Delta-alpha cross-frequency coupling before postoperative agitation delirium
Introduction
Postoperative delirium (POD) is a prevalent and severe surgical complication significantly correlated with elevated morbidity and mortality rates and prolonged hospitalization (1). Despite substantial research efforts, the precise pathophysiological mechanisms underlying POD remain elusive. Electroencephalography has emerged as a crucial instrument for investigating cerebral functions. Recent investigations have predominantly focused on the potential of intraoperative electroencephalogram (EEG)-guided anesthesia to mitigate the incidence of POD (2). However, these studies have not substantiated this hypothesis or addressed the early detection of EEG biomarkers for POD onset or its prognosis. Herein, we describe our experience of treating a patient with POD, focusing on the identification of delta-alpha (δ-to-α) neural coupling function (inter-channel) and rapid eye movement (REM) sleep δ-to-α cross-frequency coupling (CFC; intra-channel) within the comprehensive sleep cycle EEG before the manifestation of agitation during delirium. Through this report, we aim to underscore the potential of these EEG markers for the early identification and prediction of POD, which could enhance postoperative care and patient outcomes.
Case presentation
Patient information
A 53-year-old male patient with a documented history of hypertension was admitted to our institution for elective heart valve replacement surgery. Comprehensive preoperative assessments revealed no abnormalities, including standard laboratory investigations and neurological evaluations.
Surgical procedure
The patient underwent heart valve replacement under general anesthesia, which was maintained with 1,086 µg fentanyl, 182 µg dexmedetomidine, and 736 mg propofol. The total anesthesia and operative times were 334 and 293 minutes, respectively. The procedure was completed without complications, and the patient was subsequently transferred to the intensive care unit in a stable hemodynamic condition.
Postoperative course
The immediate postoperative period was unremarkable. However, on the second postoperative night, polysomnography monitoring captured spontaneous respiratory activity and 33.5 minutes of REM sleep, followed by an episode of severe confusion and agitation. POD was diagnosed using the Confusion Assessment Method for the Intensive Care Unit (3); the patient scored 26 points (delirium diagnosis threshold: >22 points). Continuous EEG monitoring was maintained throughout this period, allowing for a detailed observation of electrophysiological changes preceding the onset of agitation. The delirium was managed through a multimodal approach, including optimized analgesia, meticulous balancing of fluids and electrolytes, and environmental modifications. The patient’s condition gradually improved, and they were discharged without further complications.
EEG recording and analysis
Continuous electroencephalographic monitoring was initiated preoperatively and maintained throughout the perioperative period. EEG recordings were obtained using an 8-channel EEG system with electrodes positioned according to the international 10–20 system. All EEG data were digitally recorded and analyzed offline using MATLAB R2022b software (MathWorks, Natick, MA, USA).
Ethical considerations
All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee(s) and with the Helsinki Declaration and its subsequent amendments. Written informed consent was obtained from the patient for publication of this article and accompanying images. A copy of the written consent is available for review by the editorial office of this journal.
EEG analysis
REM sleep recognition
REM sleep was identified through characteristic EEG patterns, including low-voltage, mixed-frequency activity, REMs, and muscle atonia. The REM sleep duration was automatically scored using specialized polysomnography software and verified by an experienced EEG physician per the Rechtschaffen and Kales criteria (4).
CFC analyses
CFC analyses examined the interactions between different channels and frequency bands throughout the sleep cycle. Specifically, we focused on the phase-amplitude coupling (PAC) between the delta (1–4 Hz) and alpha (8–12 Hz) bands during REM sleep. The CFC was quantified using a PAC analysis (5). PAC is a fundamental type of CFC in neural oscillations. It describes the dynamic relationship between neural electrical activities at two different frequencies in the brain. Specifically, the ‘phase’ of low-frequency oscillations modulates the ‘amplitude’ of high-frequency oscillations, meaning the intensity variations of high-frequency oscillations depend on the phase position of low-frequency oscillations. This coupling is believed to play a crucial role in the brain’s ability to integrate information across regions and facilitate neural communication. The form of the δ-α coupling function exhibits a characteristic waveform. This waveform primarily varies along the δ-axis, while showing minimal variation along the α-axis. This form reveals that, in the dynamics of brainwave oscillations, the δ-α coupling function qα(φδ; φα) is predominantly dependent on δ dynamics, indicating a direct coupling originating from δ. Previous observations of coupling strength were limited to a single EEG channel. Herein, we extend this approach to two channels (prefrontal F3, F4 and central C3, C4) to simultaneously assess the spatial distribution of interactions, termed the distance coupling function. We present two sets of results: (I) coupling between δ waves and α brainwaves within the same channel location; (II) spatially distant δ waves and α brainwaves, i.e., combinations consisting of δ waves from one channel and α brainwaves from another channel.
Findings
The EEG data revealed significant alterations in the sleep stage distribution indicative of the patient’s delirium before the onset of postoperative agitation. We captured the complete sleep cycle and delirium spectrogram preceding postoperative agitation. The results of this case report are presented in a single large figure (Figure 1), as follows. The δ-to-α neural coupling function (intra-channel) in the C4 channel of the full sleep cycle EEG demonstrated characteristic waveforms (Figure 1A-1E). Observations of the coupling function waveform during the REM sleep phase show that α oscillations decelerate when the δ phase φδ ranges from 0 to π, and accelerate when φδ ranges from π to 2π.
This significant difference was corroborated by the δ-to-α coupling strength in the C3 and C4 channels of the sleep EEG (Figure S1).
The inter-channel spatial distance coupling function during the REM phase exhibited the highest coupling strength (Figure 1F-1I). The δ-α CFC enhancement between F3–4 or C3–4 channels suggests that POD may be related to attention and executive dysfunction in the frontal and temporal lobes.
The CFC analysis of the wakefulness and REM phases in subsequent sleep EEGs (phase-amplitude coupled frequency plot and topographic map) revealed pronounced δ-to-α low-frequency wave coupling during the REM phase (Figure 1J-1M). After comparing REM sleep with non-REM (NREM) sleep, it was found that delta-alpha CFC was enhanced in the EEG of pre-delirium REM sleep (Figure S2).
These findings suggest that EEGs can identify patients at a high risk of delirium. Moreover, postoperative EEG monitoring can predict the onset of acute POD in older patients, enabling timely intervention. Notably, this study represents the first documentation of real-time acute postoperative pre-delirium EEG changes through a neuronal CFC analysis.
Discussion
Significance of REM sleep δ-to-α CFC
Identifying increased δ-to-α CFC during REM sleep in pre-delirium episodes following postoperative agitation is a novel finding in sleep research. REM sleep, a crucial phase of the sleep cycle, has been extensively associated with various neurological and psychiatric disorders when accompanied by abnormal EEG patterns (6). The δ-to-α CFC is hypothesized to facilitate communication between disparate neural networks within the brain (7). Inter-channel coupling is primarily functional, and the corresponding anatomical locations of the channels provide new insights into the existence of anatomical connections. In the context of POD, heightened δ-to-α CFC may signify the disruption of neural connectivity and aberrant brain function, potentially underpinning the pathogenesis of delirium.
Implications for early detection and intervention
Existing literature has analyzed REM sleep in patients without delirium (7). On the first postoperative night, the patient was sedated with sedative drugs and received mechanical ventilation. Due to significant interference of the drugs with EEG signals, EEG data were not collected that night. There is currently no relevant review exploring PAC in lower frequency bands. Our study posits that electroencephalographic monitoring of δ-to-α CFC during REM sleep could be a promising biomarker for detecting POD early. We have included a segment of the EEG during REM sleep for presentation (Figure S3). The early identification of at-risk patients is pivotal for the timely deployment of preventive measures, including optimized anesthetic protocols, enhanced sleep quality interventions, and cognitive stimulation therapies (8). Future research endeavors should aim to corroborate these findings across broader patient cohorts and ascertain the optimal parameters (e.g., timing and frequency) for electroencephalographic monitoring.
Limitations
There are several limitations in this report. Primarily, the single-case nature of this study may restrict the generalizability of the findings to a broader population of patients experiencing POD. We analyzed δ-to-α CFC during REM sleep in non-delirium patients but identified no significant features (Figure S4). Additionally, the electroencephalographic analysis was confined to specific frequency bands of coupling, potentially overlooking other features that could have contributed to the development of delirium. Lastly, the mechanistic underpinnings of the observed δ-to-α CFC alterations and their associations with POD remain elusive, necessitating further in-depth investigation.
Conclusions
We present a case study of a patient who developed postoperative pre-agitated delirium and demonstrated increased REM sleep δ-to-α CFC on EEG prior to the onset of agitation. This observation offers novel insights into the electrophysiological mechanisms underlying POD and highlights the potential utility of EEG-based biomarkers for early detection and intervention. Further research is warranted to validate these findings in larger patient cohorts and to investigate the therapeutic potential of modulating abnormal EEG patterns to prevent and manage POD.
Acknowledgments
None.
Footnote
Funding: This work was supported by
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-596/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee(s) and with the Helsinki Declaration and its subsequent amendments. Written informed consent was obtained from the patient for publication of this article and accompanying images. A copy of the written consent is available for review by the editorial office of this journal.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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