Abstract
Background
Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society’s Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group.
Methods
After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into “disease core,” “basic,” “supplemental,” and “exploratory.” Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs.
Results
After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group.
Conclusions
Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.
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References
Loomis AL, Harvey EN, Hobart G. Potential rhythms of the cerebral cortex during sleep. Science. 1935;81(2111):597–8. https://doi.org/10.1126/science.81.2111.597.
Schomer DL, da Silva FHL. Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fields: sixth edition. In: Schomer DL, Lopes da Silva FH (eds) Wolters Kluver/Lippincott Williams & Wilkins. 2011. https://doi.org/10.1111/j.1468-1331.2011.03406.x
Comanducci A, Boly M, Claassen J, et al. Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group. Clin Neurophysiol. 2020;131(11):2736–65. https://doi.org/10.1016/j.clinph.2020.07.015.
Giacino JT, Schnakers C, Rodriguez-Moreno D, Kalmar K, Schiff N, Hirsch J. Behavioral assessment in patients with disorders of consciousness: gold standard or fool’s gold? Prog Brain Res. 2009;177:33–48. https://doi.org/10.1016/S0079-6123(09)17704-X.
Kondziella D, Bender A, Diserens K, et al. European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness. Eur J Neurol. 2020;27(5):741–56. https://doi.org/10.1111/ene.14151.
Hockaday JM, Potts F, Epstein E, Bonazzi A, Schwab RS. Electroencephalographic changes in acute cerebral anoxia from cardiac or respiratory arrest. Electroencephalogr Clin Neurophysiol. 1965;18(6):575–86. https://doi.org/10.1016/0013-4694(65)90075-1.
Synek VM. Prognostically important EEG coma patterns in diffuse anoxic and traumatic encephalopathies in adults. J Clin Neurophysiol. 1988;5(2):161–74. https://doi.org/10.1097/00004691-198804000-00003.
Hirsch LJ, Fong MWK, Leitinger M, et al. American clinical neurophysiology society’s standardized critical care EEG terminology: 2021 version. J Clin Neurophysiol. 2021;38(1):1–29. https://doi.org/10.1097/WNP.0000000000000806.
Gaspard N, Hirsch LJ, LaRoche SM, Hahn CD, Brandon M. Interrater agreement for critical care EEG terminology. Epilepsia. 2014;55(9):1366–73. https://doi.org/10.1111/epi.12653.
Westhall E, Rosén I, Rossetti AO, et al. Interrater variability of EEG interpretation in comatose cardiac arrest patients. Clin Neurophysiol. 2015;126(12):2397–404. https://doi.org/10.1016/j.clinph.2015.03.017.
Claassen J, Mayer SA, Kowalski RG, Emerson RG, Hirsch LJ. Detection of electrographic seizures with continuous EEG monitoring in critically ill patients. Neurology. 2004;62(10):1743–8. https://doi.org/10.1212/01.WNL.0000125184.88797.62.
Young GB, Jordan KG, Doig GS. An assessment of nonconvulsive seizures in the intensive care unit using continuous EEG monitoring: An investigation of variables associated with mortality. Neurology. 1996;47(1):83–9. https://doi.org/10.1212/WNL.47.1.83.
Rodríguez V, Rodden MF, LaRoche SM. Ictal-interictal continuum: a proposed treatment algorithm. Clin Neurophysiol. 2016;127(4):2056–64. https://doi.org/10.1016/j.clinph.2016.02.003.
Claassen J. How i treat patients with EEG patterns on the ictal-interictal continuum in the neuro ICU. Neurocrit Care. 2009;11(3):437–44. https://doi.org/10.1007/s12028-009-9295-8.
Westhall E, Rosén I, Rundgren M, et al. Time to epileptiform activity and EEG background recovery are independent predictors after cardiac arrest. Clin Neurophysiol. 2018;129(8):1660–8. https://doi.org/10.1016/j.clinph.2018.05.016.
Forgacs PB, Conte MM, Fridman EA, Voss PhD HU, Victor JD, Schiff ND. Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following. Ann Neurol. 2014;76(6):869–79. https://doi.org/10.1002/ana.24283.
Landsness E, Bruno MA, Noirhomme Q, et al. Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state. Brain. 2011;134(8):2222–32. https://doi.org/10.1093/brain/awr152.
Cloostermans MC, Van Meulen FB, Eertman CJ, Hom HW, Van Putten MJAM. Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study. Crit Care Med. 2012;40(10):2867–75. https://doi.org/10.1097/CCM.0b013e31825b94f0.
Sivaraju A, Gilmore EJ, Wira CR, et al. Prognostication of post-cardiac arrest coma: early clinical and electroencephalographic predictors of outcome. Intensive Care Med. 2015;41(7):1264–72. https://doi.org/10.1007/s00134-015-3834-x.
Hofmeijer J, Tjepkema-Cloostermans MC, van Putten MJAM. Burst-suppression with identical bursts: a distinct EEG pattern with poor outcome in postanoxic coma. Clin Neurophysiol. 2014;125(5):947–54. https://doi.org/10.1016/j.clinph.2013.10.017.
Rossetti AO, Carrera E, Oddo M. Early EEG correlates of neuronal injury after brain anoxia. Neurology. 2012;78(11):796–802. https://doi.org/10.1212/WNL.0b013e318249f6bb.
Sandroni C, D’Arrigo S, Nolan JP. Prognostication after cardiac arrest. Crit Care. 2018;22(1):150. https://doi.org/10.1186/s13054-018-2060-7.
Forgacs PB, Devinsky O, Schiff ND. Independent functional outcomes after prolonged coma following cardiac arrest: a mechanistic hypothesis. Ann Neurol. 2020;87(4):618–32. https://doi.org/10.1002/ana.25690.
Lechinger J, Bothe K, Pichler G, et al. CRS-R score in disorders of consciousness is strongly related to spectral EEG at rest. J Neurol. 2013;260(9):2348–56. https://doi.org/10.1007/s00415-013-6982-3.
Sitt JD, King JR, El Karoui I, et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain. 2014;137(8):2258–70. https://doi.org/10.1093/brain/awu141.
Engemann DA, Raimondo F, King JR, et al. Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain. 2018;141(11):3179–92. https://doi.org/10.1093/brain/awy251.
Fellinger R, Klimesch W, Schnakers C, et al. Cognitive processes in disorders of consciousness as revealed by EEG time-frequency analyses. Clin Neurophysiol. 2011;122(11):2177–84. https://doi.org/10.1016/j.clinph.2011.03.004.
Gosseries O, Schnakers C, Ledoux D, et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct Neurol. 2011;26(1):25.
Schartner M, Seth A, Noirhomme Q, et al. Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLoS ONE. 2015;10(8):e0133532. https://doi.org/10.1371/journal.pone.0133532.
King JR, Sitt JD, Faugeras F, et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr Biol. 2013;23(19):1914–9. https://doi.org/10.1016/j.cub.2013.07.075.
Schiff ND, Nauvel T, Victor JD. Large-scale brain dynamics in disorders of consciousness. Curr Opin Neurobiol. 2014;25:7–14. https://doi.org/10.1016/j.conb.2013.10.007.
Schiff ND. Mesocircuit mechanisms underlying recovery of consciousness following severe brain injuries: Model and predictions. In: Brain function and responsiveness in disorders of consciousness. 2016. https://doi.org/10.1007/978-3-319-21425-2_15.
Forgacs PB, Frey HP, Velazquez A, 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. 2017;4(2):119–29. https://doi.org/10.1002/acn3.385.
Frohlich J, Crone JS, Johnson MA, et al. Neural oscillations track recovery of consciousness in acute traumatic brain injury patients. Hum Brain Mapp. 2022;43(6):1804–20. https://doi.org/10.1002/hbm.25725.
Colombo MA, Comanducci A, Casarotto S, et al. Beyond alpha power: EEG spatial and spectral gradients robustly stratify disorders of consciousness. Cereb Cortex. 2023;33(11):7193–210. https://doi.org/10.1093/cercor/bhad031.
Sur S, Sinha V. Event-related potential: an overview. Ind Psychiatry J. 2009;18(1):70. https://doi.org/10.4103/0972-6748.57865.
Peterson NN, Schroeder CE, Arezzo JC. Neural generators of early cortical somatosensory evoked potentials in the awake monkey. Electroencephalogr Clin Neurophysiol. 1995;96(3):248–60. https://doi.org/10.1016/0168-5597(95)00006-E.
Näätänen R, Gaillard AWK, Mäntysalo S. Early selective-attention effect on evoked potential reinterpreted. Acta Psychol (Amst). 1978;42(4):313–29. https://doi.org/10.1016/0001-6918(78)90006-9.
Garrido MI, Kilner JM, Stephan KE, Friston KJ. The mismatch negativity: A review of underlying mechanisms. Clin Neurophysiol. 2009;120(3):453–63. https://doi.org/10.1016/j.clinph.2008.11.029.
Morlet D, Fischer C. MMN and novelty P3 in coma and other altered states of consciousness: a review. Brain Topogr. 2014;27(4):467–79. https://doi.org/10.1007/s10548-013-0335-5.
Kane NM, Curry SH, Butler SR, Cummins BH. Electrophysiological indicator of awakening from coma. The Lancet. 1993;341(8846):688. https://doi.org/10.1016/0140-6736(93)90453-N.
Fischer C, Luauté J, Adeleine P, Morlet D. Predictive value of sensory and cognitive evoked potentials for awakening from coma. Neurology. 2004;63(4):669–73. https://doi.org/10.1212/01.WNL.0000134670.10384.E2.
Luauté J, Fischer C, Adeleine P, Morlet D, Tell L, Boisson D. Late auditory and event-related potentials can be useful to predict good functional outcome after coma. Arch Phys Med Rehabil. 2005;86(5):917–23. https://doi.org/10.1016/j.apmr.2004.08.011.
Naccache L, Puybasset L, Gaillard R, Serve E, Willer JC. Auditory mismatch negativity is a good predictor of awakening in comatose patients: a fast and reliable procedure [1]. Clin Neurophysiol. 2005;116(4):988–9. https://doi.org/10.1016/j.clinph.2004.10.009.
Wijnen VJM, van Boxtel GJM, Eilander HJ, de Gelder B. Mismatch negativity predicts recovery from the vegetative state. Clin Neurophysiol. 2007;118(3):597–605. https://doi.org/10.1016/j.clinph.2006.11.020.
Sutton S, Braren M, Zubin J, John ER. Evoked-potential correlates of stimulus uncertainty. Science (1979). 1965;150(3700):1187–8. https://doi.org/10.1126/science.150.3700.1187.
Squires NK, Squires KC, Hillyard SA. Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalogr Clin Neurophysiol. 1975;38(4):387–401. https://doi.org/10.1016/0013-4694(75)90263-1.
Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol. 2007;118(10):2128–48. https://doi.org/10.1016/j.clinph.2007.04.019.
Picton TW. The P300 wave of the human event-related potential. J Clin Neurophysiol. 1992;9(4):456. https://doi.org/10.1097/00004691-199210000-00002.
Dehaene S, Changeux JP. Experimental and Theoretical approaches to conscious processing. Neuron. 2011;70(2):200–27. https://doi.org/10.1016/j.neuron.2011.03.018.
Bekinschtein TA, Dehaene S, Rohaut B, Tadel F, Cohen L, Naccache L. Neural signature of the conscious processing of auditory regularities. Proc Natl Acad Sci. 2009;106:1672–7.
Perez P, Valente M, Hermann B, et al. Auditory event-related “global effect” predicts recovery of overt consciousness. Front Neurol. 2021;11:588233. https://doi.org/10.3389/fneur.2020.588233.
Goldie WD, Chiappa KH, Young RR, Brooks EB. Brainstem auditory and short-latency somatosensory evoked responses in brain death. Neurology. 1981;31(3):248. https://doi.org/10.1212/wnl.31.3.248.
Zandbergen EGJ, Koelman JHTM, De Haan RJ, Hijdra A. SSEPs and prognosis in postanoxic coma: only short or also long latency responses? Neurology. 2006;67(4):583–6. https://doi.org/10.1212/01.wnl.0000230162.35249.7f.
Rothstein TL, Thomas EM, Sumi SM. Predicting outcome in hypoxic-ischemic coma. A prospective clinical and electrophysiologic study. Electroencephalogr Clin Neurophysiol. 1991;79(2):101–7. https://doi.org/10.1016/0013-4694(91)90046-7.
Madl C, Kramer L, Domanovits H, et al. Improved outcome prediction in unconscious cardiac arrest survivors with sensory evoked potentials compared with clinical assessment. Crit Care Med. 2000;28(3):721–6. https://doi.org/10.1097/00003246-200003000-00020.
Sherman AL, Tirschwell DL, Micklesen PJ, Longstreth WT, Robinson LR. Somatosensory potentials, CSF creatine kinase BB activity, and awakening after cardiac arrest. Neurology. 2000;54(4):889–94. https://doi.org/10.1212/WNL.54.4.889.
Logi F, Fischer C, Murri L, Mauguière F. The prognostic value of evoked responses from primary somatosensory and auditory cortex in comatose patients. Clin Neurophysiol. 2003;114(9):1615–27. https://doi.org/10.1016/S1388-2457(03)00086-5.
Zandbergen EGJ, de Haan RJ, Stoutenbeek CP, Koelman JHTM, Hijdra A. Systematic review of early prediction of poor outcome in anoxic- ischaemic coma. Lancet. 1998;352(9143):1808–12. https://doi.org/10.1016/S0140-6736(98)04076-8.
Amorim E, Ghassemi MM, Lee JW, et al. Estimating the false positive rate of absent somatosensory evoked potentials in cardiac arrest prognostication. Crit Care Med. 2018;46(12):e1213–21. https://doi.org/10.1097/CCM.0000000000003436.
Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science (1979). 2006;313(5792):1402. https://doi.org/10.1126/science.1130197.
Bardin JC, Fins JJ, Katz DI, et al. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain. 2011;134(3):769–82. https://doi.org/10.1093/brain/awr005.
Fernández-Espejo D, Bekinschtein T, Monti MM, et al. Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. Neuroimage. 2011;54(1):103–12. https://doi.org/10.1016/j.neuroimage.2010.08.035.
Stender J, Gosseries O, Bruno MA, et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: A clinical validation study. The Lancet. 2014;384(9942):514–22. https://doi.org/10.1016/S0140-6736(14)60042-8.
Claassen J, Doyle K, Matory A, et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med. 2019;380(26):2497–505. https://doi.org/10.1056/nejmoa1812757.
Egbebike J, Shen Q, Doyle K, 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. 2022;21(8):704–13. https://doi.org/10.1016/S1474-4422(22)00212-5.
Koch C, Massimini M, Boly M, Tononi G. Neural correlates of consciousness: progress and problems. Nat Rev Neurosci. 2016;17(5):307–21. https://doi.org/10.1038/nrn.2016.22.
Massimini M, Boly M, Casali A, Rosanova M, Tononi G. A perturbational approach for evaluating the brain’s capacity for consciousness. Prog Brain Res. 2009;177(C):25. https://doi.org/10.1016/S0079-6123(09)17714-2.
Ilmoniemi RJ, Virtanen J, Ruohonen J, et al. Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. NeuroReport. 1997;8(16):3537–40. https://doi.org/10.1097/00001756-199711100-00024.
Rosanova M, Casali A, Bellina V, Resta F, Mariotti M, Massimini M. Natural frequencies of human corticothalamic circuits. J Neurosci. 2009;29(24):7679–85. https://doi.org/10.1523/JNEUROSCI.0445-09.2009.
Massimini M, Ferrarelli F, Huber R, Esser SK, Singh H, Tononi G. Breakdown of cortical effective connectivity during sleep. Science. 2005;309(5744):2228–32.
Massimini M, Ferrarelli F, Esser SK, et al. Triggering sleep slow waves by transcranial magnetic stimulation. Proc Natl Acad Sci USA. 2007;104(20):8496–501. https://doi.org/10.1073/pnas.0702495104.
Sarasso S, Boly M, Napolitani M, et al. Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine. Curr Biol. 2015;25(23):3099–105. https://doi.org/10.1016/j.cub.2015.10.014.
Rosanova M, Gosseries O, Casarotto S, et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain. 2012;135(4):1308–20. https://doi.org/10.1093/brain/awr340.
Ragazzoni A, Pirulli C, Veniero D, et al. Vegetative versus minimally conscious states: a study using TMS-EEG, sensory and event-related potentials. PLoS ONE. 2013;8(2):e57069. https://doi.org/10.1371/journal.pone.0057069.
Bodart O, Gosseries O, Wannez S, et al. Measures of metabolism and complexity in the brain of patients with disorders of consciousness. Neuroimage Clin. 2017;14:354–62. https://doi.org/10.1016/j.nicl.2017.02.002.
Casarotto S, Comanducci A, Rosanova M, et al. Stratification of unresponsive patients by an independently validated index of brain complexity. Ann Neurol. 2016;80(5):718–29. https://doi.org/10.1002/ana.24779.
Sinitsyn DO, Poydasheva AG, Bakulin IS, et al. Detecting the potential for consciousness in unresponsive patients using the perturbational complexity index. Brain Sci. 2020;10(12):917. https://doi.org/10.3390/brainsci10120917.
Casarotto S, Fecchio M, Rosanova M, et al. The rt-TEP tool: real-time visualization of TMS-evoked potentials to maximize cortical activation and minimize artifacts. J Neurosci Methods. 2022;370:109486. https://doi.org/10.1016/j.jneumeth.2022.109486.
Russo S, Sarasso S, Puglisi GE, et al. TAAC-TMS adaptable auditory control: a universal tool to mask TMS clicks. J Neurosci Methods. 2022;370:109491. https://doi.org/10.1016/j.jneumeth.2022.109491.
Lioumis P, Rosanova M. The role of neuronavigation in TMS-EEG studies: current applications and future perspectives. J Neurosci Methods. 2022;380:109677. https://doi.org/10.1016/j.jneumeth.2022.109677.
Comanducci A, Casarotto S, Rosanova M, et al. Unconsciousness or unresponsiveness in akinetic mutism? Insights from a multimodal longitudinal exploration. Eur J Neurosci. 2023. https://doi.org/10.1111/ejn.15994.
Edlow BL, Fecchio M, Bodien YG, et al. Measuring consciousness in the intensive care unit. Neurocrit Care. 2023;38(3):584–90. https://doi.org/10.1007/s12028-023-01706-4.
Suarez JI, Sheikh MK, Macdonald RL, et al. Common data elements for unruptured intracranial aneurysms and subarachnoid hemorrhage clinical research: a National Institute for Neurological Disorders and Stroke and National Library of Medicine Project. Neurocrit Care. 2019;30:4–19. https://doi.org/10.1007/s12028-019-00723-6.
S. T, C. F, S. K, et al. National institute of neurological disorders and stroke (NINDS), national institutes of health (NIH), Common Data Element (CDE) project: Epilepsy CDE update. Epilepsia. 2009;50.
Loring DW, Lowenstein DH, Barbaro NM, et al. Common data elements in epilepsy research: Development and implementation of the NINDS epilepsy CDE project. Epilepsia. 2011;52(6):1186–91. https://doi.org/10.1111/j.1528-1167.2011.03018.x.
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The Curing Coma Campaign Collaborators are listed in the Supplementary Appendix.
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Included National Institute of Neurological Disorders and Stroke (NINDS) (R01NS106014, R03NS112760, R21NS128326) and James S. McDonnell Foundation (J.C.); Institutional KL2 Career Development Award from the Miami Clinical and Translational Science Institute (CTSI), National Center for Advancing Translational Sciences (NCATS), UL1TR002736 and by the NINDS (K23NS126577, R21NS128326) (AA); United States Department of Defense (W81XWH19-1–0514), American Heart Association (19CDA34760291) and Innovators for Neuroscience in Kids Foundation (BA); NINDS (R01NS117904, UG3NS123307), Yale New Haven Health System (YNHHS), Innovations (EG); European Research Area (ERA), PerMed JTC2019 (project PerBrain) and JTC2021 (project ModelDXConsciousness) (JDS); Calgary Health Foundation Grant for Neurocritical Care Expansion Project, Office of Health & Medical Education Scholarship Grant, Canadian Institute of Health Research Grant (BR); European Union’s Horizon2020 Framework Program for Research and Innovation under the Specific Grant Agreement No.945539 (Human Brain Project SGA3) and by the Fondazione Regionale per la Ricerca Biomedica (Regione Lombardia), Project PerBrain, call ERAPERMED2019-101,GA779282 (MR).
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EC and JC wrote the initial draft of the manuscript. CD, AA, BA, EG, JK, BR, MR, and JDS, edited the manuscript and approved the final content. All co-authors contributed equally to the case report forms released with the article.
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Carroll, E.E., Der-Nigoghossian, C., Alkhachroum, A. et al. Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group. Neurocrit Care 39, 578–585 (2023). https://doi.org/10.1007/s12028-023-01795-1
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DOI: https://doi.org/10.1007/s12028-023-01795-1