Browsing by Author "Mekonnen, Jennifer"

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    A polysomnography study examining the association between sleep and postoperative delirium in older hospitalized cardiac surgical patients
    (2021) Reine, Ibala; Mekonnen, Jennifer; Gitlin, Jacob; Hah, Eunice Y.; Ethrid, Breanna R.; Colon, Katia M.; Marota, Sophia; Orte, Cristy; Pedemonte Trewhela, Juan Cristóbal; Cobanaj, Marisa; Chamadia, Shubham; Qu, Jason; Gao, Lei; Barbieri, Riccardo; Akej, Oluwaseun
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    Delta oscillations phase limit neural activity during sevoflurane anesthesia
    (2019) Chamadia, Shubham; Pedemonte, Juan C.; Hahm, Eunice Y.; Mekonnen, Jennifer; Ibala, Reine; Gitlin, Jacob; Ethridge, Breanna R.; Qu, Jason; Vazquez, Rafael; Rhee, James; Liao, Erika T.; Brown, Emery N.; Akeju, Oluwaseun
    Understanding anesthetic mechanisms with the goal of producing anesthetic states with limited systemic side effects is a major objective of neuroscience research in anesthesiology. Coherent frontal alpha oscillations have been postulated as a mechanism of sevoflurane general anesthesia. This postulate remains unproven. Therefore, we performed a single-site, randomized, cross-over, high-density electroencephalogram study of sevoflurane and sevoflurane-plus-ketamine general anesthesia in 12 healthy subjects. Data were analyzed with multitaper spectral, global coherence, cross-frequency coupling, and phase-dependent methods. Our results suggest that coherent alpha oscillations are not fundamental for maintaining sevoflurane general anesthesia. Taken together, our results suggest that sub-anesthetic and general anesthetic sevoflurane brain states emerge from impaired information processing instantiated by a delta-higher frequency phase-amplitude coupling syntax. These results provide fundamental new insights into the neural circuit mechanisms of sevoflurane anesthesia and suggest that anesthetic states may be produced by extracranial perturbations that cause delta-higher frequency phase-amplitude interactions.
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    Dissociative and analgesic properties of ketamine are independent and unaltered by sevoflurane general anesthesia
    (2021) Hahm, Eunice Y.; Chamadia, Shubham; Locascio, Joseph J.; Pedemonte, Juan C.; Gitlin, Jacob; Mekonnen, Jennifer; Ibala, Reine; Ethridge, Breanna R.; Colon, Katia M.; Qu, Jason; Akeju, Oluwaseun
    Introduction: Ketamine, an anesthetic adjunct, is routinely administered as part of a balanced general anesthetic technique. We recently showed that the acute analgesic and dissociation properties of ketamine are separable to suggest that distinct neural circuits underlie these states. Objective: We aimed to study whether this finding is robust to the substantial neural circuit alterations associated with general anesthesia. Methods: We conducted a single-site, open-label, randomized controlled, cross-over study of sevoflurane and sevoflurane-plus-ketamine (SK) general anesthesia in healthy subjects (n = 12). Before and after general anesthesia, we assessed precalibrated cuff pain intensity and nociceptive pain quality as well as dissociation using the Clinician-Administered Dissociative States Scale (CADSS). For statistical inference, we ran a variation of backward elimination repeated-measures analysis of covariance. Models with CADSS as a covariate term were used to assess whether dissociation mediated the effect of ketamine on pain intensity and quality. Results: Sevoflurane-plus-ketamine general anesthesia was associated with a significant (P = 0.0002) pain intensity decline of 3 (SE, 0.44). There was an order effect for dissociation such that SK was associated with a significant (P = 0.0043) CADSS increase of 17.8 (3.2) when the SK treatment came first. When the pain intensity model was reanalyzed with CADSS as an additional covariate, the effect of CADSS was not significant. These results were also conserved for pain quality. Conclusions: Our findings suggest that the analgesic and dissociation properties of ketamine remain separable despite general anesthesia. Thus, ketamine may be used as a probe to advance our knowledge of dissociation independent pain circuits.
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    Improved tracking of sevoflurane anesthetic states with drug-specific machine learning models
    (2020) Kashkooli, Kimia; Polk, Sam L.; Hahm, Eunice Y.; Murphy, James; Ethridge, Breanna R.; Gitlin, Jacob; Ibala, Reine; Mekonnen, Jennifer; Pedemonte, Juan C.; Sun, Haoqi; Westover, M. Brandon; Barbieri, Riccardo; Akeju, Oluwaseun; Chamadia, Shubham
    Objective.The ability to monitor anesthetic states using automated approaches is expected to reduce inaccurate drug dosing and side-effects. Commercially available anesthetic state monitors perform poorly when ketamine is administered as an anesthetic-analgesic adjunct. Poor performance is likely because the models underlying these monitors are not optimized for the electroencephalogram (EEG) oscillations that are unique to the co-administration of ketamine.Approach.In this work, we designed twok-nearest neighbors algorithms for anesthetic state prediction.Main results.The first algorithm was trained only on sevoflurane EEG data, making it sevoflurane-specific. This algorithm enabled discrimination of the sevoflurane general anesthesia (GA) state from sedated and awake states (true positive rate = 0.87, [95% CI, 0.76, 0.97]). However, it did not enable discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.43, [0.19, 0.67]). In our second algorithm, we implemented a cross drug training paradigm by including both sevoflurane and sevoflurane-plus-ketamine EEG data in our training set. This algorithm enabled discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.91, [0.84, 0.98]).Significance.Instead of a one-algorithm-fits-all-drugs approach to anesthetic state monitoring, our results suggest that drug-specific models are necessary to improve the performance of automated anesthetic state monitors.