Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.DOI:http://dx.doi.org/10.7554/eLife.06513.001 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.DOI:http://dx.doi.org/10.7554/eLife.06513.002 In the United States, 200,000 children a year receive general anesthesia during the first year of life . AnestheA 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 .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 . For exaThese 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 .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 . Infant 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 . Power iFrontal 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 . OscillaPower across all frequencies was lower in infants at 0–3 months compared to infants at 4–6 months of age during MOSSA . InfantsEnd-tidal sevoflurane requirements during MOSSA were significantly lower in infants 0–3 months compared to infants 4–6 months of age . TherefoGroup-median spectrograms were computed for each recording electrode location in the EEG montage . InfantsTopographic maps of power were computed for slow, delta, theta, alpha, and beta frequency bands . For botWe 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  and for 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  . The froAlthough infants 4–6 months of age showed strong power in the theta and alpha bands , oscillaGlobal 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  and infaWe 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 . Power aSlow and delta power remained relatively constant in all infants during the awake state and MOSSA . There wOn emergence, infants started to display gross body movement at a median end-tidal concentration of 0.6% , and 0.4%  at 0–3 month of age and 4–6 months of age, respectively . No infaSlow and delta power remained relatively constant in infants of all ages as end-tidal sevoflurane concentration decreased during MOSSA through emergence . There wWe evaluated frontal power spectra at the frontal channel (F7) at two time periods (1) MOSSA, and (2) immediately after first body movements were recorded . InfantsWe 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 ; (2) theAge-related differences in EEG properties have been shown in previous studies of infants and children receiving general anesthesia. Davidson and colleagues monitored 8 leads of EEG in 17 infants, 0–6 months of age, who received either sevoflurane or propofol general anesthesia . These iHayashi and colleagues conducted a retrospective study on EEG data collected using a 2-electrode montage in 62 neonates and infants ranging in age from 1 day to 2 years who received sevoflurane general anesthesia . They anOur results refine the results in these reports by showing that there are readily discernible differences in the anesthetic responses of infant 0–3 months of age and infants 4–6 months of age. We report for the first time detailed spatio-temporal analyses of infant 33-lead EEGs with Laplacian referencing using spectra , spectrograms, coherograms, and global coherence computed by multitaper spectral analysis methods. These methods, which are known to have several important optimality properties, have been used successfully to characterize the EEG dynamics of adults receiving general anesthesia and sedation , 2014c. It is now appreciated that in adults at surgical levels of general anesthesia maintained by GABAergic anesthetics , the EEG shows slow oscillations and alpha oscillations, in addition to anteriorization, defined as the movement predominantly of the EEG power in the alpha band from the occipital to the frontal area of the head , 2014c. h currents that typically produce alpha oscillations at resting membrane voltages above 60 millivolts  and children. Moreover, until the question of whether anesthetics are toxic to the developing brain is answered, design of neurophysiological-based definitions of anesthetic states and design of neurophysiological-based brain monitoring strategies offer the most prudent approaches to mitigating anesthetic risk in this vulnerable population.The objective of this observational study was to evaluate the effect of postnatal age on electroencephalographic (EEG) activity during sevoflurane general anesthesia in infants 0–6 months old. We recorded multichannel EEGs during administration of sevoflurane general anesthesia for elective surgery, per clinical protocol. End-tidal anesthetic gas volume and video recordings of behavioral activity were time-locked to the EEG recording. The spatial and temporal properties of the infant EEG were evaluated during the awake state, and at two distinct periods during administration of sevoflurane general anesthesia: (1) MOSSA and (2) emergence from sevoflurane general anesthesia . The ageInfants who were scheduled for an elective surgical procedure were recruited from the pre-operative clinic at Boston Children's Hospital from December 2011 to August 2014. Eligibility criteria consisted of infants between 0 and 6 months postnatal age who required surgery below the neck. All infants were clinically stable on the day of study and American Society of Anesthesiologists' physical status I or II. Infants were not eligible for inclusion in the study if they were (1) born with congenital malformations or other genetic conditions thought to influence brain development, (2) diagnosed with a neurological or cardiovascular disorder, or (3) born at <32 weeks post-menstrual age.Ethical approval was obtained from Boston Children's Hospital Institutional Review Board (Protocol Number IRB-P000003544) and classified as a ‘no more than Minimal Risk’ study. Informed written consent was obtained from parents/legal guardians before each study. The study conformed to the standards set by the Declaration of Helsinki and Good Clinical Practice guidelines.A total of 36 EEG recordings were performed. Data are presented from 30 infants  in the MOSSA analysis. Data are presented from 24 infants  for emergence analysis. Details of the study profile and infant demographics are provided in + nitrous oxide. Nitrous oxide was added at the discretion of the anesthesiologist (29/36 cases). Nitrous oxide was discontinued after placement of an endotracheal tube or laryngeal mask. Propofol was used to facilitate tracheal intubation in 19/36 cases (mean concentration + standard deviation (SD): 15.8 + 6.3 mg/kg; range 10–30 mg/kg). No infants were prescribed midazolam or other premedication on the day of surgery. Clinical characteristics for individual infants are given in Each infant received anesthetic induction with sevoflurane MOSSA was comprised of sevoflurane administration with air and oxygen, titrated to clinical signs; end-tidal sevoflurane concentration was adjusted according to the anesthesiologist's impression of clinical need, not according to pre-set end-tidal sevoflurane concentration per IRB requirements for the pediatric risk category of ‘minimal risk, no potential for benefit’. During MOSSA, the median end-tidal sevoflurane concentration for infants 0–3 months of age was 2.0% , and for infants 4–6 months of age was 2.6% ; All infants were in the supine position throughout the study. Each infant was studied once.An EEG cap was used to record EEG activity . 33 recording electrodes were positioned according to the modified international 10/20 electrode placement system at Fz, FPz, FP1, FP2, F3, F4, F7, F8, FC1, FC2, FC5, FC6, Cz, CPz, C3, C4, CP1, CP2, CP5, CP6, Pz, P3, P4, P7, P8, T7, T8, M1, M2, POz, Oz, O1, and O2 . ReferenEEG activity from 0 to 500 Hz was recorded with an Xltek EEG recording system . Signals were digitized at a sampling rate of 1024 Hz (or 256 Hz in one case), and a resolution of 16 bit.Demographics and clinical information, including age, gender, surgical procedure, anesthetic management, were collected from the electronic medical records and from the in-house Anesthesia Information Management System (AIMS). End-tidal sevoflurane, oxygen, and nitrous oxide concentrations were downloaded from the anesthetic monitoring device  to a recording computer in real-time using ixTrend software . Signals were recorded at a sampling rate of 1 data point per second. Gross body movement was recorded with a camcorder that was time-locked to the EEG recording .Preprocessing was carried out with Natus NeuroWorks  and in-built MATLAB code . Ear electrodes (M1 and M2) were excluded from the final analysis due to poor surface-to-skin contact for the majority of infants.EEG signals were re-montaged to a nearest neighbor Laplacian reference using distances along the scalp surface to weight neighboring electrode contributions. We applied an anti-aliasing filter of 80 Hz and down-sampled the EEG data to 256 Hz.MOSSA analysis: in the first part of this study, we were interested in characterizing EEG features during MOSSA. For each infant, a 10-min EEG segment during a period of maintenance of general anesthesia adequate for surgery and where end-tidal sevoflurane concentration was maintained at a constant concentration (+0.1%) was identified. Within this time segment, a 5-min epoch was selected from ‘artifact-free’ EEG, where motion or electrocautery artifacts were not present in the EEG. Channels with noise or artifacts were excluded from the analysis by visual inspection. EEG data analyzed during MOSSA involved epochs with median time after propofol or nitrous oxide administration of 77 min (IQR 32–93 min). (2) Awake state to MOSSA analysis: in the second part of this study, we were interested in characterizing the relationship with EEG features during the awake state, and comparing them to MOSSA. For each infant, the EEG and video recording starting before any anesthetics were administered was identified. Each video was reviewed and the sleep state of the infant identified using behavioral and vocal markers. The awake state was defined as having one of three features (1) eyes open, (2) body movement, and/or (3) crying. For each corresponding EEG segment, we extracted an 11-s ‘artifact-free’ epoch by visual inspection. If there was no ‘artifact-free’ data available then we did not include that epoch in the analysis. (3) Emergence from general anesthesia analysis: in the third part of this study, we were interested in characterizing relationship with EEG features, end-tidal sevoflurane concentration and onset of body movement. For each infant, an EEG segment starting from 10 min before sevoflurane gas was turned off, and finishing when end-tidal sevoflurane concentration was 0% was identified. For each EEG segment, we extracted a 30-s ‘artifact-free’ epoch by visual inspection at 10 end-tidal sevoflurane concentration ranges: (1) 0 to <0.3%; (2) 0.3 to <0.6%; (3) 0.6 to <0.9%; (4) 0.9 to <1.2%; (5) 1.2 to <1.5%; (6) 1.5 to <1.8%; (7) 1.8 to <2.1%; (8) 2.1 to <2.4%; (9) 2.4 to <2.7%; and (10) 2.7 to <3.0%. A 15- or 20-s epoch was used in three occasions. If there was no ‘artifact-free’ data available in a specific concentration range then we did not include that epoch in the analysis.EEG dynamics were analyzed at three distinct periods of (1) MOSSA, (2) awake (pre-anesthesia), and (3) emergence from general anesthesia. (1) http://chronux.org)  . MultitaInfants were divided into two groups according to postnatal age: (1) 0–3 months and (2) 4–6 months. First, group-averaged spectrograms were computed by taking the median power across subjects at each time and frequency at the electrode of interest for each postnatal age cohort i.e., . Second,To identify the topographic distribution of specific frequency bands, we first took the mean power spectra of group-averaged spectrograms across the entire epoch  for each electrode. Then, we averaged the mean power spectra over each EEG frequency band of interest for both postnatal age groups. Scalp EEG frequency band power plots were performed using 3D interpolation of the electrode montage with the topoplot function in EEGLab .Sij is the cross-spectrum of i, jth electrode and Si is the spectrum of ith electrode at frequency f and time t. The spectrum is calculated by using multitaper methods with TW = 3, K = 5, T = 2 s, and a non-overlapping window. A coherogram graphically illustrates coherence for a range of frequencies plotted across time.Coherence analysis was performed using custom-written MATLAB code  eyes open, (b) body movement, and/or (c) crying. (2) For emergence analysis: videos were reviewed frame-by-frame to identify the time point (in seconds) where gross body movement first occurred. The corresponding end-tidal sevoflurane concentration was extracted from the ixTrend data recording. The percentage of infants who displayed gross body movement in each end-tidal sevoflurane concentration range was evaluated for each group.Behavioral data during (1) the awake state and (2) emergence from general anesthesia were analyzed post hoc. (1) Data are shown as median (95% CI of median) unless otherwise stated. Statistical analysis performed using SPSS Statistics v.21  and custom-written MATLAB code  for computing global coherence , and mul review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and three reviewers, one of whom, Jody Culham, is a member of our Board of Reviewing Editors.Thank you for sending your work entitled “Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants” for consideration at The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.Main revisions needed:All three reviewers found the study interesting and important; however, there are several issues that must be addressed in a revision. The key changes required are summarized in the numbered points below. In addition specific comments from Reviewer #2 are also appended to provide additional detail.1) One expert reviewer brought up a number of important concerns about the brain states of infants during different stages. Specifically, the revision should address the validity of using movement as a proxy for emergence, the possibility of sleep during emergence, and the lack of data from the pre-anesthesia states. See Reviewer #2's detailed points below.2) The possibility that anesthetic variances could explain the data should be explicitly discussed. This includes explicit statistics to determine whether the weights and durations of anesthesia in the two age groups differed significantly. See Reviewer #2's detailed points below.3) More statistical comparisons of the putative differences are needed. See Reviewer #2's detailed points below.4) One reviewer suggests one area that can be addressed in the Discussion is the role, if any, of the developmental change in the grey/white matter ratios that have been documented in infant MRI studies. This may provide a clinically measurable structural basis for the neurophysiological changes observed with sevoflurane anesthesia in this report.5) The authors should reconsider the framing of the conclusions of the paper, although there were some conflicting views about this.eLife editors was the need to make it clearer what the relevance/implications of the results are not just for clinical practice but for understanding brain development. eLife is a biological sciences journal so we are looking for papers that advance our understanding of the underlying biology. There is some interesting discussion about possible reasons for the differential effects of anesthesia in younger infants, older infants and adults. However, this is somewhat underplayed in the manuscript as it stands. For example, the Abstract says, “these differences in EEG dynamics are likely due to brain development”, but doesn't unpack why these are interesting.One main suggestion, which came up during the initial manuscript consultation amongst That said, given some of the concerns raised by reviewers, we do not want to push you to draw stronger conclusions than the data warrant, particularly if some of the differences may relate to potential confounds such as anesthesia levels. One reviewer also wonders if the conclusions in the final sentence of the Abstract are too strong. See Reviewer #2's detailed points below (regarding the Abstract).Detailed comments from Reviewer #2:This is a really interesting study that is challenging to perform and timely in terms of the results generated and their relevance. I am generally enthusiastic about this paper, but more work is required to be convinced by the authors' main interpretation and explanation of their results. Namely, that the developing brain is the most likely explanation for their convincing and well analysed EEG results showing clear differences during surgical anaesthesia between 0-3 and 4-6 month old babies. I describe below in more detail this issue alongside minor other concerns.1) Discussion, first paragraph. Please expand on the discussion of the flaws in using only this behavioural measure (see more below). Is it possible to relate any other measure (respiration/heart rate change) to these crude movement behavioural measures so that we can be more sure that the movement as measured relates to something that additional suggests true emergence/awake-like state (see below for more on this and complexity of sleep stages).2) In the second paragraph of the subsection headed “Studies of EEG dynamics of infant brain under general anesthesia”. Is this finding better explained by <3 month-old babies being more likely to enter a sleep stage during emergence? The authors admit this is possible later in the manuscript. This confound needs more discussion and to be offered as a viable additional explanation perhaps.3) In the subsection headed “Studies of EEG dynamics in adults under general anesthesia”. The lack of pre-anaesthetised baseline data is key. While understandably a complex environment, presumably the electrodes were already in place and so I am unclear why these data were not taken prior to induction of anaesthesia? How can you relate movement to ‘awake’ EEG if you don't know what genuine awake EEG looks like in these different age groups? These data are needed surely to make sense of anaesthetised states—possibly it's the relative EEG changes from fully awake to anaesthetised in each baby that are as relevant as the between ages changes during ‘assumed anaesthetic dose/state’?4) In the subsection headed “Anesthetic Management”. Where the methods are described we begin to unravel some of the major confounds here. These need to be brought earlier into the Introduction and Results and explanation of findings. They are buried here. Please include the anaesthetic data in 5) 6) 7) 8) 9) Abstract: I don't think you can make such a strong conclusion at this stage and based upon your conclusion. You might live to regret stating such a limiting statement regarding the current likelihood that EEG indices could be used. Arguably they might provide excellent measures once you know (and can factor in) the neurophysiological state of brain from which they derive. Please alter the final sentence. 1) One expert reviewer brought up a number of important concerns about the brain states of infants during different stages. Specifically, the revision should address the validity of using movement as a proxy for emergence, the possibility of sleep during emergence, and the lack of data from the pre-anesthesia states. See Reviewer #2's detailed points below.Please see our response to Reviewer #2 .2) The possibility that anesthetic variances could explain the data should be explicitly discussed. This includes explicit statistics to determine whether the weights and durations of anesthesia in the two age groups differed significantly. See Reviewer #2's detailed points below.Please see our response to Reviewer #2 (Comment # 4 and 8).3) More statistical comparisons of the putative differences are needed. See Reviewer #2's detailed points below.Please see our response to Reviewer #2 (Comment # 5 and 6).4) One reviewer suggests one area that can be addressed in the Discussion is the role, if any, of the developmental change in the grey/white matter ratios that have been documented in infant MRI studies. This may provide a clinically measurable structural basis for the neurophysiological changes observed with sevoflurane anesthesia in this report.Structural MRI images from infants under 6 months show relatively high water content of both grey and white matter at this age. Postmortem studies show that significant myelination occurs postnatally and proceeds in a posterior-to-anterior fashion. Myelination begins in the pons and cerebellar peduncles, and by 3 months has extended to the optic radiation and splenium of the corpus callosum. At around 8-12 months of age, the white matter associated with the frontal parietal and occipital lobes becomes apparent. The emergence of frontal alpha activity at around 3 to 4 months postnatal ages likely parallels the regional changes in cortico-cortical and thalamocortical maturation. Please see the subsection “Brain development as a source of changes in EEG dynamics with age” in the Discussion.5) The authors should reconsider the framing of the conclusions of the paper, although there were some conflicting views about this.One main suggestion, which came up during the initial manuscript consultation amongst eLife editors was the need to make it clearer what the relevance/implications of the results are not just for clinical practice but for understanding brain development. eLife is a biological sciences journal so we are looking for papers that advance our understanding of the underlying biology. There is some interesting discussion about possible reasons for the differential effects of anesthesia in younger infants, older infants and adults. However, this is somewhat underplayed in the manuscript as it stands. For example, the Abstract says, “these differences in EEG dynamics are likely due to brain development”, but doesn't unpack why these are interesting.Please see our response to Reviewer #2 (Comment # 9).That said, given some of the concerns raised by reviewers, we do not want to push you to draw stronger conclusions than the data warrant, particularly if some of the differences may relate to potential confounds such as anesthesia levels. One reviewer also wonders if the conclusions in the final sentence of the Abstract are too strong. See Reviewer #2's detailed points below (regarding the Abstract).Detailed comments from Reviewer #2:This is a really interesting study that is challenging to perform and timely in terms of the results generated and their relevance. I am generally enthusiastic about this paper, but more work is required to be convinced by the authors' main interpretation and explanation of their results. Namely, that the developing brain is the most likely explanation for their convincing and well analysed EEG results showing clear differences during surgical anaesthesia between 0-3 and 4-6 month old babies. I describe below in more detail this issue alongside minor other concerns.1) Discussion, first paragraph. Please expand on the discussion of the flaws in using only this behavioural measure (see more below). Is it possible to relate any other measure (respiration/heart rate change) to these crude movement behavioural measures so that we can be more sure that the movement as measured relates to something that additional suggests true emergence/awake-like state (see below for more on this and complexity of sleep stages).We agree with the reviewer that there are uncertainties and limitations with a variety of behavioral and physiologic measures of recovery from the anesthetic state. In studies of adult volunteers, response to verbal command is often used; this is not feasible for infants. The advantage of using gross movement as a measure of emergence is that it is well-defined in infants and children . Onset of movement relates to the anesthetic state, namely the ability of general anesthetics to suppress movement. Respiration, heart rate and blood pressure also reflect effects of anesthesia but in a more complex and derivative manner. Each of these parameters can be influenced by anesthetic state, but also can be influenced by a number of factors summarized in We collected respiration rate, heart rate and non-invasive blood pressure data during emergence. Mean respiration rate and heart rate over a 20-minute period of emergence are shown for all infants in Respiration rate data was sampled at every 1 min using a clinical movement transducer. The low temporal resolution meant that subtle changes in respiration rate at the time of first body movement were not detectable adequate second-by-second accuracy . Based oData in parenthesis represent standard deviation. Bpm, beats per min; M, months old; min, minute. A – Paired Student’s t-test.Asterisks indicate level of significance where: *, p<0.05; and **, p<0.01.Onset of body movement was associated with increased heart rate in all infants. We show that mean HR over 1 min period immediately prior to first visible body movement was 145 bpm (SD: 23) and significantly increased during first body movement  also reflect effects of anesthesia but in a more complex manner being dependent on drug administration and clinical state of the patient. ”2) In the second paragraph of the subsection headed “Studies of EEG dynamics of infant brain under general anesthesia”. Is this finding better explained by <3 month-old babies being more likely to enter a sleep stage during emergence? The authors admit this is possible later in the manuscript. This confound needs more discussion and to be offered as a viable additional explanation perhaps.We used onset of behavioral movement as a surrogate marker for onset of emergence, and this has been used with other behavioral measures such as crying (or attempting to cry), gagging on an endotracheal tube, eye opening and looking around to describe awakening in neonates and infants . All infants included in the emergence analysis required extubation. The standard anesthetic practice to extubate uses one of two ways: (1) “Awake with movement”, meaning with body movement, coughing, and return of some reflex activity, or (2) “Deep”, meaning with spontaneous breathing, but in a deeper plane of anesthesia, specifically without coughing. For >90% of infant anesthetics at Boston Children’s Hospital, awake extubation is preferred, although anesthesiologists differ in the degree of responsiveness required prior to extubation. It is plausible that some infants may remain sedated or unconscious despite recovering movement immediately following surgery. Nevertheless, our comparisons of the EEG properties between arousal states indicate striking similarities between the awake state and emergence in infants 0 to 3 months (as well as between the awake state and MOSSA). The implications of these findings demonstrate the challenges faced when using surface EEG measures to evaluate anesthetic depth in very young infants.3) In the subsection headed “Studies of EEG dynamics in adults under general anesthesia”. The lack of pre-anaesthetised baseline data is key. While understandably a complex environment, presumably the electrodes were already in place and so I am unclear why these data were not taken prior to induction of anaesthesia? How can you relate movement to ‘awake’ EEG if you don't know what genuine awake EEG looks like in these different age groups? These data are needed surely to make sense of anaesthetised states—possibly it's the relative EEG changes from fully awake to anaesthetised in each baby that are as relevant as the between ages changes during ‘assumed anaesthetic dose/state’?Collecting “awake pre-anesthesia” EEG data from infants is technically and logistically very challenging. Ideal recording conditions require the infant to be relaxed i.e. well fed, lying still, and in a cot. These conditions help avoid EEG signal contamination from muscle and movement, and external electrical noise. Most infants in the preoperative holding area were hungry . They were easily agitated and consequently episodically physically active during the Awake EEG recordings. Those infants who required comforting were often held in a parent’s arms; this also generated external electrical noise.Following the reviewer’s suggestion, we now provide awake- state data analysis in the revised report. For the reasons listed above, we made extensive effort to identify good artifact-free preoperative epochs, and used 11-second samples of artifact-free EEG data for the awake-state analysis. Awake-state data were evaluated in each group to identify age-dependent differences, and compared to 11-second samples of MOSSA data to identify sevoflurane general anesthesia dependent differences.Our findings show that slow and delta activity are the prominent frequencies during the awake state at all ages. There were no discernable changes in power between the awake state and MOSSA in infants aged 0 to 3 months. However, increased theta (>6Hz) and alpha were shown during the MOSSA state compared to the awake-state in infants 4 to 6 months of age. These findings suggest that the emergence of alpha activity is age-dependent, and a marker of anesthetic induced unconsciousness in infants 4 to 6 months.The Methods section is now edited to reflect the inclusion of awake- state data. The results include an additional section detailing the awake-state data analysis with comparisons to the “MOSSA” data .4) In the subsection headed “Anesthetic Management”. Where the methods are described we begin to unravel some of the major confounds here. These need to be brought earlier into the Introduction and Results and explanation of findings. They are buried here. Please include the anaesthetic data inand provide statistics. Clearly there are many differences here—understandable, as routine clinical care was performed and this is an opportunistic uncontrolled study, but as such the authors must alert the readership to the likelihood that anaesthetic variances might largely explain their data. As written, this information is not highlighted or discussed and the reader is very strongly led to believe this is all due to the very interesting developmental changes. I don't doubt that there is a profound developmental influence, but we need to be convinced these other factors are not relevant. Firstly, propofol was used for some babies but not others. We do not know whether this was evenly distributed between the two age groups or not. Please detail. There are differences in duration and amount of anaesthetic used between age groups, but again we're not told whether this was significantly different for the two age groups. These factors alone will confound the EEG signals; therefore, please confirm whether they are significantly different or not—hopefully not and then we can be more convinced by their developmental explanation.We have edited The findings related to this study are possibly confounded by differences in anesthetic depth among individuals during MOSSA. For example, the addition of propofol to the anesthetic or increase in end-tidal sevoflurane concentration can potentially alter the depth of anesthesia as well as the characteristics of the EEG spectra.End-tidal sevoflurane concentration requirements during MOSSA were significantly lower in the 0-3 month infants compared to the 4-6 month infants . While frontal-occipital power is not different there does appear to be more power in the slow waves in the temporal regions; is this significant?We evaluated spectral power at the temporal regions and along the midline areas. Power in the slow frequencies was significantly greater in the left (T7) temporal region when compared to key midline electrodes located in the frontal (FPz), central (Cz) and occipital (Oz) regions in all infants . These fIn neonates and infants, the skull is thinner and more vascularized than in adults. The cranial bones are un-fused at birth, separated by fontanels and sutures for several postnatal months to accommodate the rapid growth of the brain. The fontanels are located at the midline junctions of bregma and lamda, and have a thinner zone in the skull; where the skull is thinner, it is more conductive. It is plausible that a high signal to noise ratio is obtained over these regions and contributes to these spatial differences in power.6). Again, temporal lobes ‘look’ different but we're not given any statistical comparisons. I wonder whether the likely developing insula cortex within the temporal lobes might be relevant here?Please see our response to Point 5 above. We speculate that the predominance of lower frequency activity seen reflects less cortico-cortical generator activity, and more thalamocortical generator activity . Future studies using complementary techniques are needed for accurate source localization (i.e. with high density EEG electrode arrays) and evaluation of the origin of deeper cortical and subcortical signals .7). Can we segregate body movements into those with eyes open/closed or whether concomitant with being obviously awake, etc? No information is given about this crude measure, its variance and therefore whether there were systematic differences in the age groups. For example, can we eliminate the question regarding whether babies were still predominately asleep in 0-3 month group even with body movements compared to the 4-6 month olds? Also, what does the body movement do to the quality of EEG recordings and were any body movements of the head? More detail is needed considering this was the single behavioural anchor used to determine emergence and to time-lock the EEG analysis to.Please see our response to Point 2 above. The EEG power spectra in infants 0-3 months during MOSSA is comparable to that of emergence, as well as the awake state. The findings of our study demonstrate how at present, features of the EEG make it challenging to monitor anesthetic depth in infants aged 0-3 months of age, compared to older infants.The reviewer is rightly concerned about the influence of body movement on the quality of EEG recordings. We made extensive effort to identify good artifact-free emergence epochs, and used 10-30-second samples of artifact-free EEG data for the Emergence analysis.We anticipate performing future studies examining the time course of events during emergence using detailed analysis of cortical activity with behavioral (evoked and purposeful), and physiologic measures.8). We need statistics here. There is a large weight difference and differences in length of anaesthesia, which means the brains of younger babies were loaded with more anaesthetic and this will impact emergence. Was this a significant difference? More details needed about the reasons for surgery—any brain/spinal cord related? Please also include from Methods into this table the dose/duration details regarding all anaesthetic agents used with statistics.The patterns of uptake of inhalation anesthetics depend on time course of inhaled concentrations, alveolar ventilation, cardiac output and blood to gas and gas to oil partitioning. In steady-state, for an insoluble anesthetic such as sevoflurane, brain partial pressure is close to inspired partial pressure, with little dependence on body weight or brain weight. The anesthetic concentration was titrated by the anesthesiologist based on clinical considerations that we did not control. Nevertheless, the difference in mean end -tidal sevoflurane concentration between infants 0 to 3 months, and 4 to 6 months was small . Previou9) Abstract: I don't think you can make such a strong conclusion at this stage and based upon your conclusion. You might live to regret stating such a limiting statement regarding the current likelihood that EEG indices could be used. Arguably they might provide excellent measures once you know (and can factor in) the neurophysiological state of brain from which they derive. Please alter the final sentence.The last sentence of the Abstract now reads: “We demonstrate the need to apply age-adjusted analytic approaches in order to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.”


Assessment of the upper limb strength in non-ambulant neuromuscular patients remains challenging. Although potential outcome measures have been reported, longitudinal data demonstrating sensitivity to clinical evolution in spinal muscular atrophy patients are critically lacking. Our study recruited 23 non-ambulant patients, 16 patients  with spinal muscular atrophy type II and 7 patients  with type III. The Brooke functional score was on median 3 with a range from 2 to 6. The average total vital capacity was 46%, and seven patients required non-invasive ventilation at night. Patients were assessed at baseline, 6 months, and 1 year using the Motor Function Measure and innovative devices MyoGrip, MyoPinch, and MoviPlate, which assess handgrip strength, key pinch strength, and hand/finger extension-flexion function, respectively. The study demonstrated the feasibility and reliability of these measures for all patients, and sensitivity to negative changes after the age of 14 years. The younger patients showed an increase of the distal force in the follow-up period. The distal force measurements and function were correlated to different functional scales. These data represent an important step in the process of validating these devices as potential outcome measures for future clinical trials.NCT00993161ClinicalTrials.gov  SMN1 gene [Spinal muscular atrophy (SMA) is the second most frequent autosomal recessive disorder worldwide. It is caused by homozygous absence of the MN1 gene , 3. The MN1 gene . In the MN1 gene , 6. The et al. demonstrated the absence of correlation between the age of the patients and the Hammersmith Functional Motor Scale (HMFS) in type III patients. In SMA type II, a weak correlation was found, suggesting a low decline of patient’s function through time [Different outcome measures for motor assessment of ambulant and non-ambulant SMA patients have been proposed, but because of the broad clinical spectrum of these patients, assessments of strength have proven less than reliable. In a cross-sectional study of 70 SMA patients, Farrar Different outcome measures for motor assessment of ambulant and non-ambulant SMA patients have been validated and are currently used in clinical trials: motor function measure (MFM) , gross met al. showed that motor function is directly linked to muscle strength and that the loss of function is related to the loss of strength [Merlini strength . Objectistrength , 21, butstrength . SMA typThe aims of our study were 1) to assess the feasibility and reliability of different strength  and functional  measures in non-ambulant patients with SMA; 2) to assess the sensitivity to change for the same measurements over one year; and 3) to determine the sample size of non-ambulant patients with SMA needed in clinical trials to prove whether a given drug effectively stabilizes the disease.The protocol for this trial and supporting TREND checklist are available as supporting information; see The present study is part of a multicentre observational study for Upper Limb Evaluation in Non-Ambulant Patients with Neuromuscular Disorder  that took place between January 2010 and January 2013. The study design and the preliminary results concerning Duchenne Muscular Dystrophy (DMD) patients were previously reported . PatientPatient inclusion criteria for the multicentre observational study were: age 8 to 30 years, genetically confirmed neuromuscular disorder, and complete loss of ambulation. Exclusion criteria were major cognitive impairment, inability to stay seated for one hour, or recent upper limb surgery or trauma. In the analysis reported here, only data on patients with genetically confirmed SMA were included.The MoviPlate is a functional test designed to assess how the patient is able to generate specific repeated movements (extension/flexion of hand and fingers) also taking into consideration coordination and fatigue. MyoGrip and MyoPinch are dynamometers which measure isometric grip strength and key pinch with very high resolutions Upper arm strength and function were assessed for the dominant and the non-dominant sides for each patient using these devices as described .The Motor Function Measure (MFM) is a scale which enables an objective assessment of the motor abilities of patients with neuromuscular diseases whatever the motor deficiency. Three dimensions have been identified with factorial analysis in the validation study: D1 standing position and transfers, D2 axial and proximal motor function and D3 distal motor function . Since tThe Brooke score, developed initially for patients with Duchenne muscular dystrophy, is a 6-level functional scale for arms and shoulders, where level 1 is best and level 6 is worse .All patients were evaluated at baseline and at 6 and 12 months. The clinical data recorded at every visit included most recent respiratory data , type of ventilation), orthopedic and functional status , and the disease history. Time spent in the wheelchair for SMA type II patients was considered the actual age of the patients. Baseline assessment encompassed a test-retest evaluation. Both arms were tested using the MyoSet devices with a side randomly chosen for the first assessment. All patients were given between two and five trials and the maximal value was recorded on each side. For each muscle function tested, if the difference between the first two measurements was lower than 10% of the greater, the greater was accepted. If not, a subsequent measurement was made until two trials ranged within 10%. Only 5 trials were allowed for this 10% consistency criterion. The MFM was also performed at baseline and at 6 and 12 months. More details on the experimental protocol are given in a previous article .Reliability was assessed by means of intraclass correlation coefficient (ICC). ICC was computed as a single measure ICC with a two-way random-effect model (absolute agreement). Normality of the variables was assessed using a test of Shapiro-Wilk. It appeared that MoviPlate values were normally distributed, whilst MyoGrip, MyoPinch scores were not. Therefore in order to assess potential learning or fatigue effects, test and re-test results were compared using a non-parametric Wilcoxon test for paired values. Data from both dominant and non-dominant sides were pooled for each session for this analysis.The side effect was assessed by comparing strength and functional scores on dominant and non-dominant sides at baseline using a non-parametric Wilcoxon test. Baseline values corresponded to the maximum value reached between test and retest sessions. Clinical, strength and functional baseline values were compared between SMA type II and III patients using a non-parametric Mann-Whitney test.MyoGrip, MyoPinch, and MoviPlate baseline values on dominant and non-dominant sides were tested for correlation with clinical parameters  and MFM scores at baseline using a non-parametric Spearman correlation coefficient to take into account possible non-linear relationships between variables. Correlations between strength and MoviPlate scores were also tested for each side using a Spearman correlation coefficient.One-year follow up was assessed by comparing one-year MyoSets values with baseline values on both sides using a non-parametric Wilcoxon test. The same analysis was performed on the subgroups of patients classified by age: 14 or younger and older than 14. The age of 14 was chosen as the cutoff based on a graphical review of data evolution according to age for the MyoSet variables and MFM for all SMA types studied.If a new clinical trial is to be designed, the key point is to know how many participants are to be added to the sample to obtain significant results for the study. The purpose of sample size calculation is to determine an appropriate number of subjects for a given study design. We assessed the sample size for a given randomized clinical trial with two independent groups using the sample size formula described previously . The difα/2 = 1.96 and with a power of 80%, Zpower = 0.842The formula used was thus:All analyses were performed using the SPSS v.19 statistical software . The limit of statistical significance was set to 0.05.SMN1 gene. Four patients prematurely left the study: two refused to continue after the test-retest visit; one quit due to severe illness in the family, and another left for unknown reasons. None presented with intellectual disabilities. Seven patients were on nasal nocturnal ventilation, and one was on invasive ventilation.A total of 23 non-ambulant SMA type II  and type III  patients were included in the study and 19 patients completed the study . The cliSMA II patients had significantly lower FVC, time spent in the wheelchair and MFM total scores and D2 and D3 subscores than the type III patients. SMA type II patients performed less well on MFM than did type III patients (-13.6). SMA II patients had a significantly lower weight and height than SMA III patients. Comparison of the two groups is presented in The entire evaluation process including MFM, MyoGrip, MyoPinch, and MoviPlate evaluations lasted approximately 60 minutes. All patients were able to perform the MFM. One patient could not perform either the MyoGrip or the MyoPinch on the dominant side due to very severe contractures. One patient could not perform either the MyoGrip or the MoviPlate on both sides because he was too weak. One other patient could not perform the MoviPlate on either side because he was too weak, and another could not perform the MoviPlate on the non-dominant side at baseline due to difficulties in performing the task on that day. During the following visits, the patients successfully achieved the task. The lowest maximal strength detected on all visits for all patients was 0.14 kg for grip and 0.039 kg for pinch.All measurements showed very high reliability between tests according to ICC values . Correlations between test and retest sessions are displayed in Both groups scored significantly higher on the dominant side for grip (p = 0.020), pinch (p = 0.007), and MoviPlate (p = 0.011).Strength performances using MyoGrip and MyoPinch and MFM scores were significantly lower in SMA type II patients for both dominant and non-dominant hands than for SMA type III patients. In contrast, there was no significant difference in the MoviPlate scores between the groups. The results are displayed in All tests were significantly correlated with the MFM total score. All tests with the exception of MoviPlate were significantly positively correlated with the FVC and were negatively correlated with the Brooke score. MyoPinch was significantly inversely correlated with the time spent in the wheelchair. There was no significant correlation between test scores and age, weight, or height, with the exception of MyoPinch which was correlated with weight and height . Data onSMA patients younger than 14 showed an increase in distal strength from baseline to one year as measured by MyoGrip and MyoPinch. In patients older than 14, significant differences between baseline and one year visit were observed for MyoGrip on both sides, for MyoPinch on the non-dominant side, and for MoviPlate score on the dominant side. No significant evolutions of the D2 subscore, D3 subscore or total score were observed for the MFM . NormaliWe estimated the number of patients required for a clinical trial to demonstrate a significant effect of a given intervention in stabilizing the disease during one year. This estimation was performed for all patients and for patients older than 14 years for each method. Results are summarized in et al. showed that in SMA type II patients, the decline of strength as measured by Manual Muscle Testing and function as assessed by Brooke score or EK scale decrease on a decade-time scale [Our study demonstrated that the use of sensitive dynamometers and a specifically designed functional test is feasible for assessing the upper limbs in almost all non-ambulant SMA type II and III patients. Measures obtained were very reliable, even in the weakest patients, who had scores far below the lower detection limit of traditional dynamometers. This is in line with our previous work in sporadic inclusion body myositis patients  and Duchme scale .et al. [et al. reported a mean FVC of 84% whereas that in our group was 74.8%. Nasal nocturnal ventilation was used by 46% of all our SMA type II patients compared to 38% in a 100 case series of patients aged from 3 to 50 [SMA has a very heterogeneous presentation and consists of a continuous spectrum of severities rather than well-defined types . From a et al.  reported 3 to 50 . In the  3 to 50 , 29 may et al. found raw grip strength in 77 non-ambulant patients with mean age of 22.7 years at 8±11 N, which in turn must be doubled (mean: 16N). Indeed, the user manual of the Type CT 3001 Citec hand-held dynamometer recommends to multiply the value displayed by two to obtain the real strength value, which was not done in this paper . This is comparable with the mean value of 24.9 N in the present study. The two series had a similar mean FVC .Our study found similar values of grip strength as those reported previously for 120 ambulant and non-ambulant patients . MerliniIn our study group, the differences in age between groups of type II and type III patients were not significant. Grip and pinch strength were significantly lower in type II patients than in type III patients. The score measured by the MoviPlate was lower in SMA type II patients but this difference was not statistically significant. The relationship between strength and function is nonlinear, and changes in strength do not necessarily lead to difference in function scores, as it is believed that compensation mechanisms take place.The strength scores were all correlated with indices of severity of the disease, such as Brooke score, with a decrease in FVC, and with time spent in the wheelchair. Interestingly, MoviPlate scores correlated with MFM score. Correlation with strength as assessed by MyoGrip and MyoPinch appeared non-linear, as previously shown in DMD patients , 26. We et al. [et al. [et al. substantial responsiveness in the motor function scale MFM was observed with the dimension 2 subscore  in patients with SMA type II [et al. did not find statistically significant changes in muscle strength using dynamometry after one year in 24 SMA patients  [SMA is described as a slowly progressive disorder. A slow decline of the pulmonary function (FVC decrement of 1.1% per year) was reported by Steffensen et al.  in 13 pa [et al.  did not  type II . These r type II , 32. Conto walk)  and chanto walk) . In our to walk) .The age of 14 in SMA might correspond to the stage when effects of growth no longer compensate for strength loss. Data on pubertal status of patients were not collected in the present study, and it is therefore not possible to correlate our data with pubertal status (which may vary with patient) rather than with rough age. A larger group of patients must be evaluated to precisely define this cut off value and to correlate it with growth in male and female patients. To assess this point, we are currently launching a large international study including centers in France, Belgium and Germany, to assess 70 patients .et al. observed a decrease in strength over a median follow-up period of 17 years (range 12–20) in 30 SMA type II and III patients with median age of 15 (range 6–53) [It is known that upper limbs strength decreases over time when assessed over long periods. Werlauff ge 6–53) . In our Based on our data, we were able to estimate the sample size required to detect significant effects using the MyoSet devices in a placebo-controlled therapeutic trial of a compound expected to stabilize disease progression. Our data indicate that these tools should be considered for trials conducted in non-ambulant patients aged more than 14 years. To our knowledge, only one article in SMA estimated the sample size required for a clinical trial using the outcome measures reported . HoweverS1 Checklist(PDF)Click here for additional data file.S1 Protocol(PDF)Click here for additional data file.S2 Protocol(PDF)Click here for additional data file.


Understanding how cells use complex transcriptional programs to alter their fate in response to specific stimuli is an important question in biology. For the MCF-7 human breast cancer cell line, we applied gene expression trajectory models to identify the genes involved in driving cell fate transitions. We modified trajectory models to account for the scenario where cells were exposed to different stimuli, in this case epidermal growth factor and heregulin, to arrive at different cell fates, i.e. proliferation and differentiation respectively. Using genome-wide CAGE time series data collected from the FANTOM5 consortium, we identified the sets of promoters that were involved in the transition of MCF-7 cells to their specific fates versus those with expression changes that were generic to both stimuli. Of the 1,552 promoters identified, 1,091 had stimulus-specific expression while 461 promoters had generic expression profiles over the time course surveyed. Many of these stimulus-specific promoters mapped to key regulators of the ERK  signaling pathway such as FHL2 . We observed that in general, generic promoters peaked in their expression early on in the time course, while stimulus-specific promoters tended to show activation of their expression at a later stage. The genes that mapped to stimulus-specific promoters were enriched for pathways that control focal adhesion, p53 signaling and MAPK signaling while generic promoters were enriched for cell death, transcription and the cell cycle. We identified 162 genes that were controlled by an alternative promoter during the time course where a subset of 37 genes had separate promoters that were classified as stimulus-specific and generic. The results of our study highlighted the degree of complexity involved in regulating a cell fate transition where multiple promoters mapping to the same gene can demonstrate quite divergent expression profiles. Cell fate transitions occur via the tight, temporal coordination between signaling pathways used by a cell. While the makeup and control of these pathways are highly specific to the stimulus used to induce the transition, a common theme that these pathways share is the activation of cell surface receptors that trigger the initial early response of signaling cascades that then lead to the expression of genes that facilitate the transition. Defining the input and output components of the signaling cascade is feasible, however identifying the transcriptional programs that bridge these two endpoints remains somewhat of a black box. Understanding how cells are regulated by key genes and their corresponding networks during fate transitions may also present opportunities to restrict or manipulate cells towards specific endpoints, an application that has utility in the development of new cancer therapies.Elucidation of the genes expressed during fate transitions represents a critical component to understanding how cells use signaling pathways to change their fate. High-throughput technologies have improved our ability to refine the list of signaling components that contribute to a cell fate transition. However even with access to the best technology available, our limitation in understanding signaling is constrained more by the fact that pathways operate as complex, non-linear circuits, and that their usage by cells to achieve transitions is far more complicated than a simple input-output system , 2.Despite the complex arrangement of signaling components that underlie cell fate transitions, in nature, there is clearly convergence of only a finite number of possible pathways that are used by the cell. This phenomenon is most readily understood when we consider known cases where different stimuli can trigger cells to adopt the same phenotypic outcome. For example, human HL-60 promyelocytic cells when exposed to dimethyl sulfoxide (DMSO) and all-trans retinoic acid (ATRA) both lead to neutrophil differentiation , 4. ExamBoth EGF and HRG ligands share a common ErbB receptor signaling pathway. Upon activation of the ErbB receptors, a multi-layered signal transduction network is initiated, often involving the activation of the ERK and the phosphatidiylinositol 3'-kinase (PI3K) pathways , 9. EGF Gene expression trajectory models were initially developed to identify the two major sets of genes involved in a cell fate transition . One setCap analysis of gene expression (CAGE) is a technique that was pioneered to identify the location of transcription start sites (TSSs) in the genome and quantify their usage . Two CAGhttp://fantom.gsc.riken.jp/5/.Alternative promoters are defined as multiple promoter sites in the genome that control the same gene , and conThe MCF-7 human breast cancer cell line was exposed to either EGF or HRG, and samples were collected at 0h (non-treated), 15min, 30min, 45min, 60min, 80min, 100min, 2hr, 2.5hr, 3hr, 3.5hr, 4hr, 5hr, 6hr, 7hr and 8hr. These time points were selected in order to sample the early phase of cell differentiation as well as capture mid and delayed-stages of gene expression. The data was profiled using HeliscopeCAGE, resulting in time-course expression levels of 102,540 promoters under the two treatments (HRG and EGF). The time courses each had three biological replicates for each time point at all 15 time points .2-transformed data, we fitted an ANOVA model of the form:i denotes the time point indices from 1 to 15, and k denotes the three biological replicates. A promoter was removed from the analysis if it had no statistically significant change in expression over time for both treatments . The P-values from the ANOVA model were adjusted for multiple testing using the Benjamini-Hochberg method [Promoters with expression of less than 1 tag per million for all time points were removed, resulting in a total of 54,822 promoters. Using logIdentifying generic and stimulus-specific promotersAn ANOVA model was constructed for each individual promoter:stimulus term  was assigned to the fate-specific set of promoters, whereas a promoter that failed to attain significance was classified as generic. P-values were adjusted using the Benjamini-Hochberg method [A promoter that was statistically significant for the g method  for multFold change of the expression profile of HRG:EGF treatment was calculated for each promoter. An ANOVA model was fitted to detect which promoters had significant fold change differences across time . P-values were corrected for multiple testing using the Benjamini-Hochberg method.http://www.genenames.org/cgi-bin/hgnc_downloads.cgi. In most cases, there was a one-to-one relationship between a promoter and a gene. 319 out of the 461 generic promoters mapped to 306 genes with known EntrezGene IDs and 847 out of the 1,091 stimulus-specific promoters mapped to 737 genes with known EntrezGene IDs.Promoters were mapped to their respective gene-centric annotations using the FANTOM5 expression atlas  and HGNCTranscription factor sets were defined from the TFcheckpoint database  which reWe wrote R code to test for over-representation of Gene Ontology (GO) terms and KEGG pathways in the genes that were mapped by the stimulus-specific and generic promoter sets. Significance of enrichment was assessed using the Fisher’s exact test. Annotations were based on GO and KEGG terms as defined by the Bioconductor packages org.Hs.eg.db (version 2.10.1) and KEGG.db (version 2.10.1). P-values were adjusted for multiple testing using the Benjamini-Hochberg method, using a significance threshold of 0.05. Only genes with evidence codes based on experimental validation in GO were used for this analysis, specifically evidence codes EXP (inferred from experiment), IDA (inferred from direct assay), IPI , IMP (inferred from mutant phenotype) or IGI (inferred from genetic interaction). We also discarded child nodes of GO terms to improve the specificity of the results obtained. We compared our results to the DAVID  web interface  as well Expression profiles of the promoters mapping to the same gene were averaged to create a single expression profile for each unique gene. Changes in gene expression correlation in the two EGF and HRG time courses were explored using an empirical Bayesian approach for identifying differentially co-expressed gene pairs implemented in the Bioconductor EBcoexpress package  (versionshortest.paths tools from the R package igraph (version 0.7.1), and were applied to a protein-protein interaction (PPI) network constructed from iRefIndex [Sets of generic and stimulus-specific transcription factor interactions were derived using the RefIndex  data. ThRefIndex  which brRefIndex , BioGRIDRefIndex , CORUM [RefIndex , DIP [25RefIndex , IntAct RefIndex , MINT [2RefIndex , Mpact [RefIndex , MPPI [3RefIndex , and OPHRefIndex  in a nonR, as the statistic to determine whether a gene was generic (R > 0.5) or stimulus-specific (R ≤ 0.5) in the qPCR data set. A two-sided Fisher’s exact test was used to assess the significance of overlap observed between the CAGE data sets and the two other gene expression data sets. See The microarray and qPCR data sets were obtained from the authors of . For thei corresponds to each time point in the experiment and ranged from 1 to 15, j represents the stimulus applied (either EGF or HRG), and k denotes each of the three biological replicates used in the time course data  since this corresponds to a scenario where the expression profiles deviate substantially between the EGF and HRG-stimulated time courses. A promoter failing to attain statistical significance for the stimulus variable was classified in the generic group , which signifies coordination of the expression profiles between the two stimuli. An initial filtering step was applied to all promoters to exclude those with no significant change across both times courses as these promoters were unlikely to be informative for understanding the transcriptional program underlying the cell fate transition . Examples of promoters with generic and stimulus-specific expression profiles are shown in 1) that can be fit to each individual promoter is a linear regression model that is specified as follows:α is the regression coefficient that measures the direction and degree of impact that an increase in time will have on the expression of a specific promoter. We can capitalize on this property and use the direction of the estimated α from the model in (2) to infer whether promoters were associated with an overall increase (α > 0) or decrease (α < 0) in expression during the time course. The sets of generic and stimulus-specific promoters were subset further based on the direction of the estimated α  , 38 and The stimulus-specific promoters control genes that were enriched for KEGG pathways involved in focal adhesion, p53 signaling, and MAPK signaling pathways see . RelatedAlternative promoters represent an inbuilt feature of the genome for generating diversity under different cellular conditions. They also provide robustness to ensure that the proper initiation of transcription of critical genes can occur. For the MCF-7 time course, we applied the definition of an alternative promoter as the instance where more than one promoter mapped to the same gene. We found that the expression of 162 genes were controlled by an alternative promoter. Of these 162 genes, 18 were controlled by more than one generic promoter, and 107 were controlled by more than one stimulus-specific promoter. A subset of 37 genes were controlled by a combination of promoters that had expression profiles that were classified into both generic and stimulus-specific groups.Examples of transcription factors whose expression were controlled by alternative promoters were EGR1 and FOS which were each controlled by seven promoters see . For FOSWe investigated whether usage of alternative promoters was likely to be for specialization through stage-specific activation or a more general strategy for robustness by having multiple promoters available that can substitute for each other. We reasoned that if two alternative promoters could substitute for each other to activate the same gene, then their expression profiles would be highly correlated during the time course. Alternatively, low or anti-correlated expression profiles indicate that alternative promoters may have roles that differ depending on their activation time. 72.7% of 22 genes that are controlled by more than onegeneric promoter are highly correlated , and 92.9% of 126 genes are controlled by at least one stimulus-specific promoter. These results suggest that for both generic and stimulus-specific promoters, alternative promoters may substitute for each other and that the multiplicity of promoters mapping to the same gene reflect an inbuilt redundancy and robustness of the cellular system.The transcription of protein-coding genes provides a template for the production of proteins that are the molecules responsible for carrying out fundamental functions in the cell. We recognize that these genes and their corresponding proteins do not operate in isolation but instead interact via defined combinations, and one way in which these that can be represented is through a protein-protein interaction (PPI) network. We investigated whether PPI networks for genes mapping to generic promoters displayed any common or distinct features compared to the PPIs for the stimulus-specific promoters. Because overlaying gene expression data with human protein data is always somewhat of an extrapolation, we restricted our network construction to only the transcription factors that were represented in the generic and stimulus-specific promoter groups . One of the most striking results observed is that the generic and stimulus-specific PPI networks are connected through six common transcription factors that are controlled by alternative promoters, EGR1, FOS, SMAD3 (SMAD family member 3), BRIP1 , FOXA1 (forkhead box A1), and KLF6 (Kruppel-Like Factor 6) . This reHeatmaps were constructed to highlight the expression profiles of the transcription factors represented in these networks. We see that HRG drives more dramatic expression changes see  in both Regulatory information about the cell fate transition can also be obtained by probing co-expression networks since genes that share similar expression profiles are thought to be regulated by common upstream processes. We constructed co-expression networks for the transcription factors mapping to promoters in the generic and stimulus-specific groups see . Network−6). The overlap with the qPCR data set was not statistically significant , and this may have been due to the fact that only a targeted set of genes  were profiled in the qPCR experiment whereas the CAGE data set was at genome-wide resolution. The frequency at which time points were sampled was also sparser in the qPCR data set than the CAGE data set. Despite the lack of statistical significance, we observed consistent sets of genes between the qPCR and CAGE data sets that were classified as generic (32 genes) and stimulus-specific (41 genes). Amongst these lists, we detected transcription factors that were key regulators of EGF and HRG signaling such as EGR1, FOS, SP3 (Sp3 transcription factor), SMAD3, and BRIP1 for the generic genes, and FOSL2 (FOS-like antigen 2), FOXA1, and JUN (jun proto-oncogene) for the stimulus-specific genes. We inspected the expression activity for several genes from both stimulus-specific and generic sets in both CAGE and microarrays data sets and found that some profiles were qualitatively consistent. We also compared the functional enrichment results of the stimulus-specific and generic gene lists, and found a subset of significant GO terms and KEGG pathways that were enriched in both microarray and CAGE data sets. Our results from this comparison are described in more detail in To investigate the robustness of the results obtained from the CAGE data set, we applied our analyses to two other gene expression data sets from a microarray experiment  and a qPAdditionally, it is worthwhile highlighting that although different computational methods were used to analyze the qPCR and microarray data sets in the original papers from the trajectory models we have applied to the CAGE data set, similar conclusions were observed in the results we obtained , 10. In It is unsurprising that the balance between generic and stimulus-specific promoters was so skewed towards the latter group (70% versus 30%) because not only were the MCF-7 cells exposed to different stimuli but as a result, they also transition to different cell fates. As a result of the MCF-7 example that we have chosen, the stimulus-specific trajectory model captures the promoters that have both cell fate-specific and stimulus-specific expression responses of the cellular system. The generic trajectory model instead captures the promoters with behavior that is coordinated between both stimuli. We would therefore expect that a larger number of promoters are needed to participate in the transition of cells towards different fates as well as recruitment to respond to different stimuli.Our analysis indicated that both generic and stimulus-specific promoters target AP-1 complex including FOS. FOS contains a conserved DEF domain, susceptible to phosphorylation by ERK, and this domain makes FOS a good sensor for an upstream ERK signal , 43. An As for EGR1, we found this gene was represented by both generic and stimulus-specific promoters. An earlier study showed using two EGF pulse-stimulated 184A1 human mammary cells that both p53-dependent restraining processes followed by the second pulse elimination of the suppressive action of p53 via the PI3K/AKT pathway as well as ERK-EGR1 threshold mechanism is necessary for S phase entry . In addiThe observation of alternative promoters that map to the same genes demonstrated how it is possible to control the one gene via multiple signals. Amongst the alternative promoters that we identified, we saw that the majority featured alternative promoters that had stimulus-specific expression and suggested that promoters mapping to the same gene were all being expressed in a divergent manner between different stimuli. The observation that another group of genes (37 out of 162) had alternative promoters that were represented in both generic and stimulus-specific groups was interesting as this highlights the divergent nature of how genes are expressed and controlled across their corresponding promoters regions. Other CAGE studies have exposed the level of discordance that can occur for different promoters that map to the same gene, suggesting that to really understand transcription and its functional consequences, we need to move to the resolution of promoters as opposed to genes.The comparison of PPI networks for generic and stimulus-specific promoters reflect the convergence of overlapping components in the signaling pathways, specifically for the six transcription common TFs . We saw many PPIs that were unique to the generic versus stimulus-specific networks which was consistent with the idea that these genes and their proteins are required in different stages and abundances during the time course, depending on the role they play in the cell fate transition. However, the six common transcription factors mark components of the signaling pathways where their usage, at the expression level at least, occurs in both a generic and stimulus-specific manner at different timing regulated by alternative promoters. The result suggests that regulatory mechanisms of these transcription factors are in fact very diverse and precisely controlled in a time-dependent manner, and that divergent regulatory events are integrated as a core network that functions to act as a robust axis to drive cell determination.S1 FigA. 49 generic promoters and B. 100 stimulus-specific promoters, identified as being the most highly-expressed . There are many more genes controlled by stimulus-specific promoters highly expressed, particularly under the HRG treatment and at mid-delayed times.These values span the whole range of generic and stimulus-specific promoters values for (EPS)Click here for additional data file.S2 FigA. There were 16 generic promoters that mapped to genes in the GO term ‘negative regulation of cell proliferation’. B. There were 21 stimulus-specific promoters that mapped to genes in the GO term ‘positive regulation of cell proliferation’.(EPS)Click here for additional data file.S3 Fig(EPS)Click here for additional data file.S4 FigAs observed for the whole set of generic and stimulus-specific promoters, HRG drives more dramatic expression changes; while generic transcription factors are mainly expressed at immediate early times, most stimulus-specific ones are expressed at mid-delayed times.(EPS)Click here for additional data file.S5 FigEach node represents a gene, and the edges join genes that are highly-correlated during the time course (|correlation coefficient| > 0.95). The color of an edge indicates the strength of correlation, ranging from blue (negative correlation) to white (uncorrelated) to red (positively correlated). We see more dramatic co-expression in the HRG time course compared to the EGF time course, and this is observed more readily for the subset of stimulus-specific transcription factors.(EPS)Click here for additional data file.S6 FigWhile the expression of the majority of transcription factor pairs is uncorrelated under EGF treatment , the expression of the majority of transcription factor pairs is highly correlated, either positively or negatively , under HRG. These differences in correlated expression for both generic and stimulus-specific transcription factors are particularly striking for stimulus-specific transcription factors, which tend to be more positively correlated.(EPS)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableStatistical significance was defined as adjusted P-value < 0.05 using the Benjamini-Hochberg method. No KEGG pathway was found to meet statistical significant.(DOCX)Click here for additional data file.S3 TableStatistical significance was defined as adjusted P-value < 0.05 using the Benjamini-Hochberg method.(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Amatitlania nigrofasciata) has been extensively examined in relation to many behavioral topics, such as courtship, pair-bonding, bi-parental care, and territoriality. Recently, this model species has been utilized in studies on genetics, endocrinology, and neuroanatomy, with an ultimate goal of connecting behavior with its underlying mechanisms. The goal of this study was two-fold: (1) profile the circulating levels of plasma 11KT in the male convict cichlid at multiple points during the reproductive cycle and (2) generally compare the hormonal profiles of the widely used laboratory populations and those of a free-living population in the streams of Costa Rica. The results of the field experiment showed that male convict cichlids had higher levels of circulating 11KT during courtship and lower during the parental care and non-breeding phases. The profile of the laboratory population was similar to the profile of the free-living individuals, with significantly higher levels of 11KT occurring during courtship than during parental care, though the level of 11KT during non-breeding phase was elevated in the laboratory. The high levels of 11KT during courtship and low levels of 11KT during parental care found in both the field and the laboratory is similar to what has been reported in other species of teleosts, and may suggest an important function of 11KT in the expression of courtship behavior and the subsequent onset of parental behaviors in this model species.The convict cichlid ( Molothrus ater), for example, substantial differences were seen in the levels of circulating testosterone. Wild paired males had higher concentrations of circulating testosterone than unpaired males early in the season, with testosterone peaking later in the season during social hierarchy formation and mate acquisition ). In general, studies of the behavioral endocrinology of fishes have been particularly interested in the hormone 11-ketotestosterone (11KT), which is the predominant androgen in many teleost species. This non-aromatizable androgen    is respostrogen) . The cirstrogen)  (though strogen) ). It hasstrogen) . Additiostrogen) . This prOur study intentionally focused on the convict cichlid because it has become a model species that is widely used for behavioral studies and is of increasing interest to researchers that seek to connect behavior with genetics e.g., , neuroanBehaviorally, the convict cichlid has been the interest of many researchers for many decades because of their complex social system. After reaching sexual maturity, males and females court each other in order to form exclusive pair bonds . The paiIn regards to the convict cichlid and hormones specifically, recent studies have suggested a functional role for 11KT in the exhibition of social behaviors. In a laboratory study, This study had two goals: (1) profile the circulating levels of plasma 11KT in the male convict cichlid at multiple points during their natural reproductive cycle and (2) generally compare those hormonal profiles of the widely used laboratory populations and those of a free-living population in the streams of Costa Rica. Although The field portion of this study was conducted in the Rio Cabuyo, located in the Lomas Barbudal Biological Reserve  during the dry season from December 20, 2007 to January 5, 2008. All research was conducted with permission from Ministerio del Ambiente y Energia (MINAE permiso #48). Males were observed and were classified as either non-breeding, courting, or parental based upon their exhibited behavior. Adult males observed congregating in large mixed sex groups with no overt courtship or aggressive behavior were classified as non-breeding. Males observed expressing courtship behavior towards a female and aggression towards males were classified as courting. Males observed interacting with a mate and caring for offspring were classified as parental.After observations to determine reproductive stage, males were immediately caught using a weighted drop net of transparent monofilament line and a hand net. The captured male was placed on a wet, flat, floating piece of plastic. Blood was drawn from the caudal vein using a heparinized syringe with a 26-gauge needle. The syringe was inserted in the caudal peduncle, 2–3 scale rows below the midline of the body. The needle was inserted until it met resistance and then slight back pressure applied. This type of blood draw method has been used successfully in other species . ApproxiThe laboratory portion of this investigation was conducted at Lehigh University in accordance with IACUC approval . Laboratory subjects were either directly purchased from a fish distributor  or descended from purchased fish. The fish used were adults and were not sexually naive. Prior to use, the convict cichlids were maintained in uni-sexual groups of approximately 20–30 individuals, in 475 L stock tanks. The laboratory was maintained at 27 °C, with a light:dark cycle of 14:10 h. Fish were fed commercial fish food.Adult male convict cichlids were assigned to one of 3 groups; non-breeding, courting, and parental. Males in the non-breeding group were housed in male-only groups for at least 2 months in 475 L stock tanks. For the experiment, males were caught from the stock tank and immediately sampled. Males in the courting group were placed with a female and another male into a 300 L tank with an artificial breeding site for 48 h. Only males that were observed to be actively engaged in courtship behavior towards the female were sampled. Males in the parental group were males that had spawned with a female and were housed in a 300 L tank with mate and offspring either in the larval stage or free swimming fry. Only males observed to be actively engaged in parental care behavior were sampled. After sampling, males from all groups were placed in a separate tank to ensure that each male was only sampled once. Each male was caught and blood was drawn as described above. The blood samples were immediately centrifuged at 2000 × g for 10 min and the resultant plasma stored at −80 °C until assayed.Samples were analyzed using a commercially available enzyme immunoassay (EIA) kit  following the protocols provided. The EIA kits have been used with other species of teleosts  and withBecause the behavioral groups sampled in the field and in the laboratory were similar, but not identical, we analyzed the field and laboratory samples separately, using an ANOVA (SPSS 15.1) followed by Tukey HSD post-hoc comparison to determine differences in the plasma levels of 11KT among the 3 groups.n = 6) had a mean circulating 11KT level of 0.344 ± 0.102 ng/ml. The parental group (n = 5) expressed a similar level of circulating 11KT (0.387 ± 0.097 ng/ml). The courting group (n = 6) had a mean circulating 11KT level about 7.5 times higher (2.61 ± 0.850 ng/ml). The 3 groups were significantly different , with a significant difference between non-breeding and courting  as well as between parental and courting  but not between non-breeding and parental  (The non-breeding group (= 0.998) .n = 14) had a mean circulating 11KT level of 0.44 ± 0.10 ng/ml. Both the courting group  and the non-breeding group  had mean circulating levels of 11KT that were approximately five to six times higher than the parental group. The 3 groups were significantly different . A significant difference was found between the non-breeding and parental groups  as well as between the courting and parental groups  but there was not a significant difference between the non-breeding and the courting groups   .The first aim of our study was to profile the circulating levels of plasma 11KT in the male convict cichlid at multiple points during the reproductive cycle. Our findings indicate that both laboratory-housed and free-living male convict cichlids exhibit a profile of 11KT during the reproductive cycle with high levels of 11KT during courtship (prior to spawning) followed by a low levels of 11KT during parental care. This observed profile is similar to other teleost species (reviewed by The high level of circulating 11KT during courtship in both the laboratory and field subjects again suggests that 11KT may have a function in the expression of courtship behavior. This explanation is supported by research in other species in which castration led to a decrease in pre-spawning behaviors  which coThe significantly lower levels of 11KT during parental care (shown here and by The secondary goal of our study was to generally compare the hormonal profile of the widely used laboratory populations and that of a free-living population in Costa Rica. As expected, both populations of fish showed similar levels of 11KT during courtship and parental stages. An interesting and unanticipated result was the apparent difference between the laboratory and field in the non-breeding phase. In that laboratory, subjects had circulating levels of 11KT that resembled courting males, whereas field subjects had levels of 11KT that were similar to parental males.This apparent difference may be due to the fact that the nonbreeding group of laboratory subjects was composed of males that had been housed in a uni-sexual group tanks for an extended period of time, as compared to the field subjects, in which the males were in free contact with both males and females. Obviously, social interactions can greatly affect the hormonal levels of individuals. It has been shown in other teleost species that males may experience an change in androgens as a result of social interactions (reviewed by This continued examination of the circulating level of 11KT represents an important step towards elucidating the role of 11KT in the expression of reproductive behavior of teleosts, but also in using this convict cichlid model organism to better understand the mechanisms of behavior. Because of the vast knowledge of convict cichlid behavior and the current need for integrative studies, we reason that additional modest studies such as this will be necessary to not only describe this species but also to continue to make connections between the conditions of laboratory-housed animals and their naturally occurring field counterparts.10.7717/peerj.949/supp-1Supplemental Information 1Click here for additional data file.