State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intracortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Active Emergence from Propofol General Anesthesia Is Induced by Methylphenidate

Background: A recent study showed that methylphenidate induces emergence from isoflurane general anesthesia. Isoflurane and propofol are general anesthetics that may have distinct molecular mechanisms of action. The objective of this study was to test the hypothesis that methylphenidate actively induces emergence from propofol general anesthesia. Methods: Using adult rats, the effect of methylphenidate on time to emergence after a single bolus of propofol was determined. The ability of methylphenidate to restore righting during a continuous target-controlled infusion (TCI) of propofol was also tested. In a separate group of rats, a TCI of propofol was established and spectral analysis was performed on electroencephalogram recordings taken before and after methylphenidate administration. Results: Methylphenidate decreased median time to emergence after a single dose of propofol from 735 s (95% CI: 598–897 s, n = 6) to 448 s (95% CI: 371–495 s, n = 6). The difference was statistically significant (P = 0.0051). During continuous propofol anesthesia with a median final target plasma concentration of 4.0 μg/ml (95% CI: 3.2–4.6, n = 6), none of the rats exhibited purposeful movements after injection of normal saline. After methylphenidate, however, all six rats promptly exhibited arousal and had restoration of righting with a median time of 82 s (95% CI: 30–166 s). Spectral analysis of electroencephalogram data demonstrated a shift in peak power from δ (less than 4 Hz) to θ (4–8 Hz) and β (12–30 Hz) after administration of methylphenidate, indicating arousal in 4/4 rats. Conclusions: Methylphenidate decreases time to emergence after a single dose of propofol, and induces emergence during continuous propofol anesthesia in rats. Further study is warranted to test the hypothesis that methylphenidate induces emergence from propofol general anesthesia in humans.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant K08-GM094394)Massachusetts General Hospital. Dept. of Anesthesia and Critical Car

2-Methyl-4,4-dioxo-N-phenyl-5,6-di­hydro-1,4-oxathiine-3-carboxamide (Oxycarboxin)

In the title compound, C12H13NO4S, a systemic fungicide, the heterocycle adopts a lounge chair conformation and the dihedral angle between the ring planes is 25.8 (2)°. Inter­molecular C—H⋯O hydrogen bonds are noted in the crystal structure. Also observed is a short inter­action of a methyl­ene hydrogen atom with the π-electron system of a phenyl ring in an adjacent mol­ecule

Clinical Electroencephalography for Anesthesiologists

The widely used electroencephalogram-based indices for depth-of-Anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant TR01-GM104948

Activation of D1 Dopamine Receptors Induces Emergence from Isoflurane General Anesthesia

Background: A recent study showed that methylphenidate induces emergence from isoflurane anesthesia. Methylphenidate inhibits dopamine and norepinephrine reuptake transporters. The objective of this study was to test the hypothesis that selective dopamine receptor activation induces emergence from isoflurane anesthesia. Methods: In adult rats, we tested the effects of chloro-APB (D1 agonist) and quinpirole (D2 agonist) on time to emergence from isoflurane general anesthesia. We then performed a dose–response study to test for chloro-APB–induced restoration of righting during continuous isoflurane anesthesia. SCH-23390 (D1 antagonist) was used to confirm that the effects induced by chloro-APB are specifically mediated by D1 receptors. In a separate group of animals, spectral analysis was performed on surface electroencephalogram recordings to assess neurophysiologic changes induced by chloro-APB and quinpirole during isoflurane general anesthesia. Results: Chloro-APB decreased median time to emergence from 330 to 50 s. The median difference in time to emergence between the saline control group (n = 6) and the chloro-APB group (n = 6) was 222 s (95% CI: 77–534 s, Mann–Whitney test). This difference was statistically significant (P = 0.0082). During continuous isoflurane anesthesia, chloro-APB dose-dependently restored righting (n = 6) and decreased electroencephalogram δ power (n = 4). These effects were inhibited by pretreatment with SCH-23390. Quinpirole did not restore righting (n = 6) and had no significant effect on the electroencephalogram (n = 4) during continuous isoflurane anesthesia. Conclusions: Activation of D1 receptors by chloro-APB decreases time to emergence from isoflurane anesthesia and produces behavioral and neurophysiologic evidence of arousal during continuous isoflurane anesthesia. These findings suggest that selective activation of a D1 receptor–mediated arousal mechanism is sufficient to induce emergence from isoflurane general anesthesia.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant K08-GM094394)Massachusetts General Hospital. Dept. of Anesthesia and Critical Car

An assessment of Outpatient Clinic Room Ventilation Systems and Possible Relationship to Disease Transmission

BACKGROUND: With healthcare shifting to the outpatient setting, this study examined whether outpatient clinics operating in business occupancy settings were conducting procedures in rooms with ventilation rates above, at, or below thresholds defined in the American National Standards Institute/American Society of Heating, Refrigerating and Air-Conditioning Engineers/American Society for Health Care Engineering Standard 170 for Ventilation in Health Care Facilities and whether lower ventilation rates and building characteristics increase the risk of disease transmission. METHODS: Ventilation rates were measured in 105 outpatient clinic rooms categorized by services rendered. Building characteristics were evaluated as determinants of ventilation rates, and risk of disease transmission was estimated using the Gammaitoni-Nucci model. RESULTS: When compared to Standard 170, 10% of clinic rooms assessed did not meet the minimum requirement for general exam rooms, 39% did not meet the requirement for treatment rooms, 83% did not meet the requirement for aerosol-generating procedures, and 88% did not meet the requirement for procedure rooms or minor surgical procedures. CONCLUSIONS: Lower than standard air changes per hour were observed and could lead to an increased risk of spread of diseases when conducting advanced procedures and evaluating persons of interest for emerging infectious diseases. These findings are pertinent during the SARS-CoV-2 pandemic, as working guidelines are established for the healthcare community

A state-space model of the burst suppression ratio

Burst suppression is an electroencephalogram pattern observed in states of severely reduced brain activity, such as general anesthesia, hypothermia and anoxic brain injuries. The burst suppression ratio (BSR), defined as the fraction of EEG spent in suppression per epoch, is the standard quantitative measure used to characterize burst suppression. We present a state space model to compute a dynamic estimate of the BSR as the instantaneous probability of suppression. We estimate the model using an approximate EM algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia. Our approach removes the need to artificially average the ratio over long epochs and allows us to make formal statistical comparisons of burst activity at different time points. Our state-space model suggests a more principled way to analyze this key EEG feature that may offer more informative assessments of its associated brain state.Massachusetts General Hospital. Dept. of Anesthesia and Critical CareNational Institutes of Health (U.S.) (Grant DP1 OD003646-01)National Institutes of Health (U.S.) (Grant R01 MH071847)National Institutes of Health (U.S.) (Grant K08 GM094394

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award DP2-OD006454)National Institutes of Health (U.S.) (Award K08-GM094394)Burroughs Wellcome Fund (Award 1010625

A Brain-Machine Interface for Control of Medically-Induced Coma

Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.National Institutes of Health (U.S.) (Director's Transformative Award R01 GM104948)National Institutes of Health (U.S.) (Pioneer Award DP1-OD003646)National Institutes of Health (U.S.) (NIH K08-GM094394)Massachusetts General Hospital. Dept. of Anesthesia and Critical Car