Every year about 200,000 infants in the United States are given general anesthesia during their first year of life. Though anesthesia is essential to control pain during surgery and other medical procedures on infants, it involves some risks. There are some controversial studies suggesting that repeated anesthetics early in life may impact how the brain develops, but other studies have been reassuring and found no such effects. To reduce the risks, doctors carefully monitor infants' blood pressure, heart rate, body temperature, and oxygen levels while they are receiving anesthesia.

Electroencephalograms (EEGs) have proven to be a useful tool for monitoring the brain activity of adults undergoing anesthesia, but studies have found EEG-based monitoring to be unreliable in infants under anesthesia. A more reliable method of monitoring the brains of infants during anesthesia is needed. Anesthesiologists nevertheless need to better understand how the infant's brain works under general anesthesia, and novel EEG techniques hold promise for monitoring brain well-being and for adjusting anesthetic dosing in infants of different ages.

Differences in the way infants' brains respond to anesthesia may explain why current EEG-based monitoring methods developed for adults don't work as well in infants as in adults. Now, Cornelissen, Kim et al. have used a new EEG-based approach to demonstrate that as infants' brains develop, their responses to anesthesia change. The experiments involved 36 infants aged up to six months old who were going through routine surgical procedures. The brain activity of the infants was recorded using EEG—via electrodes placed on their scalps—when they were awake, during anesthesia, and as they recovered afterwards. The infants were videoed at the same time. Comparing the video with the EEG recordings allowed the brain activity of the infants to be matched up with their state of consciousness.

Cornelissen, Kim et al. detected slow waves of brain activity across the entire scalp of infants who are under six months old and under anesthesia. Infants who are older than about four months old also display some faster brain waves, which decreased in power as the infants emerged from anesthesia. However, none of the infants has the same pattern seen in adults—where faster waves appear near the front of the brain. These findings may help scientists develop more reliable ways to monitor infants' brains during anesthesia.

In the United States, 200,000 children a year receive general anesthesia during the first year of life (Rabbitts et al., 2010). Anesthetics have profound effects on all physiological systems. As a consequence, since the mid-1980s, anesthesia caregivers have been required to monitor blood pressure, heart rate, body temperature, and oxygen saturation along with anesthetic gas and oxygen delivery for all patients receiving general anesthesia. The states of general anesthesia and sedation are produced by the anesthetics acting in the brain and spinal cord (Brown et al., 2010, 2011; Ching and Brown, 2014). However, brain monitoring using electroencephalogram (EEG)-derived indices is used to only a limited extent in adults, rarely in children, and essentially not at all in infants. These EEG-derived indices, which been developed in adults can give inaccurate indications of anesthetic states in infants and younger children (Davidson, 2007; Constant and Sabourdin, 2012). The lack of principled strategies for monitoring the brains of infants and children receiving anesthesia care is especially troubling in view of growing concern about anesthetic toxicity to the developing brain (McCann and Soriano, 2012; Jevtovic-Todorovic et al., 2013; Lin et al., 2014).

A plausible inference to be drawn from the few available EEG studies of children under general anesthesia is that the inaccuracy of EEG-derived indices in pediatric practice is likely due to differences between children and adults in their brain responses to the anesthetics. A consensus has not been achieved on what these differences are because the available pediatric investigations have studied a limited set of anesthetics and frequently used EEG montages with few electrodes; different analysis methods have been used in different studies; multi-electrode recordings available in children have not been analyzed in function of age; and dose-titration experiments commonly conducted in adults cannot for ethical reasons be conducted in children (Davidson et al., 2008; Lo et al., 2009; Hayashi et al., 2012; McKeever et al., 2012; Sury et al., 2014).

For adults, characterizing the anesthetic response of the brain under general anesthesia and sedation is an active research field in which EEG, intracranial recordings, functional magnetic resonance imaging, and positron emission tomography are being used to study spatio-temporal changes in brain activity for different anesthetics and for different doses of the same anesthetic (Feshchenko et al., 2004; Boveroux et al., 2010; Breshears et al., 2010; Mhuircheartaigh et al., 2010; Cimenser et al., 2011; Martuzzi et al., 2011; Murphy et al., 2011; Boly et al., 2012; Lewis et al., 2012; Casali et al., 2013; Lee et al., 2013; Liu et al., 2013; Ní Mhuircheartaigh et al., 2013; Purdon et al., 2013; Akeju et al., 2014a; Vizuete et al., 2014). For example, a significant EEG pattern observed in adults under propofol general anesthesia and anesthesia maintained by an ether-derived anesthetic is incoherent slow oscillations across the entire head and strongly coherent alpha oscillations across only the front of the head (Purdon et al., 2013). Modeling studies suggest that the alpha oscillations are thalamo-cortical in origin (Vijayan et al., 2013). In addition, it is now appreciated that different anesthetics have different EEG signatures that are readily visible in the unprocessed EEG and its spectrogram (Purdon et al., 2013; Akeju et al., 2014b, 2014c). These EEG signatures can be related to the altered arousal states the anesthetics produce and to the mechanisms through which the drugs are believed to act at specific receptors in specific brain circuits.

These neurophysiological studies in adults have led to the idea of using the unprocessed EEG and its spectrogram as an alternative to using EEG-derived indices to monitor the brain states of adults receiving general anesthesia or sedation (Brown et al., 2015).

Use of the unprocessed EEG and its spectrogram to develop a neurophysiological-based paradigm tailored to pediatric practices for monitoring anesthetic state requires studying in children the spatio-temporal dynamics of brain responses to anesthetics. To begin to address this key knowledge gap, we used multi-electrode EEG recordings to study the spatial and temporal dynamics of brain activity in infants 0–6 months of age when awake, and during maintenance of and emergence from sevoflurane general anesthesia administered for routine surgical care.

Continuous multichannel EEG recordings were collected during the awake state, Maintenance Of a Surgical State of Anesthesia (MOSSA), and emergence from sevoflurane general anesthesia in 36 infants aged 0–6 months postnatal age (Figure 1). Infant demographics and clinical characteristics are given in Table 1. Details concerning study procedures and enrollment are given in the ‘Materials and methods’ and Figure 2.

Figure 1

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Figure 2

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EEG features during MOSSA

Slow and delta oscillations are present from 0 to 6 months of age; theta and alpha oscillations emerge around 4 months of age

Frontal power spectra analysis in individual infants at F7 showed that slow (0.1–1 Hz) and delta power (1–4 Hz) were present at all ages (Figure 3). Power in the 4–30 Hz frequency range was minimal in infants at 1, 2, and 3 months of age (Figure 3A,B). Power in the theta (4–8 Hz) and alpha (8–12 Hz) frequency ranges emerged at 4, 5, and 6 months of age (Figure 3C,D). Based on these key features of the individual power spectra, infants were divided according to postnatal age into two groups, 0–3 months (n = 11) and 4–6 months (n = 19) for subsequent analyses.

Figure 3

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Frontal group-median spectrograms computed at F7 in infants 0–3 months and 4–6 months of age show that power in the slow and delta ranges was dominant over that in other frequencies in both age groups (Figure 3B,D). Oscillations in the theta and alpha ranges were more prominent in infants aged 4–6 months during MOSSA.

Power across all frequencies was lower in infants at 0–3 months compared to infants at 4–6 months of age during MOSSA (Figure 4A). Infants 0–3 months of age showed negligible frontal alpha power (F7) with a peak of −6.52 dB at 8 Hz. Infants 4–6 months of age showed increased alpha power with a peak of 6.68 dB at 9 Hz. Group comparisons showed that power was significantly lower (95% Confidence Interval (CI), bootstrap analysis) across all of the frequencies from 0 to 30 Hz in infants 0–3 months of age, when compared with infants 4–6 months of age during MOSSA (Figure 4B–E).

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End-tidal sevoflurane requirements during MOSSA were significantly lower in infants 0–3 months compared to infants 4–6 months of age (Table 1). Therefore, we performed a secondary analysis to control for potential dose effects by comparing EEG spectra during MOSSA at identical end-tidal sevoflurane concentrations of 1.8% across infants. We chose 1.8% end-tidal sevoflurane because all the infants received this concentration for at least 30 s during surgery and did not exhibit body movement or reflex activity at this plane of anesthesia suggesting a behavioral state comparable to MOSSA. At 1.8% end-tidal sevoflurane, frontal group-median spectrograms at F7 show that slow and delta power was dominant over other frequencies in both age groups (Figure 4—figure supplement 1A,B). Higher frequency oscillations in the theta and alpha range were more prominent in infants 4–6 months of age (Figure 4—figure supplement 1C–G). The patterns of EEG activity observed during MOSSA were also seen during lighter planes of MOSSA at 1.8% end-tidal sevoflurane suggesting that the observed differences between the two groups are not likely due to a dose effect.

Frontal alpha predominance is low at 0–6 months of age

Group-median spectrograms were computed for each recording electrode location in the EEG montage (Figure 5). Infants 0–3 months of age showed slow and delta power at each electrode location and an absence of appreciable power at any frequency 5 Hz and higher. The power difference between the slow–delta range and the >5 Hz range was 30–35 dB. Infants 4–6 months of age group showed appreciable power in the slow, delta, theta, and alpha bands in the parietal, temporal, and non-central electrode locations; all the midline electrode locations showed less power in the frequencies 5 Hz and higher.

Figure 5

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Topographic maps of power were computed for slow, delta, theta, alpha, and beta frequency bands (Figure 6). For both age groups, spectral power along the midline (specifically frontal and central-parietal locations) was lower across all frequencies (Figure 6A,B). Slow and delta oscillations were broadly distributed across the entire scalp, with slightly lower power located over the frontal, centro–parietal, and occipital midline compared to the temporal regions in all infants. Theta activity followed a similar distribution pattern in all infants, although infants 0–3 months of age had minimal power in this range (<0 dB) (Figure 6A). During MOSSA, alpha oscillations, compared to lower frequency bands, were distributed more laterally across the frontal, central, and parietal regions in infants 4–6 months of age (Figure 6B). Alpha power was slightly lower over the central–parietal and occipital midline, possibly reflecting changes in skull conductivity over the locations of fontanels, rather than an artifact of referencing technique.

Figure 6

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We evaluated the potential frontal predominance of alpha power by assessing power spectra differences between frontal (FPz) and occipital (Oz) electrodes for infants 0–3 months of age (Figure 7A,C–F) and for infants 4–6 months of age (Figure 7B,G–J). During MOSSA, there were no significant frontal-occipital power differences at any frequency in infants at 0–3 months (95% CI, paired bootstrap analysis) (Figure 7C–F). In contrast, a small but significant increase in frontal alpha power compared to occipital power (95% CI, paired bootstrap analysis) was present during MOSSA in infants 4–6 months of age (Figure 7G–J). These data suggest, that unlike in adults, infants 0–3 months of age do not show frontal predominance of alpha power during MOSSA, and that it may begin to emerge at around 4 months postnatal age (Ní Mhuircheartaigh et al., 2013; Purdon et al., 2013).

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Frontal alpha coherence is absent at 0–6 months of age

We analyzed the level of local coordinated activity in the frontal regions of the scalp during MOSSA by computing the coherogram (time-varying coherence) and coherence between the left (F7) and right (F8) frontal electrodes (see ‘Materials and methods’) (Figure 8A–D). The frontal coherograms showed high coherence (∼0.6) in the slow and delta frequency ranges for all infants during MOSSA (Figure 8E,F). In infants 0–3 months of age, the peak coherence was 0.61 and occurred at 1.0 Hz (Figure 8G). In infants aged 4–6 months, peak coherence was also 0.61, and occurred at 1.0 Hz (Figure 8G). These results suggest that infants across all postnatal ages have coordinated local frontal slow and delta oscillations during MOSSA.

Figure 8

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Although infants 4–6 months of age showed strong power in the theta and alpha bands (Figure 8C,D), oscillations in these bands were not coherent (Figure 8G). This contrasts with adults who show a strong frontal coherence in the alpha band during sevoflurane general anesthesia (Akeju et al., 2014c). There were no discernable differences between age groups in the level of coherence between 0.1 and 30 Hz (95% CI, bootstrap analysis).

Global coherence is weak across all frequencies at 0–6 months of age

Global coherence analysis characterizes coordinated activity across multiple channels of EEG as a function of frequency. Global coherence is the fraction of variance at a given frequency across all EEG channels that is explained by the first eigenvector of the cross-spectral matrix. Topographic maps of coherence were computed for slow, delta, theta, alpha, and beta frequency bands in infants 0–3 months of age (Figure 9A) and infants 4–6 months of age (Figure 9B) during MOSSA. Despite slow and delta oscillations in the spectrograms at all ages, and theta and alpha oscillations in the spectrograms of infants 4–6 months of age, global coherence was weak (<0.2) in all frequency bands for all infants (Figure 9C). This finding contrasts with the pattern of highly coordinated, coherent frontal alpha activity observed in adults during propofol general anesthesia (Cimenser et al., 2011; Purdon et al., 2013; Akeju et al., 2014c). The lack of global coherence in the slow and delta frequency ranges is consistent with the lack of slow oscillation coherence observed in adults (Cimenser et al., 2011; Lewis et al., 2012; Purdon et al., 2013).

Figure 9

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EEG features during awake state

We evaluated the EEG features during the awake state (prior to anesthesia exposure) in infants whom awake EEG could be recorded. This was 7 infants in the 0–3 month age group and 12 infants in the 4–6 month age group. Body movement and/or eye opening prior to exposure of sevoflurane general anesthesia were used to confirm the presence of the awake state. Frontal group-median spectrograms at F7 showed dominant slow and delta power, with low theta and alpha power in all ages, were the predominant frequencies during the awake state at all ages (Figure 10—figure supplement 1A–C). Power across all frequencies was significantly lower in infants at 0–3 months compared to infants at 4–6 months of age (95% CI, bootstrap analysis; Figure 10—figure supplement 1D–G).

Comparison of EEG spectral features in the awake (pre-anesthesia) state and MOSSA

Theta and alpha oscillations increase in power during MOSSA in infants 4–6 months of age

Slow and delta power remained relatively constant in all infants during the awake state and MOSSA (Figure 10A,B). There were no clear associations between power in any frequency band and the awake state or MOSSA in infants 0–3 months of age (Figure 10A). Power in the theta (>5 Hz) and alpha frequency ranges were prominent features during MOSSA in infants 4–6 months of age (Figure 10B). Infants 0–3 months of age showed no change in theta or alpha activity associated with general anesthesia (95% CI paired bootstrap analysis, Figure 10D,E). In infants 4–6 months of age, the relationship between theta and alpha power and general anesthesia was significant (95% CI, paired bootstrap analysis; Figure 10H,I).

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EEG features during emergence

Theta and alpha oscillations decrease in power during emergence in infants 4–6 months of age

On emergence, infants started to display gross body movement at a median end-tidal concentration of 0.6% (95% CI, 0.2–1.0%), and 0.4% (95% CI, 0.3–0.6%) at 0–3 month of age and 4–6 months of age, respectively (Figure 11). No infants exhibited body movement at 1.2% or greater end-tidal sevoflurane concentrations.

Figure 11

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Slow and delta power remained relatively constant in infants of all ages as end-tidal sevoflurane concentration decreased during MOSSA through emergence (Figure 11). There were no clear associations between power in any frequency band and change in end-tidal sevoflurane concentration in infants 0–3 months of age (Figure 11A). Theta and alpha power was constant and sizable at end-tidal sevoflurane concentrations greater than 1.2% in infants 4–6 months of age (Figure 11B). Theta and alpha power began to decrease once end-tidal sevoflurane concentration fell below 1.2%, and gross body movement started to appear, in infants 4–6 months of age. In these older infants, theta and alpha activity decreased to negligible power levels at end-tidal sevoflurane concentrations where more than 50% of infants displayed gross body movement. Topographic mapping of the power spectral content showed that there was no spatial shift in alpha activity at any end-tidal sevoflurane concentration in older infants. This finding contrasts the adult pattern where anesthesia-induced frontal alpha oscillations disappear and are replaced by posterior alpha during recovery of consciousness (Ní Mhuircheartaigh et al., 2013; Purdon et al., 2013).

Comparison of EEG spectral features during MOSSA and first body movement

We evaluated frontal power spectra at the frontal channel (F7) at two time periods (1) MOSSA, and (2) immediately after first body movements were recorded (Figure 12). Infants 0–3 months of age showed no change in power across any frequency with recovery of movement during emergence (95% CI, paired bootstrap analysis; Figure 12C–F). In infants 4–6 months of age, the relationship between theta and alpha power and recovery of movement was significant (95% CI, paired bootstrap analysis; Figure 12G–J). We note that two infants included in the 0–3 month age group exhibited low levels of alpha power at MOSSA that decreased during emergence. These infants were 107 and 116 postnatal days, almost 4 months of age, and suggest the underlying circuitry begins to become more refined at this developmental age, starting to reflect similar properties to infants 4–6 months of age.

Figure 12

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We used multi-electrode EEG recordings to characterize the spatial and temporal dynamics of brain activity in infants 0–6 months of age during the awake state, and maintenance of and emergence from sevoflurane general anesthesia for routine surgical care. We showed that during maintenance: (1) all infants had slow oscillations across the entire scalp (Figures 3–6); (2) theta and alpha oscillations were present in addition to the slow oscillations across the entire scalp in all infants 4 months and older (Figures 3–6); (3) across all frequencies, the frontal power in the EEG was significantly greater in the 4- to 6-month-old infants compared to 0- to 3-month-old infants (Figure 4); (4) unlike in adults, all 0- to 6-month-old infants showed an absence of frontal alpha coherence as well as an absence of frontal predominance in power (Figures 5–9), although a small but significant increase in frontal alpha power compared to occipital alpha power emerged in infants 4–6 months of age (Figure 6). We also showed that alpha oscillations increase in power when switching from the awake state to MOSSA in infants 4–6 months of age (Figure 10); and that during emergence, in infants 4–6 months of age power decreased in the alpha and theta bands with decreasing end-tidal sevoflurane concentration (Figures 11, 12). Because we made video recordings during our studies, we related changes in anesthetic concentration to return of movement as an approximate behavioral marker of emergence from general anesthesia (Figures 11, 12).

1. 1. Akeju O
   2. Loggia ML
   3. Catana C
   4. Pavone KJ
   5. Vazquez R
   6. Rhee J
   7. Contreras Ramirez V
   8. Chonde DB
   9. Izquierdo-Garcia D
   10. Arabasz G
   11. Hsu S
   12. Habeeb K
   13. Hooker JM
   14. Napadow V
   15. Brown EN
   16. Purdon PL

   (2014a) Disruption of thalamic functional connectivity is a neural correlate of dexmedetomidine-induced unconsciousness

   eLife 4:e04499.

   https://doi.org/10.7554/eLife.04499

   • Google Scholar
2. 1. Akeju O
   2. Pavone KJ
   3. Westover MB
   4. Vazquez R
   5. Prerau MJ
   6. Harrell PG
   7. Hartnack KE
   8. Rhee J
   9. Sampson AL
   10. Habeeb K
   11. Gao L
   12. Pierce ET
   13. Walsh JL
   14. Brown EN
   15. Purdon PL

   (2014b) A comparison of propofol- and dexmedetomidine-induced electroencephalogram dynamics using spectral and coherence analysis

   Anesthesiology 121:978–989.

   https://doi.org/10.1097/ALN.0000000000000419

   • Google Scholar
3. 1. Akeju O
   2. Westover MB
   3. Pavone KJ
   4. Sampson AL
   5. Hartnack KE
   6. Brown EN
   7. Purdon PL

   (2014c) Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence

   Anesthesiology 121:990–998.

   https://doi.org/10.1097/ALN.0000000000000436

   • Google Scholar
4. 1. Bokil H
   2. Andrews P
   3. Kulkarni JE
   4. Mehta S
   5. Mitra PP

   (2010) Chronux: a platform for analyzing neural signals

   Journal of Neuroscience Methods 192:146–151.

   https://doi.org/10.1016/j.jneumeth.2010.06.020

   • Google Scholar
5. 1. Boly M
   2. Moran R
   3. Murphy M
   4. Boveroux P
   5. Bruno MA
   6. Noirhomme Q
   7. Ledoux D
   8. Bonhomme V
   9. Brichant JF
   10. Tononi G
   11. Laureys S
   12. Friston K

   (2012) Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness

   The Journal of Neuroscience 32:7082–7090.

   https://doi.org/10.1523/JNEUROSCI.3769-11.2012

   • Google Scholar
6. 1. Boveroux P
   2. Vanhaudenhuyse A
   3. Bruno MA
   4. Noirhomme Q
   5. Lauwick S
   6. Luxen A
   7. Degueldre C
   8. Plenevaux A
   9. Schnakers C
   10. Phillips C
   11. Brichant JF
   12. Bonhomme V
   13. Maquet P
   14. Greicius MD
   15. Laureys S
   16. Boly M

   (2010) Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness

   Anesthesiology 113:1038–1053.

   https://doi.org/10.1097/ALN.0b013e3181f697f5

   • Google Scholar
7. 1. Breshears JD
   2. Roland JL
   3. Sharma M
   4. Gaona CM
   5. Freudenburg ZV
   6. Tempelhoff R
   7. Avidan MS
   8. Leuthardt EC

   (2010) Stable and dynamic cortical electrophysiology of induction and emergence with propofol anesthesia

   Proceedings of the National Academy of Sciences of USA 107:21170–21175.

   https://doi.org/10.1073/pnas.1011949107

   • Google Scholar
8. 1. Brody BA
   2. Kinney HC
   3. Kloman AS
   4. Gilles FH

   (1987) Sequence of central nervous system myelination in human Infancy. I. an autopsy atudy of myelination

   Journal of Neuropathology and Experimental Neurology 46:283–301.

   https://doi.org/10.1097/00005072-198705000-00005

   • Google Scholar
9. 1. Brown EN
   2. Lydic R
   3. Schiff ND

   (2010) General anesthesia, sleep, and coma

   The New England Journal of Medicine 363:2638–2650.

   https://doi.org/10.1056/NEJMra0808281

   • Google Scholar
10. 1. Brown EN
    2. Purdon PL
    3. Van Dort CJ

    (2011) General anesthesia and altered states of arousal: a systems neuroscience analysis

    Annual Review of Neuroscience 34:601–628.

    https://doi.org/10.1146/annurev-neuro-060909-153200

    • Google Scholar
11. 1. Brown EN
    2. Solt K
    3. Purdon PL
    4. Akeju O

    (2015)

    Miller's anesthesia

    50, Miller's anesthesia, 8th Edition, Cambridge, Elsevier Health Sciences, Volume 1.

    • Google Scholar
12. 1. Casali AG
    2. Gosseries O
    3. Rosanova M
    4. Boly M
    5. Sarasso S
    6. Casali KR
    7. Casarotto S
    8. Bruno MA
    9. Laureys S
    10. Tononi G
    11. Massimini M

    (2013) A theoretically based index of consciousness independent of sensory processing and behavior

    Science Translational Medicine 5:198ra105.

    https://doi.org/10.1126/scitranslmed.3006294

    • Google Scholar
13. 1. Catts VS
    2. Fung SJ
    3. Long LE
    4. Joshi D
    5. Vercammen A
    6. Allen KM
    7. Fillman SG
    8. Rothmond DA
    9. Sinclair D
    10. Tiwari Y
    11. Tsai SY
    12. Weickert TW
    13. Shannon Weickert C

    (2013) Rethinking schizophrenia in the context of normal neurodevelopment

    Frontiers in Cellular Neuroscience 7:60.

    https://doi.org/10.3389/fncel.2013.00060

    • Google Scholar
14. 1. Ching S
    2. Brown EN

    (2014) Modeling the dynamical effects of anesthesia on brain circuits

    Current Opinion in Neurobiology 25:116–122.

    https://doi.org/10.1016/j.conb.2013.12.011

    • Google Scholar
15. 1. Ching S
    2. Cimenser A
    3. Purdon PL
    4. Brown EN
    5. Kopell NJ

    (2010) Thalamocortical model for a propofol-induced alpha-rhythm associated with loss of consciousness

    Proceedings of the National Academy of Sciences of USA 107:22665–22670.

    https://doi.org/10.1073/pnas.1017069108

    • Google Scholar
16. 1. Chugani HT
    2. Phelps ME

    (1986) Maturational changes in cerebral function in infants determined by 18FDG positron emission tomography

    Science 231:840–843.

    https://doi.org/10.1126/science.3945811

    • Google Scholar
17. 1. Chugani HT
    2. Phelps ME
    3. Mazziotta JC

    (1987) Positron emission tomography study of human brain functional development

    Annals of Neurology 22:487–497.

    https://doi.org/10.1002/ana.410220408

    • Google Scholar
18. 1. Cimenser A
    2. Purdon PL
    3. Pierce ET
    4. Walsh JL
    5. Salazar-Gomez AF
    6. Harrell PG
    7. Tavares-Stoeckel C
    8. Habeeb K
    9. Brown EN

    (2011) Tracking brain states under general anesthesia by using global coherence analysis

    Proceedings of the National Academy of Sciences of USA 108:8832–8837.

    https://doi.org/10.1073/pnas.1017041108

    • Google Scholar
19. 1. Constant I
    2. Sabourdin N

    (2012) The EEG signal: a window on the cortical brain activity

    Paediatric Anaesthesia 22:539–552.

    https://doi.org/10.1111/j.1460-9592.2012.03883.x

    • Google Scholar
20. 1. Davidson AJ

    (2007) Monitoring the anaesthetic depth in children—an update

    Current Opinion in Anaesthesiology 20:236–243.

    https://doi.org/10.1097/ACO.0b013e3280c60c66

    • Google Scholar
21. 1. Davidson AJ
    2. Sale SM
    3. Wong C
    4. McKeever S
    5. Sheppard S
    6. Chan Z
    7. Williams C

    (2008) The electroencephalograph during anesthesia and emergence in infants and children

    Paediatric Anaesthesia 18:60–70.

    https://doi.org/10.1111/j.1460-9592.2007.02359.x

    • Google Scholar
22. 1. Dehorter N
    2. Vinay L
    3. Hammond C
    4. Ben-Ari Y

    (2012) Timing of developmental sequences in different brain structures: physiological and pathological implications

    The European Journal of Neuroscience 35:1846–1856.

    https://doi.org/10.1111/j.1460-9568.2012.08152.x

    • Google Scholar
23. 1. Delorme A
    2. Makeig S

    (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Journal of Neuroscience Methods 134:9–21.

    https://doi.org/10.1016/j.jneumeth.2003.10.009

    • Google Scholar
24. 1. Feshchenko VA
    2. Veselis RA
    3. Reinsel RA

    (2004) Propofol-induced alpha rhythm

    Neuropsychobiology 50:257–266.

    https://doi.org/10.1159/000079981

    • Google Scholar
25. 1. Hayashi K
    2. Shigemi K
    3. Sawa T

    (2012) Neonatal electroencephalography shows low sensitivity to anesthesia

    Neuroscience Letters 517:87–91.

    https://doi.org/10.1016/j.neulet.2012.04.028

    • Google Scholar
26. 1. Hensch T

    (2004) Critical period regulation

    Annual Review of Neuroscience 27:549–579.

    https://doi.org/10.1146/annurev.neuro.27.070203.144327

    • Google Scholar
27. 1. Huttenlocher PR
    2. de Courten C
    3. Garey LJ
    4. Van der Loos H

    (1982) Synaptogenesis in human visual cortex—evidence for synapse elimination during normal development

    Neuroscience Letters 33:247–252.

    https://doi.org/10.1016/0304-394090379-2

    • Google Scholar
28. 1. Huttenlocher PR
    2. Dabholkar AS

    (1997) Regional differences in synaptogenesis in human cerebral cortex

    The Journal of Comparative Neurology 387:167–178.

    https://doi.org/10.1002/(SICI)1096-9861(19971020)387:2<167::AID-CNE1>3.0.CO;2-Z

    • Google Scholar
29. 1. Jevtovic-Todorovic V
    2. Absalom AR
    3. Blomgren K
    4. Brambrink A
    5. Crosby G
    6. Culley DJ
    7. Fiskum G
    8. Giffard RG
    9. Herold KF
    10. Loepke AW
    11. Ma D
    12. Orser BA
    13. Planel E
    14. Slikker W Jr
    15. Soriano SG
    16. Stratmann G
    17. Vutskits L
    18. Xie Z
    19. Hemmings HC Jr

    (2013) Anaesthetic neurotoxicity and neuroplasticity: an expert group report and statement based on the BJA Salzburg Seminar

    British Journal of Anaesthesia 111:143–151.

    https://doi.org/10.1093/bja/aet177

    • Google Scholar
30. 1. Johnson MH

    (2001) Functional brain development in humans

    Nature Reviews Neuroscience 2:475–483.

    https://doi.org/10.1038/35081509

    • Google Scholar
31. 1. Kanold PO
    2. Luhmann HJ

    (2010) The subplate and early cortical circuits

    Annual Review of Neuroscience 33:23–48.

    https://doi.org/10.1146/annurev-neuro-060909-153244

    • Google Scholar
32. 1. Kinnala A
    2. Suhonen-Polvi H
    3. Aärimaa T
    4. Kero P
    5. Korvenranta H
    6. Ruotsalainen U
    7. Bergman J
    8. Haaparanta M
    9. Solin O
    10. Nuutila P
    11. Wegelius U

    (1996) Cerebral metabolic rate for glucose during the first six months of life: An FDG positron emission tomography study

    Archives of Disease in Childhood. Fetal and Neonatal Edition 74:F153–F157.

    https://doi.org/10.1136/fn.74.3.F153

    • Google Scholar
33. 1. Kinney HC
    2. Brody BA
    3. Kloman AS
    4. Gilles FH

    (1988) Sequence of central nervous system myelination in human infancy. II. patterns of myelination in autopsied infants

    Journal of Neuropathology and Experimental Neurology 47:217–234.

    https://doi.org/10.1097/00005072-198805000-00003

    • Google Scholar
34. 1. Kirch C
    2. Politis DN

    (2011) Tft-bootstrap: resampling time series in the frequency domain to obtain replicates in the time domain

    The Annals of Statistics 39:1427–1470.

    https://doi.org/10.1214/10-AOS868

    • Google Scholar
35. 1. Kostović I
    2. Judas M

    (2010) The development of the subplate and thalamocortical connections in the human foetal brain

    Acta Paediatrica 99:1119–1127.

    https://doi.org/10.1111/j.1651-2227.2010.01811.x

    • Google Scholar
36. 1. Lee U
    2. Ku S
    3. Noh G
    4. Baek S
    5. Choi B
    6. Mashour GA

    (2013) Disruption of frontal-parietal communication by ketamine, propofol, and sevoflurane

    Anesthesiology 118:1264–1275.

    https://doi.org/10.1097/ALN.0b013e31829103f5

    • Google Scholar
37. 1. Lewis LD
    2. Weiner VS
    3. Mukamel EA
    4. Donoghue JA
    5. Eskandar EN
    6. Madsen JR
    7. Anderson WS
    8. Hochberg LR
    9. Cash SS
    10. Brown EN
    11. Purdon PL

    (2012) Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness

    Proceedings of the National Academy of Sciences of USA 109:E3377–E3386.

    https://doi.org/10.1073/pnas.1210907109

    • Google Scholar
38. 1. Lin EP
    2. Soriano SG
    3. Loepke AW

    (2014) Anesthetic neurotoxicity

    Anesthesiology Clinics 32:133–155.

    https://doi.org/10.1016/j.anclin.2013.10.003

    • Google Scholar
39. 1. Liu X
    2. Lauer KK
    3. Ward BD
    4. Li SJ
    5. Hudetz AG

    (2013) Differential effects of deep sedation with propofol on the specific and nonspecific thalamocortical systems: a functional magnetic resonance imaging study

    Anesthesiology 118:59–69.

    https://doi.org/10.1097/ALN.0b013e318277a801

    • Google Scholar
40. 1. Lo SS
    2. Sobol JB
    3. Mallavaram N
    4. Carson M
    5. Chang C
    6. Grieve PG
    7. Emerson RG
    8. Stark RI
    9. Sun LS

    (2009) Anesthetic-specific electroencephalographic patterns during emergence from sevoflurane and isoflurane in infants and children

    Paediatric Anaesthesia 19:1157–1165.

    https://doi.org/10.1111/j.1460-9592.2009.03128.x

    • Google Scholar
41. 1. Martuzzi R
    2. Ramani R
    3. Qiu M
    4. Shen X
    5. Papademetris X
    6. Constable RT

    (2011) A whole-brain voxel based measure of Intrinsic connectivity contrast reveals local changes in tissue connectivity with anesthetic without a priori assumptions on thresholds or regions of interest

    Neuroimage 58:1044–1050.

    https://doi.org/10.1016/j.neuroimage.2011.06.075

    • Google Scholar
42. 1. McCann ME
    2. Soriano SG

    (2012) General anesthetics in pediatric anesthesia: influences on the developing brain

    Current Drug Targets 13:944–951.

    https://doi.org/10.2174/138945012800675768

    • Google Scholar
43. 1. McKeever S
    2. Johnston L
    3. Davidson AJ

    (2012)

    An observational study exploring amplitude-integrated electroencephalogram and spectral edge frequency during paediatric anaesthesia

    Anaesthesia and Intensive Care 40:275–284.

    • Google Scholar
44. 1. Mhuircheartaigh RN
    2. Rosenorn-Lanng D
    3. Wise R
    4. Jbabdi S
    5. Rogers R
    6. Tracey I

    (2010) Cortical and subcortical connectivity changes during decreasing levels of consciousness in humans: a functional magnetic resonance imaging study using propofol

    The Journal of Neuroscience 30:9095–9102.

    https://doi.org/10.1523/JNEUROSCI.5516-09.2010

    • Google Scholar
45. 1. Murphy M
    2. Bruno MA
    3. Riedner BA
    4. Boveroux P
    5. Noirhomme Q
    6. Landsness EC
    7. Brichant JF
    8. Phillips C
    9. Massimini M
    10. Laureys S
    11. Tononi G
    12. Boly M

    (2011)

    Propofol anesthesia and sleep: a high-density EEG study

    Sleep 34:283–291A.

    • Google Scholar
46. 1. Ní Mhuircheartaigh R
    2. Warnaby C
    3. Rogers R
    4. Jbabdi S
    5. Tracey I

    (2013) Slow-wave activity saturation and thalamocortical isolation during propofol anesthesia in humans

    Science Translational Medicine 5:208ra148.

    https://doi.org/10.1126/scitranslmed.3006007

    • Google Scholar
47. 1. Purdon PL
    2. Pierce ET
    3. Mukamel EA
    4. Prerau MJ
    5. Walsh JL
    6. Wong KF
    7. Salazar-Gomez AF
    8. Harrell PG
    9. Sampson AL
    10. Cimenser A
    11. Ching S
    12. Kopell NJ
    13. Tavares-Stoeckel C
    14. Habeeb K
    15. Merhar R
    16. Brown EN

    (2013) Electroencephalogram signatures of loss and recovery of consciousness from propofol

    Proceedings of the National Academy of Sciences of USA 110:E1142–E1151.

    https://doi.org/10.1073/pnas.1221180110

    • Google Scholar
48. 1. Rabbitts JA
    2. Groenewald CB
    3. Moriarty JP
    4. Flick R

    (2010) Epidemiology of ambulatory anesthesia for children in the United States: 2006 and 1996

    Anesthesia and Analgesia 111:1011–1015.

    https://doi.org/10.1213/ANE.0b013e3181ee8479

    • Google Scholar
49. 1. Semple BD
    2. Blomgren K
    3. Gimlin K
    4. Ferriero DM
    5. Noble-Haeusslein LJ

    (2013) Brain development in rodents and humans: identifying benchmarks of maturation and vulnerability to injury across species

    Progress in Neurobiology 106–107:1–16.

    https://doi.org/10.1016/j.pneurobio.2013.04.001

    • Google Scholar
50. 1. Sury MR
    2. Worley A
    3. Boyd SG

    (2014) Age-related changes in EEG power spectra in infants during sevoflurane wash-out

    British Journal of Anaesthesia 112:686–694.

    https://doi.org/10.1093/bja/aet409

    • Google Scholar
51. 1. Tau GZ
    2. Peterson BS

    (2010) Normal development of brain circuits

    Neuropsychopharmacology 35:147–168.

    https://doi.org/10.1038/npp.2009.115

    • Google Scholar
52. 1. Vijayan S
    2. Ching S
    3. Purdon PL
    4. Brown EN
    5. Kopell NJ

    (2013) Thalamocortical mechanisms for the anteriorization of Α rhythms during propofol-induced unconsciousness

    The Journal of Neuroscience 33:11070–11075.

    https://doi.org/10.1523/JNEUROSCI.5670-12.2013

    • Google Scholar
53. 1. Vizuete JA
    2. Pillay S
    3. Ropella KM
    4. Hudetz AG

    (2014) Graded defragmentation of cortical neuronal firing during recovery of consciousness in rats

    Neuroscience 275:340–351.

    https://doi.org/10.1016/j.neuroscience.2014.06.018

    • Google Scholar

Author details

1. Laura Cornelissen

   Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, United States

   Contribution

   LC, The work presented here was undertaken in collaboration between all authors. LC and CBB designed and performed the study. SK, PLP and ENB designed the computational analysis. SK and LC carried out the data analysis. All authors contributed to data analysis and data interpretation. LC, SK, CBB and ENB wrote the initial manuscript; all authors edited and revised the manuscript. All authors approved the final version of this manuscript, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Seong-Eun Kim

   For correspondence

   laura.cornelissen@childrens.harvard.edu

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0001-8579-0870

2. Seong-Eun Kim

   Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   S-EK, The work presented here was undertaken in collaboration between all authors. LC and CBB designed and performed the study. SK, PLP and ENB designed the computational analysis. SK and LC carried out the data analysis. All authors contributed to data analysis and data interpretation. LC, SK, CBB and ENB wrote the initial manuscript; all authors edited and revised the manuscript. All authors approved the final version of this manuscript, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Laura Cornelissen

   Competing interests

   No competing interests declared.

3. Patrick L Purdon

   Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, United States

   Contribution

   PLP, The work presented here was undertaken in collaboration between all authors. LC and CBB designed and performed the study. SK, PLP and ENB designed the computational analysis. SK and LC carried out the data analysis. All authors contributed to data analysis and data interpretation. LC, SK, CBB and ENB wrote the initial manuscript; all authors edited and revised the manuscript. All authors approved the final version of this manuscript, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Emery N Brown and Charles B Berde

   Competing interests

   PLP: PLP has patents pending on brain monitoring during general anesthesia and sedation, and a patent licensing agreement with Masimo Corporation. Application Numbers: 20150080754, 20150011907, 20140323898, 20140323897, 20140316218, 20140316217, 20140187973, 20140180160, 20080306397.

4. Emery N Brown

   1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
   2. Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, United States

   Contribution

   ENB, The work presented here was undertaken in collaboration between all authors. LC and CBB designed and performed the study. SK, PLP and ENB designed the computational analysis. SK and LC carried out the data analysis. All authors contributed to data analysis and data interpretation. LC, SK, CBB and ENB wrote the initial manuscript; all authors edited and revised the manuscript. All authors approved the final version of this manuscript, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Patrick L Purdon and Charles B Berde

   Competing interests

   ENB: ENB has patents pending on brain monitoring during general anesthesia and sedation, and a patent licensing agreement with Masimo Corporation. Application Numbers: 20150080754, 20150011907, 20140323898, 20140323897, 20140316218, 20140316217, 20140187973, 20140180160. ENB is a Reviewing Editor for eLife.

5. Charles B Berde

   Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, United States

   Contribution

   CBB, The work presented here was undertaken in collaboration between all authors. LC and CBB designed and performed the study. SK, PLP and ENB designed the computational analysis. SK and LC carried out the data analysis. All authors contributed to data analysis and data interpretation. LC, SK, CBB and ENB wrote the initial manuscript; all authors edited and revised the manuscript. All authors approved the final version of this manuscript, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Patrick L Purdon and Emery N Brown

   Competing interests

   No competing interests declared.

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