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Perioperative Medicine| November 2021
Neurophysiologic Complexity in Children Increases with Developmental Age and Is Reduced by General Anesthesia
This article has a Visual Abstract
Michael P. Puglia, II, M.D., Ph.D.;
Michael P. Puglia, II, M.D., Ph.D.
From the Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan; the Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan
Duan Li, Ph.D.;
Duan Li, Ph.D.
Aleda M. Leis, M.S.;
Aleda M. Leis, M.S.
From the Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan
Elizabeth S. Jewell, M.S.;
Elizabeth S. Jewell, M.S.
Chelsea M. Kaplan, Ph.D.;
Chelsea M. Kaplan, Ph.D.
Megan Therrian, B.S.;
Megan Therrian, B.S.
Minkyung Kim, Ph.D.;
Minkyung Kim, Ph.D.
UnCheol Lee, Ph.D.;
UnCheol Lee, Ph.D.
George A. Mashour, M.D., Ph.D.;
George A. Mashour, M.D., Ph.D.
From the Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan; the Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan; the Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan
Phillip E. Vlisides, M.D.
This article is featured in "This Month in Anesthesiology," page A1.
Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal's Web site (www.anesthesiology.org).
Submitted for publication February 1, 2021. Accepted for publication July 14, 2021. Published online first on September 7, 2021.
Address correspondence to Dr. Puglia: 4-911 Mott Hospital, University of Michigan, 1540 E. Hospital Dr., SPC 4245, Ann Arbor, Michigan 48109-4245. mpuglia@med.umich.edu. Anesthesiology's articles are made freely accessible to all readers on www.anesthesiology.org, for personal use only, 6 months from the cover date of the issue.
Anesthesiology November 2021, Vol. 135, 813–828.
https://doi.org/10.1097/ALN.0000000000003929
Michael P. Puglia, Duan Li, Aleda M. Leis, Elizabeth S. Jewell, Chelsea M. Kaplan, Megan Therrian, Minkyung Kim, UnCheol Lee, George A. Mashour, Phillip E. Vlisides; Neurophysiologic Complexity in Children Increases with Developmental Age and Is Reduced by General Anesthesia. Anesthesiology 2021; 135:813–828 doi: https://doi.org/10.1097/ALN.0000000000003929
Neurophysiologic complexity in the cortex has been shown to reflect changes in the level of consciousness in adults but remains incompletely understood in the developing brain. This study aimed to address changes in cortical complexity related to age and anesthetic state transitions. This study tested the hypotheses that cortical complexity would (1) increase with developmental age and (2) decrease during general anesthesia.
This was a single-center, prospective, cross-sectional study of healthy (American Society of Anesthesiologists physical status I or II) children (n = 50) of age 8 to 16 undergoing surgery with general anesthesia at Michigan Medicine. This age range was chosen because it reflects a period of substantial brain network maturation. Whole scalp (16-channel), wireless electroencephalographic data were collected from the preoperative period through the recovery of consciousness. Cortical complexity was measured using the Lempel–Ziv algorithm and analyzed during the baseline, premedication, maintenance of general anesthesia, and clinical recovery periods. The effect of spectral power on Lempel–Ziv complexity was analyzed by comparing the original complexity value with those of surrogate time series generated through phase randomization that preserves power spectrum.
Baseline spatiotemporal Lempel–Ziv complexity increased with age (yr; slope [95% CI], 0.010 [0.004, 0.016]; P < 0.001); when normalized to account for spectral power, there was no significant age effect on cortical complexity (0.001 [–0.004, 0.005]; P = 0.737). General anesthesia was associated with a significant decrease in spatiotemporal complexity (median [25th, 75th]; baseline, 0.660 [0.620, 0.690] vs. maintenance, 0.459 [0.402, 0.527]; P < 0.001), and spatiotemporal complexity exceeded baseline levels during postoperative recovery (0.704 [0.642, 0.745]; P = 0.009). When normalized, there was a similar reduction in complexity during general anesthesia (baseline, 0.913 [0.887, 0.923] vs. maintenance 0.851 [0.823, 0.877]; P < 0.001), but complexity remained significantly reduced during recovery (0.873 [0.840, 0.902], P < 0.001).
Cortical complexity increased with developmental age and decreased during general anesthesia. This association remained significant when controlling for spectral changes during anesthetic-induced perturbations in consciousness but not with developmental age.
Editor's Perspective
What We Already Know about This Topic
Cortical complexity refers to the differentiation or diversity of neural activity patterns in the cerebral cortex
In adults, changes in cortical complexity have been shown to reflect changes in the level of consciousness across different classes of general anesthetics
Changes in cortical complexity with age and during general anesthesia in pediatric populations are incompletely understood
What This Article Tells Us That Is New
Using the Lempel–Ziv algorithm, a mathematical method for assessing neural signal complexity, a positive correlation of cortical complexity with age was found in awake, 8- to 16-yr-old children
During anesthetic state transitions in this pediatric population, cortical complexity decreased during the maintenance phase and, upon recovery of consciousness, remained reduced when compared with preanesthesia baseline levels
anesthesia, general, brain, child, developmental age, electroencephalography, neurophysiology-biologic function, premedication, consciousness related finding
Although the brain is a major target organ of general anesthetics, there remains no standard neurophysiologic monitor in the perioperative period. The lack of a standardized monitoring strategy likely reflects an incomplete understanding of the precise neural correlates of consciousness. Identifying neurobiological processes underlying consciousness is particularly challenging in the pediatric population, because the brain undergoes considerable structural and functional changes during development, resulting in substantial brain network formation and refinement.1,2 Cortical oscillatory patterns and spectral properties can thus vary considerably with age3,4 and during general anesthesia.5–8 In addition, network hubs (highly connected brain regions that facilitate information transfer) undergo significant developmental maturation9–11 and are highly susceptible to functional disruption with general anesthesia.12 As such, identifying age-invariant markers of anesthetic-induced unconsciousness is difficult for pediatric patients.
Candidate strategies for perioperative brain monitoring in the pediatric population need to account for neurodevelopmental changes that occur with age. One such strategy is the measure of neurophysiologic complexity in the cortex, hereafter referred to as cortical complexity. Cortical complexity can be broadly thought of as representing the differentiation or diversity of neural activity and can be analyzed using mathematical algorithms.13–22 The Lempel–Ziv algorithm serves as one such method for assessing neural signal complexity. It is a method of symbolic sequence analysis used to measure the "compressibility" or variability of a data series. In adults, previous studies have demonstrated a correlation between changes in Lempel–Ziv complexity with changes in the level of consciousness across different classes of anesthetic agents.14,15,21,23 However, cortical complexity has not been rigorously studied in pediatric populations requiring surgery and anesthesia. Analyzing Lempel–Ziv complexity thus serves as a candidate strategy for identifying changes in cortical signal complexity across various ages of neurodevelopment and during anesthetic-mediated perturbations in states of consciousness.
The objectives of this empirical and theoretical study were to determine the changes in cortical complexity with age and during general anesthesia. Specifically, this study tested the hypothesis that cortical complexity would increase with developmental age and decrease during general anesthesia. We studied a population of children 8 to 16 yr old because this age range reflects a period of dramatic brain network maturation. Additionally, children of this age are more likely to participate in preanesthetic assessment of baseline consciousness.
This was a prospective, single-center, cross-sectional, observational study assessing cortical complexity in children undergoing general anesthesia for elective outpatient surgery. The study was approved by the University of Michigan Medical School Institutional Review Board (Ann Arbor, Michigan; approval No. HUM00142298). After careful discussion, written informed consent by parents/guardians and verbal or written assent by pediatric patients were obtained before study enrollment. All study operations were conducted at C.S. Mott Children's Hospital, Michigan Medicine, University of Michigan. Recruitment took place from November 2018 to March 2020. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.24 Statistical analysis plans for the primary and secondary outcomes and meaningful effect size were defined a priori.
Pediatric patients aged 8 to 16 yr old, with American Society of Anesthesiologists physical status I or II, and scheduled for outpatient elective surgery with a halogenated ether as the primary anesthetic were eligible for study enrollment. Exclusion criteria included a patient history of seizure disorder, developmental delay, neurologic disease, current use of stimulant medications (e.g., amphetamine, dextroamphetamine), surgery above the neck (which might preclude neurophysiologic monitoring), history or suspicion of a difficult airway, physical characteristics that prevent electrode contact with scalp, enrollment in conflicting research protocol, or where English was not the primary language.
Anesthetic and Perioperative Management
The goal of this study was to determine the changes in cortical complexity across age in the preoperative, baseline state of consciousness and through the perioperative period. As such, no protocol was implemented for altering patient care. Clinical teams provided care as indicated, based on standard perioperative protocols, and were blinded to the electroencephalogram data to prevent additional sources of bias.
Electroencephalographic Data Acquisition
The electroencephalogram was recorded from 16 Ag/AgCl scalp electrodes using a wireless electroencephalographic system (mobile 128 system, Cognionics Inc., USA) and applied based on the international 10-20 system (Supplemental Digital Content, fig. 1, http://links.lww.com/ALN/C668). Head circumference was measured to ensure proper cap size (EASYCAP, Germany). Cap placement was based on electrode position Cz, which was localized to half of the distance between the nasion and inion and the preauricular notch measurements. Data recordings were sampled at 500 samples/s and referenced to the mastoid. Electrode impedances were continuously monitored and maintained at less than 100 kΩ per manufacturer recommendations. The raw electroencephalogram signals were exported into MATLAB (version 2019b; MathWorks, Inc., USA) and downsampled to 250 Hz. The 60-Hz power-line interference, if present, was removed using a multitaper regression technique and Thomas F-statistics implemented in CleanLine plugin for EEGLAB toolbox.25
Epoch Selection and Preprocessing
Electroencephalogram epochs selected for data analysis are shown in figure 1. Baseline (n = 50) electroencephalogram data (range, 3.27 to 5.97 min) were recorded in the preoperative eyes-closed resting state. Premedication electroencephalogram data for all subjects were extracted from a 2-min segment that was recorded within 5 min before and as close as possible to the induction of general anesthesia. If premedication was administered, the selected segment was at least 2 min after intravenous administration (n = 18) or 20 min after oral administration (n = 3). Maintenance (n = 49) electroencephalogram data (5 min) were recorded during the maintenance of general anesthesia and approximately halfway between surgical incision and cessation of the anesthetic maintenance agent. The specific time epoch chosen was based on the following additional criteria: constant age-adjusted minimum alveolar concentration value (greater than 0.7 and less than ±0.1% change) and electroencephalogram data suitable for analysis (i.e., free from artifact by visual inspection). During this maintenance period, the mean age-adjusted minimum alveolar concentration value was 1.26 ± 0.35. Recovery (n = 45) electroencephalogram data (range, 1.18 to 5.75 min) were recorded in a postoperative, eyes-closed resting state after clinical recovery determined by achieving a University of Michigan Sedation Scale score of 0 to 1.26 For the maintenance phase, one patient was excluded because of a minimum alveolar concentration below selection criteria, and five patients in the recovery phase were excluded because of data loss (e.g., associated with emergence delirium).
Study paradigm. Purple squares represent epochs of electroencephalogram data that were extracted for analysis. PACU, postanesthesia care unit.
For each epoch, the electroencephalogram signals were preprocessed as previously described.27 First, bad channels and noisy time segments with obvious artifacts were rejected by visual inspection; the data after artifact removal had an average length (mean ± SD) of 4.88 ± 0.39 min with 15 to 16 channels for the baseline, 5 ± 0 min with 14 to 16 channels for the maintenance, and 3.06 ± 1.21 min with 11 to 16 channels for the recovery epochs. Second, the signals were detrended using a local linear regression method with a 10-s window at a 5-s step size in the Chronux analysis toolbox,28 and low-pass-filtered at 50 Hz using the eegfiltnew function in the EEGLAB toolbox.25 Third, the signals underwent independent component analysis using the extended-Infomax algorithm in the EEGLAB toolbox.25 Independent components representing cardiac, eye, muscle, or other transient artifacts were identified and removed using visual inspection of the time-domain waveform, power spectrum, and spatial scalp topography. The number of independent components removed were (median [25th, 75th]) 3 [2, 4] for the baseline epochs, 0 [0, 0] for the maintenance epochs, and 4 [2, 5] for the recovery epochs.
Spectral Power
The power spectrogram was estimated using the multitaper method in the Chronux analysis toolbox,28 with a window length of 4 s with 50% overlap, a time-bandwidth product of 2, and a number of tapers of 3; the estimates were then averaged over all available windows to obtain the averaged power spectrum for each available channel. The normalized power spectrum was further calculated as the absolute power spectrum divided by the total power at 0 to 50 Hz. Electroencephalogram power was calculated for delta (0.5 to 4 Hz), theta (4 to 7 Hz), alpha (7 to 13 Hz), beta (13 to 25 Hz), and gamma (25 to 50 Hz) from both absolute and normalized power spectra. The topographic maps of group-level spectral power across all subjects were constructed for each frequency band using the topoplot function in the EEGLAB toolbox.25
Complexity Analysis
We used the Lempel–Ziv algorithm to determine the complexity of cortical dynamics across different states. Lempel–Ziv complexity is a method of symbolic sequence analysis18 that serves as a surrogate measure of temporal and spatiotemporal complexity of brain activity.14–16,21,23 In this study, we assessed both spatiotemporal complexity across multiple channels and temporal complexity in individual channels.
The spatiotemporal complexity was measured as previously described.23 Specifically, the instantaneous amplitude was estimated by applying the Hilbert transform, which was then segmented into 4-s windows with 50% overlap (additional analysis was performed to test the effect of window length; Supplemental Digital Content, fig. 2, http://links.lww.com/ALN/C668). The data were then converted to a binary value using the mean value as the threshold for each channel. The data window was then converted into a binary matrix in which rows represent channels and columns represent time points. The complexity of the matrix was assessed by spatiotemporal Lempel–Ziv algorithm, which reflects the number of different spatial patterns across time. If the matrix is random, the spatiotemporal Lempel–Ziv complexity tends to be high; if channels behave similarly (or identically), Lempel–Ziv complexity is low. Because the Lempel–Ziv complexity value for a sequence of fixed length is maximal if it is entirely random, we normalized the raw spatiotemporal Lempel–Ziv complexity by the mean of those from n = 50 surrogate data generated by randomly shuffling the original spatial order for each time point; thus, the resultant spatiotemporal Lempel–Ziv complexity values range from 0 to 1. To test whether the Lempel–Ziv complexity is accounted for by spectral changes, we normalized the spatiotemporal Lempel–Ziv complexity by the mean of those from a surrogate time series (n = 50) generated through phase randomization that preserves the spectral profiles of the signal for each channel. If the complexity change is entirely due to spectral changes, the difference in the Lempel–Ziv complexity values across the states will be completely preserved in the Lempel–Ziv complexity values from the surrogate data; thus, the normalized spatiotemporal Lempel–Ziv complexity will be close to 1 and equal across the states. If the change in complexity is not due to spectral changes, the alterations of Lempel–Ziv complexity values from the surrogate data will be distinct from those of spatiotemporal Lempel–Ziv complexity, and the normalized spatiotemporal Lempel–Ziv complexity will reflect the signal diversity beyond the spectral changes. Last, the spatiotemporal Lempel–Ziv complexity and normalized spatiotemporal Lempel–Ziv complexity values were averaged across all available windows as the final estimate for each state and participant.
To measure individual-channel temporal complexity, the number of different temporal patterns in each individual channel was analyzed. These data were then normalized by the mean of those from n = 50 surrogate data generated by randomly shuffling the temporal order for each channel to obtain temporal Lempel–Ziv complexity. We further tested whether the difference in the Lempel–Ziv complexity across states is due to spectral changes by comparing the temporal Lempel–Ziv complexity with the mean of those from n = 50 surrogate time series generated through phase randomization. The temporal Lempel–Ziv complexity and normalized temporal Lempel–Ziv complexity values were averaged across all available windows as the final estimate for each channel, state, and participant. The topographic maps of group-level temporal complexity across all subjects were constructed using the topoplot function in the EEGLAB toolbox.25 For each epoch and subject, the mean temporal complexity was further obtained by averaging the temporal Lempel–Ziv complexity (and normalized temporal Lempel–Ziv complexity) in prefrontal (Fp1, Fp2), frontal (F5, F6, Fz), central (C3, C4, Cz), parietal (P5, P6, Pz), and occipital (O1, O2) regions, as well as all further channels across the scalp.
Human Brain Network Simulations of Maturation
To assess the general relationship between brain network maturation and complexity, a large-scale functional brain network was simulated using a coupled Stuart–Landau model implemented in a neuroanatomically informed scaffolding derived from human diffusion tensor imaging. Complexity values of oscillations in the simulated brain network models were compared to assess the effect of developmental changes in the network hub structure. We chose the Stuart–Landau model because it can replicate the oscillatory dynamics of different types of brain signals.29–32 The coupled Stuart–Landau model is defined as follows:
zj̇t=λj+iωj−zjt2zjt+∑k=1N Ajk Kjkzkt−τjk+βξj(t) (1)
Here, the complex variable zj(t) determined a state of the node (brain region) j at time t, j=1,2,…,N. The anatomical structure A was acquired from group-averaged diffusion tensor imaging with n = 82 nodes.33 The Ajk is determined by the connection weight between brain regions j and k. We modulated Ajk to simulate brain network maturation based on previous studies demonstrating that hub structure is associated with the developmental age.9–11,34 Therefore, we additionally used weak (one tenth lower in connection strength compared with the ten strongest hubs) and strong (five times larger in connection strengths compared with the ten strongest hubs) hub structures to simulate the effect of brain network maturation. The brain network model simulation described below was performed with three different brain anatomical structures. The dynamics of the oscillator settle on a limit cycle if λj>0 and on a stable focus if λj<0. We modulated the λj=λ from –3 to 3 with δλ=0.2. The ωj is an initial angular natural frequency of each oscillator j. To simplify the model, we used a Gaussian distribution for natural frequency with a mean frequency of 10 Hz and SD of 0.3 Hz to simulate the narrow bandwidth of human electroencephalogram activity in the eyes-closed resting state.30–32,35 We controlled a coupling term Kjk=K between oscillators j and k from 0 to 0.5 with δK=0.01, which determines the global connection strength among brain regions. To make the model more realistic, we introduced a time delay between brain regions, τjk=Djk/s, with the average speed of axons in brain areas, s = 7 ms36 and the distance Djk between brain regions. The brain region j receives input from connected region k after the time delay τjk. The model results are not qualitatively different if the time delay is smaller than a quarter of the period of oscillation.31 A Gaussian white noise ξj(t) for each region was added with the SD β=0.005. We numerically solved the differential equations of the Stuart–Landau model using the Stratonovich–Heun method with 1,000 discretization steps. The first 10 s of the generated signals were discarded, and the last 50 s were used for the analysis of each simulation. Each brain region generates its own spontaneous oscillatory dynamics in a network at each bifurcation parameter λ and coupling strength K for one simulation. The simulation was repeated 50 times with different frequency configurations to obtain statistical robustness.
Among the simulated brain signals at various bifurcation parameter λ and coupling strength K, we selected brain states at certain parameter sets that can represent conscious states. A variance of the level of global synchronization in a network, termed the pair correlation function, was calculated, and the state at certain coupling strength K with the largest pair correlation function was chosen as the state that can represent the conscious state for each bifurcation parameter λ.32 The instantaneous global synchronization level r(t) at time t was calculated using phase difference Δθjk(t)=θj(t)−θk(t) at each coupling strength K.
r(t)=〈1N∑k=1N eiΔθjk(t)〉
Here r(t)=1 if all phases are equal, but r(t) is nearly 0 if all phases are randomly distributed.
Global pulsatile stimuli to the whole brain network were induced at the states with the largest pair correlation function to observe the complexity from the response to the stimuli. The coupled Stuart–Landau model with the stimulation term u(t) is as follows:
zj̇t=λj+iωj−zjt2zjt+∑k=1N AjkKjkzkt−τjk +βξjt+u(t)
u(t)=p, t1<t<t20, otherwise
Here p is the strength of the stimulus during a period T= t2−t1. We fixed p=30 and set duration of the stimulus as T = 100 ms. We induced the stimulus at 10 different random timings for one iteration. Each stimulus was applied independently to generated signals within one frequency configuration.
A significant response was calculated by comparing the instantaneous amplitude values I(t) before and after the stimuli. For each iteration, the I(t) of each node j after stimuli was normalized by the mean and SD of the baseline amplitude values of node j. Baseline values were obtained by using a total of 100 s, consisting of 10 trials of a 10-s prestimulus segment for each iteration. We considered the one tail (1−α)∗100 th quantile with α=0.05 as a significantly increased amplitude. A perturbation response (represented in equations as PR) of node j at time t was defined in a binary fashion: PRj(t)=1, for the significantly increased amplitude for node j, and PRj(t)=0, otherwise. The complexity was calculated by measuring Lempel–Ziv complexity LZc (t) of the PR(t) over 1 s after the stimuli. The Lempel–Ziv complexity was calculated for brain network models with low, intermediate, and high hub structure connectivity.
Effect of Premedication on the Resting Electroencephalogram
To assess the effects of premedication on cortical complexity and spectral properties, the resting electroencephalogram data during the preinduction period were analyzed. The electroencephalogram signals were preprocessed as described for the primary analysis and, after preprocessing, 12 to 16 channels remained for n = 50 subjects. The spectral power and cortical complexity measures were calculated as described for the complexity analysis.
Statistical Analysis and Power Calculation
Statistical analyses were performed using MATLAB, and the data were tested for normality of distribution by Lilliefors corrected Kolmogorov–Smirnov tests. Because the null hypothesis of normality of distribution was rejected in some of the data sets (P < 0.05), the two-sided Wilcoxon signed rank test was used to compare the electroencephalogram measure (spectral power and cortical complexity) across baseline, maintenance, and recovery. With Bonferroni correction, P < 0.017 (0.05/3; 3 pairs) was considered statistically significant. Spearman correlation was used to investigate the relationship between age and each electroencephalogram measure. Univariate linear regressions, with age as the independent variable and complexity measures as the dependent exposure variables, were used to determine the slope [95% CI] of the associations, with R2 used to assess goodness-of-fit. For the effect of premedication, the two-sided Wilcoxon rank sum test was used to compare the electroencephalogram measure (median [25th, 75th]) between subjects without (n = 29) and with premedication (n = 21). A Kruskal–Wallis test was used to calculate the statistical differences (median [25th, 75th]) across simulated brain networks, and P < 0.05 was considered statistically significant.
A sample size of 41 achieved 80% power to detect a change in slope between complexity and age from 0.000 under the null hypothesis to 0.011 under the alternative hypothesis when the statistical hypothesis is two-sided, the significance level is 0.050, the variance of age is 1.0816, the variance of complexity is 0.0009, the error variance of complexity is 0.5, and the correlation between observations within an individual is 0.25, assuming a compound symmetry correlation structure. This was a conservative estimate based on a previous study of complexity in healthy control patients of similar ages.19
In total, 175 children were screened for study eligibility. Of the 175 who met the study criteria, 36 children declined participation, 52 were excluded because of operative time change or time constraints, 37 because of enrollment in another study, 21 because of research staff availability, 10 because of cancellation or no show for surgery, 3 because of changes in anesthetic plan, and 2 because of technical complications, leaving 50 participants who completed the study and were included for analysis. The participant demographic, anesthetic, and surgical characteristics are presented in table 1. Five participants had incomplete data for race.
Age-related changes in spectral properties are shown in figure 2. Baseline normalized gamma power in the frontal and parietal region and beta in the parietal region were correlated with age (gamma: frontal r = 0.30, P = 0.034, and parietal r = 0.31, P = 0.027; beta: parietal r = 0.40, P = 0.003), whereas baseline theta was inversely correlated with age (r = –0.45, P = 0.001 and r = –0.57, P < 0.001 for frontal and parietal regions, respectively). Total power decreased with age, with a peak observed at 8 yr, and subsequent decreases were present during the baseline (slope [95% CI], –0.63 [–0.91, –0.35]; P < 0.001; R2 = 0.29), maintenance (–0.65 [–0.94, –0.35]; P < 0.001; R2 = 0.28), and recovery phases (–1.2 [–1.6, –0.79]; P < 0.001; R2 = 0.43); Supplemental Digital Content, fig. 3, http://links.lww.com/ALN/C668). Total power also tended to decrease across regions and frequency bands with increasing age (Supplemental Digital Content, fig. 3, http://links.lww.com/ALN/C668). During maintenance, there was an inverse correlation for normalized theta power in the parietal region (r = –0.32, P = 0.027). During recovery, there was an inverse correlation for normalized delta power r = –0.44, P = 0.002 and r = –0.35, P = 0.020 for frontal and parietal regions, respectively). Conversely, higher (beta and gamma) frequencies were positively correlated during recovery (beta: frontal r = 0.45, P = 0.002 and parietal r = 0.57, P < 0.001; gamma: frontal r = 0.51, P < 0.001 and parietal r = 0.53, P < 0.001; fig. 2, A and B).
Age-related changes in normalized spectral properties. (A) Group-level spectra of normalized electroencephalogram power as a function of age in the frontal (top row) and parietal (bottom row) brain regions across the baseline, maintenance, and recovery periods. (B) Spearman correlation between age and normalized electroencephalogram power in the frontal and parietal brain regions for each frequency band across the baseline, maintenance, and recovery periods. *Significant correlation with P < 0.05.
Group-level changes in spectral properties are shown in figure 3 (and Supplemental Digital Content, fig. 4, http://links.lww.com/ALN/C668). Overall, during general anesthesia, there were global increases in delta power and alpha anteriorization and decreases in the higher frequency band power (beta and gamma; fig. 3, A and B). In terms of total power, general anesthesia induced an overall increase in power (except for gamma), which returned to baseline level after recovery for most frequencies (except alpha; Supplemental Digital Content, fig. 4, http://links.lww.com/ALN/C668).
Group-level spectral properties of normalized power. (A) Normalized power as a function of frequency for the baseline, maintenance, and recovery periods in the frontal (top) and parietal (bottom) brain regions. The solid line represents the median value, with shading representing the interquartile range. (B) Topographical representation of the normalized power for each frequency band for the baseline, maintenance, and recovery periods (note the differences in scale bars for each frequency band).
Age-related Changes in Cortical Complexity
In the preanesthetic baseline state of consciousness, there was a positive correlation with age and spatiotemporal Lempel–Ziv complexity (r = 0.41, P = 0.003), as well as age and averaged temporal Lempel–Ziv complexity (r = 0.39, P = 0.005). Likewise, there was a significant linear association with age for both measures (slope [95% CI], 0.010 [0.004, 0.016], P < 0.001, R2 = 0.20; and 0.007 [0.003, 0.011], P < 0.001, R2 = 0.20, respectively; fig. 4, A and B). When these data were normalized to account for spectral effects, there was no correlation for the normalized spatiotemporal Lempel–Ziv complexity or normalized averaged temporal Lempel–Ziv complexity (r = 0.02, P = 0.916 and r = 0.08, P = 0.572). Likewise, the linear association was not statistically significant (slope [95% CI], 0.001 [–0.004, 0.005], P = 0.737, R2 < 0.01, and 0.002 [–0.001, 0.004], P = 0.234, R2 = 0.03, respectively; fig. 4, C and D). Regional analysis of temporal Lempel–Ziv complexity and normalized temporal Lempel–Ziv complexity mirrored the findings above for the whole brain average (data not shown). The results were qualitatively similar when we applied robustness testing by varying the duration of window analysis (Supplemental Digital Content, fig. 2A, http://links.lww.com/ALN/C668).
Age-related changes in cortical complexity during the preanesthetic baseline. (A) Spatiotemporal Lempel–Ziv complexity. (B) Average temporal Lempel–Ziv complexity. (C) Normalized spatiotemporal Lempel–Ziv complexity. (D) Normalized average temporal Lempel–Ziv complexity of electroencephalogram data from the eyes-closed preanesthetic baseline period. The blue line represents the linear regression for each complexity measure with the line equation in the bottom right of each box. *Significant linear association (P < 0.001).
Effect of Brain Network Maturation on Complexity in Simulated Human Brain Models
Because our study design presumed but did not measure brain network development in the cohort of participants, we employed a principled approach to assess complexity in developing networks, as defined by networks that had progressively increased hub structures. After a simulated perturbation, complexity was measured in computational brain models informed by human neuroanatomy and compared across weak, intermediate, and strong hub structures (as quantitatively defined under "Materials and Methods"). Lempel–Ziv complexity significantly increased across simulated brain models with increasing strength of connectivity in the network hub structure (P < 0.001; fig. 5).
Lempel–Ziv complexity of simulated brain models with changes in the connection strength of network hubs. The central mark in each box represents the median value, with the edges representing the 25th and 75th percentiles. The extending whiskers are the most extreme data points determined by the MATLAB algorithm (version 2019b; MathWorks, Inc., USA) to be nonoutliers, and the red crosses are those values deemed to be outliers. *Significance was measured using the Kruskal–Wallis test (P < 0.001).
Effect of Midazolam Premedication on Electroencephalogram Power and Cortical Complexity
Midazolam premedication was not associated with a change in cortical complexity when measured across the entire brain (fig. 6A). However, regional increases in complexity were found for both the temporal Lempel–Ziv complexity and normalized temporal Lempel–Ziv complexity in the central region (median [25th, 75th], 0.384 [0.352, 0.418] vs. 0.434 [0.414, 0.452], P = 0.002; 0.915 [0.898, 0.933] vs. 0.945 [0.915, 0.961], P = 0.014, respectively) and parietal region (0.377 [0.365, 0.401] vs. 0.420 [0.412, 0.456], P < 0.001; 0.923 [0.900, 0.939] vs. 0.938 [0.915, 0.958], P = 0.031, respectively; fig. 6, B and C). Midazolam premedication was associated with frequency- and region-specific changes in normalized electroencephalogram power (Supplemental Digital Content, fig. 5, http://links.lww.com/ALN/C668). In all brain regions, there was a decrease in theta and increase in beta power, as well as a decrease in frontal and prefrontal alpha power (P < 0.05).
Effect of premedication with midazolam on cortical complexity. (A) Spatiotemporal Lempel–Ziv complexity, normalized spatiotemporal Lempel–Ziv complexity, average temporal Lempel–Ziv complexity, and normalized average temporal Lempel–Ziv complexity of electroencephalogram data before the first intraoperative anesthetic medication administration. (B) Topographical representation of temporal Lempel–Ziv complexity and normalized temporal Lempel–Ziv complexity with and without midazolam premedication. (C) Region-wise analysis of the temporal Lempel–Ziv complexity and normalized temporal Lempel–Ziv complexity. For (A) and (C), the central mark in each box represents the median values, with the edges representing the 25th and 75th percentiles. The extending whiskers are the most extreme data points determined by the MATLAB algorithm (version 2019b; MathWorks, Inc., USA) to be nonoutliers, and the red crosses are those values deemed to be outliers. *Significance (P value shown); Wilcoxon rank sum test.
Effects of General Anesthesia on Cortical Complexity
During the stable maintenance phase of general anesthesia, cortical complexity decreased from baseline values for both complexity measures tested, with the greatest decrease from baseline in spatiotemporal Lempel–Ziv complexity (median [25th, 75th] for baseline 0.660 [0.620, 0.690] vs. maintenance 0.459 [0.402, 0.527], P < 0.001; fig. 7; Supplemental Digital Content, fig. 6, http://links.lww.com/ALN/C668). Changes in complexity were independent of spectral power during the maintenance phase of general anesthesia. During recovery, spatiotemporal Lempel–Ziv complexity exceeded the baseline level (0.704 [0.642, 0.745], P < 0.001); however, after controlling for spectral changes, the normalized spatiotemporal Lempel–Ziv complexity during recovery was significantly decreased compared with baseline (baseline 0.913 [0.887, 0.923], vs. recovery 0.873 [0.840, 0.902], P < 0.001). Similar results were found for the average temporal Lempel–Ziv complexity analysis, with the exception of the normalized average temporal Lempel–Ziv complexity when the recovery period returned to baseline levels (0.960 [0.949, 0.966] vs. 0.957 [0.945, 0.965], P = 0.404, respectively). Regional changes in normalized temporal Lempel–Ziv complexity results were similar to data from the whole brain average (Supplemental Digital Content, fig. 7, http://links.lww.com/ALN/C668). Overall, these results were consistent when we varied the analysis window duration (Supplemental Digital Content, fig. 2B, http://links.lww.com/ALN/C668).
Effect of general anesthesia on cortical complexity. (A) Spatiotemporal Lempel–Ziv complexity (top left), average temporal Lempel–Ziv complexity (bottom left), normalized spatiotemporal Lempel–Ziv complexity (top right), and normalized average temporal Lempel–Ziv complexity (bottom right) of electroencephalogram data for the baseline, maintenance, and recovery periods. (B) Topographical representation of the temporal Lempel–Ziv complexity and normalized temporal Lempel–Ziv complexity for the baseline, maintenance, and recovery periods. For (A), the central mark in each box represents the median value, with the edges being the 25th and 75th percentiles. The extending whiskers are the most extreme data points determined by the MATLAB algorithm (version 2019b; MathWorks, Inc., USA) to be nonoutliers, and the red crosses are those values deemed to be outliers. *Significance (P value shown); Wilcoxon signed rank test with Bonferroni correction for three pairwise comparisons.
In a post hoc analysis of changes in spatiotemporal Lempel–Ziv complexity during the maintenance epoch with (n = 11) or without (n = 38) the use of nitrous oxide, there was no difference except that the normalized spatiotemporal Lempel–Ziv complexity was higher in the nitrous oxide group (P = 0.045, Wilcoxon rank sum test; data not shown). In an additional post hoc analysis of the association of spatiotemporal Lempel–Ziv complexity and the average age-adjusted minimum alveolar concentration (maintenance epoch) with or without the use of nitrous oxide, we found a negative correlation (with nitrous oxide r = –0.64, P = 0.040, without r = –0.36, P = 0.028; Spearman correlation; data not shown), but this was not significant after controlling for spectral changes (with nitrous oxide r = –0.37, P = 0.261, without r = –0.02, P = 0.916; similar results were found for average temporal Lempel–Ziv complexity; data not shown).
This study of children undergoing surgical anesthesia tested the hypothesis that electroencephalogram cortical complexity would increase with developmental age and decrease with general anesthesia. Age was positively correlated with cortical complexity during baseline recordings, and further analyses revealed that this was attributable to spectral changes. We supported this empirical finding through a principled approach by investigating complexity in simulated brain networks representative of development. This analysis demonstrated increased complexity with increasing connectivity strength of the network hub structure. During anesthetic state transitions, we found that cortical complexity decreased during the maintenance phase of general anesthesia compared with the eyes-closed baseline. Furthermore, after recovery of consciousness, normalized spatiotemporal complexity remained reduced compared with baseline. Overall, we found that age and anesthetic-mediated perturbations in the level of consciousness were associated with changes in cortical complexity, with age-related changes likely resulting from spectral changes and, as suggested by our simulation results, evolving functional architecture.
The hypothesis that cortical complexity would increase as a function of age was informed by the developmental maturation occurring through this period. Structural and functional changes, such as increased white matter density, long-range connections,1,37 and network connectivity,1,38 could yield a less reducible and more "complex" brain network. Additionally, studies of childhood and adolescent brain network development have demonstrated increased differentiation, specialization, and organization of network function,1,9,38 potentially leading to a greater diversity of neural oscillatory activity and measurable using complexity algorithms. Indeed, previous studies of resting state magnetoencephalogram data analyzed using the Lempel–Ziv complexity algorithm demonstrated a significant increase in complexity during childhood and adolescence that continued throughout the lifespan, peaking in the sixth decade.19,20 Our results are consistent with this, but the effect is attributable to changes in spectral properties, which were not controlled for in the studies by Fernández et al.19,20 The lack of an age effect in complexity beyond spectral properties is somewhat surprising given the magnitude of brain network change occurring during this period but is possibly accounted for by several factors. First, we did not control for developmental heterogeneity. It also remains possible that the normalized measure of complexity is not sensitive enough to measure a developmental difference but requires significantly larger perturbations (e.g., anesthetic state transitions). The robust finding of significant decreases during general anesthesia, even when controlling for spectral changes, supports this interpretation. It should also be considered that despite the large developmental changes occurring in children 8 to 16 yr old, it remains possible that this age range may not capture the structural and functional changes required for discernable differences in normalized complexity. Additionally, a developing network may give rise to both spectral and complexity changes that temporally align.39 Our computational brain model findings that networks with stronger hub structures yield a more complex network further support the empirical age-related results. Moreover, previous work from our group has shown that network hub structure is disrupted with propofol administration and consistent with reductions in complexity with general anesthesia.12
Complexity analysis using the Lempel–Ziv algorithm yielded dynamic results. Preoperative midazolam administration increased complexity in the central and parietal regions, whereas complexity was globally reduced during general anesthesia. Previous studies from our group demonstrated a similar increase in complexity with subanesthetic ketamine administration and reduction during ketamine anesthesia.23 Although the mechanism for increased complexity during subanesthetic states remains unknown, it has been shown that γ-aminobutyric acid type A receptor agonists, such as propofol, can induce paradoxical excitation.40 Additionally, benzodiazepines have been shown to increase beta activity in the central and parietal regions consistent with the increased beta activity we observed.41,42 In support, a post hoc analysis of temporal complexity and beta power demonstrated a positive correlation and after controlling for spectral changes, was mitigated in the no-premedication group, but remained significant for the midazolam group (Supplemental Digital Content, fig. 8, http://links.lww.com/ALN/C668). This suggests an association between complexity and beta power, which could be a concurrent effect of midazolam on the electroencephalogram. However, a recent study of subanesthetic nitrous oxide administration demonstrated decreased electroencephalographic complexity (Shannon complexity).13 The effect of various subanesthetic medications on cortical complexity in children remains an open question. In addition, there may be region specific changes (fig. 7B), but further investigation including high-density recordings would be required to address this fully.
Leading theories of consciousness posit that the diversity of information in both space and time is important for conscious processing.43,44 The biologic interpretation of decreases in cortical complexity may represent reduced information sharing or processing. In this study, decreases in cortical complexity induced by general anesthesia were similar to what has been shown in animal models16,45 and adult patients.14,17,21,23 The significant decrease during general anesthesia of cortical complexity, independent of spectral changes, suggests that measures of complexity may be helpful in the development of real-time monitoring modalities across the adult and pediatric populations. It is important to note that complexity measures in this study reflect the level of consciousness despite the age-related changes in power occurring during this period of massive neurodevelopment. When developing monitoring systems, measures of the conscious state need to be robust, have high fidelity, and be accurate across the population. In our study, there was a decrease in all children with both spatiotemporal and averaged temporal complexity; however, when the data were normalized, nine children in the spatiotemporal and seven in the averaged temporal complexity analysis groups had actual increases in complexity during the maintenance of general anesthesia (Supplemental Digital Content, fig. 6, http://links.lww.com/ALN/C668). Additional post hoc analysis of age, anesthetic dose, or channel rejection did not account for this increase (data not shown). Recent work by our group in an animal model artificially dissociated the observed level of consciousness from various electroencephalographic measures, including complexity.16 However, as noted in this study, just because state-related and neurophysiologic dynamic changes can be dissociated in the laboratory setting does not mean that they are not correlated during spontaneous physiologic, pharmacologic, or pathologic state transitions. These findings suggest that a single quantitative surrogate of consciousness using normalized complexity analysis may be insufficient to reliably determine the level of consciousness in all patients and future developmental studies should also address combinatorial surrogate measures. In addition, the finding of decreased normalized spatiotemporal complexity after clinical recovery is not surprising considering that functional brain network recovery extends beyond the initial return of consciousness.46,47
In an effort to confirm that our findings were consistent with the spectral characteristics across development shown in previous works, we analyzed both the absolute and normalized power of the electroencephalogram in this cohort. Similar to previous studies of school-age and adolescent children, we observed an age-dependent decrease in absolute electroencephalogram power over most brain regions during general anesthesia.5,8 In addition, when the power data were normalized, regional differences were aligned with developmental structural and functional changes occurring during this period.1,48 Specifically, there was a general shift toward faster oscillatory activity (i.e., decreased theta and increased beta and gamma power).3,4 When we analyzed the spectral data during general anesthesia, we found increased absolute power, increased delta power, decreased gamma power, and anteriorization of the alpha activity, consistent with what has been previously shown during this developmental period.5,49,50
There are several limitations of this investigation. First, this was an observational study without a protocol for a standard anesthetic regimen, and although this was pragmatic, we are unable to dissect the role of individual anesthetic agents. We performed a post hoc analysis to investigate the role of nitrous oxide and minimum alveolar concentration values influence on complexity. Although we are cautious in interpreting these data because of the small subsamples, the findings were consistent with the overall findings. Additionally, patient characteristics that may be present and attributable to heightened preoperative anxiety could also influence complexity and spectral properties. Further, sex differences have been shown to affect cortical complexity with development, and future larger-scale studies should address this.20 When considering the Lempel–Ziv complexity measure, there are several notable strengths: it is a nonparametric measure, can be applied to relatively short data segments, and can be used with nonstationary data. However, because the measure requires binarization of the original signal, there exists the possibility for the loss of information during the analysis as well as influence by slow waves in the electroencephalogram (Supplemental Digital Content, fig. 9, http://links.lww.com/ALN/C668). Additionally, this study was conducted in the perioperative environment and limited by a low electroencephalogram (16 channel) density and, in some instances, a loss of information from an electroencephalogram channel that has the potential to influence the assessment of cortically complexity. However, when we repeated our analysis excluding participants with channel rejection, the results were similar (data not shown).
In summary, cortical complexity increased with developmental age and decreased during general anesthesia. The age-related increase in cortical complexity could be accounted for by spectral properties but, based on modeling data, is consistent with a maturing brain network. By contrast, reductions in complexity during general anesthesia extended beyond spectral properties. Overall, these findings contribute to the growing body of literature on cortical complexity and how this potential neural correlate of consciousness is affected by age and anesthetic state transitions.
The authors thank the children and their families for their participation in this study.
Supported by Foundation for Anesthesia Education and Research (Schaumburg, Illinois) grant No. 20-PAF00823 and by the Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan.
Dr. Kaplan has received research funding from Lundbeck Biopharmaceutical Company (Copenhagen, Denmark). The other authors declare no competing interests.
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Visual Abstract
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text:
Letter from the Co-Chairs
Tags Youth at the CenterOptimizing School Accreditation and Transfer
Fostering Racial Equity
Summary of Terms
Authorizing Appropriate Use of Force
Conducting Just Use of Force Investigations
Encouraging Appropriate Use of Technology in Citizen-Law Enforcement Interactions
Encouraging Efficiency and Transparency
Expanding Civilian Oversight
Facilitating Efficiency Through Consolidation of Police Departments
Improving Efficiency and Effectiveness Through Consolidation of Municipal Courts
Improving Officer Training
Increasing Awareness of Rights and Procedures
Preventing Conflicts of Interest
Promoting Officer Wellness
Protecting Rights and Effectively Administering Courts
Re-Envisioning Response to Demonstration
Redefining Courts' Response to Nonviolent Offenses
Restoring Relations Through Community Policing
Strengthening Anti-Bias and Cultural Competency
Utilizing Community-Based Alternatives to Traditional Sentencing
Building Safe and Trauma-Informed Environments
Ending Childhood Hunger
Enhancing Auxiliary Services' Ability to Support Youth
Enhancing College Access and Affordability
Fostering Innovation and Organizational Capacity
Increasing Access to Care for Children
Monitoring Child Well-being
Optimizing School Accreditation and Transfers
Promoting Lifelong Wellness and Personal Development
Providing Quality Early Childhood Education
Providing Rigorous Primary and Secondary Education
Summary of Terms – Youth at the Center
Supporting Career Readiness
Bolstering Employer-Educator Collaboration
Building Equity through Enhanced Access to Banking
Enhancing Access to Transportation
Ensuring Communities' Ability to Advocate for Equity
Ensuring Robust Minority Participation in the Job Market
Increasing Access to Care
Optimizing Existing Housing Support
Prioritizing Youth-Focused Job Creation and Training
Promoting Asset Building
Realigning Incentives and Funding to Improve Job Training and Creation
Back to Nav
The expert testimony, research, scholarship, and lived experience collected by the Commission revealed the following:
In January 2013, the Normandy School District in St. Louis County lost its accreditation, joining the Riverview Gardens School District, which lost its accreditation in 2006 (FOCUS St. Louis, 2014).
The State Board of Education awards accreditation to districts that meet minimum quality and rigor standards (Missouri Department of Elementary and Secondary Education). According to a 1993 state law, students in an unaccredited district are eligible to transfer to an accredited district in the same or adjoining county without expense to their family. However, that expense falls on the unaccredited school district—the statute requires the unaccredited school district to cover tuition and transportation costs (to at least one "receiving school") for any students who wish to transfer. (Mo. Rev. Stat. § 167.131)
The Missouri Legislature has repeatedly introduced legislation to address the issues raised by the current transfer statute. In the 2015 legislative session, this bill was HB 42, and it, among other things, proposed adding public charter and virtual schools as transfer options (School Accreditation and Transfers). Notably, the bill did not set a cap on the tuition that receiving school districts could charge home districts. The bill was vetoed by Governor Nixon in late June 2015 (Office of Governor Jay Nixon, 2015).
As of early 2014, Normandy and Riverview Gardens paid up to $20,000 per year per child, or over $9 million total, in tuition for transfer students (Crouch & Bock, 2014). In some cases, Normandy and Riverview Gardens are paying out more in per-pupil tuition than they are receiving in per-pupil tax revenue, resulting in the two districts enrolling 80% of their original student population with only 70% of their original budget (Crouch & Bock, 2014).
In May of 2014, with Normandy nearly bankrupt, the Missouri Board of Education voted to replace the Normandy School District with the Normandy Schools Collaborative in order to avoid having to dissolve it entirely and assign its students to other schools. The Collaborative carried a non-accredited status (rather than unaccredited), effectively removing it from the school transfer law. The Board of Education later deemed it accredited, a decision that was overturned in a strongly written opinion issued by a St. Louis County Judge in early 2015 (Circuit Court of St. Louis County, 2015; Singer, 2015).
Last year, nearly 40 percent of the class of 2014 at Normandy High failed to graduate (Crouch, 2015).
These findings prompted the Commission to draft several calls to action for changes to the accreditation system and student transfer laws and conditions with the hope that these calls will help all students access high quality education.
To that end, the Commission issues the following calls to action.
Calls to Action Within This Area of Focus
Identify Convenient Transfer Schools
Prioritize accredited schools in same district when transferring students from unaccredited schools.
Accountable Bodies: Missouri Legislature Missouri Governor
Tags Youth at the CenterEducation
Hold Schools Accepting Transfer Students Accountable
Mandate accountability. If districts are going to accept students and funds, they must accept accountability.
Adopt the Voluntary Interdistrict Choice Corporation (VICC) Calculation
Adopt the Voluntary Inter-District Choice Corporation (VICC) calculation of $7,200 as the maximum rate.
Tags Youth at the Center
Limit Criteria for Denial of Transfers
No student shall be denied a transfer based on criteria that does not exclude him/her from attending a school in their resident district. Only students who have committed a safe schools violation be ineligible for transfer.
Amend Role of Assistance Teams
Ensure members of assistance teams are qualified, based on their past performance in failing districts with similar circumstances and omit or change language to "may" consider the recommendations of the assistance teams.
Negotiate Reasonable and Equitable Transfer Rates
Negotiate a reasonable and equitable transfer rate between local school districts. Without an established tuition cap, unaccredited school districts are at great risk of going bankrupt and further compromising their remaining students' hope for a quality education.
Accountable Bodies: School Districts Missouri Governor
Veto HB42
Veto HB42. *Note:The Commission affirmed this call prior to Governor Nixon's veto of the bill on 6/26/15
School Accreditation System
Revise the Missouri accreditation system (MSIP5). Ensure that the process of revision incorporates the following:Inclusive Participation – ensure that the revision team includes broad representation including:K-12 – district superintendents, principals and teachers Higher education representatives Parents and students Business, philanthropic and community social support representatives Lens Assessment – ensure that the new system…
Accountable Bodies: Missouri Legislature Missouri Department of Elementary and Secondary Education (DESE) Missouri Governor
Relationships: Rasheen and Kevin
Commissioners Rasheen Aldridge, an activist, and Kevin Ahlbrand, a detective sergeant with the St. Louis Metropolitan Police Department, talk about their experiences joining the commission and working together towards positive change in the St. Louis region.
Tags YouthYouth at the CenterRacial EquityPolice Reform
Understand the issues
Understand the limitations with the current state accreditation system, including becoming familiar with the measures for which accreditation is based, to become a more informed advocate for struggling school districts.
Tags Optimizing School Accreditation and Transfer
Change your environment
Build trauma-informed communities and spaces.
Tags Youth at the CenterEnhancing Auxiliary Services Ability to Support YouthIncreasing Access to Care
FAQ:What you need to know about Missouri School Transfers. St. Louis Public Radio. Retrieved from:https://news.stlpublicradio.org/post/faq-what-you-need-know-about-missouri-school-transfers
Gilbert, V. (2014). Missouri uses flawed data to penalize poor, minority students. St. Louis Post Dispatch. Retrieved from:https://www.stltoday.com/news/opinion/columns/missouri-uses-flawed-data-to-penalize-poor-minority-students/article_fd33baf3-1871-5209-8697-8aa902fb94e7.html
School Accreditation-Student Transfers Discussion Guide. FOCUS St. Louis. Retrieved from:https://www.focus-stl.org/?page=StudentTransfers
Circuit Court of St. Louis County, State of Missouri (2015). Final ruling in Janine Masset, et al., v. The Normandy Schools Collaborative, et al. Retrieved from:https://mediad.publicbroadcasting.net/p/kwmu/files/201502/2014.02.11_Findings_of_Fact%2C_Conclusion_of_Law%2C_Final_Order_and_Judgment_14SL-CC02359.pdf
Crouch, E. and Bock, J (2014). Money being paid by Normandy, Riverview Gardens to other districts not being spent. St. Louis Post Dispatch. Retrieved from:https://www.stltoday.com/news/local/education/money-being-paid-by-normandy-riverview-gardens-to-other-districts/article_f2ae8233-bf03-57b3-9270-dcc5366de090.html
Crouch, E. (2015). A senior year mostly lost for a Normandy honor student. St. Louis Post Dispatch. Retrieved from:https://www.stltoday.com/news/local/education/a-senior-year-mostly-lost-for-a-normandy-honor-student/article_ce759a06-a979-53b6-99bd-c87a430dc339.html
Focus St. Louis. (2014). School Accreditation – Student Transfers. Retrieved from:https://www.focus-stl.org/?page=StudentTransfers
Mo. Rev. Stat. § 167.131. Retrieved from:https://www.moga.mo.gov/mostatutes/stathtml/16700001311.HTML
Missouri Department of Elementary and Primary Education. Missouri School Improvement Program. Retrieved from:https://dese.mo.gov/quality-schools/mo-school-improvement-program
Office of Governor Jay Nixon. (2015). Gov. Nixon vetoes bill that fails to solve problems of unaccredited schools, creates new problems for districts across the state .Retrieved from:https://governor.mo.gov/news/archive/gov-nixon-vetoes-bill-fails-solve-problems-unaccredited-schools-creates-new-problems
Singer, D. (2015). Judge rules Normandy schools unaccredited. St. Louis Public Radio. Retrieved from:https://news.stlpublicradio.org/post/judge-rules-normandy-schools-unaccredited
School Accreditation and Transfers. HB 42. Retrieved from: https://www.house.mo.gov/billtracking/bills151/sumpdf/HB0042I.pdf

redpajama_set_name: RedPajamaCommonCrawl


text:
The Partisan | A Sad New Cal Am Tactic New mailer, a signal of desperation, is filled with serious deceptions
It should surprise no one, not even Cal Am's pals in the business community, that the company will stop at nothing in its latest effort to prevent a public takeover of its water monopoly on the Monterey Peninsula.
Four years ago, in the run-up to ballot Measure O, the utility outspent takeover advocates by some 20-1 and ended up winning a close race that likely would have gone the other way if the company hadn't bought so much deceptive campaign advertising.
At the heart of the Cal Am campaign in 2014, and an earlier contest, was the argument that the takeover process would distract the company from the crucial task of completing its much delayed desalination project. There were TV ads featuring actors walking along the beach, looking out to sea and declaring that the desal plant was just beyond the horizon. At the heart of the Cal Am campaign this year, predictably, is the same argument even though the project has barely drifted any closer to the shore. (Which doesn't really matter to Cal Am because state law lets it charge its customers 110 percent of the cost of the project no matter the outcome. In the world of regulated utilities, failure is just as profitable as success.)
Now faced with a better-organized takeover effort following years of out-of-control rate increases, Cal Am has added a new trick. Last week it sent out an exceptionally deceptive mailer that instructs voters on how they can remove their names from the takeover petitions being circulated by Public Water Now.
Public Water Now (PWN) has just two weeks left to collect enough signatures to qualify a new takeover measure for the November ballot. PWN founder George Riley says the petition drive is doing just fine and called the latest Cal Am tactic "an act of desperation."
The Cal Am mailer includes a coupon that petition signers can use to request that their signatures be removed. It also repeatedly accuses PWN of trying to deceive voters by claiming that residential water bills would be reduced under public ownership, which has proved to be the case elsewhere no matter how loudly Cal Am disagrees. See Missoula, Mt., and Nashua, N.H.
Cal Am maintains that water customers would see rate increases as a result of a takeover and that their property taxes would climb dramatically to cover the acquisition costs. PWN, however, forecasts that simply eliminating Cal Am's automatic profit margin of 10 percent could allow the purchase to be financed without increasing property taxes.
Not surprisingly, considering the company's record, Cal Am's mailer contains other serious deceptions of its own. The most blatant involves what occurred after voters in the Santa Cruz County community of Felton agreed to buy out the Cal Am operation there.
PWN touts a Food & Water Watch study that found Peninsula water rates to be the highest in the country. Cal Am, which makes a practice of creating false and indecipherable comparisons, argues in the mailer that Felton's rates under public ownership are higher yet.
Trying to make valid rate comparisons is beyond tricky when Cal Am is involved because it is willing to bend any number to suit its purposes.
But Cal Am says the average bill in Felton is $111 per month when it actually is significantly less. Cal Am correctly argues that property taxes in Felton went up dramatically to cover the purchase cost, but even accounting for that leaves Felton customers paying less than Cal Am claims.
Trying to make valid rate comparisons is beyond tricky when Cal Am is involved because it is willing to bend any number to suit its purposes. The company employs number crunchers with no real incentive to provide meaningful or accurate figures.
Cal Am's view of the Felton takeover is largely contradicted by a recent study by a national economic consulting firm. The work by the Analysis Group was paid for by the California Water Association, a trade group dedicated to combating public takeover efforts. Cal Am is a member. It concluded that water customers receive no financial benefits from public ownership, in large part because of acquisition costs. It failed to recognize, however, that acquisition costs are financed up front, eliminating a major expense within 20 to 30 years. In other words, if rates don't go down immediately under public ownership, they will eventually, and the benefit will accrue to future generations upon generations.
The Analysis Group study even indicates that rates in Felton actually declined marginally. PWN's Reilly says the Felton rates now are closely aligned with what takeover proponents there expected, numbers that the community is comfortable with.
The PWN ballot measure would set up an intense cost analysis. The acquisition costs would be determined either by negotiation or court order. In other words, the impact of a takeover would be well understood before it came about, so there is no reason for anyone to pay any attention now to anything Cal Am has to say on the subject.
The bottom line: Cal Am is barely acquainted with the facts and has millions of reasons to obscure them. The company maintains an able and conscientious workforce in the field locally. But upper management locally and nationally stumbles badly when it comes to technology, engineering, cost controls, public relations and fair play. The company's top rate expert once argued publicly that the words "median" and "mean" mean the same thing.
Public Water Now's members might occasionally get carried away in their zeal to remove a bad corporate actor from the scene, but it has already won the credibility contest by a landslide. Peninsula voters should do themselves a favor by paying close attention to PWN and ignoring almost everything the company has to say.
Calkins writes a weekly column on local politics and public affairs. He welcomes communications at calkinsroyal@gmail.com
11 thoughts on "The Partisan | A Sad New Cal Am Tactic New mailer, a signal of desperation, is filled with serious deceptions"
Susan Ragsdale-Cronin says:
I love this piece… taking something very complicated and distilling it down so us averge jill's can get the message.
bill leone says:
Cal Am, a subsidiary of American Water, a White Collar Criminal Organization, like the cigarette industry, the oil industry, or La Cosa Nostra, they will do whatever it takes to make obscene profits at the expense of The Public; that is, their employees & their consumers. What is at stake now is nothing less than our Democracy.
Power to the Public!!!
The salient part of Royal's article is that the ballot measure simply sets the stage for "an intensive cost analysis" of a public take-over. What does Cal Am have to fear? Perhaps investigation into their operating costs, the true numbers involved in a public take-over? From their view, any kind of detailed analysis is a lose-lose. When you can make a 10% profit doing business regardless of the outcome, there is nothing in this for them. welcome to Public Sector over site of our private water monopoly. I do appreciate the offer from Cal Am to do my thinking for me.
amy anderson says:
Thank you again Royal! Great article – You always hit the nail on the head. Would that all of the Peninsula voters were clued into the "Partisan" so they could read this. PWN will certainly get it out to our volunteers and fans.
Luana Conley says:
In addition to the fact-free mailer, they're doing phone banking to voters insulting the character of the PWN signature gatherers, our neighbors and friends, who generously volunteered their time to get this on the ballot.
There is more immediate benefit to public ownership than generously doing the right thing for future generations. CalAm is not incentivized to do maintenance, it's a total loss on their books, and have been caught red-handed falsifying records to their advantage, claiming work done when it wasn't. Local control by those who drink the same water and pay the same bills as ratepayers do is the answer.
Cal Am has added deceptive phone calls to their campaign. I received a call last night asking me to watch for the mailer so I can remove my name from the petition being circulated by Public Water Now. The caller said that the petition will increase my water rates and put the future of my water security at risk. When I asked how much the rates will increase, he said he did not know. When I asked how the future of my water security was going to be at risk, he said he did not know. When I asked who was paying him to call and where he was calling from, he said he was not at liberty to tell me this information, but he was a third-party outbound call center. He did say he got my phone number from the Voter Registrars Office.
I really resent having to pay for the mailers and the phone calls as part of my inflated water rates with Cal Am.
Bob Coble says:
I too received both the mailer and a phone call. I returned the form from the mailer (without my signature but with some negative comments about the dishonesty of Cal Am) in their postage-paid envelope. The young man who called me would not give me his name but did start reading his script. After he said that the Public Water Now petition endangers our future water supply I interrupted him, saying, "Wait a minute. How would the petition endanger the future water supply?" He responded, "I don't know." Then I told him "Well if you don't know why are you saying that," to which his answer was "that's what I'm told to say." Of course he wouldn't tell me who told him, or who is paying for the phone calls. He reverted back to reading his script until I told him that what he was saying was lies and dishonest." Finally he gave up and to me to have a nice afternoon.
I am not pro or con to water companies in charge at this time.
I am wondering who is interested in investing into the services of CalAm?
The writers are all knowledgeable about CalAm failures, but who wants private public water
Company , that we do not know who they are?
Is it true that MRWPCA the most qualified to assume the responsibility of owning the water company? Personally I do not know what MRWPC responsibilities are. The charges are clearly paid by residences, but why? CalAm is all there is? So who wants to be a private water company? I always understood the CPUC was responsible for all that is done in utilities. Has the CPUC lost control of what CalAm can do? Please identify problems with good solutions.
Bill Hood says:
Note that the Ph.D. who wrote the report for the California Water Assoc also wrote the recent Herald commentary wherein he strongly concludes, and cites his own report to support that conclusion, that private water is much better and should be preferred over public water. His reputation should be in some dispute, because in the intro to the study, he states that the purpose of the study is NOT to reach conclusions as to which option – private or public – are clearly more beneficial. Looks like he either has a short memory, or, more likely, he's been paid to blab about Cal-Am as the hero and PWN as the villain.
Let's hear from a few dozen property owners from Santa Cruz County (Felton)
What was and what is the cost per month
for water usage now after the take over
John M. Moore says:
The Pure Water Monterey(PWM) project headed by the Water District and Monterey One Water mixes human sewage with toxic agriculture waste treats it with state of the art processes, the processes used to recycle waste for non drinking purposes, but then, with no tests for unknown pathogens, will inject that mix into mthe Seaside Basin where it is commingled with water from other sources, and then sold to Cal Am (again with no comprehensive pathogen testing) and finally sold to us for drinking water.
Every expert about the health risks of such a project is that it is an extremely high risk project that should not be attempted until a way to test for known, but mostly for unknown pathogens, is perfected by the FDA. Ca. has not authorized even the study of a pathogen detecting system for recycled waste water systems that purport to be safe to drink.
PWM claims that it has obtained all of the permits required by law. One thing they do not claim is the opinion of a neutral disease expert that the water will be safe to drink. So they bought the unsubstantiated opinion of an engineer instead. See my May 2, 2018 letter for more info,


text:
Gold Octadrachm of Ptolemy II & Arsinöe II
by The British Museum
This massive gold coin weighing approximately 27.7-8 grams was known as an octadrachm (equivalent in worth to 8 drachmae). Under the Ptolemies, mints in cities like Alexandria and Ptolemais produced ever larger denominations in gold, silver and bronze. This coin was minted in Alexandria, Egypt between 260-40 BCE. On its obverse it bears the diademed heads of Ptolemy II and Arsinöe II with the legend "Adelphon" (literally Greek for "Siblings"). On the reverse, Ptolemy I and Berenike I are depicted. (British Museum, London)
© Trustees of the British Museum. Republished under the British Museum Standard Terms of Use for non-profit educational purposes. Original image by The British Museum. Uploaded by Arienne King, published on 27 February 2018 under the following license: Copyright. You cannot use, copy, distribute, or modify this item without explicit permission from the author. Please note that content linked from this page may have different licensing terms.
Ptolemy II & Arsinoe II
The stela, from a chapel in the royal couple's honors, shows Ptolemy...
Ptolemy II Philadelphus Founds the Library of Alexandria
"Tolomeo Filadelfo nella biblioteca di Alessandria", or "Ptolemy...
Bronze Statues of Ptolemy II & Arsinöe II
These early 3rd Century BCE bronze statues from Alexandria, Egypt...
Bronze Bust of Ptolemy II Philadelphus
This bronze bust is usually attributed to the Ptolemaic king Ptolemy...
Ptolemy XV
This larger than life 1st Century BCE granite head was found in...
Museum, T. B. (2018, February 27). Gold Octadrachm of Ptolemy II & Arsinöe II. Ancient History Encyclopedia. Retrieved from https://www.ancient.eu/image/8210/
Museum, The B. "Gold Octadrachm of Ptolemy II & Arsinöe II." Ancient History Encyclopedia. Last modified February 27, 2018. https://www.ancient.eu/image/8210/.
Museum, The B. "Gold Octadrachm of Ptolemy II & Arsinöe II." Ancient History Encyclopedia. Ancient History Encyclopedia, 27 Feb 2018. Web. 18 Jan 2020.


The goal of Story Club is to mix the spontaneity of an open mic with the experience of live theater. We create a forum for writers/performers to display their nonfiction storytelling prowess, read from the page or memorized.
We give you a mic and 5-8 minutes. You tell us a story.
We have open mic slots waiting for you!
Open mic participants are given 8 minutes (1300 words) and can tell a story on the theme or not.
Open mic sign-up starts at 8:00pm and we'll start the show at 8:30pm. If more than 3 storytellers sign up we'll draw names for the slots.
At the end of the night, the audience will vote for their favorite story. The winner receives a shiny, golden pile of adoration!
As always, a portion of our donations will be supporting an organization that's making the world better and better.
Story Club North Side is produced and hosted by Stephanie Douglass.
Or we're on the prowl.
Horns of plenty and horns that announce the hunt.
Some days, we're not sure if we're the predator or the prey.
We know it's out there, and we're going to find it.
Or we know it's after us, and we're going to make sure it never gets there.
Either way, we're welcoming November by filling our satchel with arrows and chasing down the things we deserve.
This month, Story Club North Side is all about Bounty/Hunters: endless supplies, lying in wait, going after what we need to survive, and surviving what tries to destroy us.


text:
Comedy > Romantic Comedy
Drama > Psychological Drama
Romance > Romantic Drama
Drama > Family Drama
Marty (1955)
directed by Delbert Mann
featuring Ernest Borgnine, Betsy Blair, Esther Minciotti, Augusta Ciolli, Joe Mantell
The Country Girl (1954)
directed by George Seaton
featuring Bing Crosby, Grace Kelly, William Holden, Anthony Ross, Gene Reynolds
From Here to Eternity (1953)
directed by Fred Zinnemann
featuring Burt Lancaster, Montgomery Clift, Deborah Kerr, Donna Reed, Frank Sinatra
Roman Holiday (1953)
directed by William Wyler
featuring Audrey Hepburn, Gregory Peck, Eddie Albert, Hartley Power, Harcourt Williams
The Bad and the Beautiful (1952)
directed by Vincente Minnelli
featuring Kirk Douglas, Lana Turner, Walter Pidgeon, Dick Powell, Barry Sullivan
Used CD, 1996
A Place in the Sun (1951)
directed by George Stevens
featuring Montgomery Clift, Elizabeth Taylor, Shelley Winters, Anne Revere, Keefe Brasselle, Fred Clark
All About Eve (1950)
directed by Joseph L. Mankiewicz
featuring Bette Davis, Anne Baxter, George Sanders, Celeste Holm, Gary Merrill
featuring Jeanne Crain, Linda Darnell, Ann Sothern, Kirk Douglas, Paul Douglas
Miracle on 34th Street (1947)
featuring Maureen O'Hara, John Payne, Edmund Gwenn, Natalie Wood, Harry Antrim, Jerome Cowan, William Forrest, Herbert Heyes, Gene Lockhart
The Best Years of Our Lives (1946)
featuring Fredric March, Myrna Loy, Dana Andrews, Harold Russell, Virginia Mayo
The Lost Weekend (1945)
directed by Billy Wilder
featuring Ray Milland, Jane Wyman, Howard Da Silva, Phillip Terry, Doris Dowling
Going My Way (1944)
directed by Leo McCarey
featuring Bing Crosby, Rise Stevens, Barry Fitzgerald, Gene Lockhart, Frank McHugh, James Brown
directed by Michael Curtiz
featuring Humphrey Bogart, Ingrid Bergman, Paul Henreid, Claude Rains, Conrad Veidt, Peter Lorre
featuring Greer Garson, Walter Pidgeon, Teresa Wright, Dame May Whitty, Reginald Owen
Here Comes Mr. Jordan (1941)
directed by Alexander Hall
featuring Robert Montgomery, Evelyn Keyes, Claude Rains, Rita Johnson, Edward Everett Horton
The Philadelphia Story (1940)
directed by George Cukor
featuring Cary Grant, Katharine Hepburn, James Stewart, Ruth Hussey, John Howard
Gone With the Wind (1939)
directed by Victor Fleming
featuring Clark Gable, Vivien Leigh, Leslie Howard, Olivia de Havilland, Hattie McDaniel
Pygmalion (1938)
directed by Anthony Asquith, Leslie Howard
featuring Wendy Hiller, Leslie Howard, Wilfred Lawson, Marie Lohr, Scott Sunderland
The Life of Emile Zola (1937)
directed by Irving Rapper, William Dieterle
featuring Paul Muni, Gloria Holden, Gale Sondergaard, Joseph Schildkraut, Robert Warwick, Donald Crisp
The Story of Louis Pasteur (1936)
directed by William Dieterle
featuring Paul Muni, Akim Tamiroff, Josephine Hutchinson, Anita Louise, Donald Woods, Fritz Leiber
directed by John Ford
featuring Victor McLaglen, Heather Angel, Preston S. Foster, Margot Grahame, Wallace Ford
The Big House (1930)
directed by George W. Hill
featuring Chester Morris, Wallace Beery, Robert Montgomery, Leila Hyams, Lewis Stone
Gene Lockhart


to the webpages of the Institute of Theoretical Computer Science. The vision of our team is the computer-aided construction of concurrent systems, on all levels in the design space. To this end, our ambition is to understand the principles underlying concurrent computation and exploit them in the development of efficient verification and synthesis algorithms. Harnessing methods from automata theory, games, semantics, and computer-aided verification the institute is specialized in inference techniques for qualitative as well as quantitative properties, and currently extends verification algorithms towards automatic system correction, optimization, and synthesis.
reading more about our teaching.
If you would like to join us, if you are interested in a Bachelor's, Master's, or PhD thesis, or in case you are just curious about who we are - we warmly welcome you to visit us. We offer beautiful topics, interesting projects, competitive positions, and an international research environment.


Find out what the mischievous aliens are up to in author Denis Hayes' Silly Alien Stories for Bigger Kids.
Wild, wacky and wonderful, Silly Alien Space War Stories for Bigger Kids follows incredible aliens and humans in unusual adventures causing chaos and concern throughout the universe.
Follow the wild, whacky and wonderful adventures of incredible aliens and humans in an unusual journey causing chaos and concern throughout the universe.
Follow how some wild, wacky and wonderful fantastic creatures in unusual adventures overcome their problems in great style.
Silly Fishy Stories for Kids is about wonderful stories of sea creatures who live wild and whacky lives on a reef beneath the waves.
The Ghosts that haunt Highfield Towers, the most haunted house anywhere, are indeed frightening, scary and fearsome, but they are entertaining because that is their job. Let them entertain you.


I'm approaching my 3 year TDRL mark and haven't heard a word from anyone. My address, phone, and email are up to date as I made sure 4 months ago. The soldier over the phone even said he would send out my paper work for examination. That never happened. Is the DoD backed up? Does anyone know what going on?
The max for TDRL is now 3 years, but based on your date you still fall under the 5 year rule.
end change of address information to U.S. Army Physical Agency, ATTN: TDRL Branch, 2530 Crystal Drive, Arlington, Virginia 22202. You may contact us by phone at 1-855-863-0426 (toll free) or email at: [email protected].
If so, I would contact the USAPDA again.
So I believe my last day in the army was January 10 2017. Does that qualify for the new 3 year cutoff or 5? I think I accepted my ratings in November of 2016.
Since you were placed onto the DoD military TDRL after 1 January 2017, you shall be subjected to the maximum of three years on TDRL until your PEB unfitting medical condition(s) become "permanent and stable" for DoD rating purposes.
As such, if your TDRL re-evaluation didn't occur at the 18 month duration point in March 2018 then you need to follow-up (again) with the USAPDA TDRL Branch to ensure that all contact information is accurate and the scheduling of an immediate TDRL re-evaluation. It's important to remember that all military disabled retired pay to include military retirement benefits are automatically subject to termination after the three year maximum limit is reached without any exceptions to the federal law.
In retrospect when on TDRL via an U.S. Army perspective, about one month prior to reaching my third TDRL re-evaluation date under the previous federal law's five year maximum duration limitation, I telephonically contacted the USAPDA TDRL Branch and they informed me that my TDRL information was "not on file" and the third TDRL medical re-examination portion wasn't scheduled via a civilian medical contractor. They also stated I will be contacted by a PEBLO for the scheduling of my third TDRL re-evaluation; it didn't happen. Upon reaching the third TDRL re-evaluation date, I still wasn't scheduled for my third TDRL re-evaluation and now I started my fifth anniversary year of being placed onto the TDRL. So, I wrote a very detailed email to the USAPDA TDRL Branch explaining my situation with concerns of a fast approaching the five-year maximum limit for TDRL and possibility of the termination of all military disabled retired pay to include military benefits due to no fault of my own. Within two hours of sending that email, I was surprised with a telephone call from the USAPDA TDRL Branch stating that the process has started to initiate my third TDRL re-evaluation. Within two months there afterwards, I was finally placed onto the DoD military PDRL just a couple of months ago in 2018 after a long 4 years and 17 days on the TDRL.
Nonetheless, with that all said, it's very vital that you continue to be "positively proactive" in your interactions with the USAPDA TDRL Branch either telephonically or electronically via email; or via both courses of action NOW! Hmm, it has been 21+ months (and counting) on the TDRL at this point in time without a TDRL re-evaluation, right? Please don't allow the automatic termination of all of your military disabled retirement pay and benefits after 36 months of placement onto the TDRL due to no fault of your own! Take care!
"The National Defense Authorization Act (NDAA) for fiscal year 2017 modified the period of time a veteran may remain on the TDRL. For a veteran placed on the TDRL prior to January 01, 2017, he or she may remain on the TDRL for no greater than five (5) years. For those placed on the TDRL on or after January 01, 2017, he or she may remain on the TDRL for no greater than three (3) years."