Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia (original) (raw)
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Dynamic Cortical Connectivity during General Anesthesia in Healthy Volunteers
Anesthesiology
Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Recent studies of anesthetic-induced unconsciousness in healthy volunteers have focused on functional brain connectivity patterns, but the protocols rarely parallel the depth and duration of surgical anesthesia. Furthermore, it is unknown whether there is a single functional connectivity pattern that correlates with general anesthesia for the duration of prolonged anesthetic exposure. Methods The authors analyzed electroencephalographic data in 30 healthy participants who underwent induction of anesthesia with propofol followed by 3 h of isoflurane anesthesia at age-adjusted 1.3 minimum alveolar concentration. Functional connectivity was assessed by frequency-resolved weighted phase lag index between frontal and parietal channels and between prefrontal and frontal channels, which were classified into a discrete set of states through k-means cluster analysis. Temporal dynamics...
Anesthesia & Analgesia, 2017
BACKGROUND: To reduce head movement during resting state functional magnetic resonance imaging, post-coma patients with disorders of consciousness (DOC) are frequently sedated with propofol. However, little is known about the effects of this sedation on the brain connectivity patterns in the damaged brain essential for differential diagnosis. In this study, we aimed to assess these effects. METHODS: Using resting state functional magnetic resonance imaging 3T data obtained over several years of scanning patients for diagnostic and research purposes, we employed a seed-based approach to examine resting state connectivity in higher-order (default mode, bilateral external control, and salience) and lower-order (auditory, sensorimotor, and visual) resting state networks and connectivity with the thalamus, in 20 healthy unsedated controls, 8 unsedated patients with DOC, and 8 patients with DOC sedated with propofol. The DOC groups were matched for age at onset, etiology, time spent in DO...
Intrinsic brain activity is characterized by the presence of highly structured networks of correlated fluctuations between different regions of the brain. Such networks encompass different functions, whose properties are known to be modulated by the ongoing global brain state and are altered in several neurobiological disorders. In the present study, we induced a deep state of anesthesia in rats by means of a ketamine/medetomidine peri-toneal injection, and analyzed the time course of the correlation between the brain activity in different areas while anesthesia spontaneously decreased over time. We compared results separately obtained from fMRI and local field potentials (LFPs) under the same anesthesia protocol, finding that while most profound phases of anesthesia can be described by overall sparse connectivity, stereotypical activity and poor functional integration, during lighter states different frequency-specific functional networks emerge, endowing the gradual restoration of structured large-scale activity seen during rest. Noteworthy, our in vivo results show that those areas belonging to the same functional network (the default-mode) exhibited sustained correlated oscillations around 10 Hz throughout the protocol, suggesting the presence of a specific functional backbone that is preserved even during deeper phases of anesthesia. Finally, the overall pattern of results obtained from both imaging and in vivo-recordings suggests that the progressive emergence from deep anesthesia is reflected by a corresponding gradual increase of organized correlated oscillations across the cortex.
Neuronal Synchrony during Anesthesia: A Thalamocortical Model
Biophysical Journal, 2008
There is growing evidence in favour of the temporal-coding hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations and, in particular, that θ (3.5-7.5 Hz) and δ (0.5 <3.5 Hz) oscillations facilitate information coding. The θ and δ rhythms are shown to be involved in various sleep stages, and during anaesthesia, and they undergo changes with the depth of anaesthesia. We introduce a thalamocortical model of interacting neuronal ensembles to describe phase relationships between θ and δ oscillations, especially during deep and light anaesthesia. Asymmetric and long range interactions among the thalamocortical neuronal oscillators are taken into account. The model results are compared with the experimental observations of Musizza et al. J. Physiol. (London) 2007 580:315-326. The δ and θ activities are found to be separately generated and are governed by the thalamus and cortex respectively. Changes in the degree of intra-ensemble and interensemble synchrony imply that the neuronal ensembles inhibit information coding during deep anaesthesia and facilitate it during light anaesthesia.
Dynamic local connectivity uncovers altered brain synchrony during propofol sedation
Human consciousness is considered a result of the synchronous " humming " of multiple dynamic networks. We performed a dynamic functional connectivity analysis using resting state functional magnetic resonance imaging (rsfMRI) in 14 patients before and during a propofol infusion to characterize the sedation-induced alterations in consciousness. A sliding 36-second window was used to derive 59 time points of whole brain integrated local connectivity measurements. Significant changes in the connectivity strength (Z Corr) at various time points were used to measure the connectivity fluctuations during awake and sedated states. Compared with the awake state, sedation was associated with reduced cortical connectivity fluctuations in several areas connected to the default mode network and around the perirolandic cortex with a significantly decreased correlation of connectivity between their anatomical homologues. In addition, sedation was associated with increased connectivity fluctuations in the frequency range of 0.027 to 0.063 Hz in several deep nuclear regions, including the cerebellum, thalamus, basal ganglia and insula. These findings advance our understanding of sedation-induced altered consciousness by visualizing the altered dynamics in several cortical and subcortical regions and support the concept of defining consciousness as a dynamic and integrated network. Human consciousness is an enigma, and states of reversible loss of consciousness are useful channels to unravel this mystery. Understanding the neurobiological basis of sedation has, thus, captured the interest of many scholars, and studies using resting state fMRI (rsfMRI) have provided important but contradictory evidence of increased 1–3 , decreased 4–6 or heterogenous connectivity 1, 7. The discrepancy in the results could be partially due to the varying analysis techniques, and the majority of studies have used hypothesis driven techniques, such as region of interest (ROI) and seed to voxel analyses 2, 4, 6, 8 , that focus on a default mode network (DMN) and a sensorimotor network; some studies have used data driven global (independent component analysis (ICA)) 7 , local (integrated local connectivity (ILC)) 5 and intrinsic connectivity contrast (ICC) 1 connectivity measures. The levels of sedation are also known to alter functional connectivity, and lower sedation levels (Observer Assessment of Alertness/Sedation scores (OAAS) 3 and above) are linked to an increased somatosensory cortical connec-tivity 2, 3, 7 , while deeper levels of sedation (OAAS 3 and below) are linked to a decrease cortical connectivity 1, 5, 9. Several multistage studies exploring awake, sedated, unconscious and recovery states have captured the dynamic changes in connectivity during the loss and recovery of consciousness 10, 11. One of the major limitations of these connectivity methods is that the measurement is averaged over the data acquisition duration and, hence, does not consider the fluctuations or variability in the system. According to a large scale dynamic model of consciousness 12 , human consciousness is not static and exists at the verge of instability between a multi-stable attractor state and an unstable spontaneous state 13, 14. This state of instability is considered essential as it assumes that the system is periodically monitoring and is maximally sensitive to any external stimulus 15. Experimental investigations of consciousness also reveal a dynamic state of connectivity between several networks 13, 16 , particularly those involving the parietal and frontal cortices 16. Dynamic connectivity (DC) rsfMRI analysis 17 is a novel technique that uses sliding temporal windows to yield continuous series of connectivity snap shots that can be used to characterize the temporal evolution of networks.
BOLD temporal variability differentiates wakefulness from anesthesia-induced unconsciousness
Journal of neurophysiology, 2017
Even though a number of findings, based on information content or information integration, are shown to define neural underpinnings characteristic of a conscious experience, the neurophysiological mechanism of consciousness is still poorly understood. Here, we investigated the brain activity and functional connectivity changes that occur in the isoflurane anesthetized unconscious state in contrast to the awake-state in rats (awake and/or anesthetized n=68 rats). We examined nine information measures previously shown to distinguish between conscious states; BOLD variability, functional connectivity strength, modularity, weighted modularity, efficiency, clustering coefficient, small-worldness, and spatial and temporal Lempel-Ziv complexity measure. We also identified modular membership, seed based network connectivity, and absolute and normalized power spectrums to assess the integrity of the BOLD functional networks between awake and anesthesia. fMRI BOLD variability and related abso...
Functional connectivity under six anesthesia protocols and the awake condition in rat brain
NeuroImage
Resting-state functional magnetic resonance imaging (rsfMRI) is a translational imaging method with great potential in several neurobiologic applications. Most preclinical rsfMRI studies are performed in anesthetized animals, but the confounding effects of anesthesia on the measured functional connectivity (FC) are poorly understood. Therefore, we measured FC under six commonly used anesthesia protocols and compared the findings with data obtained from awake rats. The results demonstrated that each anesthesia protocol uniquely modulated FC. Connectivity patterns obtained under propofol and urethane anesthesia were most similar to that observed in awake rats. FC patterns in the α-chloralose and isoflurane-medetomidine combination groups had moderate to good correspondence with that in the awake group. The FC patterns in the isoflurane and medetomidine groups differed most from that in the awake rats. These results can be directly exploited in rsfMRI study designs to improve the data quality, comparability, and interpretation.
Anesthesiology
Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. Methods We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a m...
Current Pharmaceutical Design, 2013
Persistent vegetative state (PVS) Resting-state electroencephalography (EEG) Low-resolution brain electromagnetic source tomography (LORETA) Human cerebral cortex a b s t r a c t Objective: High power of pre-stimulus cortical alpha rhythms (about 8-12 Hz) underlies conscious perception in normal subjects. Here we tested the hypothesis that these rhythms are abnormal in persistent vegetative state (PVS) patients, who are awake but not aware of self and environment. Methods: Clinical and resting-state, eyes-closed electroencephalographic (EEG) data were taken from a clinical archive. These data were recorded in 50 PVS subjects (level of cognitive functioning -LCF score: I-II) and in 30 cognitively normal subjects. Rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Cortical sources were estimated by low-resolution electromagnetic tomography (LORETA). Based on LCF score at 3-months follow-up, PVS patients were retrospectively divided into three groups: 30 subjects who did not recover (NON-REC patients; follow-up LCF: I-II), 8 subjects classified as minimally conscious state patients (MCS patients; follow-up LCF: III-IV), and 12 subjects who recovered (REC patients; follow-up LCF: V-VIII). Results: Occipital source power of alpha 1 and alpha 2 was high in normal subjects, low in REC patients, and practically null in NON-REC patients. A Cox regression analysis showed that the power of alpha source predicted the rate of the follow up recovery, namely the higher its power, the higher the chance to recover consciousness. Furthermore, the MCS patients showed intermediate values of occipital alpha source power between REC and NON-REC patients. Conclusions: These results suggest that cortical sources of alpha rhythms are related to the chance of recovery at a 3-months follow-up in patients in persistent vegetative state. Significance: Cortical sources of resting alpha rhythms might predict recovery in PVS patients.
Frontiers in Computational Neuroscience, 2016
Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.