1
|
Smith SK, Kafashan M, Rios RL, Brown EN, Landsness EC, Guay CS, Palanca BJA. Daytime dexmedetomidine sedation with closed-loop acoustic stimulation alters slow wave sleep homeostasis in healthy adults. BJA Open 2024; 10:100276. [PMID: 38571816 PMCID: PMC10990715 DOI: 10.1016/j.bjao.2024.100276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 02/29/2024] [Indexed: 04/05/2024]
Abstract
Background The alpha-2 adrenergic agonist dexmedetomidine induces EEG patterns resembling those of non-rapid eye movement (NREM) sleep. Fulfilment of slow wave sleep (SWS) homeostatic needs would address the assumption that dexmedetomidine induces functional biomimetic sleep states. Methods In-home sleep EEG recordings were obtained from 13 healthy participants before and after dexmedetomidine sedation. Dexmedetomidine target-controlled infusions and closed-loop acoustic stimulation were implemented to induce and enhance EEG slow waves, respectively. EEG recordings during sedation and sleep were staged using modified American Academy of Sleep Medicine criteria. Slow wave activity (EEG power from 0.5 to 4 Hz) was computed for NREM stage 2 (N2) and NREM stage 3 (N3/SWS) epochs, with the aggregate partitioned into quintiles by time. The first slow wave activity quintile served as a surrogate for slow wave pressure, and the difference between the first and fifth quintiles as a measure of slow wave pressure dissipation. Results Compared with pre-sedation sleep, post-sedation sleep showed reduced N3 duration (mean difference of -17.1 min, 95% confidence interval -30.0 to -8.2, P=0.015). Dissipation of slow wave pressure was reduced (P=0.02). Changes in combined durations of N2 and N3 between pre- and post-sedation sleep correlated with total dexmedetomidine dose, (r=-0.61, P=0.03). Conclusions Daytime dexmedetomidine sedation and closed-loop acoustic stimulation targeting EEG slow waves reduced N3/SWS duration and measures of slow wave pressure dissipation on the post-sedation night in healthy young adults. Thus, the paired intervention induces sleep-like states that fulfil certain homeostatic NREM sleep needs in healthy young adults. Clinical trial registration ClinicalTrials.gov NCT04206059.
Collapse
Affiliation(s)
- S. Kendall Smith
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA
| | - MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA
| | - Rachel L. Rios
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA
| | - Emery N. Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eric C. Landsness
- Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Division of Sleep Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Christian S. Guay
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ben Julian A. Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
2
|
Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness. Sci Transl Med 2024; 16:eadj4303. [PMID: 38691619 DOI: 10.1126/scitranslmed.adj4303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.
Collapse
Affiliation(s)
- Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Holly J Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Samuel B Snider
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - J Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Robin L Haynes
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Bram R Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Hannah C Kinney
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
3
|
Adam E, Kowalski M, Akeju O, Miller EK, Brown EN, McCarthy MM, Kopell N. Ketamine can produce oscillatory dynamics by engaging mechanisms dependent on the kinetics of NMDA receptors. bioRxiv 2024:2024.04.03.587998. [PMID: 38617266 PMCID: PMC11014619 DOI: 10.1101/2024.04.03.587998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Ketamine is an NMDA-receptor antagonist that produces sedation, analgesia and dissociation at low doses and profound unconsciousness with antinociception at high doses. At high and low doses, ketamine can generate gamma oscillations (>25 Hz) in the electroencephalogram (EEG). The gamma oscillations are interrupted by slow-delta oscillations (0.1-4 Hz) at high doses. Ketamine's primary molecular targets and its oscillatory dynamics have been characterized. However, how the actions of ketamine at the subcellular level give rise to the oscillatory dynamics observed at the network level remains unknown. By developing a biophysical model of cortical circuits, we demonstrate how NMDA-receptor antagonism by ketamine can produce the oscillatory dynamics observed in human EEG recordings and non-human primate local field potential recordings. We have discovered how impaired NMDA-receptor kinetics can cause disinhibition in neuronal circuits and how a disinhibited interaction between NMDA-receptor-mediated excitation and GABA-receptor-mediated inhibition can produce gamma oscillations at high and low doses, and slow-delta oscillations at high doses. Our work uncovers general mechanisms for generating oscillatory brain dynamics that differs from ones previously reported, and provides important insights into ketamine's mechanisms of action as an anesthetic and as a therapy for treatment-resistant depression.
Collapse
Affiliation(s)
- Elie Adam
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Marek Kowalski
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Department of Anesthesia, Harvard Medical School, Boston, MA 02215
| | - Earl K. Miller
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Emery N. Brown
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Department of Anesthesia, Harvard Medical School, Boston, MA 02215
| | | | - Nancy Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215
| |
Collapse
|
4
|
Bardon AG, Ballesteros JJ, Brincat SL, Roy JE, Mahnke MK, Ishizawa Y, Brown EN, Miller EK. Convergent effects of different anesthetics are due to changes in phase alignment of cortical oscillations. bioRxiv 2024:2024.03.20.585943. [PMID: 38562734 PMCID: PMC10983946 DOI: 10.1101/2024.03.20.585943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Many different anesthetics cause loss of responsiveness despite having diverse underlying molecular and circuit actions. To explore the convergent effects of these drugs, we examined how ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, and dexmedetomidine, an α2 adrenergic receptor agonist, affected neural oscillations in the prefrontal cortex of nonhuman primates. Previous work has shown that anesthesia increases phase locking of low-frequency local field potential activity across cortex. We observed similar increases with anesthetic doses of ketamine and dexmedetomidine in the ventrolateral and dorsolateral prefrontal cortex, within and across hemispheres. However, the nature of the phase locking varied between regions. We found that oscillatory activity in different prefrontal subregions within each hemisphere became more anti-phase with both drugs. Local analyses within a region suggested that this finding could be explained by broad cortical distance-based effects, such as a large traveling wave. By contrast, homologous areas across hemispheres increased their phase alignment. Our results suggest that the drugs induce strong patterns of cortical phase alignment that are markedly different from those in the awake state, and that these patterns may be a common feature driving loss of responsiveness from different anesthetic drugs.
Collapse
|
5
|
Tauber JM, Brincat SL, Stephen EP, Donoghue JA, Kozachkov L, Brown EN, Miller EK. Propofol-mediated Unconsciousness Disrupts Progression of Sensory Signals through the Cortical Hierarchy. J Cogn Neurosci 2024; 36:394-413. [PMID: 37902596 DOI: 10.1162/jocn_a_02081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
A critical component of anesthesia is the loss of sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of nonhuman primates before and during propofol-mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-related synchronization between brain areas in the local field potential of Awake animals. By contrast, propofol-mediated unconsciousness eliminated stimulus-related synchrony and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in Awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just because of asynchronous Down states. Rather, both Down states and Up states reflect disrupted dynamics.
Collapse
Affiliation(s)
- John M Tauber
- Massachusetts Institute of Technology, Cambridge, MA
| | | | | | | | - Leo Kozachkov
- Massachusetts Institute of Technology, Cambridge, MA
| | - Emery N Brown
- Massachusetts Institute of Technology, Cambridge, MA
- Massachusetts General Hospital, Boston
- Harvard University, Cambridge, MA
| | - Earl K Miller
- Massachusetts Institute of Technology, Cambridge, MA
| |
Collapse
|
6
|
Garwood IC, Major AJ, Antonini MJ, Correa J, Lee Y, Sahasrabudhe A, Mahnke MK, Miller EK, Brown EN, Anikeeva P. Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques. Sci Adv 2023; 9:eadh0974. [PMID: 37801492 PMCID: PMC10558126 DOI: 10.1126/sciadv.adh0974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/05/2023] [Indexed: 10/08/2023]
Abstract
Recording and modulating neural activity in vivo enables investigations of the neurophysiology underlying behavior and disease. However, there is a dearth of translational tools for simultaneous recording and localized receptor-specific modulation. We address this limitation by translating multifunctional fiber neurotechnology previously only available for rodent studies to enable cortical and subcortical neural recording and modulation in macaques. We record single-neuron and broader oscillatory activity during intracranial GABA infusions in the premotor cortex and putamen. By applying state-space models to characterize changes in electrophysiology, we uncover that neural activity evoked by a working memory task is reshaped by even a modest local inhibition. The recordings provide detailed insight into the electrophysiological effect of neurotransmitter receptor modulation in both cortical and subcortical structures in an awake macaque. Our results demonstrate a first-time application of multifunctional fibers for causal studies of neuronal activity in behaving nonhuman primates and pave the way for clinical translation of fiber-based neurotechnology.
Collapse
Affiliation(s)
- Indie C. Garwood
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex J. Major
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marc-Joseph Antonini
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Josefina Correa
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Youngbin Lee
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Atharva Sahasrabudhe
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Meredith K. Mahnke
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Earl K. Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emery N. Brown
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Anaesthesia, Harvard Medical School, Boston, MA, USA
| | - Polina Anikeeva
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
7
|
Chakravarty S, Donoghue J, Waite AS, Mahnke M, Garwood IC, Gallo S, Miller EK, Brown EN. Closed-loop control of anesthetic state in nonhuman primates. PNAS Nexus 2023; 2:pgad293. [PMID: 37920551 PMCID: PMC10619513 DOI: 10.1093/pnasnexus/pgad293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 11/04/2023]
Abstract
Research in human volunteers and surgical patients has shown that unconsciousness under general anesthesia can be reliably tracked using real-time electroencephalogram processing. Hence, a closed-loop anesthesia delivery (CLAD) system that maintains precisely specified levels of unconsciousness is feasible and would greatly aid intraoperative patient management. The US Federal Drug Administration has approved no CLAD system for human use due partly to a lack of testing in appropriate animal models. To address this key roadblock, we implement a nonhuman primate (NHP) CLAD system that controls the level of unconsciousness using the anesthetic propofol. The key system components are a local field potential (LFP) recording system; propofol pharmacokinetics and pharmacodynamic models; the control variable (LFP power between 20 and 30 Hz), a programmable infusion system and a linear quadratic integral controller. Our CLAD system accurately controlled the level of unconsciousness along two different 125-min dynamic target trajectories for 18 h and 45 min in nine experiments in two NHPs. System performance measures were comparable or superior to those in previous CLAD reports. We demonstrate that an NHP CLAD system can reliably and accurately control in real-time unconsciousness maintained by anesthesia. Our findings establish critical steps for CLAD systems' design and testing prior to human testing.
Collapse
Affiliation(s)
- Sourish Chakravarty
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jacob Donoghue
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Ayan S Waite
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Meredith Mahnke
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Indie C Garwood
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Sebastian Gallo
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Emery N Brown
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- Institute for Medical Engineering and Sciences, MIT, Cambridge, MA 02139, USA
- Department of Anaesthesia, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
8
|
Xiong YS, Donoghue JA, Lundqvist M, Mahnke M, Major AJ, Brown EN, Miller EK, Bastos AM. Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity. bioRxiv 2023:2023.09.02.555990. [PMID: 37732234 PMCID: PMC10508719 DOI: 10.1101/2023.09.02.555990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from deep-layer cortex via alpha/beta (8-30Hz) oscillations. They inhibit the gamma (40-100Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs (e.g., AAAAB) before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late (> 200 ms from stimulus onset) period spiking, and superficial layer sinks in sensory cortex. Therefore, auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in auditory cortex, there was a loss of differential spiking to oddballs in higher order cortex. This may be a consequence of a loss of within-area and inter-area spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness. Significance statement Neurophysiology studies have found alpha/beta oscillations (8-30Hz), gamma oscillations (40-100Hz), and spiking activity during cognition. Alpha/beta power has an inverse relationship with gamma power/spiking. This inverse relationship suggests that gamma/spiking are under the inhibitory control of alpha/beta. The predictive routing model hypothesizes that alpha/beta oscillations selectively inhibit (and thereby control) cortical activity that is predictable. We tested whether this inhibitory control is a signature of consciousness. We used multi-area neurophysiology recordings in monkeys presented with tone sequences that varied in predictability. We recorded brain activity as the anesthetic propofol was administered to manipulate consciousness. Compared to conscious processing, propofol-mediated unconsciousness disrupted alpha/beta inhibitory control during predictive processing. This led to a disinhibition of gamma/spiking, consistent with the predictive routing model.
Collapse
|
9
|
Adam E, Kwon O, Montejo KA, Brown EN. Modulatory dynamics mark the transition between anesthetic states of unconsciousness. Proc Natl Acad Sci U S A 2023; 120:e2300058120. [PMID: 37467269 PMCID: PMC10372635 DOI: 10.1073/pnas.2300058120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 04/26/2023] [Indexed: 07/21/2023] Open
Abstract
Unconsciousness maintained by GABAergic anesthetics, such as propofol and sevoflurane, is characterized by slow-delta oscillations (0.3 to 4 Hz) and alpha oscillations (8 to 14 Hz) that are readily visible in the electroencephalogram. At higher doses, these slow-delta-alpha (SDA) oscillations transition into burst suppression. This is a marker of a state of profound brain inactivation during which isoelectric (flatline) periods alternate with periods of the SDA patterns present at lower doses. While the SDA and burst suppression patterns have been analyzed separately, the transition from one to the other has not. Using state-space methods, we characterize the dynamic evolution of brain activity from SDA to burst suppression and back during unconsciousness maintained with propofol or sevoflurane in volunteer subjects and surgical patients. We uncover two dynamical processes that continuously modulate the SDA oscillations: alpha-wave amplitude and slow-wave frequency modulation. We present an alpha modulation index and a slow modulation index which characterize how these processes track the transition from SDA oscillations to burst suppression and back to SDA oscillations as a function of increasing and decreasing anesthetic doses, respectively. Our biophysical model reveals that these dynamics track the combined evolution of the neurophysiological and metabolic effects of a GABAergic anesthetic on brain circuits. Our characterization of the modulatory dynamics mediated by GABAergic anesthetics offers insights into the mechanisms of these agents and strategies for monitoring and precisely controlling the level of unconsciousness in patients under general anesthesia.
Collapse
Affiliation(s)
- Elie Adam
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Ohyoon Kwon
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Karla A Montejo
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Emery N Brown
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Department of Anaesthesia, Harvard Medical School, Boston, MA 02115
| |
Collapse
|
10
|
Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Sustaining wakefulness: Brainstem connectivity in human consciousness. bioRxiv 2023:2023.07.13.548265. [PMID: 37502983 PMCID: PMC10369992 DOI: 10.1101/2023.07.13.548265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 μm resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness.
Collapse
Affiliation(s)
- Brian L. Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Holly J. Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Samuel B. Snider
- Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - J. Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Yelena G. Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Robin L. Haynes
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Bram R. Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Joseph T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Emery N. Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hannah C. Kinney
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
11
|
Guay CS, Bean CD, Kwon O, Brown EN. Recovery From Acute Respiratory Distress Syndrome Is Associated With Increasing Alpha Power in the Frontal Electroencephalogram During Propofol Sedation: A Case Report. A A Pract 2023; 17:e01698. [PMID: 37409746 DOI: 10.1213/xaa.0000000000001698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
The effects of critical illness on electroencephalographic (EEG) signatures of sedatives have not been described, limiting the use of EEG-guided sedation in the intensive care unit (ICU). We report the case of a 36-year-old man recovering from acute respiratory distress syndrome (ARDS). Severe ARDS was characterized by slow-delta (0.1-4 Hz) and theta (4-8 Hz) oscillations but lacked the alpha (8-14 Hz) power expected during propofol sedation in a patient of this age. The alpha power emerged as ARDS resolved. This case raises the question of whether inflammatory states can alter EEG signatures during sedation.
Collapse
Affiliation(s)
- Christian S Guay
- From the Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christopher D Bean
- From the Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ohyoon Kwon
- From the Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Emery N Brown
- From the Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
| |
Collapse
|
12
|
Tauber JM, Brincat SL, Stephen EP, Donaghue JA, Kozachkov L, Brown EN, Miller EK. Propofol Mediated Unconsciousness Disrupts Progression of Sensory Signals through the Cortical Hierarchy. bioRxiv 2023:2023.06.25.546463. [PMID: 37425684 PMCID: PMC10327085 DOI: 10.1101/2023.06.25.546463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
A critical component of anesthesia is the loss sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential (LFP) and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of non-human primates before and during propofol mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-induced coherence between brain areas in the LFP of awake animals. By contrast, propofol mediated unconsciousness eliminated stimulus-induced coherence and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just due to asynchronous down states. Rather, both Down states and Up states reflect disrupted dynamics.
Collapse
Affiliation(s)
- John M. Tauber
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Scott L. Brincat
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Emily P. Stephen
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Jacob A. Donaghue
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Leo Kozachkov
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Emery N. Brown
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - Earl K. Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| |
Collapse
|
13
|
Soplata AE, Adam E, Brown EN, Purdon PL, McCarthy MM, Kopell N. Rapid thalamocortical network switching mediated by cortical synchronization underlies propofol-induced EEG signatures: a biophysical model. J Neurophysiol 2023. [PMID: 37314079 DOI: 10.1152/jn.00068.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023] Open
Abstract
Propofol-mediated unconsciousness elicits strong alpha/low-beta and slow oscillations in the electroencephalogram (EEG) of patients. As anesthetic dose increases, the EEG signal changes in ways that give clues to the level of unconsciousness; the network mechanisms of these changes are only partially understood. Here, we construct a biophysical thalamocortical network involving brainstem influences that reproduces transitions in dynamics seen in the EEG involving the evolution of the power and frequency of alpha/low beta and slow rhythm, as well as their interactions.Our model suggests propofol engages thalamic spindle and cortical sleep mechanisms to elicit persistent alpha/low-beta and slow rhythms, respectively. The thalamocortical network fluctuates between two mutually exclusive states on the timescale of seconds. One state is characterized by continuous alpha/low-beta frequency spiking in thalamus (C-state), while in the other, thalamic alpha spiking is interrupted by periods of co-occurring thalamic and cortical silence (I-state). In the I-state, alpha co-localizes to the peak of the slow oscillation; in the C-state, there is a variable relationship between an alpha/beta rhythm and the slow oscillation. The C-state predominates near loss of consciousness; with increasing dose, the proportion of time spent in the I-state increases, recapitulating EEG phenomenology. Cortical synchrony drives the switch to the I-state by changing the nature of the thalamocortical feedback. Brainstem influence on the strength of thalamocortical feedback mediates the amount of cortical synchrony. Our model implicates loss of low-beta, cortical synchrony, and coordinated thalamocortical silent periods as contributing to the unconscious state.
Collapse
Affiliation(s)
- Austin E Soplata
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Mathematics and Statistics, Boston University, Boston, MA, United States
| | - Elie Adam
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Michelle M McCarthy
- Department of Mathematics and Statistics, Boston University, Boston, MA, United States
| | - Nancy Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA, United States
| |
Collapse
|
14
|
Tian F, Lewis LD, Zhou DW, Balanza GA, Paulk AC, Zelmann R, Peled N, Soper D, Santa Cruz Mercado LA, Peterfreund RA, Aglio LS, Eskandar EN, Cosgrove GR, Williams ZM, Richardson RM, Brown EN, Akeju O, Cash SS, Purdon PL. Characterizing brain dynamics during ketamine-induced dissociation and subsequent interactions with propofol using human intracranial neurophysiology. Nat Commun 2023; 14:1748. [PMID: 36991011 PMCID: PMC10060225 DOI: 10.1038/s41467-023-37463-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/17/2023] [Indexed: 03/31/2023] Open
Abstract
Ketamine produces antidepressant effects in patients with treatment-resistant depression, but its usefulness is limited by its psychotropic side effects. Ketamine is thought to act via NMDA receptors and HCN1 channels to produce brain oscillations that are related to these effects. Using human intracranial recordings, we found that ketamine produces gamma oscillations in prefrontal cortex and hippocampus, structures previously implicated in ketamine's antidepressant effects, and a 3 Hz oscillation in posteromedial cortex, previously proposed as a mechanism for its dissociative effects. We analyzed oscillatory changes after subsequent propofol administration, whose GABAergic activity antagonizes ketamine's NMDA-mediated disinhibition, alongside a shared HCN1 inhibitory effect, to identify dynamics attributable to NMDA-mediated disinhibition versus HCN1 inhibition. Our results suggest that ketamine engages different neural circuits in distinct frequency-dependent patterns of activity to produce its antidepressant and dissociative sensory effects. These insights may help guide the development of brain dynamic biomarkers and novel therapeutics for depression.
Collapse
Affiliation(s)
- Fangyun Tian
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Department of Radiology, MGH/HST Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
- Institute for Medical Engineering and Sciences, Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David W Zhou
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gustavo A Balanza
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Noam Peled
- Department of Radiology, MGH/HST Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Daniel Soper
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura A Santa Cruz Mercado
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert A Peterfreund
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Linda S Aglio
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Albert Einstein College of Medicine, Bronx, NY, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
15
|
Guay CS, Hight D, Gupta G, Kafashan M, Luong AH, Avidan MS, Brown EN, Palanca BJA. Breathe–squeeze: pharmacodynamics of a stimulus-free behavioural paradigm to track conscious states during sedation☆. Br J Anaesth 2023; 130:557-566. [PMID: 36967282 DOI: 10.1016/j.bja.2023.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 01/03/2023] [Accepted: 01/16/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Conscious states are typically inferred through responses to auditory tasks and noxious stimulation. We report the use of a stimulus-free behavioural paradigm to track state transitions in responsiveness during dexmedetomidine sedation. We hypothesised that estimated dexmedetomidine effect-site (Ce) concentrations would be higher at loss of responsiveness (LOR) compared with return of responsiveness (ROR), and both would be lower than comparable studies that used stimulus-based assessments. METHODS Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine data were analysed for secondary analysis. Fourteen healthy volunteers were asked to perform the breathe-squeeze task of gripping a dynamometer when inspiring and releasing it when expiring. LOR was defined as five inspirations without accompanied squeezes; ROR was defined as the return of five inspirations accompanied by squeezes. Brain states were monitored using 64-channel EEG. Dexmedetomidine was administered as a target-controlled infusion, with Ce estimated from a pharmacokinetic model. RESULTS Counter to our hypothesis, mean estimated dexmedetomidine Ce was lower at LOR (0.92 ng ml-1; 95% confidence interval: 0.69-1.15) than at ROR (1.43 ng ml-1; 95% confidence interval: 1.27-1.58) (paired t-test; P=0.002). LOR was characterised by progressively increasing fronto-occipital EEG power in the 0.5-8 Hz band and loss of occipital alpha (8-12 Hz) and global beta (16-30 Hz) power. These EEG changes reverted at ROR. CONCLUSIONS The breathe-squeeze task can effectively track changes in responsiveness during sedation without external stimuli and might be more sensitive to state changes than stimulus-based tasks. It should be considered when perturbation of brain states is undesirable. CLINICAL TRIAL REGISTRATION NCT04206059.
Collapse
|
16
|
Wang W, Yuan RK, Mitchell JF, Zitting KM, St Hilaire MA, Wyatt JK, Scheer FAJL, Wright KP, Brown EN, Ronda JM, Klerman EB, Duffy JF, Dijk DJ, Czeisler CA. Desynchronizing the sleep---wake cycle from circadian timing to assess their separate contributions to physiology and behaviour and to estimate intrinsic circadian period. Nat Protoc 2023; 18:579-603. [PMID: 36376588 DOI: 10.1038/s41596-022-00746-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022]
Abstract
Circadian clocks drive cyclic variations in many aspects of physiology, but some daily variations are evoked by periodic changes in the environment or sleep-wake state and associated behaviors, such as changes in posture, light levels, fasting or eating, rest or activity and social interactions; thus, it is often important to quantify the relative contributions of these factors. Yet, circadian rhythms and these evoked effects cannot be separated under typical 24-h day conditions, because circadian phase and the length of time awake or asleep co-vary. Nathaniel Kleitman's forced desynchrony (FD) protocol was designed to assess endogenous circadian rhythmicity and to separate circadian from evoked components of daily rhythms in multiple parameters. Under FD protocol conditions, light intensity is kept low to minimize its impact on the circadian pacemaker, and participants have sleep-wake state and associated behaviors scheduled to an imposed non-24-h cycle. The period of this imposed cycle, Τ, is chosen so that the circadian pacemaker cannot entrain to it and therefore continues to oscillate at its intrinsic period (τ, ~24.15 h), ensuring circadian components are separated from evoked components of daily rhythms. Here we provide detailed instructions and troubleshooting techniques on how to design, implement and analyze the data from an FD protocol. We provide two procedures: one with general guidance for designing an FD study and another with more precise instructions for replicating one of our previous FD studies. We discuss estimating circadian parameters and quantifying the separate contributions of circadian rhythmicity and the sleep-wake cycle, including statistical analysis procedures and an R package for conducting the non-orthogonal spectral analysis method that enables an accurate estimation of period, amplitude and phase.
Collapse
Affiliation(s)
- Wei Wang
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Robin K Yuan
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jude F Mitchell
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
| | - Kirsi-Marja Zitting
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Melissa A St Hilaire
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - James K Wyatt
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Frank A J L Scheer
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Kenneth P Wright
- Sleep and Chronobiology Laboratory, Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Data Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Joseph M Ronda
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Elizabeth B Klerman
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jeanne F Duffy
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, Guildford, UK
| | - Charles A Czeisler
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine and Department of Medicine, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
17
|
Qu JZ, Mueller A, McKay TB, Westover MB, Shelton KT, Shaefi S, D'Alessandro DA, Berra L, Brown EN, Houle TT, Akeju O. Nighttime dexmedetomidine for delirium prevention in non-mechanically ventilated patients after cardiac surgery (MINDDS): A single-centre, parallel-arm, randomised, placebo-controlled superiority trial. EClinicalMedicine 2023; 56:101796. [PMID: 36590787 PMCID: PMC9800196 DOI: 10.1016/j.eclinm.2022.101796] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The delirium-sparing effect of nighttime dexmedetomidine has not been studied after surgery. We hypothesised that a nighttime dose of dexmedetomidine would reduce the incidence of postoperative delirium as compared to placebo. METHODS This single-centre, parallel-arm, randomised, placebo-controlled superiority trial evaluated whether a short nighttime dose of intravenous dexmedetomidine (1 μg/kg over 40 min) would reduce the incidence of postoperative delirium in patients 60 years of age or older undergoing elective cardiac surgery with cardiopulmonary bypass. Patients were randomised to receive dexmedetomidine or placebo in a 1:1 ratio. The primary outcome was delirium on postoperative day one. Secondary outcomes included delirium within three days of surgery, 30-, 90-, and 180-day abbreviated Montreal Cognitive Assessment scores, Patient Reported Outcome Measures Information System quality of life scores, and all-cause mortality. The study was registered as NCT02856594 on ClinicalTrials.gov on August 5, 2016, before the enrolment of any participants. FINDINGS Of 469 patients that underwent randomisation to placebo (n = 235) or dexmedetomidine (n = 234), 75 met a prespecified drop criterion before the study intervention. Thus, 394 participants (188 dexmedetomidine; 206 placebo) were analysed in the modified intention-to-treat cohort (median age 69 [IQR 64, 74] years; 73.1% male [n = 288]; 26·9% female [n = 106]). Postoperative delirium status on day one was missing for 30 (7.6%) patients. Among those in whom it could be assessed, the primary outcome occurred in 5 of 175 patients (2.9%) in the dexmedetomidine group and 16 of 189 patients (8.5%) in the placebo group (OR 0.32, 95% CI: 0.10-0.83; P = 0.029). A non-significant but higher proportion of participants experienced delirium within three days postoperatively in the placebo group (25/177; 14.1%) compared to the dexmedetomidine group (14/160; 8.8%; OR 0.58; 95% CI, 0.28-1.15). No significant differences between groups were observed in secondary outcomes or safety. INTERPRETATION Our findings suggested that in elderly cardiac surgery patients with a low baseline risk of postoperative delirium and extubated within 12 h of ICU admission, a short nighttime dose of dexmedetomidine decreased the incidence of delirium on postoperative day one. Although non-statistically significant, our findings also suggested a clinical meaningful difference in the three-day incidence of postoperative delirium. FUNDING National Institute on Aging (R01AG053582).
Collapse
Affiliation(s)
- Jason Z. Qu
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ariel Mueller
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina B. McKay
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kenneth T. Shelton
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shahzad Shaefi
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - David A. D'Alessandro
- Division of Cardiac Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lorenzo Berra
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Respiratory Care Services, Massachusetts General Hospital, Boston, MA, USA
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Timothy T. Houle
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding author. Massachusetts General Hospital, 55 Fruit Street, Gray Bigelow 444, Boston, MA 02114, USA.
| | | |
Collapse
|
18
|
Subramanian S, Tseng B, Barbieri R, Brown EN. An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting. Physiol Meas 2022; 43. [PMID: 36113446 DOI: 10.1088/1361-6579/ac92bd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/16/2022] [Indexed: 02/07/2023]
Abstract
Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible.Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it.Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances.Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring.
Collapse
Affiliation(s)
- Sandya Subramanian
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
| | - Bryan Tseng
- Picower Institute for Learning and Memory, Cambridge, MA, United States of America
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Engineering, Politecnico di Milano, Milano, Italy.,Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Emery N Brown
- Picower Institute for Learning and Memory, Cambridge, MA, United States of America.,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.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| |
Collapse
|
19
|
Mashour GA, Pal D, Brown EN. Prefrontal cortex as a key node in arousal circuitry. Trends Neurosci 2022; 45:722-732. [PMID: 35995629 PMCID: PMC9492635 DOI: 10.1016/j.tins.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/02/2022] [Accepted: 07/31/2022] [Indexed: 10/15/2022]
Abstract
The role of the prefrontal cortex (PFC) in the mechanism of consciousness is a matter of active debate. Most theoretical and empirical investigations have focused on whether the PFC is critical for the content of consciousness (i.e., the qualitative aspects of conscious experience). However, there is emerging evidence that, in addition to its well-established roles in cognition, the PFC is a key regulator of the level of consciousness (i.e., the global state of arousal). In this opinion article we review recent data supporting the hypothesis that the medial PFC is a critical node in arousal-promoting networks.
Collapse
Affiliation(s)
- George A Mashour
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA; Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA; Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.
| | - Dinesh Pal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA; Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA; Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Emery N Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
20
|
Tseng B, Subramanian S, Barbieri R, Brown EN. Tonic Electrodermal Activity is a Robust Marker of Psychological and Physiological Changes during Induction of Anesthesia. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:418-421. [PMID: 36086567 DOI: 10.1109/embc48229.2022.9871080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electrodermal activity (EDA), which tracks sweat gland activity as a proxy for sympathetic activation, has the potential to be a biomarker of physiological and psychological changes in the clinic. To show this, in this study, we demonstrate that the tonic component of EDA responds consistently and robustly during induction of anesthesia in the operating room in 8 subjects during surgery. This response is seen bilaterally. The response shows a significant increase in EDA in anticipation of induction and then a gradual decrease in response to the administration of medication, which agrees with both the expected psychological effects of stress and anxiety and the physiological effects of anesthetic medication on sweat glands. The results also show a slightly faster response to drug in the arm directly receiving the medication intravenously compared to the opposite, though the magnitude of the effect evens out over time. Clinical Relevance- EDA can serve as a robust non-invasive biomarker in the clinic to track both psychologically and physiologically induced autonomic changes.
Collapse
|
21
|
Davis KC, Meschede-Krasa B, Cajigas I, Prins NW, Alver C, Gallo S, Bhatia S, Abel JH, Naeem JA, Fisher L, Raza F, Rifai WR, Morrison M, Ivan ME, Brown EN, Jagid JR, Prasad A. Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury. J Neuroeng Rehabil 2022; 19:53. [PMID: 35659259 PMCID: PMC9166490 DOI: 10.1186/s12984-022-01026-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 05/13/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). BACKGROUND BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home. METHODS The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject's wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use. RESULTS Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject's caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining. CONCLUSIONS The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015.
Collapse
Affiliation(s)
- Kevin C Davis
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Benyamin Meschede-Krasa
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Iahn Cajigas
- Department of Neurological Surgery, University of Miami, 1095 NW 14th Terrace, Miami, FL, 33136, USA
| | - Noeline W Prins
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
- Department of Electrical and Information Engineering, University of Ruhuna, Matara, Sri Lanka
| | - Charles Alver
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Sebastian Gallo
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Shovan Bhatia
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - John H Abel
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Jasim A Naeem
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Letitia Fisher
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, 33136, USA
| | - Fouzia Raza
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Wesley R Rifai
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Matthew Morrison
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Michael E Ivan
- Department of Neurological Surgery, University of Miami, 1095 NW 14th Terrace, Miami, FL, 33136, USA
| | - Emery N Brown
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jonathan R Jagid
- Department of Neurological Surgery, University of Miami, 1095 NW 14th Terrace, Miami, FL, 33136, USA.
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, 33136, USA.
| | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA.
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, 33136, USA.
| |
Collapse
|
22
|
Waldrop G, Safavynia SA, Barra ME, Agarwal S, Berlin DA, Boehme AK, Brodie D, Choi JM, Doyle K, Fins JJ, Ganglberger W, Hoffman K, Mittel AM, Roh D, Mukerji SS, Der Nigoghossian C, Park S, Schenck EJ, Salazar‐Schicchi J, Shen Q, Sholle E, Velazquez AG, Walline MC, Westover MB, Brown EN, Victor J, Edlow BL, Schiff ND, Claassen J. Prolonged Unconsciousness is Common in COVID-19 and Associated with Hypoxemia. Ann Neurol 2022; 91:740-755. [PMID: 35254675 PMCID: PMC9082460 DOI: 10.1002/ana.26342] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 02/20/2022] [Accepted: 02/28/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The purpose of this study was to estimate the time to recovery of command-following and associations between hypoxemia with time to recovery of command-following. METHODS In this multicenter, retrospective, cohort study during the initial surge of the United States' pandemic (March-July 2020) we estimate the time from intubation to recovery of command-following, using Kaplan Meier cumulative-incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID-19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6). RESULTS Five hundred seventy-one patients of the 795 patients recovered command-following. The median time to recovery of command-following was 30 days (95% confidence interval [CI] = 27-32 days). Median time to recovery of command-following increased by 16 days for patients with at least one episode of an arterial partial pressure of oxygen (PaO2 ) value ≤55 mmHg (p < 0.001), and 25% recovered ≥10 days after cessation of mechanical ventilation. The time to recovery of command-following was associated with hypoxemia (PaO2 ≤55 mmHg hazard ratio [HR] = 0.56, 95% CI = 0.46-0.68; PaO2 ≤70 HR = 0.88, 95% CI = 0.85-0.91), and each additional day of hypoxemia decreased the likelihood of recovery, accounting for confounders including sedation. These findings were confirmed among patients without any imagining evidence of structural brain injury (n = 199), and in a non-overlapping second surge cohort (N = 427, October 2020 to April 2021). INTERPRETATION Survivors of severe COVID-19 commonly recover consciousness weeks after cessation of mechanical ventilation. Long recovery periods are associated with more severe hypoxemia. This relationship is not explained by sedation or brain injury identified on clinical imaging and should inform decisions about life-sustaining therapies. ANN NEUROL 2022;91:740-755.
Collapse
Affiliation(s)
- Greer Waldrop
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Seyed A. Safavynia
- New York Presbyterian HospitalNew YorkNYUSA
- Department of AnesthesiologyWeill Cornell Medical CollegeNew YorkNYUSA
| | - Megan E. Barra
- Department of PharmacyMassachusetts General HospitalBostonMAUSA
- Center for Neurotechnology and NeurorecoveryMassachusetts General HospitalBostonMAUSA
| | - Sachin Agarwal
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - David A. Berlin
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineWeill Cornell Medical CollegeNew YorkNYUSA
| | - Amelia K Boehme
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - Daniel Brodie
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - Jacky M. Choi
- Division of Biostatistics, Department of Population Health SciencesWeill Cornell Medical CollegeNew YorkNYUSA
| | - Kevin Doyle
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Joseph J. Fins
- New York Presbyterian HospitalNew YorkNYUSA
- Division of Medical Ethics, Department of MedicineWeill Cornell Medical CollegeNew YorkNYUSA
| | - Wolfgang Ganglberger
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Katherine Hoffman
- Division of Biostatistics, Department of Population Health SciencesWeill Cornell Medical CollegeNew YorkNYUSA
| | - Aaron M. Mittel
- New York Presbyterian HospitalNew YorkNYUSA
- Department of AnesthesiaColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - David Roh
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Shibani S. Mukerji
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Caroline Der Nigoghossian
- New York Presbyterian HospitalNew YorkNYUSA
- Department of PharmacyNew York Presbyterian HospitalNew YorkNYUSA
| | - Soojin Park
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Edward J. Schenck
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineWeill Cornell Medical CollegeNew YorkNYUSA
| | - John Salazar‐Schicchi
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - Qi Shen
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Evan Sholle
- Information Technologies & Services DepartmentWeill Cornell MedicineNew YorkNYUSA
| | - Angela G. Velazquez
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Maria C. Walline
- New York Presbyterian HospitalNew YorkNYUSA
- Department of AnesthesiologyWeill Cornell Medical CollegeNew YorkNYUSA
| | - M. Brandon Westover
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Emery N. Brown
- Department of Brain and Cognitive ScienceInstitute of Medical Engineering and Sciences, the Picower Institute for Learning and Memory, and the Institute for Data Systems and Society, Massachusetts Institute of TechnologyBostonMAUSA
- Department of AnesthesiaCritical Care and Pain Medicine, Massachusetts General HospitalBostonMAUSA
| | - Jonathan Victor
- New York Presbyterian HospitalNew YorkNYUSA
- Department of NeurologyWeill Cornell Medical CollegeNew YorkNYUSA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical CenterNew YorkNYUSA
| | - Brian L. Edlow
- Center for Neurotechnology and NeurorecoveryMassachusetts General HospitalBostonMAUSA
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Nicholas D. Schiff
- New York Presbyterian HospitalNew YorkNYUSA
- Department of NeurologyWeill Cornell Medical CollegeNew YorkNYUSA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical CenterNew YorkNYUSA
| | - Jan Claassen
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| |
Collapse
|
23
|
Adam EM, Brown EN, Kopell N, McCarthy MM. Deep brain stimulation in the subthalamic nucleus for Parkinson's disease can restore dynamics of striatal networks. Proc Natl Acad Sci U S A 2022; 119:e2120808119. [PMID: 35500112 PMCID: PMC9171607 DOI: 10.1073/pnas.2120808119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/25/2022] [Indexed: 12/03/2022] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in alleviating movement disability in patients with Parkinson’s disease (PD). However, its therapeutic mechanism of action is unknown. The healthy striatum exhibits rich dynamics resulting from an interaction of beta, gamma, and theta oscillations. These rhythms are essential to selection and execution of motor programs, and their loss or exaggeration due to dopamine (DA) depletion in PD is a major source of behavioral deficits. Restoring the natural rhythms may then be instrumental in the therapeutic action of DBS. We develop a biophysical networked model of a BG pathway to study how abnormal beta oscillations can emerge throughout the BG in PD and how DBS can restore normal beta, gamma, and theta striatal rhythms. Our model incorporates STN projections to the striatum, long known but understudied, found to preferentially target fast-spiking interneurons (FSI). We find that DBS in STN can normalize striatal medium spiny neuron activity by recruiting FSI dynamics and restoring the inhibitory potency of FSIs observed in normal conditions. We also find that DBS allows the reexpression of gamma and theta rhythms, thought to be dependent on high DA levels and thus lost in PD, through cortical noise control. Our study highlights that DBS effects can go beyond regularizing BG output dynamics to restoring normal internal BG dynamics and the ability to regulate them. It also suggests how gamma and theta oscillations can be leveraged to supplement DBS treatment and enhance its effectiveness.
Collapse
Affiliation(s)
- Elie M. Adam
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Emery N. Brown
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Nancy Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215
| | | |
Collapse
|
24
|
Whitaker J, Baum TE, Qian PC, Prassl AJ, Plank G, Brown EN, Cochet H, Sauer WH, Bishop MJ, Tedrow UB. PO-664-07 FREQUENCY DOMAIN ANALYSIS OF UNIPOLAR ELECTROGRAMS IDENTIFIES PRESENCE OF MID-MYOCARDIAL FIBROSIS IN NON-ISCHEMIC CARDIOMYOPATHY. Heart Rhythm 2022. [DOI: 10.1016/j.hrthm.2022.03.357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
25
|
Abstract
Oscillatory dynamics in cortex seem to organize into traveling waves that serve a variety of functions. Recent studies show that propofol, a widely used anesthetic, dramatically alters cortical oscillations by increasing slow-delta oscillatory power and coherence. It is not known how this affects traveling waves. We compared traveling waves across the cortex of non-human primates before, during, and after propofol-induced loss of consciousness (LOC). After LOC, traveling waves in the slow-delta (∼1 Hz) range increased, grew more organized, and traveled in different directions relative to the awake state. Higher frequency (8-30 Hz) traveling waves, by contrast, decreased, lost structure, and switched to directions where the slow-delta waves were less frequent. The results suggest that LOC may be due, in part, to increases in the strength and direction of slow-delta traveling waves that, in turn, alter and disrupt traveling waves in the higher frequencies associated with cognition.
Collapse
Affiliation(s)
| | | | | | | | - Emery N Brown
- Massachusetts Institute of Technology, Cambridge.,Massachusetts General Hospital/Harvard Medical School, Boston
| | | |
Collapse
|
26
|
Luppi AI, Cain J, Spindler LRB, Górska UJ, Toker D, Hudson AE, Brown EN, Diringer MN, Stevens RD, Massimini M, Monti MM, Stamatakis EA, Boly M. Correction: Mechanisms Underlying Disorders of Consciousness: Bridging Gaps to Move Toward an Integrated Translational Science. Neurocrit Care 2022; 36:1080. [PMID: 35359223 PMCID: PMC9110433 DOI: 10.1007/s12028-022-01488-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK. .,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Joshua Cain
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Lennart R B Spindler
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK. .,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Urszula J Górska
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.
| | - Daniel Toker
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrew E Hudson
- Department of Anesthesia and Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, MA, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael N Diringer
- Department of Neurology and Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Robert D Stevens
- Departments of Anesthesiology and Critical Care Medicine, Neurology and Neurosurgery, and Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Marcello Massimini
- Dipartimento di Scienze Biomediche e Cliniche "L. Sacco", Università Degli Studi Di Milano, Milan, Italy.,Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Martin M Monti
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.,Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Melanie Boly
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | |
Collapse
|
27
|
Schamberg G, Badgeley M, Meschede-Krasa B, Kwon O, Brown EN. Continuous action deep reinforcement learning for propofol dosing during general anesthesia. Artif Intell Med 2022; 123:102227. [PMID: 34998516 DOI: 10.1016/j.artmed.2021.102227] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/26/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Anesthesiologists simultaneously manage several aspects of patient care during general anesthesia. Automating administration of hypnotic agents could enable more precise control of a patient's level of unconsciousness and enable anesthesiologists to focus on the most critical aspects of patient care. Reinforcement learning (RL) algorithms can be used to fit a mapping from patient state to a medication regimen. These algorithms can learn complex control policies that, when paired with modern techniques for promoting model interpretability, offer a promising approach for developing a clinically viable system for automated anesthestic drug delivery. METHODS We expand on our prior work applying deep RL to automated anesthetic dosing by now using a continuous-action model based on the actor-critic RL paradigm. The proposed RL agent is composed of a policy network that maps observed anesthetic states to a continuous probability density over propofol-infusion rates and a value network that estimates the favorability of observed states. We train and test three versions of the RL agent using varied reward functions. The agent is trained using simulated pharmacokinetic/pharmacodynamic models with randomized parameters to ensure robustness to patient variability. The model is tested on simulations and retrospectively on nine general anesthesia cases collected in the operating room. We utilize Shapley additive explanations to gain an understanding of the factors with the greatest influence over the agent's decision-making. RESULTS The deep RL agent significantly outperformed a proportional-integral-derivative controller (median episode median absolute performance error 1.9% ± 1.8 and 3.1% ± 1.1). The model that was rewarded for minimizing total doses performed the best across simulated patient demographics (median episode median performance error 1.1% ± 0.5). When run on real-world clinical datasets, the agent recommended doses that were consistent with those administered by the anesthesiologist. CONCLUSIONS The proposed approach marks the first fully continuous deep RL algorithm for automating anesthestic drug dosing. The reward function used by the RL training algorithm can be flexibly designed for desirable practices (e.g. use less anesthetic) and bolstered performances. Through careful analysis of the learned policies, techniques for interpreting dosing decisions, and testing on clinical data, we confirm that the agent's anesthetic dosing is consistent with our understanding of best-practices in anesthesia care.
Collapse
Affiliation(s)
- Gabriel Schamberg
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | | | - Benyamin Meschede-Krasa
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ohyoon Kwon
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Emery N Brown
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| |
Collapse
|
28
|
Koch S, Windmann V, Chakravarty S, Kruppa J, Yürek F, Brown EN, Winterer G, Spies C. Perioperative Electroencephalogram Spectral Dynamics Related to Postoperative Delirium in Older Patients. Anesth Analg 2021; 133:1598-1607. [PMID: 34591807 DOI: 10.1213/ane.0000000000005668] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Intraoperative electroencephalography (EEG) signatures related to the development of postoperative delirium (POD) in older patients are frequently studied. However, a broad analysis of the EEG dynamics including preoperative, postinduction, intraoperative and postoperative scenarios and its correlation to POD development is still lacking. We explored the relationship between perioperative EEG spectra-derived parameters and POD development, aiming to ascertain the diagnostic utility of these parameters to detect patients developing POD. METHODS Patients aged ≥65 years undergoing elective surgeries that were expected to last more than 60 minutes were included in this prospective, observational single center study (Biomarker Development for Postoperative Cognitive Impairment [BioCog] study). Frontal EEGs were recorded, starting before induction of anesthesia and lasting until recovery of consciousness. EEG data were analyzed based on raw EEG files and downloaded excel data files. We performed multitaper spectral analyses of relevant EEG epochs and further used multitaper spectral estimate to calculate a corresponding spectral parameter. POD assessments were performed twice daily up to the seventh postoperative day. Our primary aim was to analyze the relation between the perioperative spectral edge frequency (SEF) and the development of POD. RESULTS Of the 237 included patients, 41 (17%) patients developed POD. The preoperative EEG in POD patients was associated with lower values in both SEF (POD 13.1 ± 4.6 Hz versus no postoperative delirium [NoPOD] 17.4 ± 6.9 Hz; P = .002) and corresponding γ-band power (POD -24.33 ± 2.8 dB versus NoPOD -17.9 ± 4.81 dB), as well as reduced postinduction absolute α-band power (POD -7.37 ± 4.52 dB versus NoPOD -5 ± 5.03 dB). The ratio of SEF from the preoperative to postinduction state (SEF ratio) was ~1 in POD patients, whereas NoPOD patients showed a SEF ratio >1, thus indicating a slowing of EEG with loss of unconscious. Preoperative SEF, preoperative γ-band power, and SEF ratio were independently associated with POD (P = .025; odds ratio [OR] = 0.892, 95% confidence interval [CI], 0.808-0.986; P = .029; OR = 0.568, 95% CI, 0.342-0.944; and P = .009; OR = 0.108, 95% CI, 0.021-0.568, respectively). CONCLUSIONS Lower preoperative SEF, absence of slowing in EEG while transitioning from preoperative state to unconscious state, and lower EEG power in relevant frequency bands in both these states are related to POD development. These findings may suggest an underlying pathophysiology and might be used as EEG-based marker for early identification of patients at risk to develop POD.
Collapse
Affiliation(s)
- Susanne Koch
- From the Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Technical Transfer Department, Berlin Institute of Health (BIH), Berlin, Germany
| | - Victoria Windmann
- From the Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sourish Chakravarty
- Harvard-MIT, Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Jochen Kruppa
- Technical Transfer Department, Berlin Institute of Health (BIH), Berlin, Germany.,Department of Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Fatima Yürek
- From the Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Emery N Brown
- Harvard-MIT, Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Georg Winterer
- From the Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Spies
- From the Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | |
Collapse
|
29
|
Chan D, Suk H, Jackson BL, Milman N, Stark D, Fernandez V, Banerjee A, Kitchener E, Klerman EB, Boyden ES, Brown EN, Dickerson BC, Tsai L. Gamma frequency sensory stimulation prevents brain atrophy, improves sleep and memory in probable mild Alzheimer’s patients. Alzheimers Dement 2021. [DOI: 10.1002/alz.054218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Diane Chan
- Massachusetts Institute of Technology Cambridge MA USA
- Massachusetts General Hospital Boston MA USA
- Harvard Medical School Boston MA USA
| | - Ho‐Jun Suk
- Massachusetts Institute of Technology Cambridge MA USA
| | | | - Noah Milman
- Massachusetts Institute of Technology Cambridge MA USA
- Northeastern University Boston MA USA
| | | | | | - Arit Banerjee
- Massachusetts Institute of Technology Cambridge MA USA
| | | | | | | | - Emery N Brown
- Massachusetts Institute of Technology Cambridge MA USA
- Massachusetts General Hospital Boston MA USA
| | - Brad C. Dickerson
- Harvard Medical School Boston MA USA
- Martinos Center for Biomedical Imaging Charlestown MA USA
- Frontotemporal Disorders Unit Department of Neurology Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Li‐Huei Tsai
- Massachusetts Institute of Technology Cambridge MA USA
| |
Collapse
|
30
|
Barra ME, Edlow BL, Lund JT, DeSanctis KS, Vetrano J, Reilly-Tremblay C, Zhang ER, Bodien YG, Brown EN, Solt K. Stability of extemporaneously prepared preservative-free methylphenidate 5 mg/mL intravenous solution. Am J Health Syst Pharm 2021; 79:359-363. [PMID: 34788364 DOI: 10.1093/ajhp/zxab420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
DISCLAIMER In an effort to expedite the publication of articles , AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE To advance the implementation of consciousness-promoting therapies in patients with acute disorders of consciousness, the availability of potential therapeutic agents in formulations suitable for administration in hospitalized patients in the presence of complex comorbid conditions is paramount. The purpose of this study is to evaluate the long-term stability of extemporaneously prepared preservative-free methylphenidate hydrochloride (HCl) 5 mg/mL intravenous solution for experimental use. METHODS A methylphenidate 5 mg/mL solution was prepared under proper aseptic techniques with Methylphenidate Hydrochloride, USP, powder mixed in sterile water for solution. Methylphenidate HCl 5 mg/mL solution was sterilized by filtration technique under USP <797>-compliant conditions. Samples were stored refrigerated (2-8°C) and analyzed at approximately days 1, 30, 60, 90, 180, and 365. At each time point, chemical and physical stability were evaluated by visual inspection, pH measurement, membrane filtration procedure, turbidometric or photometric technique, and high-performance liquid chromatography analysis. RESULTS Over the 1-year study period, the samples retained 96.76% to 102.04% of the initial methylphenidate concentration. There was no significant change in the visual appearance, pH level, or particulate matter during the study period. The sterility of samples was maintained and endotoxin levels were undetectable throughout the 1-year stability period. CONCLUSION Extemporaneously prepared preservative-free methylphenidate 5 mg/mL intravenous solution was physically and chemically stable at 32, 61, 95, 186, and 365 days when stored in amber glass vials at refrigerated temperatures (2-8°C).
Collapse
Affiliation(s)
- Megan E Barra
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - James T Lund
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | | | - John Vetrano
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | | | - Edlyn R Zhang
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Boston, MA, USA
| | - Ken Solt
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
31
|
Shanker A, Abel JH, Narayanan S, Mathur P, Work E, Schamberg G, Sharkey A, Bose R, Rangasamy V, Senthilnathan V, Brown EN, Subramaniam B. Perioperative Multimodal General Anesthesia Focusing on Specific CNS Targets in Patients Undergoing Cardiac Surgeries: The Pathfinder Feasibility Trial. Front Med (Lausanne) 2021; 8:719512. [PMID: 34722563 PMCID: PMC8551571 DOI: 10.3389/fmed.2021.719512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Multimodal general anesthesia (MMGA) is a strategy that utilizes the well-known neuroanatomy and neurophysiology of nociception and arousal control in designing a rational and clinical practical paradigm to regulate the levels of unconsciousness and antinociception during general anesthesia while mitigating side effects of any individual anesthetic. We sought to test the feasibility of implementing MMGA for seniors undergoing cardiac surgery, a high-risk cohort for hemodynamic instability, delirium, and post-operative cognitive dysfunction. Twenty patients aged 60 or older undergoing on-pump coronary artery bypass graft (CABG) surgery or combined CABG/valve surgeries were enrolled in this non-randomized prospective observational feasibility trial, wherein we developed MMGA specifically for cardiac surgeries. Antinociception was achieved by a combination of intravenous remifentanil, ketamine, dexmedetomidine, and magnesium together with bupivacaine administered as a pecto-intercostal fascial block. Unconsciousness was achieved by using electroencephalogram (EEG)-guided administration of propofol along with the sedative effects of the antinociceptive agents. EEG-guided MMGA anesthesia was safe and feasible for cardiac surgeries, and exploratory analyses found hemodynamic stability and vasopressor usage comparable to a previously collected cohort. Intraoperative EEG suppression events and postoperative delirium were found to be rare. We report successful use of a total intravenous anesthesia (TIVA)-based MMGA strategy for cardiac surgery and establish safety and feasibility for studying MMGA in a full clinical trial. Clinical Trial Number:www.clinicaltrials.gov; identifier NCT04016740 (https://clinicaltrials.gov/ct2/show/NCT04016740).
Collapse
Affiliation(s)
- Akshay Shanker
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.,Department of Anesthesiology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, United States
| | - John H Abel
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.,Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Shilpa Narayanan
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Pooja Mathur
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Erin Work
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Gabriel Schamberg
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.,Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Aidan Sharkey
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Ruma Bose
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Valluvan Rangasamy
- Department of Anesthesia, Critical Care, and Pain Medicine, Riley Hospital for Children, Indiana University, Indianapolis, IN, United States
| | - Venkatachalam Senthilnathan
- Department of Cardiothoracic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.,Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Balachundhar Subramaniam
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
32
|
Subramanian S, Tseng B, Barbieri R, Brown EN. Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:399-402. [PMID: 34891318 DOI: 10.1109/embc46164.2021.9630535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artifact detection and removal is a crucial step in all data preprocessing pipelines for physiological time series data, especially when collected outside of controlled experimental settings. The fact that such artifact is often readily identifiable by eye suggests that unsupervised machine learning algorithms may be a promising option that do not require manually labeled training datasets. Existing methods are often heuristic-based, not generalizable, or developed for controlled experimental settings with less artifact. In this study, we test the ability of three such unsupervised learning algorithms, isolation forests, 1-class support vector machine, and K-nearest neighbor distance, to remove heavy cautery-related artifact from electrodermal activity (EDA) data collected while six subjects underwent surgery. We first defined 12 features for each halfsecond window as inputs to the unsupervised learning methods. For each subject, we compared the best performing unsupervised learning method to four other existing methods for EDA artifact removal. For all six subjects, the unsupervised learning method was the only one successful at fully removing the artifact. This approach can easily be expanded to other modalities of physiological data in complex settings.Clinical Relevance- Robust artifact detection methods allow for the use of diverse physiological data even in complex clinical settings to inform diagnostic and therapeutic decisions.
Collapse
|
33
|
Cajigas I, Davis KC, Meschede-Krasa B, Prins NW, Gallo S, Naeem JA, Palermo A, Wilson A, Guerra S, Parks BA, Zimmerman L, Gant K, Levi AD, Dietrich WD, Fisher L, Vanni S, Tauber JM, Garwood IC, Abel JH, Brown EN, Ivan ME, Prasad A, Jagid J. Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury. Brain Commun 2021; 3:fcab248. [PMID: 34870202 PMCID: PMC8637800 DOI: 10.1093/braincomms/fcab248] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/27/2021] [Accepted: 08/19/2021] [Indexed: 11/12/2022] Open
Abstract
Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain-computer interface within the home environment. The brain-computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-weeks in-laboratory study with a mean accuracy of 89.0% (range 78-93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80-98.95%) and an accuracy of 88.3% (range 77.6-95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain-computer interface can be safely used to reliably decode movement-intent from motor cortex, allowing for accurate volitional control of hand grasp.
Collapse
Affiliation(s)
- Iahn Cajigas
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
| | - Kevin C Davis
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Benyamin Meschede-Krasa
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Noeline W Prins
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
- Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Galle 80000, Sri Lanka
| | - Sebastian Gallo
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Jasim Ahmad Naeem
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Anne Palermo
- Department of Physical Therapy, University of Miami, Miami, FL 33146, USA
| | - Audrey Wilson
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Santiago Guerra
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Brandon A Parks
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Lauren Zimmerman
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Katie Gant
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Allan D Levi
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Letitia Fisher
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Steven Vanni
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - John Michael Tauber
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Indie C Garwood
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - John H Abel
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Emery N Brown
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Michael E Ivan
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
| | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| |
Collapse
|
34
|
Abstract
Objective: We present a statistical model for extracting physiologic
characteristics from electrodermal activity (EDA) data in observational
settings. Methods: We based our model on the integrate-and-fire physiology of sweat
gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval
structure. At the core of our model-based paradigm is a subject-specific
amplitude threshold selection process for EDA pulses based on the
statistical properties of four right-skewed models including the IG. By
performing a sensitivity analysis across thresholds and fitting all four
models, we selected for IG-like structure and verified the pulse selection
with a goodness-of-fit analysis, maximizing capture of physiology at the
time scale of EDA responses. Results: We tested the model-based paradigm on simulated EDA time series and
data from two different experimental cohorts recorded during different
experimental conditions, using different equipment. In both the simulated
and experimental data, our model-based method robustly recovered pulses that
captured the IG-like structure predicted by physiology, despite large
differences in noise level. In contrast, established EDA analysis tools,
which attempted to estimate neural activity from slower EDA responses, did
not provide physiological validation and were susceptible to noise. Conclusion: We present a computationally efficient, statistically rigorous, and
physiology-informed paradigm for pulse selection from EDA data that is
robust across individuals and experimental conditions, yet adaptable to
varying noise level. Significance: The robustness of the model-based paradigm and its physiological
basis provide empirical support for the use of EDA as a clinical marker for
sympathetic activity in conditions such as pain, anxiety, depression, and
sleep states.
Collapse
|
35
|
Le Mau T, Hoemann K, Lyons SH, Fugate JMB, Brown EN, Gendron M, Barrett LF. Professional actors demonstrate variability, not stereotypical expressions, when portraying emotional states in photographs. Nat Commun 2021; 12:5037. [PMID: 34413313 PMCID: PMC8376986 DOI: 10.1038/s41467-021-25352-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 08/02/2021] [Indexed: 02/07/2023] Open
Abstract
It is long hypothesized that there is a reliable, specific mapping between certain emotional states and the facial movements that express those states. This hypothesis is often tested by asking untrained participants to pose the facial movements they believe they use to express emotions during generic scenarios. Here, we test this hypothesis using, as stimuli, photographs of facial configurations posed by professional actors in response to contextually-rich scenarios. The scenarios portrayed in the photographs were rated by a convenience sample of participants for the extent to which they evoked an instance of 13 emotion categories, and actors' facial poses were coded for their specific movements. Both unsupervised and supervised machine learning find that in these photographs, the actors portrayed emotional states with variable facial configurations; instances of only three emotion categories (fear, happiness, and surprise) were portrayed with moderate reliability and specificity. The photographs were separately rated by another sample of participants for the extent to which they portrayed an instance of the 13 emotion categories; they were rated when presented alone and when presented with their associated scenarios, revealing that emotion inferences by participants also vary in a context-sensitive manner. Together, these findings suggest that facial movements and perceptions of emotion vary by situation and transcend stereotypes of emotional expressions. Future research may build on these findings by incorporating dynamic stimuli rather than photographs and studying a broader range of cultural contexts.
Collapse
Affiliation(s)
- Tuan Le Mau
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for High Performance Computing, Social and Cognitive Computing, Connexis North, Singapore
| | - Katie Hoemann
- Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Sam H Lyons
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer M B Fugate
- Department of Psychology, University of Massachusetts at Dartmouth, Dartmouth, MA, 02747, USA
| | - Emery N Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Maria Gendron
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA.
- Massachusetts General Hospital/Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
| |
Collapse
|
36
|
Garwood IC, Chakravarty S, Donoghue J, Mahnke M, Kahali P, Chamadia S, Akeju O, Miller EK, Brown EN. A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms. PLoS Comput Biol 2021; 17:e1009280. [PMID: 34407069 PMCID: PMC8405019 DOI: 10.1371/journal.pcbi.1009280] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 08/30/2021] [Accepted: 07/15/2021] [Indexed: 11/18/2022] Open
Abstract
Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine's neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.
Collapse
Affiliation(s)
- Indie C. Garwood
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Sourish Chakravarty
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jacob Donoghue
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Meredith Mahnke
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Pegah Kahali
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Shubham Chamadia
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Earl K. Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Emery N. Brown
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| |
Collapse
|
37
|
Subramanian S, Purdon PL, Barbieri R, Brown EN. Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity. PLoS Comput Biol 2021; 17:e1009099. [PMID: 34232965 PMCID: PMC8289084 DOI: 10.1371/journal.pcbi.1009099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/19/2021] [Accepted: 05/21/2021] [Indexed: 11/19/2022] Open
Abstract
Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA. Electrodermal activity (EDA) is an indirect read-out of the body’s sympathetic nervous system, or fight-or-flight response, measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Our previous worked showed that the times in between EDA pulses obeyed a specific statistical distribution, the inverse Gaussian, that arises from the physiology of EDA production. In this work, we build on that insight to analyze the amplitudes of EDA pulses. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness and 11 different healthy volunteers during controlled propofol sedation, we establish that the amplitudes of EDA pulses also have specific statistical structure, as the differences of inverse Gaussians, that arises from the physiology of sweat production. We capture that structure using a series of progressively simpler models that each prioritize different parts of the pulse amplitude distribution. Our findings show that a physiologically-based statistical model provides a parsimonious and accurate description of EDA. This enables increased reliability and robustness in analyzing EDA data collected under any circumstance.
Collapse
Affiliation(s)
- Sandya Subramanian
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- * E-mail:
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Riccardo Barbieri
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 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, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Picower Institute of Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| |
Collapse
|
38
|
Abstract
Burst-suppression electroencephalography (EEG) patterns of electrical activity, characterized by intermittent high-power broad-spectrum oscillations alternating with isoelectricity, have long been observed in the human brain during general anesthesia, hypothermia, coma and early infantile encephalopathy. Recently, commonalities between conditions associated with burst-suppression patterns have led to new insights into the origin of burst-suppression EEG patterns, their effects on the brain, and their use as a therapeutic tool for protection against deleterious neural states. These insights have been further supported by advances in mechanistic modeling of burst suppression. In this Perspective, we review the origins of burst-suppression patterns and use recent insights to weigh evidence in the controversy regarding the extent to which burst-suppression patterns observed during profound anesthetic-induced brain inactivation are associated with adverse clinical outcomes. Whether the clinical intent is to avoid or maintain the brain in a state producing burst-suppression patterns, monitoring and controlling neural activity presents a technical challenge. We discuss recent advances that enable monitoring and control of burst suppression.
Collapse
Affiliation(s)
- Akshay Shanker
- Department of Anesthesiology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, United States
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - John H. Abel
- Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, MA, United States
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
| | - Gabriel Schamberg
- Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, MA, United States
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Emery N. Brown
- Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, MA, United States
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States
| |
Collapse
|
39
|
Schamberg G, Brown EN. The Drug Titration Paradox is Simpson's Paradox. Clin Pharmacol Ther 2021; 110:1424-1425. [PMID: 34038588 DOI: 10.1002/cpt.2280] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 04/21/2021] [Indexed: 11/10/2022]
Affiliation(s)
- Gabriel Schamberg
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Emery N Brown
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| |
Collapse
|
40
|
Abel JH, Badgeley MA, Meschede-Krasa B, Schamberg G, Garwood IC, Lecamwasam K, Chakravarty S, Zhou DW, Keating M, Purdon PL, Brown EN. Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia. PLoS One 2021; 16:e0246165. [PMID: 33956800 PMCID: PMC8101756 DOI: 10.1371/journal.pone.0246165] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 01/14/2021] [Indexed: 11/22/2022] Open
Abstract
In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.
Collapse
Affiliation(s)
- John H. Abel
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States of America
| | - Marcus A. Badgeley
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Benyamin Meschede-Krasa
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Gabriel Schamberg
- Picower Institute for Learning and Memory, 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
| | - Indie C. Garwood
- Picower Institute for Learning and Memory, 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
- Harvard-MIT Health Sciences and Technology, Cambridge, MA, United States of America
| | - Kimaya Lecamwasam
- Department of Neuroscience, Wellesley College, Wellesley, MA, United States of America
| | - Sourish Chakravarty
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - David W. Zhou
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Picower Institute for Learning and Memory, 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
| | - Matthew Keating
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Patrick L. Purdon
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Emery N. Brown
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Picower Institute for Learning and Memory, 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
- Harvard-MIT Health Sciences and Technology, Cambridge, MA, United States of America
| |
Collapse
|
41
|
Bastos AM, Donoghue JA, Brincat SL, Mahnke M, Yanar J, Correa J, Waite AS, Lundqvist M, Roy J, Brown EN, Miller EK. Neural effects of propofol-induced unconsciousness and its reversal using thalamic stimulation. eLife 2021; 10:60824. [PMID: 33904411 PMCID: PMC8079153 DOI: 10.7554/elife.60824] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 03/28/2021] [Indexed: 01/05/2023] Open
Abstract
The specific circuit mechanisms through which anesthetics induce unconsciousness have not been completely characterized. We recorded neural activity from the frontal, parietal, and temporal cortices and thalamus while maintaining unconsciousness in non-human primates (NHPs) with the anesthetic propofol. Unconsciousness was marked by slow frequency (~1 Hz) oscillations in local field potentials, entrainment of local spiking to Up states alternating with Down states of little or no spiking activity, and decreased coherence in frequencies above 4 Hz. Thalamic stimulation ‘awakened’ anesthetized NHPs and reversed the electrophysiologic features of unconsciousness. Unconsciousness is linked to cortical and thalamic slow frequency synchrony coupled with decreased spiking, and loss of higher-frequency dynamics. This may disrupt cortical communication/integration.
Collapse
Affiliation(s)
- André M Bastos
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Jacob A Donoghue
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Scott L Brincat
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Meredith Mahnke
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Jorge Yanar
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Josefina Correa
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Ayan S Waite
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Mikael Lundqvist
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Jefferson Roy
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Emery N Brown
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.,The Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, United States.,The Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, United States
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| |
Collapse
|
42
|
Abel JH, Badgeley MA, Baum TE, Chakravarty S, Purdon PL, Brown EN. Constructing a control-ready model of EEG signal during general anesthesia in humans. IFAC Pap OnLine 2021; 53:15870-15876. [PMID: 34184002 PMCID: PMC8236287 DOI: 10.1016/j.ifacol.2020.12.243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.
Collapse
Affiliation(s)
- John H. Abel
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
| | - Marcus A. Badgeley
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Taylor E. Baum
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Sourish Chakravarty
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| |
Collapse
|
43
|
Chakravarty S, Waite AS, Abel JH, Brown EN. A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery. IFAC Pap OnLine 2021; 53:15898-15903. [PMID: 34184003 PMCID: PMC8236286 DOI: 10.1016/j.ifacol.2020.12.369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady state response with respect to PID. The numerical analysis framework and findings reported here can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.
Collapse
Affiliation(s)
- Sourish Chakravarty
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA
- Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital, Boston, MA
| | - Ayan S Waite
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA
- Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital, Boston, MA
| | - John H Abel
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA
- Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital, Boston, MA
| | - Emery N Brown
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA
- Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital, Boston, MA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA
| |
Collapse
|
44
|
Guay CS, Labonte AK, Montana MC, Landsness EC, Lucey BP, Kafashan M, Haroutounian S, Avidan MS, Brown EN, Palanca BJA. Closed-Loop Acoustic Stimulation During Sedation with Dexmedetomidine (CLASS-D): Protocol for a Within-Subject, Crossover, Controlled, Interventional Trial with Healthy Volunteers. Nat Sci Sleep 2021; 13:303-313. [PMID: 33692642 PMCID: PMC7939493 DOI: 10.2147/nss.s293160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 02/10/2021] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION The relative power of slow-delta oscillations in the electroencephalogram (EEG), termed slow-wave activity (SWA), correlates with level of unconsciousness. Acoustic enhancement of SWA has been reported for sleep states, but it remains unknown if pharmacologically induced SWA can be enhanced using sound. Dexmedetomidine is a sedative whose EEG oscillations resemble those of natural sleep. This pilot study was designed to investigate whether SWA can be enhanced using closed-loop acoustic stimulation during sedation (CLASS) with dexmedetomidine. METHODS Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine (CLASS-D) is a within-subject, crossover, controlled, interventional trial with healthy volunteers. Each participant will be sedated with a dexmedetomidine target-controlled infusion (TCI). Participants will undergo three CLASS conditions in a multiple crossover design: in-phase (phase-locked to slow-wave upslopes), anti-phase (phase-locked to slow-wave downslopes) and sham (silence). High-density EEG recordings will assess the effects of CLASS across the scalp. A volitional behavioral task and sequential thermal arousals will assess the anesthetic effects of CLASS. Ambulatory sleep studies will be performed on nights immediately preceding and following the sedation session. EEG effects of CLASS will be assessed using linear mixed-effects models. The impacts of CLASS on behavior and arousal thresholds will be assessed using logistic regression modeling. Parametric modeling will determine differences in sleepiness and measures of sleep homeostasis before and after sedation. RESULTS The primary outcome of this pilot study is the effect of CLASS on EEG slow waves. Secondary outcomes include the effects of CLASS on the following: performance of a volitional task, arousal thresholds, and subsequent sleep. DISCUSSION This investigation will elucidate 1) the potential of exogenous sensory stimulation to potentiate SWA during sedation; 2) the physiologic significance of this intervention; and 3) the connection between EEG slow-waves observed during sleep and sedation.
Collapse
Affiliation(s)
- Christian S Guay
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Alyssa K Labonte
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Michael C Montana
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Eric C Landsness
- Department of Neurology, Division of Sleep Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Brendan P Lucey
- Department of Neurology, Division of Sleep Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Simon Haroutounian
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Emery N Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ben Julian A Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
45
|
Casile A, Faghih RT, Brown EN. Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains. PLoS Comput Biol 2021; 17:e1007675. [PMID: 33493162 PMCID: PMC7861554 DOI: 10.1371/journal.pcbi.1007675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/04/2021] [Accepted: 11/17/2020] [Indexed: 11/18/2022] Open
Abstract
Assessing directional influences between neurons is instrumental to understand how brain circuits process information. To this end, Granger causality, a technique originally developed for time-continuous signals, has been extended to discrete spike trains. A fundamental assumption of this technique is that the temporal evolution of neuronal responses must be due only to endogenous interactions between recorded units, including self-interactions. This assumption is however rarely met in neurophysiological studies, where the response of each neuron is modulated by other exogenous causes such as, for example, other unobserved units or slow adaptation processes. Here, we propose a novel point-process Granger causality technique that is robust with respect to the two most common exogenous modulations observed in real neuronal responses: within-trial temporal variations in spiking rate and between-trial variability in their magnitudes. This novel method works by explicitly including both types of modulations into the generalized linear model of the neuronal conditional intensity function (CIF). We then assess the causal influence of neuron i onto neuron j by measuring the relative reduction of neuron j's point process likelihood obtained considering or removing neuron i. CIF's hyper-parameters are set on a per-neuron basis by minimizing Akaike's information criterion. In synthetic data sets, generated by means of random processes or networks of integrate-and-fire units, the proposed method recovered with high accuracy, sensitivity and robustness the underlying ground-truth connectivity pattern. Application of presently available point-process Granger causality techniques produced instead a significant number of false positive connections. In real spiking responses recorded from neurons in the monkey pre-motor cortex (area F5), our method revealed many causal relationships between neurons as well as the temporal structure of their interactions. Given its robustness our method can be effectively applied to real neuronal data. Furthermore, its explicit estimate of the effects of unobserved causes on the recorded neuronal firing patterns can help decomposing their temporal variations into endogenous and exogenous components.
Collapse
Affiliation(s)
- Antonino Casile
- Istituto Italiano di Tecnologia, Center for Translational Neurophysiology of Speech and Communication (CTNSC), Ferrara, Italy
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States of America
- * E-mail: ,
| | - Rose T. Faghih
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, United States of America
| | - Emery N. Brown
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| |
Collapse
|
46
|
Edlow BL, Claassen J, Victor JD, Brown EN, Schiff ND. Delayed reemergence of consciousness in survivors of severe COVID-19. Neurocrit Care 2020; 33:627-629. [PMID: 33174149 PMCID: PMC7654564 DOI: 10.1007/s12028-020-01133-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022]
Affiliation(s)
- Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jan Claassen
- Department of Neurology, Columbia University, New York, NY, USA
| | - Jonathan D Victor
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical Center, New York, NY, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Picower Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas D Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical Center, New York, NY, USA.
| |
Collapse
|
47
|
Shao YR, Kahali P, Houle TT, Deng H, Colvin C, Dickerson BC, Brown EN, Purdon PL. Low Frontal Alpha Power Is Associated With the Propensity for Burst Suppression: An Electroencephalogram Phenotype for a "Vulnerable Brain". Anesth Analg 2020; 131:1529-1539. [PMID: 33079876 PMCID: PMC7553194 DOI: 10.1213/ane.0000000000004781] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND A number of recent studies have reported an association between intraoperative burst suppression and postoperative delirium. These studies suggest that anesthesia-induced burst suppression may be an indicator of underlying brain vulnerability. A prominent feature of electroencephalogram (EEG) under propofol and sevoflurane anesthesia is the frontal alpha oscillation. This frontal alpha oscillation is known to decline significantly during aging and is generated by prefrontal brain regions that are particularly prone to age-related neurodegeneration. Given that burst suppression and frontal alpha oscillations are both associated with brain vulnerability, we hypothesized that anesthesia-induced frontal alpha power could also be associated with burst suppression. METHODS We analyzed EEG data from a previously reported cohort in which 155 patients received propofol (n = 60) or sevoflurane (n = 95) as the primary anesthetic. We computed the EEG spectrum during stable anesthetic maintenance and identified whether or not burst suppression occurred during the anesthetic. We characterized the relationship between burst suppression and alpha power using logistic regression. We proposed 5 different models consisting of different combinations of potential contributing factors associated with burst suppression: (1) a Base Model consisting of alpha power; (2) an Extended Mechanistic Model consisting of alpha power, age, and drug dosing information; (3) a Clinical Confounding Factors Model consisting of alpha power, hypotension, and other confounds; (4) a Simplified Model consisting only of alpha power and propofol bolus administration; and (5) a Full Model consisting of all of these variables to control for as much confounding as possible. RESULTS All models show a consistent significant association between alpha power and burst suppression while adjusting for different sets of covariates, all with consistent effect size estimates. Using the Simplified Model, we found that for each decibel decrease in alpha power, the odds of experiencing burst suppression increased by 1.33-fold. CONCLUSIONS In this study, we show how a decrease in anesthesia-induced frontal alpha power is associated with an increased propensity for burst suppression, in a manner that captures individualized information above and beyond a patient's chronological age. Lower frontal alpha band power is strongly associated with higher propensity for burst suppression and, therefore, potentially higher risk of postoperative neurocognitive disorders. We hypothesize that low frontal alpha power and increased propensity for burst suppression together characterize a "vulnerable brain" phenotype under anesthesia that could be mechanistically linked to brain metabolism, cognition, and brain aging.
Collapse
Affiliation(s)
- Yu Raymond Shao
- From the Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina
| | - Pegah Kahali
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Timothy T. Houle
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Hao Deng
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christopher Colvin
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
48
|
Subramanian S, Barbieri R, Purdon PL, Brown EN. Detecting Loss and Regain of Consciousness during Propofol Anesthesia using Multimodal Indices of Autonomic State. Annu Int Conf IEEE Eng Med Biol Soc 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] [What about the content of this article? (0)] [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.
Collapse
|
49
|
Edlow BL, Barra ME, Zhou DW, Foulkes AS, Snider SB, Threlkeld ZD, Chakravarty S, Kirsch JE, Chan ST, Meisler SL, Bleck TP, Fins JJ, Giacino JT, Hochberg LR, Solt K, Brown EN, Bodien YG. Personalized Connectome Mapping to Guide Targeted Therapy and Promote Recovery of Consciousness in the Intensive Care Unit. Neurocrit Care 2020; 33:364-375. [PMID: 32794142 DOI: 10.1007/s12028-020-01062-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 04/18/2020] [Indexed: 01/05/2023]
Abstract
There are currently no therapies proven to promote early recovery of consciousness in patients with severe brain injuries in the intensive care unit (ICU). For patients whose families face time-sensitive, life-or-death decisions, treatments that promote recovery of consciousness are needed to reduce the likelihood of premature withdrawal of life-sustaining therapy, facilitate autonomous self-expression, and increase access to rehabilitative care. Here, we present the Connectome-based Clinical Trial Platform (CCTP), a new paradigm for developing and testing targeted therapies that promote early recovery of consciousness in the ICU. We report the protocol for STIMPACT (Stimulant Therapy Targeted to Individualized Connectivity Maps to Promote ReACTivation of Consciousness), a CCTP-based trial in which intravenous methylphenidate will be used for targeted stimulation of dopaminergic circuits within the subcortical ascending arousal network (ClinicalTrials.gov NCT03814356). The scientific premise of the CCTP and the STIMPACT trial is that personalized brain network mapping in the ICU can identify patients whose connectomes are amenable to neuromodulation. Phase 1 of the STIMPACT trial is an open-label, safety and dose-finding study in 22 patients with disorders of consciousness caused by acute severe traumatic brain injury. Patients in Phase 1 will receive escalating daily doses (0.5-2.0 mg/kg) of intravenous methylphenidate over a 4-day period and will undergo resting-state functional magnetic resonance imaging and electroencephalography to evaluate the drug's pharmacodynamic properties. The primary outcome measure for Phase 1 relates to safety: the number of drug-related adverse events at each dose. Secondary outcome measures pertain to pharmacokinetics and pharmacodynamics: (1) time to maximal serum concentration; (2) serum half-life; (3) effect of the highest tolerated dose on resting-state functional MRI biomarkers of connectivity; and (4) effect of each dose on EEG biomarkers of cerebral cortical function. Predetermined safety and pharmacodynamic criteria must be fulfilled in Phase 1 to proceed to Phase 2A. Pharmacokinetic data from Phase 1 will also inform the study design of Phase 2A, where we will test the hypothesis that personalized connectome maps predict therapeutic responses to intravenous methylphenidate. Likewise, findings from Phase 2A will inform the design of Phase 2B, where we plan to enroll patients based on their personalized connectome maps. By selecting patients for clinical trials based on a principled, mechanistic assessment of their neuroanatomic potential for a therapeutic response, the CCTP paradigm and the STIMPACT trial have the potential to transform the therapeutic landscape in the ICU and improve outcomes for patients with severe brain injuries.
Collapse
Affiliation(s)
- Brian L Edlow
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Megan E Barra
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | - David W Zhou
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrea S Foulkes
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel B Snider
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Zachary D Threlkeld
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Sourish Chakravarty
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.,The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Suk-Tak Chan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven L Meisler
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas P Bleck
- Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Joseph J Fins
- Division of Medical Ethics and Consortium for the Advanced Study of Brain Injury (CASBI), Weill Cornell Medical College, New York, NY, USA.,The Rockefeller University, New York, NY, USA.,Solomon Center for Health Law and Policy, Yale Law School, New Haven, CT, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA, USA
| | - Leigh R Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA.,Veterans Affairs RR&D Center for Neurorestoration and Neurotechnology, VA Medical Center, Providence, RI, USA
| | - Ken Solt
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Emery N Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.,The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yelena G Bodien
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA, USA
| |
Collapse
|
50
|
Conklin J, Frosch MP, Mukerji S, Rapalino O, Maher M, Schaefer PW, Lev MH, Gonzalez RG, Das S, Champion SN, Magdamo C, Sen P, Harrold GK, Alabsi H, Normandin E, Shaw B, Lemieux J, Sabeti P, Branda JA, Brown EN, Westover MB, Huang SY, Edlow BL. Cerebral Microvascular Injury in Severe COVID-19. medRxiv 2020. [PMID: 32743599 DOI: 10.1101/2020.07.21.20159376] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
IMPORTANCE Microvascular lesions are common in patients with severe COVID-19. Radiologic-pathologic correlation in one case suggests a combination of microvascular hemorrhagic and ischemic lesions that may reflect an underlying hypoxic mechanism of injury, which requires validation in larger studies. OBJECTIVE To determine the incidence, distribution, and clinical and histopathologic correlates of microvascular lesions in patients with severe COVID-19. DESIGN Observational, retrospective cohort study: March to May 2020. SETTING Single academic medical center. PARTICIPANTS Consecutive patients (16) admitted to the intensive care unit with severe COVID-19, undergoing brain MRI for evaluation of coma or focal neurologic deficits. EXPOSURES Not applicable. MAIN OUTCOME AND MEASURES Hypointense microvascular lesions identified by a prototype ultrafast high-resolution susceptibility-weighted imaging (SWI) MRI sequence, counted by two neuroradiologists and categorized by neuroanatomic location. Clinical and laboratory data (most recent measurements before brain MRI). Brain autopsy and cerebrospinal fluid PCR for SARS-CoV 2 in one patient who died from severe COVID-19. RESULTS Eleven of 16 patients (69%) had punctate and linear SWI lesions in the subcortical and deep white matter, and eight patients (50%) had >10 SWI lesions. In 4/16 patients (25%), lesions involved the corpus callosum. Brain autopsy in one patient revealed that SWI lesions corresponded to widespread microvascular injury, characterized by perivascular and parenchymal petechial hemorrhages and microscopic ischemic lesions. CONCLUSIONS AND RELEVANCE SWI lesions are common in patients with neurological manifestations of severe COVID-19 (coma and focal neurologic deficits). The distribution of lesions is similar to that seen in patients with hypoxic respiratory failure, sepsis, and disseminated intravascular coagulation. Collectively, these radiologic and histopathologic findings suggest that patients with severe COVID-19 are at risk for multifocal microvascular hemorrhagic and ischemic lesions in the subcortical and deep white matter.
Collapse
|