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Subramanian S, Purdon PL, Barbieri R, Brown EN. Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness. PLoS One 2021; 16:e0254053. [PMID: 34379623 PMCID: PMC8357089 DOI: 10.1371/journal.pone.0254053] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 06/19/2021] [Indexed: 12/30/2022] Open
Abstract
During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.
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Affiliation(s)
- Sandya Subramanian
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Riccardo Barbieri
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Emery N. Brown
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Picower Institute of Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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Subramanian S, Barbieri R, Purdon PL, Brown EN. Detecting Loss and Regain of Consciousness during Propofol Anesthesia using Multimodal Indices of Autonomic State. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:824-827. [PMID: 33018112 DOI: 10.1109/embc44109.2020.9175366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We have traditionally defined `loss of consciousness' (LOC) and `regain of consciousness' (ROC) during general anesthesia in terms of behavioral correlates. We are starting to understand the dynamics in brain activity that may help define those events; however, we have not yet explored the possible autonomic correlates of LOC and ROC. In this study, we investigated the autonomic dynamics immediately surrounding loss and regain of consciousness in nine healthy volunteers under controlled propofol sedation. We used multimodal autonomic indices generated from physiologically accurate models and found that just before and after LOC and ROC could be differentiated with an AUC of 0.80. In addition, we saw that some of the autonomic changes accompanying LOC and ROC verify known information about the mechanism of action of propofol, while others indicate new avenues for exploration of propofol's effect on the autonomic nervous system. Overall, our work suggests that the autonomic dynamics surrounding the events of loss and regain of consciousness are worthy of further investigation.Clinical Relevance-This introduces the possibility of autonomic biomarkers for loss and regain of consciousness during general anesthesia that are more precise than behavioral tracking alone.
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