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Cortical Oscillations and Connectivity During Postoperative Recovery

Zierau, Mackenzie BSN*; Li, Duan PhD*,†; Lapointe, Andrew P. PhD; Ip, Ka I. MS§; McKinney, Amy M. MA*; Thompson, Aleda MS*; Puglia, Michael P. MD, PhD*; Vlisides, Phillip E. MD*,†

Author Information
Journal of Neurosurgical Anesthesiology: January 2021 - Volume 33 - Issue 1 - p 87-91
doi: 10.1097/ANA.0000000000000636

Abstract

Brain health is a growing concern for older surgical patients and perioperative clinicians. In fact, the American Society of Anesthesiologists recently launched the Brain Health Initiative, which aims to provide tools and resources for optimizing cognitive recovery in older surgical patients. Unfortunately, cognitive impairment in the immediate postoperative period is an everyday problem, occurring in 21% to 45% of high-risk patients.1,2 Severe emergence agitation, for example, can result in self-inflicted injury, injury to health care providers, additional use of health care resources, and poor cognitive and clinical trajectories after surgery.1–3 Delirium in the postanesthesia care unit (PACU) is associated with subsequent delirium on surgical wards and increased health care utilization.2,3 The underlying neurophysiology of such altered postoperative brain states is not well-understood, and pathophysiological knowledge gaps likely contribute to poor neurocognitive and clinical recovery patterns.

A fundamental step in understanding postoperative brain function is to identify cortical oscillatory patterns in relation to neurocognitive and clinical recovery. Two electroencephalographic (EEG) measures, posterior alpha power and frontal-parietal connectivity, have emerged as candidate biomarkers that may track with postoperative neurocognitive recovery. Parietal alpha rhythms have been correlated with brain network recovery after general anesthesia in healthy volunteers,4 and reduced posterior alpha power is associated with encephalopathy and delirium.5 The parietal region also includes network hubs that may play a vital role in states of consciousness.6 Frontal-parietal connectivity dynamically correlates with levels of consciousness perioperatively7 along with cognitive strength and flexibility.8 To study these neurophysiological patterns and their clinical significance, whole-scalp EEG recordings need to be pragmatically obtained from the PACU setting. As such, the objectives of this study were to (1) characterize cortical oscillatory and connectivity patterns in the immediate postoperative period, and (2) test whether parietal alpha power and frontal-parietal connectivity were associated with measures of PACU recovery. Specifically, this study tested the hypothesis that parietal alpha power and frontal-parietal connectivity were inversely associated with time until PACU Phase I discharge criteria were met.

MATERIALS AND METHODS

This was a secondary analysis of a prospective cohort study analyzing cortical oscillatory and connectivity patterns in adult surgical patients. This substudy was approved by the University of Michigan Medical School Institutional Review Board (HUM00138196), and all procedures were conducted at Michigan Medicine, University Hospital (Ann Arbor, MI). Written informed consent was obtained from all participants, who were previously enrolled in a parent observational cohort study (HUM00113764). However, this previous study was a distinct investigation that characterized intraoperative connectivity patterns.9

Study Population

Participants were recruited from the preoperative evaluation clinic or on the day of surgery at the surgical check-in desk (March 2017 to August 2017). Eligible patients were identified by performing chart reviews, and a research assistant then approached eligible patients before surgery. Inclusion criteria included adult surgical patients (≥18 y old) requiring general anesthesia for noncardiac, nonintracranial, and nonmajor vascular surgery (ie, operating above the inguinal ligament). Exclusion criteria were the following: emergency surgery, surgery involving the head and neck, patients known to have difficulty with intubation, non-English speaking, or enrolled in a conflicting research study.

Anesthetic and Perioperative Procedures

As previously described,9 anesthetic and perioperative management were left to the discretion of anesthesia teams and perioperative clinicians. No specific protocol was implemented for this observational study, as the goal was to elicit neurophysiological patterns—and clinical associations—in a real-world, pragmatic surgical setting. General anesthesia was induced with propofol for all patients, and maintenance techniques varied and included any of the following: isoflurane, sevoflurane, desflurane, nitrous oxide, and propofol infusions.9

EEG Data Acquisition and Analysis

Full EEG methodologic details are available in the Supplementary Digital Content. EEG data were acquired using a wireless 16-channel cap (Cognionics, Inc., San Diego, CA) that covered the scalp (Fig., Supplemental Digital Content 1, http://links.lww.com/JNA/A194). Data were reference-linked to the left mastoid, grounded at the right mastoid, and sampled at 500 Hz. After emergence from anesthesia, a 2-minute resting state EEG epoch was recorded in eyes-closed conditions after initial PACU admission and assessment. Data were derived from 30 to 60 seconds of clean neurophysiological signaling during this time frame.

EEG data were cleaned and analyzed as previously described9 for generating spectral power results and estimating functional connectivity. For EEG data abstraction, at least 1 usable channel was required for each area of interest (frontal: F5, F6, Fz; parietal: P5, P6, Pz) for a given participant. Data preprocessing then occurred in a stepwise manner by visual inspection—and removal of bad channels—followed by detrending and low-pass filtering at 55 Hz. Independent component analysis was then conducted to remove cardiac artifact, eye movement, muscle movement, and other artifacts by extended-Infomax algorithm in EEGLAB Toolbox.9,10

Outcome Measures

The primary outcome was length of time (minutes) until PACU Phase I discharge criteria were met. Time was calculated using nursing documentation of the first PACU assessment until Phase I discharge criteria were met. Phase I discharge was chosen given the direct relevance to physiological and neurocognitive recovery after surgery.11 EEG measures included absolute posterior (parietal) alpha power, relative alpha power (in relation to all other bandwidths), and frontal-parietal connectivity. University of Michigan Sedation Scale data were also collected (0=awake and alert; 1=minimally sedated, appropriate response to verbal conversation; 2=moderately sedated, aroused with tactile stimulation; 3=deeply sedated, aroused only with significant stimulation; 4=unarousable) via routine, clinical screening. There were no clinically recorded cases of PACU delirium, so this measure was not available for analysis. Additional, confounding variables may impact both length of PACU stay and levels of arousal. Data for these explanatory mediators were also collected, which included the following: Charlson Comorbidity Index,12 total opioid consumption (oral morphine equivalents, mg)13 during surgery and immediately before PACU EEG data collection, body mass index,14 length of surgery (minutes),3,14 presence of postoperative nausea/vomiting,15 and type of surgery (eg, urologic, orthopedic, plastic, surgical oncology, minimally invasive surgery, and neurosurgery).3,14 A chart review was performed by the research team to obtain these data, and the Charlson Comorbidity Index was calculated for each patient using an online calculator (www.mdcalc.com/charlson-comorbidity-index-cci).

Statistical Analysis

Descriptive statistics were presented for all data as frequencies with percentages for categorical variables and continuous variables with either means (±SDs) or medians (interquartile range), as appropriate. Continuous data were assessed for normality using the Kolmogorov-Smirnov test. Unadjusted correlations between PACU Phase I time to discharge readiness and primary covariates of interest (relative parietal alpha and frontal-parietal connectivity) were computed using a Pearson or Spearman correlation, as appropriate. Bivariate linear regression models were also constructed with the above EEG measures along with potential confounders listed previously. Finally, 2 multivariable linear regression models were constructed to analyze the association between time to PACU Phase I discharge readiness (dependent variable) and each EEG measure, relative parietal alpha power, and frontal-parietal connectivity; any additional covariates deemed significant via bivariate linear regression were included to adjust for confounding. For the secondary University of Michigan Sedation Scale analysis, simple ordinal regression models were constructed with sedation scale score (0 to 4) as the dependent variable and EEG measures as the independent variables. To adjust for 2 EEG outcome comparisons, a P-value of 0.025 was considered statistically significant (P=0.05/2). All statistical analyses were performed in SPSS 24 (IBM, Armonk, NY).

RESULTS

Demographic and surgical characteristics are presented in Table 1. Mean participant age was 50 (±17), and cases from 6 surgical subspecialties were included. PACU EEG oscillatory and connectivity patterns were then obtained 24.7 (±11.6) minutes after PACU admission (Fig. 1).

TABLE 1 - Patient Characteristics (N=53)
Demographics
 Age (y) 50 (17)
 Female patients, n (%) 24 (45)
 BMI 28 (4.3)
 Comorbidities
   ASA status
   1 7 (13.2)
   2 33 (62.3)
   3 13 (24.5)
  Charlson Comorbidity Index 2 [0-4]
Procedure characteristics
 Surgical type
  Urologic 26 (49.1)
  Orthopedic 10 (18.9)
  Plastics 6 (11.3)
  Surgical oncology 6 (11.3)
  Minimally invasive surgery 2 (3.8)
  Neurosurgery 3 (5.7)
 Morphine equivalents 20 [10-23]
 Length of case (minutes) 85 [51-116]
Postoperative characteristics
 PONV 10 (18.9)
 Posterior alpha (relative, %) 32 (14)
 Posterior alpha (mean, dB) 2.7 (4.2)
 Frontal-parietal wPLI 0.2 (0.1)
 Time to Phase I PACU discharge readiness (minutes) 67 [45-105]
 Initial University of Michigan Sedation Scale Score (n) 1 [0-3]
Of note, EEG data excluded from 8 (15.1%) participants due to bad or incomplete recordings, and PACU discharge criteria time was only recorded in the medical chart for 42/53 (79%) participants. SDs are reported in parentheses unless otherwise indicated, and interquartile ranges are reported in square brackets.
ASA indicates American Society of Anesthesiologists; BMI, body mass index; dB, decibels; PACU, postanesthesia care unit; PONV, postoperative nausea and vomiting; wPLI, weighted phase lag index.

FIGURE 1
FIGURE 1:
Spectral and connectivity data presented for all participants with available EEG data (n=45). A, Shift in spectral power evident in the alpha band (7 to 15 Hz), particularly in the parietal channels. Red line indicates the group-level (median across subjects) normalized power (relative to total power) spectrum in parietal channels, and the black line indicates the group-level normalized power spectrum throughout frontal channels. Shaded area represents interquartile range for each EEG region. B, Functional connectivity, estimated using weight phase lag index. Median (average) connectivity presented for frontal-parietal channels (red line) and prefrontal-frontal channels (black line). As with the spectral data, shaded regions represent the interquartile range for each channel region. EEG indicates electroencephalographic; PACU, postanesthesia care unit; wPLI, weighted phase lag index.

Postoperative Cortical Oscillations and Connectivity

Oscillatory and connectivity patterns are presented in Figure 1. The normalized spectrogram is characterized by a shift to alpha (7 to 15 Hz) in the parietal channels (Fig. 1A), with mean relative parietal alpha power 32% (±14) compared with total bandwidth (0 to 40 Hz) power. Similarly, connectivity was highest in frontal-parietal channels in the alpha band (Fig. 1B) (weighted phase lag index [wPLI], mean 0.2 [±0.1] wPLI). Considerable variability was appreciated for both absolute power and connectivity values, with mean ranges of 17 dB and 0.34 dB (wPLI), respectively.

PACU Time Course and Clinical Recovery

Relative parietal alpha power and frontal-parietal connectivity were then tested for associations with length of time until PACU Phase I discharge criteria were met. As illustrated in Table 2, there were no statistically significant bivariable associations between relative parietal alpha power (%)—or frontal-parietal alpha connectivity—and time (minutes) until Phase I discharge readiness criteria were achieved. Similarly, these associations remained non-significant for relative parietal alpha power (% alpha; −0.25, 95% conficence interval [CI], −1.41 to 0.90; P=0.657) and frontal-parietal connectivity (wPLI; −82, 95% CI, −237 to 73; P=0.287) after adjusting for age, oral morphine equivalents, surgical length, and type of surgery. In addition, neither relative parietal alpha (−0.03; 95% CI, −0.07 to 0.01; P=0.206) nor frontal-parietal connectivity (−4.2; 95% CI, −11 to 2.6; P=0.226) were associated with initial University of Michigan Sedation Scale score after PACU admission.

TABLE 2 - Bivariable Associations With Time Until PACU Phase I Discharge Criteria Met
Bivariable Correlation P Unadjusted Estimate (β) 95% CI P
Posterior alpha (relative) −0.22 0.192 −0.57 −1.74, 0.60 0.330
Frontal-parietal wPLI −0.29 0.086 −101.48 −261.18, 58.22 0.206
Age (y) −0.40 0.008 −0.90 −1.69, −0.12 0.024
Morphine equivalents 0.39 0.011 1.26 0.30, 2.22 0.011
BMI −0.21 0.176 −0.59 −4.00, 2.81 0.728
Length of case 0.35 0.022 0.36 0.09, 0.63 0.009
ASA class
 (Ref) 1
 2 9.14 −30.49, 48.77 0.651
 3 −8.15 −52.05, 35.76 0.716
PONV 22.26 −11.22, 55.74 0.187
Charlson comorbidity index 0.80 −4.81, 6.40 0.775
Urologic −26.12 −51.44, −0.79 0.044
Orthopedic 9.23 −23.36, 41.82 0.570
Plastics 37.21 −7.09, 81.50 0.097
Surgical Oncology −11.58 −57.26, 34.1 0.611
Minimally Invasive Surgery 86.26 29.20, 143.31 0.004
Neurosurgery −22.29 −85.11, 40.52 0.478
Spearman’s rank-order was used for bivariable correlation.
ASA indicates American Society of Anesthesiologists; BMI, body mass index; PACU, postanesthesia care unit; wPLI, weight phase lag index.

DISCUSSION

The neurodynamic recovery of the brain after a major perturbation, such as general anesthesia, is of clinical and scientific interest. In this study investigating clinically relevant surrogate measures of brain recovery, we found no correlation with the time of meeting PACU Phase I discharge criteria or clinically reported sedation scores at PACU admission with relative parietal alpha power or frontal-parietal connectivity, measured via wPLI.

Previous studies have demonstrated a correlation between the return of posterior alpha power with recovery from general anesthetics.4,16 The lack of correlation in our study may be a result of using PACU discharge criteria as a marker of clinical recovery as the target endpoint, as opposed to a more direct measure (ie, neurocognitive function testing). The most utilized scoring rubric for assessment of PACU discharge readiness, and the one used for patients in this study, is the modified Aldrete score.11 This scoring system assesses multiple recovery domains, including the following: activity, respiration, circulation, consciousness, and oxygen saturation. These indirect factors may have contributed to the lack of association with candidate EEG measures. In addition, unmeasured variables, such as nursing personnel, may have contributed to PACU discharge readiness times.17 When we investigated associations with the University of Michigan Sedation Scale, a more direct measure of anesthetic-mediated changes in states of consciousness, we did not find a correlation with relative parietal alpha power or frontal-parietal connectivity. Sedation scale scoring is also limited by a subjectivity, and further studies of more direct neurocognitive testing may be insightful.

Despite being a prominent brain rhythm, the role of the alpha band frequency is still incompletely understood. Multiple lines of evidence highlight both an inhibitory and a facilitatory role in cortical excitability and neuronal processing, supporting an influential role in attention processing.18 Although clinically realistic, the PACU environment is noisy and busy, and patients are often in close proximity to one another. These factors may have provided spurious artifact during recording sessions. It is possible that more direct measures of attention processing are needed to delineate an effect. Furthermore, previous studies demonstrating an effect of these measures were pharmacologically controlled, whereas anesthetic administration in our study was at the discretion of the anesthesia provider—possibly implicating a molecular mechanism for the difference.

Frontal-parietal connectivity has been demonstrated to reflect anesthetic-mediated changes in states of consciousness19; however, recent data have demonstrated that the brain may exist in multiple dynamic states during the unconscious period, and a single measure of functional connectivity may be unlikely to predict depth of anesthesia or brain recovery.9,20 Consistent with this, we found no association between PACU frontal-parietal wPLI and discharge time or sedation scale scores.

This study has numerous limitations. First, it is a small sample size with a heterogeneous population of patients where the anesthetic was not standardized. Future experiments controlling for these factors may be considered; however, a strength and goal of the current study design was to define neurodynamic surrogates of brain recovery that are correlated with relevant, clinically useful metrics that are independent of anesthetic agent or adjuvant. Our study was limited to parietal alpha power and a measure of functional connectivity; future studies should consider additional measures. EEG-derived measures also serve as a surrogate assessment of functional connectivity among brain regions. Lastly, preoperative, baseline EEG measures were not collected as part of the original study design. Thus, ratios comparing preoperative and postoperative values were not available for analysis. Such ratios are likely of importance, as the coefficient of variation (CV) for relative parietal alpha power increased from 44% (PACU) to 82% when calculating the preinduction-to-PACU ratio CV. Similarly, the CV for frontal-parietal connectivity increased from 45% (PACU) to 75% for preinduction-to-PACU ratios. Thus, a single postoperative EEG measurement might be insufficient for correlation with clinical outcomes; rather, individual preoperative ratios may be required given this increased variability.

In conclusion, in a pragmatic study investigating clinically relevant postoperative recovery endpoints, we found no association with surrogate measures of brain neurodynamics. These data contribute to the overall impetus of developing anesthetic-invariant and generalizable markers of brain recovery.

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Keywords:

cognitive dysfunction; delayed emergence from anesthesia; delirium; electroencephalography; postanesthesia nursing

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