Temporal dynamics of the default mode network characterise meditation induced alterations in consciousness

Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network

Mapping the functional brain state of a world champion freediver in static dry apnea

peer reviewedVoluntary apnea showcases extreme human adaptability in trained individuals like professional free divers. We evaluated the psychological and physiological adaptation and the functional cerebral changes using electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) to 6.5 min of dry static apnea performed by a world champion free diver. Compared to resting state at baseline, breath holding was characterized by increased EEG power and functional connectivity in the alpha band, along with decreased delta band connectivity. fMRI connectivity was increased within the default mode network (DMN) and visual areas but decreased in pre- and postcentral cortices. While these changes occurred in regions overlapping with cerebral signatures of several meditation practices, they also display some unique features that suggest an altered somatosensory integration. As suggested by self-reports, these findings could reflect the ability of elite free divers to create a state of sensory dissociation when performing prolonged apnea

How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs – the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) – to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 minutes of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior ‘hot zone’, frontoparietal connections, and between-RSN connections. We estimate – for the first time – a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness

Identification and neuromodulation of brain states to promote recovery of consciousness

Experimental and clinical studies of consciousness identify brain states (i.e., transient, relevant features of the brain associated with the state of consciousness) in a non-systematic manner and largely independent from the research into the induction of state changes. In this narrative review with a focus on patients with a disorder of consciousness (DoC), we synthesize advances on the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anesthesia) and pathological (DoC) states of altered consciousness in human. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication in the brain is sparser, less efficient, and less complex. The results also highlight damaged resting state networks, in particular the default mode network, decreased connectivity in long-range connections and in the thalamocortical loops. Next, we show that therapeutic approaches to treat DoC, through pharmacology (e.g., amantadine, zolpidem), and (non-)invasive brain stimulation (e.g., transcranial current stimulation, deep brain stimulation) have shown some effectiveness to promote consciousness recovery. It seems that these deteriorated features of conscious brain states may improve in response to these neuromodulation approaches, yet, targeting often remains non-specific and does not always lead to (behavioral) improvements. Furthermore, in silico model-based approaches allow the development of personalized assessment of the effect of treatment on brain-wide dynamics. Although still in infancy, the fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when united, might propel the development of new and better targeted techniques for DoC. For example, brain states could be identified in a predictive setting, and the theoretical and empirical testing (i.e., in animals, under anesthesia and patients with a DoC) of neuromodulation techniques to promote consciousness could be investigated. This review further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multi-modal and multi-species approaches outlined throughout the review. This paper presents interactive figures, supported by the Live Paper initiative of the Human Brain Project, enabling the interaction with data and figures illustrating the concepts in the paper through EBRAINS (go to https://wiki.ebrains.eu/bin/view/Collabs/live-paper-states-altered-consciousness and get started with an EBRAINS account).NA is research fellow, OG is Research Associate, and SL is research director at FRS-FNRS. JA is postdoctoral fellow at the FWO. The study was further supported by the University and University Hospital of Liège, the BIAL Foundation, the Belgian National Funds for Scientific Research (FRS-FNRS), the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the FNRS PDR project (T.0134.21), the ERA-Net FLAG-ERA JTC2021 project ModelDXConsciousness (Human Brain Project Partnering Project), the fund Generet, the King Baudouin Foundation, the Télévie Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Public Utility Foundation 'Université Européenne du Travail', "Fondazione Europea di Ricerca Biomedica", the BIAL Foundation, the Mind Science Foundation, the European Commission, the Fondation Leon Fredericq, the Mind-Care foundation, the DOCMA project (EU-H2020-MSCA–RISE–778234), the National Natural Science Foundation of China (Joint Research Project 81471100) and the European Foundation of Biomedical Research FERB Onlus

Unifying turbulent dynamics framework distinguishes different brain states.

peer reviewedSignificant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto's turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states

Criticality of electrical safety for medical devices

peer reviewedEvery department that has "ownership" of biomedical devices must designate one or more persons to be responsible for device control. Everyone that uses a biomedical device in the course of their work is part of the safety process too. Here's how Devices cannot be put into service until completion of an initial safety test and they cannot stay in service if they are past due for preventive maintenance or inspection. The test procedures are designed to find out if equipment has deteriorated from its original design specification. In many cases this specification and the appropriate test method are set out in British Standards. It will be safe to test equipment to those standards by the test method specified in the relevant standard. A life case study was carried out on a very common and widely used syringe pumps malfunction and a major cause of patient/user electrical safety with analysis a pilot was run and huge important reason to avoid patient risk was observed. The objective of the Study is to find out the prevalence of health hazards in the local hospital of my area and awareness level in the health care professionals to establish best practices and process. © 2010 IEEE

Classification of EEG signals for epileptic seizure evaluation

peer reviewedFeature extraction and classification of biosignals is an important issue in development of disease diagnostic expert system (DDES). In this paper we propose a simple method for EEG classification based on Fourier features. Parameters like energy, entropy, power, and kurtosis were considered for discrimination of various categories of EEG signals. After calculating the above mentioned parameters of the discussed signals, we found that without going for rigorous time-frequency domain analysis, only frequency based analysis is well suitable to classify various EEG signals. © 2010 IEEE

Expert system design for classification of brain waves and epileptic-seizure detection

peer reviewedFeature extraction and classification of electro-physiological signals is an important issue in development of disease diagnostic expert system (DDES). Classification of electroencephalogram (EEGs) signals (normal and abnormal) is still a challenge for engineers and scientists. Various signal processing techniques have already been proposed to solve this puzzle of classification of non linear signals like EEG. In this work, attempts have been taken to distinguish between normal, epileptic and non-epileptic EEG waves by use of Support Vector Machine (SVM). EEG signals from (healthy subject with eye open condition, healthy subject with eye close condition, signal from hippocampus region and signal from opposite to epileptogenic region and signal with seizure) were considered for the analysis. The signals were processed by using wavelet-chaos techniques. The nonlinear dynamics of the original EEGs are quantified in the form of the correlation dimension (CD, representing system complexity) and the largest Lyapunov exponent (LLE, representing system chaoticity), Capacitive Dimension (CAD) which show the randomness nature of the signal. SVM classifier applied on the extracted feature vectors for the classification purpose. From the results, it was clearly found that the classification accuracy was significantly higher i.e. more than ninety percentage. Hence the techniques can be implemented to design knowledge based expert disease diagnostic system. © 2011 IEEE

Analysis of brain activity for motor task using simultaneous EEG - fMRI

peer reviewedIntegration of electroencephalogram (EEG) Signal and functional magnetic resonance imaging (fMRI) has opened a new avenue in Computational Neuro Cognitive Science studies as these two are complimentary methods for the analysis of brain activity. We are proposing a novel approach for the correlation between the EEG signal and fMRI to evaluate the blood oxygen level dependent (BOLD) signal changes and electrophysiological activity with high gain of spatio temporal resolution of the brain activity during a task. In this study, simultaneous EEG-fMRI was performed for hand motor task. sLORETA (Standardized low-resolution brain electromagnetic tomography) was reconstructed using segmented EEG for right-hand, left-hand movement and resting state. We estimated condition-specific effects by using statistical parametric maps (SPMs) with general linear model (GLM) approach .By correlating the sLORETA images of EEG and the fMRI images on MNI template during motor task we could demonstrate similar brain activity in both. However with the advantage of simultaneous EEG-fMRI signal acquired at same time points we had a better insight on temporo-spatial information of brain activity during a given task. This method has a huge utility value both in a clinical setting and cognitive studies for example in cryptogenic lesions on imaging with epileptic form discharges detected on EEG combined approach can explain the focal point of seizure and path of its spread to rest of the neuronal substrates on an anatomical image. © 2013 Springer

Large-scale brain network modelling using graph-theory approach in neuroscience

peer reviewedFrom decade, neuroscientist trying to understand the brain functional dynamics of healthy individuals and the patients suffering with neuro-psychiatric disorders. In this study, we propose a large-scale brain network modelling using graph theory approach to understand the brain dynamics of healthy individual's by exploring the ageing. We used resting-state functional magnetic resonance imaging (rsfMRI) data. We selected two groups of subject's 1) young healthy subjects (mean age: 23 ±6) and 2) old healthy subject (mean age: 40±5). The rsfMRI data pre-processed for ac-pc baseline correction, realignment, registration, segmentation, normalization and band pass filtering. The preprocessed data were parcellated using 'Dosenbach-160' atlas and connectivity matrix computed using Pearson correlation. Large-scale brain network computed by network segregation (i.e. clustering coefficient), integration (i.e. participation coefficient), efficiency and small-worldness. Individual nodal graph measures were computed through integrated nodal clustering coefficient and participation coefficient. Finally, statistical analysis were carried out using two-sample t-test with FDR correction. We found the older healthy individuals have lower clustering coefficient, small-worldness and higher participation coefficient. Our finding suggests graph theory connectivity measure is a potential technique to understand the neural dynamics of ageing, cognitive processes in healthy individual and may be a potential methods to study the alter brain functions in patients with neuro-psychiatric disorders