Brain activity complexity has a nonlinear relation to the level of propofol sedation

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Neural markers of reduced arousal and consciousness in mild cognitive impairment

OBJECTIVES

People with Alzheimer's Disease (AD) experience changes in their level and content of consciousness, but there is little research on biomarkers of consciousness in pre-clinical AD and Mild Cognitive Impairment (MCI). This study investigated whether levels of consciousness are decreased in people with MCI.

METHODS

A multi-site site magnetoencephalography (MEG) dataset, BIOFIND, comprising 83 people with MCI and 83 age matched controls, was analysed. Arousal (and drowsiness) was assessed by computing the theta-alpha ratio (TAR). The Lempel-Ziv algorithm (LZ) was used to quantify the information content of brain activity, with higher LZ values indicating greater complexity and potentially a higher level of consciousness.

RESULTS

LZ was lower in the MCI group versus controls, indicating a reduced level of consciousness in MCI. TAR was higher in the MCI group versus controls, indicating a reduced level of arousal (i.e. increased drowsiness) in MCI. LZ was also found to be correlated with mini-mental state examination (MMSE) scores, suggesting an association between cognitive impairment and level of consciousness in people with MCI.

CONCLUSIONS

A decline in consciousness and arousal can be seen in MCI. As cognitive impairment worsens, measured by MMSE scores, levels of consciousness and arousal decrease. These findings highlight how monitoring consciousness using biomarkers could help understand and manage impairments found at the preclinical stages of AD. Further research is needed to explore markers of consciousness between people who progress from MCI to dementia and those who do not, and in people with moderate and severe AD, to promote person-centred care.

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms

OBJECTIVE

This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI).

METHODS

A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score.

RESULTS

The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance.

CONCLUSIONS

This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.

Permutation Entropy Does Not Track the Electroencephalogram-Related Manifestations of Paradoxical Excitation During Propofol-Induced Loss of Responsiveness: Results From a Prospective Observational Cohort Study

BACKGROUND

During the anesthetic-induced loss of responsiveness (LOR), a "paradoxical excitation" with activation of β-frequencies in the electroencephalogram (EEG) can be observed. Thus, spectral parameters-as widely used in commercial anesthesia monitoring devices-may mistakenly indicate that patients are awake when they are actually losing responsiveness. Nonlinear time-domain parameters such as permutation entropy (PeEn) may analyze additional EEG information and appropriately reflect the change in cognitive state during the transition. Determining which parameters correctly track the level of anesthesia is essential for designing monitoring algorithms but may also give valuable insight regarding the signal characteristics during state transitions.

METHODS

EEG data from 60 patients who underwent general anesthesia were extracted and analyzed around LOR. We derived the following information from the power spectrum: (i) spectral band power, (ii) the spectral edge frequency as well as 2 parameters known to be incorporated in monitoring systems, (iii) beta ratio, and (iv) spectral entropy. We also calculated (v) PeEn as a time-domain parameter. We used Friedman's test and Bonferroni correction to track how the parameters change over time and the area under the receiver operating curve to separate the power spectra between time points.

RESULTS

Within our patient collective, we observed a "paradoxical excitation" around the time of LOR as indicated by increasing beta-band power. Spectral edge frequency and spectral entropy values increased from 19.78 [10.25-34.18] Hz to 25.39 [22.46-30.27] Hz (P = .0122) and from 0.61 [0.54-0.75] to 0.77 [0.64-0.81] (P < .0001), respectively, before LOR, indicating a (paradoxically) higher level of high-frequency activity. PeEn and beta ratio values decrease from 0.78 [0.77-0.82] to 0.76 [0.73-0.81] (P < .0001) and from -0.74 [-1.14 to -0.09] to -2.58 [-2.83 to -1.77] (P < .0001), respectively, better reflecting the state transition into anesthesia.

CONCLUSIONS

PeEn and beta ratio seem suitable parameters to monitor the state transition during anesthesia induction. The decreasing PeEn values suggest a reduction of signal complexity and information content, which may very well describe the clinical situation at LOR. The beta ratio mainly focuses on the loss of power in the gamma-band. PeEn, in particular, may present a single parameter capable of tracking the LOR transition without being affected by paradoxical excitation.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective.General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.Approach.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Main results.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p< 0.001), PFC increased (p< 0.001) and PCMI decreased (p< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p< 0.001) and nodal efficiency (NE) (p< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (p< 0.01) and modularity (Q) (p< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/LrandQwere not significantly different from the preoperative resting state in sensor space.Significance.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

Complexity and 1/f slope jointly reflect brain states

Characterization of brain states is essential for understanding its functioning in the absence of external stimuli. Brain states differ on their balance between excitation and inhibition, and on the diversity of their activity patterns. These can be respectively indexed by 1/f slope and Lempel-Ziv complexity (LZc). However, whether and how these two brain state properties relate remain elusive. Here we analyzed the relation between 1/f slope and LZc with two in-silico approaches and in both rat EEG and monkey ECoG data. We contrasted resting state with propofol anesthesia, which directly modulates the excitation-inhibition balance. We found convergent results among simulated and empirical data, showing a strong, inverse and non trivial monotonic relation between 1/f slope and complexity, consistent at both ECoG and EEG scales. We hypothesize that differentially entropic regimes could underlie the link between the excitation-inhibition balance and the vastness of the repertoire of brain systems.

EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia

The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15-45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth.

Linear thinking does not reflect the newer 21st-century anesthesia concepts. A narrative review

The brain constitutes a good example of a chaotic, nonlinear biological system where large neuronal networks operate chaotically with random connectivity. This critical state is significantly affected by the anesthetic loss of consciousness induced by drugs whose pharmacological behavior has been classically based on linear kinetics and dynamics. Recent developments in pharmacology and brain monitoring during anesthesia suggest a different view that we tried to explore in this article. The concepts of effect-site for hypnotic drugs modeling a maximum effect, electroencephalographic dynamics during induction, maintenance, and recovery from anesthesia are discussed, integrated into this alternative view, and how it may be applied in daily clinical practice.

Clinical features and power spectral entropy of electroencephalography in Wilson's disease with dystonia

OBJECTIVE

To study the clinical features and power spectral entropy (PSE) of electroencephalography signals in Wilson's disease (WD) patients with dystonia.

METHODS

Several scale evaluations were performed to assess the clinical features of WD patients. Demographic information and electroencephalography signals were obtained in all subjects.

RESULTS

34 WD patients with dystonia were recruited in the case group and 24 patients without dystonia were recruited in the control group. 20 healthy individuals were included in the healthy control group. The mean body mass index (BMI) in the case group was significantly lower than that in the controls (p < .05). The case group had significantly higher SAS, SDS, and Bucco-Facial-Apraxia Assessment scores (p < .05). Total BADS scores in the case group were lower than those in the control group (p < .01). Note that 94.11% of the case group presented with dysarthria and 70.59% of them suffered from dysphagia. Dysphagia was mainly related to the oral preparatory stage and oral stage. Mean power spectral entropy (PSE) values in the case group were significantly different (p < .05) from those in the control group and the healthy group across the different tasks.

CONCLUSIONS

The patients with dystonia were usually accompanied with low BMI, anxiety, depression, apraxia, executive dysfunction, dysarthria and dysphagia. The cortical activities of the WD patients with dystonia seemed to be more chaotic during the eyes-closed and reading tasks but lower during the swallowing stages than those in the control group.

The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction

Background

Improving anesthesia administration for elderly population is of particular importance because they undergo considerably more surgical procedures and are at the most risk of suffering from anesthesia-related complications. Intraoperative brain monitors electroencephalogram (EEG) have proved useful in the general population, however, in elderly subjects this is contentious. Probably because these monitors do not account for the natural differences in EEG signals between young and older patients. In this study we attempted to systematically characterize the age-dependence of different EEG measures of anesthesia hypnosis.

Methods

We recorded EEG from 30 patients with a wide age range (19–99 years old) and analyzed four different proposed indexes of depth of hypnosis before, during and after loss of behavioral response due to slow propofol infusion during anesthetic induction. We analyzed Bispectral Index (BIS), Alpha Power and two entropy-related EEG measures, Lempel-Ziv complexity (LZc), and permutation entropy (PE) using mixed-effect analysis of variances (ANOVAs). We evaluated their possible age biases and their trajectories during propofol induction.

Results

All measures were dependent on anesthesia stages. BIS, LZc, and PE presented lower values at increasing anesthetic dosage. Inversely, Alpha Power increased with increasing propofol at low doses, however this relation was reversed at greater effect-site propofol concentrations. Significant group differences between elderly patients (>65 years) and young patients were observed for BIS, Alpha Power, and LZc, but not for PE.

Conclusion

BIS, Alpha Power, and LZc show important age-related biases during slow propofol induction. These should be considered when interpreting and designing EEG monitors for clinical settings. Interestingly, PE did not present significant age differences, which makes it a promising candidate as an age-independent measure of hypnotic depth to be used in future monitor development.

Evaluation of Anesthetic Specific EEG Dynamics during State Transitions between Loss and Return of Responsiveness

Purpose: electroencephalographic (EEG) information is used to monitor the level of cortical depression of a patient undergoing surgical intervention under general anesthesia. The dynamic state transitions into and out of anesthetic-induced loss and return of responsiveness (LOR, ROR) present a possibility to evaluate the dynamics of the EEG induced by different substances. We evaluated changes in the EEG power spectrum during anesthesia emergence for three different anesthetic regimens. We also assessed the possible impact of these changes on processed EEG parameters such as the permutation entropy (PeEn) and the cerebral state index (CSI). Methods: we analyzed the EEG from 45 patients, equally assigned to three groups. All patients were induced with propofol and the groups differed by the maintenance anesthetic regimen, i.e., sevoflurane, isoflurane, or propofol. We evaluated the EEG and parameter dynamics during LOR and ROR. For the emergence period, we focused on possible differences in the EEG dynamics in the different groups. Results: depending on the substance, the EEG emergence patterns showed significant differences that led to a substance-specific early activation of higher frequencies as indicated by the “wake” CSI values that occurred minutes before ROR in the inhalational anesthetic groups. Conclusion: our results highlight substance-specific differences in the emergence from anesthesia that can influence the EEG-based monitoring that probably have to be considered in order to improve neuromonitoring during general anesthesia.