For over a decade my research career as a systems neuroscientist has been centered on measuring the brain in different states of consciousness using electrophysiology. Two ways to study conscious transitions empirically are by investigating the brain during sleep and while under anesthesia. I spent my doctoral and early postdoctoral work studying how sleep improves learning and memory at the neural network level. Currently, I study the brain activity associated with anesthetic state transitions to broaden my understanding of the neural dynamics associated with altered conscious states. In fact, the brain shares similar electrophysiological activity patterns during sleep with some anesthetic transitions. With anesthetics, however, one can compare how different anesthetic agents interact with different neuromodulatory systems to cause similar behavioral outcomes (i.e. sedation and unconsciousness).

My current projects include exploring and evaluating different computational approaches to quantify anesthetic depth using electrophysiology in various anesthetic protocols, identifying perioperative anesthesia risk factors for poor cognitive outcomes, and understanding the influence of anesthesia on neural circuits in patients with epilepsy. A thorough characterization of the brain activity associated with brain state transitions during anesthesia administration is of critical importance to better monitor patients and improve outcomes.

Academic Appointments

Honors & Awards

  • Pathway to Independence Award (K99/R00), NIH NIGMS (2020-2025)
  • Helena Anna Henzl Gabor Young Women in Science Fund Travel Grant, Stanford University (2019)
  • Dean's Postdoctoral Fellowship Award, Stanford University (2018)
  • Anesthesia Training Program in Biomedical Research T32, Department of Anesthesiology, Stanford School of Medicine (2018)
  • President's Research Scholarship Award, UT Health Science Center Houston (2014)
  • Dean’s Research Scholarship Award, UT Health Science Center Houston (2013)
  • Roberta M. and Jean M. Worsham Endowed Scholarship, UT Health Science Center Houston (2012)
  • Tzu-Chi Foundation Scholarship for Excellence, UT Health Science Center Houston (2012)
  • Eka Francian Chemistry Honor Society, Ripon College (2007)
  • Beta Beta Beta Biological Honor Society, Ripon College (2006-2007)
  • The Laurel Honor Society, Ripon College (2006)
  • Psi Chi National Honor Society in Psychology, Ripon College (2005 - 2007)

Boards, Advisory Committees, Professional Organizations

  • Member, International Anesthesia Research Society (2017 - Present)
  • Member, Society for Neuroscience (2008 - Present)
  • Student Member, Association for the Scientific Study of Consciousness (2007 - 2011)
  • Student Member, Mind Science Foundation (2007 - 2011)
  • Chapter President, Psi Chi National Honor Society in Psychology (2006 - 2007)
  • Member, Psi Chi National Honor Society in Psychology (2005 - Present)

Professional Education

  • Postdoctoral Fellowship, Stanford. University, Neuroscience, Anesthesiology (2021)
  • Postdoctoral Fellowship, Rice University, Electrical and Computer Engineering, Neuroscience (2015)
  • PhD, UT Health Science Center at Houston, Neuroscience (2014)
  • B.A., Ripon College, Psychobiology, Biology, Chemistry (2007)

2021-22 Courses

All Publications

  • High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas. Neuron Chelaru, M. I., Eagleman, S., Andrei, A. R., Milton, R., Kharas, N., Dragoi, V. 2021


    One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured 90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions.

    View details for DOI 10.1016/j.neuron.2021.09.042

    View details for PubMedID 34665999

  • Offline comparison of brain function monitors for geriatric anaesthetic-induced electroencephalogram changes British Journal of Anaesthesia Eagleman, S., Drover, C., Li, X., MacIver, B., Drover, D. 2021
  • Molecular diversity of anesthetic actions is evident in electroencephalogram effects in humans and animals International Journal of Molecular Sciences Eagleman, S., MacIver, M. 2021; 22 (2)

    View details for DOI 10.3390/ijms22020495

  • Advances in precision anaesthesia may be found by testing our resistance to change British Journal of Anaesthesia Eagleman, S. L., MacIver, M. 2020
  • Nonlinear dynamics captures brain states at different levels of consciousness in patients anesthetized with propofol. PloS one Eagleman, S. L., Chander, D. n., Reynolds, C. n., Ouellette, N. T., MacIver, M. B. 2019; 14 (10): e0223921


    The information processing capability of the brain decreases during unconscious states. Capturing this decrease during anesthesia-induced unconsciousness has been attempted using standard spectral analyses as these correlate relatively well with breakdowns in corticothalamic networks. Much of this work has involved the use of propofol to perturb brain activity, as it is one of the most widely used anesthetics for routine surgical anesthesia. Propofol administration alone produces EEG spectral characteristics similar to most hypnotics; however, inter-individual and drug variation render spectral measures inconsistent. Complexity measures of EEG signals could offer better measures to distinguish brain states, because brain activity exhibits nonlinear behavior at several scales during transitions of consciousness. We tested the potential of complexity analyses from nonlinear dynamics to identify loss and recovery of consciousness at clinically relevant timepoints. Patients undergoing propofol general anesthesia for various surgical procedures were identified as having changes in states of consciousness by the loss and recovery of response to verbal stimuli after induction and upon cessation of anesthesia, respectively. We demonstrate that nonlinear dynamics analyses showed more significant differences between consciousness states than spectral measures. Notably, attractors in conscious and anesthesia-induced unconscious states exhibited significantly different shapes. These shapes have implications for network connectivity, information processing, and the total number of states available to the brain at these different levels. They also reflect some of our general understanding of the network effects of consciousness in a way that spectral measures cannot. Thus, complexity measures could provide a universal means for reliably capturing depth of consciousness based on EEG changes at the beginning and end of anesthesia administration.

    View details for DOI 10.1371/journal.pone.0223921

    View details for PubMedID 31665174

  • Remifentanil and Nitrous Oxide Anesthesia Produces a Unique Pattern of EEG Activity During Loss and Recovery of Response FRONTIERS IN HUMAN NEUROSCIENCE Eagleman, S. L., Drover, C. M., Drover, D. R., Ouellette, N. T., MacIver, M. 2018; 12: 173


    Nitrous oxide (N2O) and remifentanil (remi) are used along with other anesthetic and adjuvant agents for routine surgical anesthesia, yet the electroencephalogram (EEG) changes produced by this combination are poorly described. N2O administered alone produces EEG spectral characteristics that are distinct from most hypnotics. Furthermore, EEG frequency-derived trends before and after clinically relevant time points vary depending on N2O concentration. Remifentanil typically increases low frequency and decreases high frequency activity in the EEG, but how it influences N2O's EEG effect is not known. Previous attempts to characterize EEG signals of patients anesthetized with N2O using frequency-derived measures have shown conflicts and inconsistencies. Thus, in addition to determining the spectral characteristics of this unique combination, we also test whether a newly proposed characterization of time-delayed embeddings of the EEG signal tracks loss and recovery of consciousness significantly at clinically relevant time points. We retrospectively investigated the effects of remi and N2O on EEG signals recorded from 32 surgical patients receiving anesthesia for elective abdominal surgeries. Remifentanil and N2O (66%) were co-administered during the procedures. Patients were tested for loss and recovery of response (ROR) to verbal stimuli after induction and upon cessation of anesthesia, respectively. We found that the addition of remifentanil to N2O anesthesia improves the ability of traditional frequency-derived measures, including the Bispectral Index (BIS), to discriminate between loss and ROR. Finally, we found that a novel analysis of EEG using nonlinear dynamics showed more significant differences between states than most spectral measures.

    View details for PubMedID 29867405

  • Can you hear me now? Information processing in primary auditory cortex at loss of consciousness British Journal of Anaesthesia Eagleman, S. L., MacIver, M. B. 2018; 121 (3): 526-529
  • Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia? Frontiers in neuroscience Eagleman, S. L., Vaughn, D. A., Drover, D. R., Drover, C. M., Cohen, M. S., Ouellette, N. T., MacIver, M. B. 2018; 12: 645


    While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration-differences which future work may reveal to improve geriatric patient monitoring.

    View details for PubMedID 30294254

  • Calculations of consciousness: electroencephalography analyses to determine anesthetic depth. Current opinion in anaesthesiology Eagleman, S. L., Drover, D. R. 2018; 31 (4): 431–38


    Electroencephalography (EEG) was introduced into anesthesia practice in the 1990s as a tool to titrate anesthetic depth. However, limitations in current analysis techniques have called into question whether these techniques improve standard of care, or instead call for improved, more ubiquitously applicable measures to assess anesthetic transitions and depth. This review highlights emerging analytical approaches and techniques from neuroscience research that have the potential to better capture anesthetic transitions to provide better measurements of anesthetic depth.Since the introduction of electroencephalography, neuroscientists, engineers, mathematicians, and clinicians have all been developing new ways of analyzing continuous electrical signals. Collaborations between these fields have proliferated several analytical techniques that demonstrate how anesthetics affect brain dynamics and conscious transitions. Here, we review techniques in the following categories: network science, integration and information, nonlinear dynamics, and artificial intelligence.Up-and-coming techniques have the potential to better clinically define and characterize altered consciousness time points. Such new techniques used alongside traditional measures have the potential to improve depth of anesthesia measurements and enhance an understanding of how the brain is affected by anesthetic agents. However, new measures will be needed to be tested for robustness in real-world environments and on diverse experimental protocols.

    View details for PubMedID 29847364

  • Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state Nature Communications Beaman, C., Eagleman, S., Dragoi, V. 2017
  • Image sequence reactivation in awake V4 networks PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Eagleman, S. L., Dragoi, V. 2012; 109 (47): 19450-19455


    In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we describe reactivation of stimulus-evoked activity in awake visual cortical networks. We found that continuous exposure to randomly flashed image sequences induces reactivation in macaque V4 cortical networks in the absence of visual stimulation. This reactivation of previously evoked activity is stimulus-specific, occurs only in the same temporal order as the original response, and strengthens with increased stimulus exposures. Importantly, cells exhibiting significant reactivation carry more information about the stimulus than cells that do not reactivate. These results demonstrate a surprising degree of experience-dependent plasticity in visual cortical networks as a result of repeated exposure to unattended information. We suggest that awake reactivation in visual cortex may underlie perceptual learning by passive stimulus exposure.

    View details for DOI 10.1073/pnas.1212059109

    View details for Web of Science ID 000311997200085

    View details for PubMedID 23129638

    View details for PubMedCentralID PMC3511092

  • Examining Local Network Processing using Multi-contact Laminar Electrode Recording JOVE-JOURNAL OF VISUALIZED EXPERIMENTS Hansen, B. J., Eagleman, S., Dragoi, V. 2011

    View details for DOI 10.3791/2806

    View details for Web of Science ID 000209222100003

  • Testing pigeon memory in a change detection task PSYCHONOMIC BULLETIN & REVIEW Wright, A. A., Katz, J. S., Magnotti, J., Elmore, L. C., Babb, S., Alwin, S. 2010; 17 (2): 243-249


    Six pigeons were trained in a change detection task with four colors. They were shown two colored circles on a sample array, followed by a test array with the color of one circle changed. The pigeons learned to choose the changed color and transferred their performance to four unfamiliar colors, suggesting that they had learned a generalized concept of color change. They also transferred performance to test delays several times their 50-msec training delay without prior delay training. The accurate delay performance of several seconds suggests that their change detection was memory based, as opposed to a perceptual attentional capture process. These experiments are the first to show that an animal species (pigeons, in this case) can learn a change detection task identical to ones used to test human memory, thereby providing the possibility of directly comparing short-term memory processing across species.

    View details for DOI 10.3758/PBR.17.2.243

    View details for Web of Science ID 000281812500018

    View details for PubMedID 20382927