Sleep spindles as a predictor of cognitive motor dissociation and recovery of consciousness after acute brain injury | Nature Medicine
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Cognitive motor dissociation (CMD) can improve the accuracy to predict recovery of behaviorally unresponsive patients with acute brain injury, but acquisition and analysis of task-based electroencephalography (EEG) are technically challenging. N2 sleep patterns, such as sleep spindles on EEG, have been associated with good outcomes, rely on similar thalamocortical networks as consciousness and could provide less technically challenging complementary outcome predictors. In this prospective observational cohort study of 226 acutely brain injured patients, well-formed sleep spindles (WFSS) were more likely present in those with CMD than in those without CMD, often preceding the detection of CMD. WFSS were associated with a shorter time to recovery of consciousness, and both CMD and WFSS independently predicted recovery of independence, controlling for age, admission neurological status and injury type. WFSS are seen in approximately every third behaviorally unresponsive patient after acute brain injury, frequently precede detection of CMD and are a promising complementary predictor for recovery of consciousness and functional independence.
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The primary patient-level data that support all the main analyses are available as part of Extended Data Figs. 4 and 5, including sleep data, CMD status, sedation data and outcomes data. Other individual patient data are not openly available due to reasons of sensitivity for patient privacy reasons.
The code to analyze EEG for cognitive motor dissociation was previously shared (see Claassen et al.26). All other codes used based on publicly available R packages are non-proprietary (R version 4.0.3, R package GLM version 4.1-2, R package survival version 3.2-7, R-package survminer version 0.4.8, R package tidycmprsk version 1.0.0 and R package riskRegression version 2023.03.22).
Kondziella, D. et al. European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness. Eur. J. Neurol. 27, 739–740 (2020).
Kondziella, D. et al. A precision medicine framework for classifying patients with disorders of consciousness: Advanced Classification of Consciousness Endotypes (ACCESS). Neurocrit. Care 35, 27–36 (2021).
PubMed Google Scholar
Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: a report from the American Heart Association. Circulation 147, e93–e621 (2023).
Kittner, S. J. et al. Ethnic and racial variation in intracerebral hemorrhage risk factors and risk factor burden. JAMA Netw. Open 4, e2121921 (2021).
PubMed PubMed Central Google Scholar
Maas, A. I. R. et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol. 21, 1004–1060 (2022).
PubMed PubMed Central Google Scholar
Russell, M. E., Hammond, F. M. & Murtaugh, B. Prognosis and enhancement of recovery in disorders of consciousness. NeuroRehabilitation 54, 43–59 (2024).
PubMed Google Scholar
Fitzgerald, E. et al. Functional outcomes at 12 months for patients with traumatic brain injury, intracerebral haemorrhage and subarachnoid haemorrhage treated in an Australian neurocritical care unit: a prospective cohort study. Aust. Crit. Care 33, 497–503 (2020).
PubMed Google Scholar
Karras, C. L. et al. Outcomes following penetrating brain injuries in military settings: a systematic review and meta-analysis. World Neurosurg. 166, 39–48 (2022).
PubMed Google Scholar
Ponfick, M., Wiederer, R. & Nowak, D. A. Outcome of intensive care unit–dependent, tracheotomized patients with cerebrovascular diseases. J. Stroke Cerebrovasc. Dis. 24, 1527–1531 (2015).
PubMed Google Scholar
Humble, S. S. et al. Prognosis of diffuse axonal injury with traumatic brain injury. J. Trauma Acute Care Surg. 85, 155–159 (2018).
PubMed PubMed Central Google Scholar
Hayamizu, M. et al. Delayed neurologic improvement and long-term survival of patients with poor neurologic status after out-of-hospital cardiac arrest: a retrospective cohort study in Japan. Resuscitation 188, 109790 (2023).
PubMed Google Scholar
Edlow, B. L., Claassen, J., Schiff, N. D. & Greer, D. M. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat. Rev. Neurol. 17, 135–156 (2021).
PubMed Google Scholar
Egawa, S. et al. Long-term outcomes of patients with stroke predicted by clinicians to have no chance of meaningful recovery: a Japanese cohort study. Neurocrit. Care 38, 733–740 (2023).
PubMed Google Scholar
Dijkland, S. A. et al. Prognosis in moderate and severe traumatic brain injury: a systematic review of contemporary models and validation studies. J. Neurotrauma 37, 1–13 (2020).
PubMed Google Scholar
Witsch, J. et al. Prognostication after intracerebral hemorrhage: a review. Neurol. Res. Pract. 3, 22 (2021).
PubMed PubMed Central Google Scholar
Turgeon, A. F. et al. Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study. CMAJ 183, 1581–1588 (2011).
PubMed PubMed Central Google Scholar
Elmer, J. et al. Long-term survival benefit from treatment at a specialty center after cardiac arrest. Resuscitation 108, 48–53 (2016).
PubMed PubMed Central Google Scholar
Giacino, J. T. et al. Behavioral recovery and early decision making in patients with prolonged disturbance in consciousness after traumatic brain injury. J. Neurotrauma 37, 357–365 (2020).
Giacino, J. T. et al. Practice guideline update recommendations summary: disorders of consciousness: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Neurology 91, 450–460 (2018).
Alkhachroum, A. et al. Withdrawal of life-sustaining treatment mediates mortality in patients with intracerebral hemorrhage with impaired consciousness. Stroke 52, 3891–3898 (2021).
CAS PubMed PubMed Central Google Scholar
Alkhachroum, A. et al. Association of acute alteration of consciousness in patients with acute ischemic stroke with outcomes and early withdrawal of care. Neurology 98, e1470–e1478 (2022).
CAS PubMed PubMed Central Google Scholar
Amiri, M. et al. Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study. Brain 146, 50–64 (2023).
PubMed Google Scholar
Rohaut, B. et al. Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury. Nat. Med. 30, 2349–2355 (2024).
Owen, A. M. et al. Detecting awareness in the vegetative state. Science 313, 1402 (2006).
CAS PubMed Google Scholar
Edlow, B. L. et al. Early detection of consciousness in patients with acute severe traumatic brain injury. Brain 140, 2399–2414 (2017).
PubMed PubMed Central Google Scholar
Claassen, J. et al. Detection of brain activation in unresponsive patients with acute brain injury. N. Engl. J. Med. 380, 2497–2505 (2019).
PubMed Google Scholar
Egbebike, J. et al. Cognitive-motor dissociation and time to functional recovery in patients with acute brain injury in the USA: a prospective observational cohort study. Lancet Neurol. 21, 704–713 (2022).
Giacino, J. T., Kalmar, K. & Whyte, J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch. Phys. Med. Rehabil. 85, 2020–2029 (2004).
Bodien, Y. G., Carlowicz, C. A., Chatelle, C. & Giacino, J. T. Sensitivity and specificity of the Coma Recovery Scale–Revised total score in detection of conscious awareness. Arch. Phys. Med. Rehabil. 97, 490–492 (2016).
PubMed Google Scholar
Giacino, J. T. et al. The minimally conscious state: definition and diagnostic criteria. Neurology 58, 349–353 (2002).
Giacino, J. T. et al. Comprehensive systematic review update summary: disorders of consciousness: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Arch. Phys. Med. Rehabil. 99, P1710–P1719 (2018).
Schiff, N. D. Cognitive motor dissociation following severe brain injuries. JAMA Neurol. 72, 1413–1415 (2015).
Fernández-Espejo, D., Rossit, S. & Owen, A. M. A thalamocortical mechanism for the absence of overt motor behavior in covertly aware patients. JAMA Neurol. 72, 1442–1450 (2015).
PubMed Google Scholar
Sanders, W. R. et al. Recovery potential in patients who died after withdrawal of life-sustaining treatment: a TRACK-TBI propensity score analysis. J. Neurotrauma 41, 2336–2348 (2024).
PubMed Google Scholar
Bodien, Y. G. et al. Cognitive motor dissociation in disorders of consciousness. N. Engl. J. Med. 391, 598–608 (2024).
PubMed PubMed Central Google Scholar
Claassen, J. et al. Cognitive motor dissociation: gap analysis and future directions. Neurocrit. Care 40, 81–98 (2024).
PubMed Google Scholar
Jacobson, S. D. et al. Impact of aphasia on brain activation to motor commands in patients with acute intracerebral hemorrhage. Neurocrit. Care https://doi.org/10.1007/s12028-024-02086-z (2024).
Raciti, L. et al. Sleep in disorders of consciousness: a brief overview on a still under investigated issue. Brain Sci. 13, 275 (2023).
Sandsmark, D. K. et al. Sleep features on continuous electroencephalography predict rehabilitation outcomes after severe traumatic brain injury. J. Head Trauma Rehabil. 31, 101–107 (2016).
PubMed PubMed Central Google Scholar
Gottshall, J. L. & Rossi Sebastiano, D. Sleep in disorders of consciousness: diagnostic, prognostic, and therapeutic considerations. Curr. Opin. Neurol. 33, 684–690 (2020).
Rossi Sebastiano, D. et al. Sleep patterns associated with the severity of impairment in a large cohort of patients with chronic disorders of consciousness. Clin. Neurophysiol. 129, 687–693 (2018).
Duclos, C. et al. Parallel recovery of consciousness and sleep in acute traumatic brain injury. Neurology 88, 268–275 (2017).
Forgacs, P. B. et al. Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following. Ann. Neurol. 76, 869–879 (2014).
Grigg-Damberger, M. M., Hussein, O. & Kulik, T. Sleep spindles and K-complexes are favorable prognostic biomarkers in critically ill patients. J. Clin. Neurophysiol. 39, 372–382 (2022).
PubMed Google Scholar
Van Der Lande, G. J. M., Blume, C. & Annen, J. Sleep and circadian disturbance in disorders of consciousness: current methods and the way towards clinical implementation. Semin. Neurol. 42, 283–298 (2022).
Arnaldi, D. et al. The prognostic value of sleep patterns in disorders of consciousness in the sub-acute phase. Clin. Neurophysiol. 127, 1445–1451 (2016).
Yang, X. A. et al. Prognostic roles of sleep electroencephalography pattern and circadian rhythm biomarkers in the recovery of consciousness in patients with coma: a prospective cohort study. Sleep Med. 69, 204–212 (2020).
PubMed Google Scholar
Fernandez, L. M. J. & Lüthi, A. Sleep spindles: mechanisms and functions. Physiol. Rev. 100, 805–868 (2020).
PubMed Google Scholar
Urakami, Y. Relationship between sleep spindles and clinical recovery in patients with traumatic brain injury: a simultaneous EEG and MEG study. Clin. EEG Neurosci. 43, 39–47 (2012).
PubMed Google Scholar
De Gennaro, L. & Ferrara, M. Sleep spindles: an overview. Sleep Med. Rev. 7, 423–440 (2003).
Anderer, P. et al. Low-resolution brain electromagnetic tomography revealed simultaneously active frontal and parietal sleep spindle sources in the human cortex. Neuroscience 103, 581–592 (2001).
Franzova, E. et al. Injury patterns associated with cognitive motor dissociation. Brain 146, 4645–4658 (2023).
Zhu, H. et al. Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain. Preprint at bioRxiv https://doi.org/10.1101/2024.07.08.600416 (2024).
Schiff, N. D. in Brain Function and Responsiveness in Disorders of Consciousness (eds Monti, M. M. & Sannita, W. G.) 195–204 (Springer, 2016).
Forgacs, P. B. et al. Dynamic regimes of neocortical activity linked to corticothalamic integrity correlate with outcomes in acute anoxic brain injury after cardiac arrest. Ann. Clin. Transl. Neurol. 4, 119–129 (2017).
PubMed PubMed Central Google Scholar
Du, B. et al. Zolpidem arouses patients in vegetative state after brain injury: quantitative evaluation and indications. Am. J. Med. Sci. 347, 178–182 (2014).
PubMed Google Scholar
Whyte, J. & Myers, R. Incidence of clinically significant responses to zolpidem among patients with disorders of consciousness: a preliminary placebo controlled trial. Am. J. Phys. Med. Rehabil. 88, 410–418 (2009).
PubMed Google Scholar
Sutton, J. A. & Clauss, R. P. A review of the evidence of zolpidem efficacy in neurological disability after brain damage due to stroke, trauma and hypoxia: a justification of further clinical trials. Brain Inj. 31, 1019–1027 (2017).
CAS PubMed Google Scholar
Schiff, N. D. Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends Neurosci. 33, 1–9 (2010).
CAS PubMed Google Scholar
Stender, J. et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet 384, 514–522 (2014).
Fine, J. & Gray, R. A proportional hazards model for the subdistribution of a competing risk. J. Am. Stat. Assoc. 94, 496–509 (1999).
Google Scholar
Berry, R. B. et al. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications version 2.6 (American Academy of Sleep Medicine, 2020).
Majersik, J. J. et al. A shortage of neurologists—we must act now: a report from the AAN 2019 Transforming Leaders Program. Neurology 96, 1122–1134 (2021).
Hirsch, L. J. et al. American Clinical Neurophysiology Society’s standardized critical care EEG terminology: 2021 version. J. Clin. Neurophysiol. 38, 1–29 (2021).
PubMed PubMed Central Google Scholar
Kwon, H. et al. Sleep spindles in the healthy brain from birth through 18 years. Sleep 46, zsad017 (2023).
Felten, M. et al. Circadian rhythm disruption in critically ill patients. Acta Physiol. 238, e13962 (2023).
CAS Google Scholar
Curley, W. H., Forgacs, P. B., Voss, H. U., Conte, M. M. & Schiff, N. D. Characterization of EEG signals revealing covert cognition in the injured brain. Brain 141, 1404–1421 (2018).
Wolpert, E. A. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Arch. Gen. Psychiatry 20, 246–247 (1969).
Google Scholar
Purcell, S. M. et al. Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat. Commun. 8, 15930 (2017).
CAS PubMed PubMed Central Google Scholar
Valente, M. et al. Sleep organization pattern as a prognostic marker at the subacute stage of post-traumatic coma. Clin. Neurophysiol. 113, 1798–1805 (2002).
CAS PubMed Google Scholar
Ambrogio, C., Koebnick, J., Quan, S. F., Ranieri, M. & Parthasarathy, S. Assessment of sleep in ventilator-supported critically III patients. Sleep 31, 1559–1568 (2008).
PubMed PubMed Central Google Scholar
McHugh, M. L. Interrater reliability: the kappa statistic. Biochem. Med. 22, 276–282 (2012).
Google Scholar
Landis, J. & Koch, G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977).
CAS PubMed Google Scholar
Claassen, J. et al. Bedside quantitative electroencephalography improves assessment of consciousness in comatose subarachnoid hemorrhage patients. Ann. Neurol. 80, 541–553 (2016).
PubMed PubMed Central Google Scholar
Blanche, P. et al. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks. Biometrics 71, 102–113 (2015).
PubMed Google Scholar
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We thank the nurses, attending physicians, fellows and neurology residents of the neuroscience ICU for their overall support of this project. We are grateful to the National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NS106014; LM011826) and the Clinical and Translational Science Awards (UL1TR001873 from the National Center for Advancing Translational Sciences/NIH) for support of this study.
These authors contributed equally: Elizabeth E. Carroll, Qi Shen.
Department of Neurology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, NY, USA
Elizabeth E. Carroll, Qi Shen, Vedant Kansara, Nicole Casson, Andrew Michalak, Itamar Niesvizky-Kogan, Jaehyung Lim, Amy Postelnik, Matthew J. Viereck, Satoshi Egawa, Joshua Kahan, Jerina C. Carmona, Lucie Kruger, You Lim Song, Angela Velazquez, Catherine A. Schevon, Shivani Ghoshal, Sachin Agarwal, David Roh, Soojin Park, Paul Kent & Jan Claassen
NewYork-Presbyterian Hospital, New York, NY, USA
Elizabeth E. Carroll, Andrew Michalak, Itamar Niesvizky-Kogan, Jaehyung Lim, Amy Postelnik, Joshua Kahan, E. Sander Connolly, Shivani Ghoshal, Sachin Agarwal, David Roh, Soojin Park, Paul Kent & Jan Claassen
Department of Neurosurgery, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, NY, USA
E. Sander Connolly
Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
Soojin Park
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The study was conceived and designed by J. Claassen. Data were acquired and analyzed by E.E.C., Q.S., V.K., N.C., A.M., I.N.-K., J.L., A.P., M.V., S.E., J. Carmona, L.K., Y.L.S., A.V. and J. Claassen. The paper and figures were drafted by E.E.C., Q.S., V.K. and J. Claassen. Edits to the paper were provided by C.A.S., J.K., E.S.C., S.G., S.A., D.R., S.P. and P.K.
Correspondence to Jan Claassen.
The authors declare no competing interests. J. Claassen is a minority shareholder at iCE Neurosystems, but this amounts to less than $10,000 and less than 5% equity in the company. No technology from iCE Neurosystems was used for any of the study procedures, data acquisition or analysis presented here. None of the patients included in this study were managed using any technology from iCE Neurosystems.
Nature Medicine thanks Brian Edlow, Daniel Kondziella and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
EEG, electroencephalography; WLST, withdrawal of list sustaining treatment.
The green box indicates K-complex followed by a well-formed sleep spindle (Extended Data Fig. 2A), the light blue box indicates well-formed sleep spindles (Extended Data Fig. 2B and 2C), and the purple box indicates rudimentary sleep spindles (Extended Data Fig. 2D and 2E).
Presence of K-complexes and vertex waves in the entire patient cohort (a), CMD positive patients (b), and CMD negative patients (c) was highly correlated with concomitant presence of sleep spindles.
Well-formed sleep spindles frequently preceded the detection of CMD.
Of those CMD negative patients with delayed sleep spindles, well-formed sleep spindles were still predictive of recovery of consciousness, and 12-month outcome. Similarly to CMD positive patients, when well-formed sleep spindles were present on EEG, they were likely to persist on subsequent EEG recordings.
Patients receiving “moderate” (p = 0.0002, OR = 0.4 [0.3, 0.7]) and “minimal” or “low” levels of sedation (p < 0.0001 OR = 0.5 [0.3, 0.7]) were less likely to have well-formed sleep spindles present when compared to those with no sedation.
Time to CRS-R ≥ 8 is significantly shorter than time to MCS + /EMCS (p = 0.04).
Time to detect WFSS is significantly shorter than time to detect CMD in the overall cohort (Panel a). Time to CRS-R ≥ 8 is shorter for those with CMD (Panel b) but not for those without CMD (Panel c) except for non-CMD patients with WFSS (Panel d). Amongst patients with WFSS, 35% (6 of 17) of patients with and 46% (25 of 54) of patients without CMD recovered consciousness (Panel e). Amongst patients without WFSS, 25% (4 of 16) of patients with and 21% (30 of 139) of patients without CMD recovered consciousness. Patients with CRS-R ≥ 8 are kept in orange and first detecting WFSS, CMD and CRS-R are indicated by a green diamond, blue triangle, and orange dot, respectively. CMD, cognitive motor dissociation; CRS-R, coma recovery scale-revised; well-formed sleep spindles, WFSS.
This schematic depicts the timeline of testing and results for an exemplary patient found to have well-formed sleep spindles, followed by positive CMD testing, and ultimately recovery of consciousness and long-term functional recovery.
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Carroll, E.E., Shen, Q., Kansara, V. et al. Sleep spindles as a predictor of cognitive motor dissociation and recovery of consciousness after acute brain injury. Nat Med 31, 1578–1585 (2025). https://doi.org/10.1038/s41591-025-03578-x
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Received: 04 October 2024
Accepted: 07 February 2025
Published: 03 March 2025
Issue Date: May 2025
DOI: https://doi.org/10.1038/s41591-025-03578-x
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