High-energy brain dynamics during anesthesia-induced unconsciousness.
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ABSTRACT:
SUBMITTER: Riehl JR
PROVIDER: S-EPMC6063715 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:General anesthesia (GA) is a reversible manipulation of consciousness whose mechanism is mysterious at the level of neural networks leaving space for several competing hypotheses. We recorded electrocorticography (ECoG) signals in patients who underwent intracranial monitoring during awake surgery for the treatment of cerebral tumors in functional areas of the brain. Therefore, we recorded the transition from unconsciousness to consciousness directly on the brain surface. Using frequency resolved interferometry; we studied the intermediate ECoG frequencies (4-40?Hz). In the theoretical study, we used a computational Jansen and Rit neuron model to simulate recovery of consciousness (ROC). During ROC, we found that f increased by a factor equal to 1.62?±?0.09, and ?f varied by the same factor (1.61?±?0.09) suggesting the existence of a scaling factor. We accelerated the time course of an unconscious EEG trace by an approximate factor 1.6 and we showed that the resulting EEG trace match the conscious state. Using the theoretical model, we successfully reproduced this behavior. We show that the recovery of consciousness corresponds to a transition in the frequency (f, ?f) space, which is exactly reproduced by a simple time rescaling. These findings may perhaps be applied to other altered consciousness states.
| S-EPMC5902625 | biostudies-literature
Project description:In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.
| S-EPMC8101756 | biostudies-literature
Project description:During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.
| S-EPMC8357089 | biostudies-literature
Project description:BackgroundGeneral anesthesia (GA) provides an invaluable experimental tool to understand the essential neural mechanisms underlying consciousness. Previous neuroimaging studies have shown the functional integration and segregation of brain functional networks during anesthetic-induced alteration of consciousness. However, the organization pattern of hubs in functional brain networks remains unclear. Moreover, comparisons with the well-characterized physiological unconsciousness can help us understand the neural mechanisms of anesthetic-induced unconsciousness.MethodsResting-state functional magnetic resonance imaging was performed during wakefulness, mild propofol-induced sedation (m-PIS), and deep PIS (d-PIS) with clinical unconsciousness on 8 healthy volunteers and wakefulness and natural sleep on 9 age- and sex-matched healthy volunteers. Large-scale functional brain networks of each volunteer were constructed based on 160 regions of interest. Then, rich-club organizations in brain functional networks and nodal properties (nodal strength and efficiency) were assessed and analyzed among the different states and groups.ResultsRich-clubs in the functional brain networks were reorganized during alteration of consciousness induced by propofol. Firstly, rich-club nodes were switched from the posterior cingulate cortex (PCC), angular gyrus, and anterior and middle insula to the inferior parietal lobule (IPL), inferior parietal sulcus (IPS), and cerebellum. When sedation was deepened to unconsciousness, the rich-club nodes were switched to the occipital and angular gyrus. These results suggest that the rich-club nodes were switched among the high-order cognitive function networks (default mode network [DMN] and fronto-parietal network [FPN]), sensory networks (occipital network [ON]), and cerebellum network (CN) from consciousness (wakefulness) to propofol-induced unconsciousness. At the same time, compared with wakefulness, local connections were switched to rich-club connections during propofol-induced unconsciousness, suggesting a strengthening of the overall information commutation of networks. Nodal efficiency of the anterior and middle insula and ventral frontal cortex was significantly decreased. Additionally, from wakefulness to natural sleep, a similar pattern of rich-club reorganization with propofol-induced unconsciousness was observed: rich-club nodes were switched from the DMN (including precuneus and PCC) to the sensorimotor network (SMN, including part of the frontal and temporal gyrus). Compared with natural sleep, nodal efficiency of the insula, frontal gyrus, PCC, and cerebellum significantly decreased during propofol-induced unconsciousness.ConclusionsOur study demonstrated that the rich-club reorganization in functional brain networks is characterized by switching of rich-club nodes between the high-order cognitive and sensory and motor networks during propofol-induced alteration of consciousness and natural sleep. These findings will help understand the common neurological mechanism of pharmacological and physiological unconsciousness.
| S-EPMC6997627 | biostudies-literature
Project description:How the brain recovers from general anaesthesia is poorly understood. Neurocognitive problems during anaesthesia recovery are associated with an increase in morbidity and mortality in patients. We studied intracortical neuronal dynamics during transitions from propofol-induced unconsciousness into consciousness by directly recording local field potentials and single neuron activity in a functionally and anatomically interconnecting somatosensory (S1, S2) and ventral premotor (PMv) network in primates. Macaque monkeys were trained for a behavioural task designed to determine trial-by-trial alertness and neuronal response to tactile and auditory stimulation. We found that neuronal dynamics were dissociated between S1 and higher-order PMv prior to return of consciousness. The return of consciousness was distinguishable by a distinctive return of interregionally coherent beta oscillations and disruption of the slow-delta oscillations. Clustering analysis demonstrated that these state transitions between wakefulness and unconsciousness were rapid and unstable. In contrast, return of pre-anaesthetic task performance was observed with a gradual increase in the coherent beta oscillations. We also found that recovery end points significantly varied intra-individually across sessions, as compared to a rather consistent loss of consciousness time. Recovery of single neuron multisensory responses appeared to be associated with the time of full performance recovery rather than the length of recovery time. Similar to loss of consciousness, return of consciousness was identified with an abrupt shift of dynamics and the regions were dissociated temporarily during the transition. However, the actual dynamics change during return of consciousness is not simply an inverse of loss of consciousness, suggesting a unique process.
| S-EPMC7868001 | biostudies-literature
Project description:BackgroundThere is limited understanding of cortical neurochemistry and cortical connectivity during ketamine anaesthesia. We conducted a systematic study to investigate the effects of ketamine on cortical acetylcholine (ACh) and electroencephalographic coherence.MethodsMale Sprague-Dawley rats (n=11) were implanted with electrodes to record electroencephalogram (EEG) from frontal, parietal, and occipital cortices, and with a microdialysis guide cannula for simultaneous measurement of ACh concentrations in prefrontal cortex before, during, and after ketamine anaesthesia. Coherence and power spectral density computed from the EEG, and ACh concentrations, were compared between conscious and unconscious states. Loss of righting reflex was used as a surrogate for unconsciousness.ResultsKetamine-induced unconsciousness was associated with a global reduction of power (P=0.02) in higher gamma bandwidths (>65 Hz), a global reduction of coherence (P≤0.01) across a broad frequency range (0.5-250 Hz), and a significant increase in ACh concentrations (P=0.01) in the prefrontal cortex. Compared with the unconscious state, recovery of righting reflex was marked by a further increase in ACh concentrations (P=0.0007), global increases in power in theta (4-10 Hz; P=0.03) and low gamma frequencies (25-55 Hz; P=0.0001), and increase in power (P≤0.01) and coherence (P≤0.002) in higher gamma frequencies (65-250 Hz). Acetylcholine concentrations, coherence, and spectral properties returned to baseline levels after a prolonged recovery period.ConclusionsKetamine-induced unconsciousness is characterized by suppression of high-frequency gamma activity and a breakdown of cortical coherence, despite increased cholinergic tone in the cortex.
| S-EPMC4436930 | biostudies-literature
Project description:The temporal trajectories and neural mechanisms of recovery of cognitive function after a major perturbation of consciousness is of both clinical and neuroscientific interest. The purpose of the present study was to investigate network-level changes in functional brain connectivity associated with the recovery and return of six cognitive functions after general anesthesia. High-density electroencephalograms (EEG) were recorded from healthy volunteers undergoing a clinically relevant anesthesia protocol (propofol induction and isoflurane maintenance), and age-matched healthy controls. A battery of cognitive tests (motor praxis, visual object learning test, fractal-2-back, abstract matching, psychomotor vigilance test, digital symbol substitution test) was administered at baseline, upon recovery of consciousness (ROC), and at half-hour intervals up to 3 h following ROC. EEG networks were derived using the strength of functional connectivity measured through the weighted phase lag index (wPLI). A partial least squares (PLS) analysis was conducted to assess changes in these networks: (1) between anesthesia and control groups; (2) during the 3-h recovery from anesthesia; and (3) for each cognitive test during recovery from anesthesia. Networks were maximally perturbed upon ROC but returned to baseline 30-60 min following ROC, despite deficits in cognitive performance that persisted up to 3 h following ROC. Additionally, during recovery from anesthesia, cognitive tests conducted at the same time-point activated distinct and dissociable functional connectivity networks across all frequency bands. The results highlight that the return of cognitive function after anesthetic-induced unconsciousness is task-specific, with unique behavioral and brain network trajectories of recovery.
| S-EPMC8477048 | biostudies-literature
Project description:General anesthesia (GA) is a reversible drug-induced state of altered arousal required for more than 60,000 surgical procedures each day in the United States alone. Sedation and unconsciousness under GA are associated with stereotyped electrophysiological oscillations that are thought to reflect profound disruptions of activity in neuronal circuits that mediate awareness and cognition. Computational models make specific predictions about the role of the cortex and thalamus in these oscillations. In this paper, we provide in vivo evidence in rats that alpha oscillations (10-15 Hz) induced by the commonly used anesthetic drug propofol are synchronized between the thalamus and the medial prefrontal cortex. We also show that at deep levels of unconsciousness where movement ceases, coherent thalamocortical delta oscillations (1-5 Hz) develop, distinct from concurrent slow oscillations (0.1-1 Hz). The structure of these oscillations in both cortex and thalamus closely parallel those observed in the human electroencephalogram during propofol-induced unconsciousness. During emergence from GA, this synchronized activity dissipates in a sequence different from that observed during loss of consciousness. A possible explanation is that recovery from anesthesia-induced unconsciousness follows a "boot-up" sequence actively driven by ascending arousal centers. The involvement of medial prefrontal cortex suggests that when these oscillations (alpha, delta, slow) are observed in humans, self-awareness and internal consciousness would be impaired if not abolished. These studies advance our understanding of anesthesia-induced unconsciousness and altered arousal and further establish principled neurophysiological markers of these states.
| S-EPMC5558998 | biostudies-literature
Project description:Individual differences in the structure of parietal and prefrontal cortex predict the stability of bistable visual perception. However, the mechanisms linking such individual differences in brain structures to behaviour remain elusive. Here we demonstrate a systematic relationship between the dynamics of brain activity, cortical structure and behaviour underpinning bistable perception. Using fMRI in humans, we find that the activity dynamics during bistable perception are well described as fluctuating between three spatially distributed energy minimums: visual-area-dominant, frontal-area-dominant and intermediate states. Transitions between these energy minimums predicted behaviour, with participants whose brain activity tend to reflect the visual-area-dominant state exhibiting more stable perception and those whose activity transits to frontal-area-dominant states reporting more frequent perceptual switches. Critically, these brain activity dynamics are correlated with individual differences in grey matter volume of the corresponding brain areas. Thus, individual differences in the large-scale dynamics of brain activity link focal brain structure with bistable perception.
| S-EPMC4174295 | biostudies-literature
Project description:Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15-30Hz) amplitude over frontocentral channels and with a suppression of alpha (8-13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness.
| S-EPMC6545227 | biostudies-literature