steven L.; Smith, Dana; Simpson, David M.

doi:10.1088/1361-6579/aa81b5

Cited by 12 publications

(11 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These conflicting findings are likely to result from the use of different estimators of connectivity that may quantify diverse effects. Partial directed coherence has also been investigated during propofol anaesthesia , with similar results to ours, although, in our previous study on sleep, we found that Partial directed coherence was less sensitive to changes in the level of consciousness than directed coherence .…”

Section: Discussionsupporting

confidence: 84%

“…Only continuous and artefact‐free epochs were analysed. Directional connectivity was estimated from the multivariate model of multi‐channel EEG time series using directed coherence as previously described . Due to its straightforward interpretation in terms of amount of spectral power transferred from one source channel to another , we used the squared modulus of directed coherence to estimate functional connectivity.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

View full text Add to dashboard Cite

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 stepwise slow induction of anaesthesia in 10 patients to assess graded changes in electroencephalographic directional connectivity at propofol effect-site concentrations of 2 lg.ml À1 , 3 lg.ml À1 and 4 lg.ml À1 . 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 lg.ml À1 . 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.

show abstract

Section: Discussionsupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…, ,…, are the model terms selected from the candidate term set, written as Eq. (4). Matrix can be decomposed as = × where…”

Section: Nonlinear Functional Connectivity Estimationmentioning

confidence: 99%

“…Empirical interpretation of the EEG is largely based on recognising abnormal frequencies in specific biological states (e.g. wakefulness versus sleep [4]), the spatio-temporal and morphological (e.g. sharp waves, spikes etc.)…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Durongbhan

et al. 2020

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

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 paper 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

show abstract

“…In addition to the DTF, several other measures of connectivity have been suggested as a relevant markers for changes in the state or level of consciousness, including measures of transfer entropy (Ku et al 2011; Lee et al 2013), directed coherence (Lioi et al 2017; Maksimow et al 2014), and Granger causality measures (Barrett et al 2012; Seth, Barrett, and Barnett 2011). Specifically, measures of the connectivity between frontal and parietal regions were found to differ between conscious and unconscious states, both for humans undergoing various forms of anesthesia (Ku et al 2011; Lee et al 2013) or falling asleep (Lioi et al 2017), and for patients suffering from DOC (Boly et al 2012). For example, in a study using directed coherence – a measure closely related to DTF – the directionality of frontal-parietal functional connectivity covaried with NREM sleep stages and wakefulness (Lioi et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Validation of a new approach for distinguishing anesthetized from awake state in patients using Directed Transfer Function applied to raw EEG

Juel

Romundstad

Storm

et al. 2020

Preprint

View full text Add to dashboard Cite

Aim: In a previous study, we found that the state of wakefulness in patients undergoing general anesthesia with propofol can effectively be monitored with high temporal resolution using an automatic measure of connectivity based on the Directed Transfer Function (DTF) calculated from short segments of electroencephalography (EEG) time-series. The study described here was designed to test whether the same measure can be used to monitor the state of the patients also during sevoflurane anesthesia. Methods: Twenty-five channel EEG recordings were collected from 8 patients undergoing surgical anesthesia with sevoflurane. The EEG data were segmented into one second epochs and labeled as awake or anesthetized in accordance with the clinician's judgement, and the sensor space directed connectivity was quantified for every epoch using the DTF. The resulting DTF derived connectivity parameters were compared to corresponding parameters from our previous study using permutation statistics. A data driven classification algorithm was then employed to objectively classify the individual 1-second epochs as coming from awake or anesthetized state, using a leave-one-out cross-validation approach. The classifications were made for every epoch using the median DTF parameters across the five preceding 1-second EEG epochs. Results: The DTF derived connectivity parameters showed a significant difference between the awake and sevoflurane-induced general anesthesia at the group level (p<0.05). In contrast, the DTF parameters were not significantly different when comparing sevoflurane and propofol data neither in the awake nor in anesthetized state (p>0.05 for both comparisons). The classification algorithm reached a maximum accuracy of 96.8% (SE=0.63%). Optimizing the algorithm for simultaneously having high sensitivity and specificity in classification reduced the accuracy to 95.1% (SE=0.96%), with sensitivity of 98.4% (SE=0.80%) and specificity of 94.8% (SE=0.10%). Conclusion: 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. This seems to support the idea that brain connectivity is related to the level of consciousness in humans, although further studies are needed to clarify whether our results may be contaminated by confounding factors.

show abstract

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

Abstract: Directed coherence provides a promising new means of assessing level of consciousness, firmly based on current physiological understanding of consciousness.

Cited by 12 publications

References 66 publications

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

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

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

Validation of a new approach for distinguishing anesthetized from awake state in patients using Directed Transfer Function applied to raw EEG

Contact Info

Product

Resources

About