Monitoring anesthesia using simultaneous functional Near Infrared Spectroscopy and Electroencephalography

Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications

Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains-such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy-demonstrating the fNIRS-EEG system's potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine.

Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO2) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69.27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

Inter-brain entrainment (IBE) during interoception. A multimodal EEG-fNIRS coherence-based hyperscanning approach

This work examined the impact of interoceptive manipulation and the presence of a shared goal on inter-brain entrainment (IBE) during a motor synchronization task. A multimodal functional Near Infrared Spectroscopy - Electroencephalogram (fNIRS-EEG) system-based hyperscanning approach was applied to 13 dyads performing the motor synchrony task during an interoceptive (focus on the breath) and control condition. Additionally, two version of the motor task-one with and one without a clearly defined common goal-were presented to participants to emphasize the task's collaborative purpose. The multimodal approach was exploited to record the electrophysiological (EEG) cortical oscillation and hemodynamic (oxy-Hb and deoxy-Hb) levels. Results revealed significant correlations between EEG delta, theta, and alpha band and hemodynamic oxy-Hb in the left compared to right hemisphere for the interoceptive confronted with the control condition. This significant EEG/fNIRS IBE correlation was also found for delta and theta band whereas the task was presented with an explicit shared goal confronted with the no-social version. In addition to separate functional connectivity EEG and fNIRS analysis, this study proposed a novel analysis pipeline including statistical tests for examining the coherence between functional connectivity EEG-fNIRS signals within couples. Besides proposing methodological advancements on EEG-fNIRS signals hyperscanning analysis, this research demonstrated that, in dyads undertaking a motor synchronization task, both the interoceptive attention to respiration and an explicit joint intention activate left anterior regions.

The most fundamental and popular literature on functional near-infrared spectroscopy: a bibliometric analysis of the top 100 most cited articles

Background

Functional near infrared spectroscopy (fNIRS) has developed rapidly in recent years, and there are more and more studies on fNIRS. At present, there is no bibliometric analysis of the top 100 most cited articles on fNIRS research.

Objective

To identify the top 100 most cited articles on fNIRS and analyze those most fundamental and popular articles through bibliometric research methods.

Methods

The literature on fNIRS of web of science from 1990 to 2023 was searched and the top 100 most cited articles were identified by citations. Use the bibliometrix package in R studio and VOSviewer for data analysis and plotting to obtain the output characteristics and citation status of these 100 most cited articles, and analyze research trends in this field through keywords.

Results

A total of 9,424 articles were retrieved from web of science since 1990. The average citation number of the 100 articles was 457.4 (range from 260 to 1,366). Neuroimage published the most articles (n = 31). Villringer, A. from Leipzig University had the largest number of top 100 papers. Harvard University (n = 22) conducted most cited articles. The United States, Germany, Japan, and the United Kingdom had most cited articles, respectively. The most common keywords were near-infrared spectroscopy, activation, cerebral-blood-flow, brain, newborn-infants, oxygenation, cortex, fMRI, spectroscopy. The fund sources mostly came from National Institutes of Health Unitd States (NIH) and United States Department of Health Human Services (n = 28).

Conclusion

Neuroimage was the most popular journal. The top countries, institutions, and authors were the United States, Harvard University, and Villringer, A., respectively. Researchers and institutions from North America and Europe contributed the most. Near-infrared spectroscopy, activation, cerebral-blood-flow, brain, newborn-infants, oxygenation, cortex, fmri, spectroscopy, stimulation, blood-flow, light-propagation, infants, tissue comprise the future research directions and potential topic hotspots for fNIRS.

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.

Functional near-infrared spectroscopy guided mapping of frontal cortex, a novel modality for assessing emergence delirium in children: A prospective observational study

INTRODUCTION

Despite an 18%-30% prevalence, there is no consensus regarding pathogenesis of emergence delirium after anesthesia in children. Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging modality that relies on blood oxygen level-dependent response, translating to a mean increase in oxyhemoglobin and a decrease in deoxyhemoglobin. We aimed to correlate the emergence delirium in the postoperative period with the changes in the frontal cortex utilizing fNIRS reading primarily and also with blood glucose, serum electrolytes, and preoperative anxiety scores.

METHODS

A total of 145 ASA I and II children aged 2-5 years, undergoing ocular examination under anesthesia, were recruited by recording the modified Yale Preoperative Anxiety Score after acquiring the Institute Ethics Committee approval and written informed parental consent. Induction and maintenance were done with O2, N2O, and Sevoflurane. The emergence delirium was assessed using the PAED score in the postoperative period. The frontal cortex fNIRS recordings were taken throughout anesthesia.

RESULTS

A total of 59 children (40.7%) had emergence delirium. The ED+ group had a significant activation left superior frontal cortex (t = 2.26E+00; p = .02) and right middle frontal cortex (t = 2.27E+00; p = .02) during induction, significant depression in the left middle frontal (t = -2.22E+00; p = .02), left superior frontal and bilateral medial (t = -3.01E+00; p = .003), right superior frontal and bilateral medial (t = -2.44E+00; p = .015), bilateral medial and superior (t = -3.03E+00; p = .003), and right middle frontal cortex (t = -2.90E+00; p = .004) during the combined phase of maintenance, and significant activation in cortical activity in the left superior frontal cortex (t = 2.01E+00; p = .0047) during the emergence in comparison with the ED- group.

CONCLUSION

There is significant difference in the change in oxyhemoglobin concentration during induction, maintenance, and emergence in specific frontal brain regions between children with and without emergence delirium.

Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods

Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.

A Narrative Review Illustrating the Clinical Utility of Electroencephalogram-Guided Anesthesia Care in Children

The major therapeutic end points of general anesthesia include hypnosis, amnesia, and immobility. There is a complex relationship between general anesthesia, responsiveness, hemodynamic stability, and reaction to noxious stimuli. This complexity is compounded in pediatric anesthesia, where clinicians manage children from a wide range of ages, developmental stages, and body sizes, with their concomitant differences in physiology and pharmacology. This renders anesthetic requirements difficult to predict based solely on a child's age, body weight, and vital signs. Electroencephalogram (EEG) monitoring provides a window into children's brain states and may be useful in guiding clinical anesthesia management. However, many clinicians are unfamiliar with EEG monitoring in children. Young children's EEGs differ substantially from those of older children and adults, and there is a lack of evidence-based guidance on how and when to use the EEG for anesthesia care in children. This narrative review begins by summarizing what is known about EEG monitoring in pediatric anesthesia care. A key knowledge gap in the literature relates to a lack of practical information illustrating the utility of the EEG in clinical management. To address this gap, this narrative review illustrates how the EEG spectrogram can be used to visualize, in real time, brain responses to anesthetic drugs in relation to hemodynamic stability, surgical stimulation, and other interventions such as cardiopulmonary bypass. This review discusses anesthetic management principles in a variety of clinical scenarios, including infants, children with altered conscious levels, children with atypical neurodevelopment, children with hemodynamic instability, children undergoing total intravenous anesthesia, and those undergoing cardiopulmonary bypass. Each scenario is accompanied by practical illustrations of how the EEG can be visualized to help titrate anesthetic dosage to avoid undersedation or oversedation when patients experience hypotension or other physiological challenges, when surgical stimulation increases, and when a child's anesthetic requirements are otherwise less predictable. Overall, this review illustrates how well-established clinical management principles in children can be significantly complemented by the addition of EEG monitoring, thus enabling personalized anesthesia care to enhance patient safety and experience.

Dose-related adaptive reconstruction of DMN in isoflurane administration: a study in the rat

BACKGROUND

The anesthetic states are accompanied by functional alterations. However, the dose-related adaptive alterations in the higher-order network under anesthesia, e. g. default mode network (DMN), are poorly revealed.

METHODS

We implanted electrodes in brain regions of the rat DMN to acquire local field potentials to investigate the perturbations produced by anesthesia. Relative power spectral density, static functional connectivity (FC), fuzzy entropy of dynamic FC, and topological features were computed from the data.

RESULTS

The results showed that adaptive reconstruction was induced by isoflurane, exhibiting reduced static and stable long-range FC, and altered topological features. These reconstruction patterns were in a dose-related fashion.

CONCLUSION

These results might impart insights into the neural network mechanisms underlying anesthesia and suggest the potential of monitoring the depth of anesthesia based on the parameters of DMN.

Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia

General anesthesia is an indispensable procedure in clinical practice. Anesthetic drugs induce dramatic changes in neuronal activity and cerebral metabolism. However, the age-related changes in neurophysiology and hemodynamics during general anesthesia remain unclear. Therefore, the objective of this study was to explore the neurovascular coupling between neurophysiology and hemodynamics in children and adults during general anesthesia. We analyzed frontal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals recorded from children (6-12 years old, n = 17) and adults (18-60 years old, n = 25) during propofol-induced and sevoflurane-maintained general anesthesia. The neurovascular coupling was evaluated in wakefulness, maintenance of a surgical state of anesthesia (MOSSA), and recovery by using correlation, coherence and Granger-causality (GC) between the EEG indices [EEG power in different bands and permutation entropy (PE)], and hemodynamic responses the oxyhemoglobin (Δ[HbO]) and deoxy-hemoglobin (Δ[Hb]) from fNIRS in the frequency band in 0.01-0.1 Hz. The PE and Δ[Hb] performed well in distinguishing the anesthesia state (p > 0.001). The correlation between PE and Δ[Hb] was higher than those of other indices in the two age groups. The coherence significantly increased during MOSSA (p < 0.05) compared with wakefulness, and the coherences between theta, alpha and gamma, and hemodynamic activities of children are significantly stronger than that of adults' bands. The GC from neuronal activities to hemodynamic responses decreased during MOSSA, and can better distinguish anesthesia state in adults. Propofol-induced and sevoflurane-maintained combination exhibited age-dependent neuronal activities, hemodynamics, and neurovascular coupling, which suggests the need for separate rules for children's and adults' brain states monitoring during general anesthesia.

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient’s state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.

How to Study the Brain While Anesthetizing It?! A Scoping Review on Running Neuroanesthesiologic Studies and Trials That Include Neurosurgical Patients

This scoping review addresses the challenges of neuroanesthesiologic research: the population, the methods/treatment/exposure, and the outcome/results. These challenges are put into the context of a future research agenda for peri-/intraoperative anesthetic management, neurocritical care, and applied neurosciences. Finally, the opportunities of adaptive trial design in neuroanesthesiologic research are discussed.

Spatial complexity method for tracking brain development and degeneration using functional near-infrared spectroscopy

Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (Δ[HbO]) or two deoxy-hemoglobin (Δ[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE's performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDE_HbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.

How general anesthetics work: from the perspective of reorganized connections within the brain

General anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.