CoChat
Sign Up
Cited by 89Traumatic Brain Injury ResearchFunctional Brain Connectivity StudiesTraumatic Brain Injury and Neurovascular Disturbances

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Open access
Amiri, Moshgan, Fisher, Patrick M, Raimondo, Federico, Sidaros, Annette, Cacic Hribljan, Melita, Othman, Marwan H, Zibrandtsen, Ivan, Albrechtsen, Simon S +22 more·2023·Brain
DOI 10.1093/brain/awac335PMC PMC9825454
Open this paper in CoChat — free accountOpen PDF

Abstract

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study ‘Consciousness in neurocritical care cohort study using EEG and fMRI’ (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77–0.80) and 0.71 (95% CI 0.77–0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71–0.86) and 0.83 (95% CI 0.75–0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/brain/awac335

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Early fusion approaches typically aggregated features within predefined time windows for joint modeling.

    “Early fusion approaches typically aggregated features within predefined time windows for joint modeling [50–53]”

    From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [51]

    The Materials and methods describe multimodal models that jointly use EEG and fMRI features, and EEG markers summarized across resting, stimulation, or combined periods. They do not establish that early-fusion approaches typically aggregate features within predefined time windows for joint modeling; the specified 800 ms epochs are described for an EEG classifier, not as the multimodal fusion window. No page numbers are available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 60

  1. [1]Majdan M, Plancikova D, Brazinova A, et al Epidemiology of traumatic brain injuries in Europe: A cross-sectional analysis. Lancet Public Health. 2016; 1: e76– e83. 29253420
  2. [2]Rosenfeld JV, Maas AI, Bragge P, Morganti-Kossmann MC, Manley GT, Gruen RL. Early management of severe traumatic brain injury. Lancet. 2012; 380: 1088– 1098. 22998718
  3. [3]Schnakers C, Vanhaudenhuyse A, Giacino J, et al Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment. BMC Neurol. 2009; 9: 35. 19622138 PMC2718857
  4. [4]Kondziella D, Frontera JA. Pearls & Oy-sters: eyes-open coma. Neurology. 2021; 96: 864– 867. 33593866
  5. [5]Turgeon AF, Lauzier F, Simard JF, et al Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: A Canadian multicentre cohort study. CMAJ. 2011; 183: 1581– 1588. 21876014 PMC3185074
  6. [6]Albrechtsen SS, Riis RGC, Amiri M, et al Impact of MRI on decision-making in ICU patients with disorders of consciousness. Behav Brain Res. 2022; 421: 113729. 34973968
  7. [7]Kondziella D, Friberg CK, Frokjaer VG, Fabricius M, Møller K. Preserved consciousness in vegetative and minimal conscious states: Sstematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2016; 87: 485– 492. 26139551
  8. [8]Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science. 2006; 313: 1402. 16959998
  9. [9]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: 2497– 2505. 31242361
  10. [10]Edlow BL, Claassen J, Schiff ND, Greer DM. Recovery from disorders of consciousness: Mechanisms, prognosis and emerging therapies. Nat Rev Neurol. 2021; 17: 135– 156. 33318675 PMC7734616
  11. [11]Bruno MA, Vanhaudenhuyse A, Thibaut A, Moonen G, Laureys S. From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: Recent advances in our understanding of disorders of consciousness. J Neurol. 2011; 258: 1373– 1384. 21674197
  12. [12]Cole DM, Smith SM, Beckmann CF. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci. 2010; 4: 8. 20407579 PMC2854531
  13. [13]Edlow BL, Chatelle C, Spencer CA, et al Early detection of consciousness in patients with acute severe traumatic brain injury. Brain. 2017; 140: 2399– 2414. 29050383 PMC6059097
  14. [14]Bodien YG, Chatelle C, Edlow BL. Functional networks in disorders of consciousness. Semin Neurol. 2017; 37: 485– 502. 29207410 PMC5884076
  15. [15]Giacino JT, Katz DI, Schiff ND, et al Practice guideline update recommendations summary: Disorders of consciousness. Neurology. 2018; 91: 450– 460. 30089618 PMC6139814
  16. [16]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: 741– 756. 32090418
  17. [17]Golkowski D, Merz K, Mlynarcik C, et al Simultaneous EEG-PET-fMRI measurements in disorders of consciousness: An exploratory study on diagnosis and prognosis. J Neurol. 2017; 264: 1986– 1995. 28819796
  18. [18]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: 1804– 1820. 35076993 PMC8933330
  19. [19]Skibsted AP, Amiri M, Fisher PM, et al Consciousness in neurocritical care cohort study using fMRI and EEG (CONNECT-ME): Protocol for a longitudinal prospective study and a tertiary clinical care service. Front Neurol. 2018; 9: 1012. 30542319 PMC6278242
  20. [20]Bruno MA, Laureys S, Demertzi A. Coma and disorders of consciousness. Handb Clin Neurol. 2013; 118: 205– 213. 24182379
  21. [21]Giacino JT, Ashwal S, Childs N, et al The minimally conscious state: Definition and diagnostic criteria. Neurology. 2002; 58: 349– 353. 11839831
  22. [22]DASAIM. Sedationsstrategi - Målrettet behandling af gener forbundet med kritisk sygdom. Guidel - Dansk Selsk Anæstesiologi og Intensiv Med. 2020; 3: 1– 39.
  23. [24]Kondziella D, Fisher PM, Larsen VA, et al Functional MRI for assessment of the default mode network in acute brain injury. Neurocrit Care. 2017; 27: 401– 406. 28484929
  24. [25]Othman MH, Bhattacharya M, Møller K, et al Resting-state NIRS–EEG in unresponsive patients with acute brain injury: A proof-of-concept study. Neurocrit Care. 2021; 34: 31– 44. 32333214
  25. [26]Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974; 304: 81– 84. 4136544
  26. [27]Wijdicks EFM, Bamlet WR, Maramattom BV, Manno EM, McClelland RL. Validation of a new coma scale: The FOUR score. Ann Neurol. 2005; 58: 585– 593. 16178024
  27. [28]Acharya JN, Acharya VJ. Overview of EEG montages and principles of localization. J Clin Neurophysiol. 2019; 36: 325– 329. 31490449
  28. [29]Hirsch LJ, Laroche SM, Gaspard N, et al American Clinical neurophysiology society’s standardized critical care EEG terminology: 2012 version. J Clin Neurophysiol. 2013; 30: 1– 27. 23377439
  29. [30]Beniczky S, Aurlien H, Brøgger JC, et al Standardized computer-based organized reporting of EEG: SCORE. Epilepsia. 2013; 54: 1112– 1124. 23506075 PMC3759702
  30. [31]André-Obadia N, Zyss J, Gavaret M, et al Recommendations for the use of electroencephalography and evoked potentials in comatose patients. Neurophysiol Clin. 2018; 48: 143– 169. 29784540
  31. [32]Leitinger M, Beniczky S, Rohracher A, et al Salzburg consensus criteria for non-convulsive status epilepticus—Approach to clinical application. Epilepsy Behav. 2015; 49: 158– 163. 26092326
  32. [33]Admiraal MM, van Rootselaar AF, Horn J. Electroencephalographic reactivity testing in unconscious patients: A systematic review of methods and definitions. Eur J Neurol. 2017; 24: 245– 254. 27981707
  33. [34]Synek VM. Prognostically important EEG coma patterns in diffuse anoxic and traumatic encephalopathies in adults. J Clin Neurophysiol. 1988; 5: 161– 174. 3074973
  34. [35]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: 119– 129. 28168211 PMC5288467
  35. [36]Schomer DL, Da Silva FL. Niedermeyer's electroencephalography: basic principles, clinical applications, and related fields. 7th ed. Lippincott Williams & Wilkins (LWW); 2019.
  36. [37]Thomson DJ. Spectrum estimation and harmonic analysis. Proc IEEE. 1982; 70: 1055– 1096.
  37. [38]Engemann DA, Raimondo F, King JR, et al Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain. 2018; 141: 3179– 3192. 30285102
  38. [39]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: 2258– 2270. 24919971 PMC4610185
  39. [40]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 USA. 2009; 106: 1672– 1677. 19164526 PMC2635770
  40. [42]Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007; 37: 90– 101. 17560126 PMC2214855
  41. [43]Demertzi A, Tagliazucchi E, Dehaene S, et al Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv. 2019; 5: eaat7603. 30775433 PMC6365115
  42. [45]Pedregosa F, Varoquaux G, Gramfort A, et al Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011; 12: 2825– 2830.
  43. [46]Bagnato S, Boccagni C, Sant’Angelo A, Prestandrea C, Mazzilli R, Galardi G. EEG Predictors of outcome in patients with disorders of consciousness admitted for intensive rehabilitation. Clin Neurophysiol. 2015; 126: 959– 966. 25238957
  44. [48]Demertzi A, Antonopoulos G, Heine L, et al Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain. 2015; 138(Pt 9): 2619– 2631. 26117367
  45. [49]Bodien YG, Threlkeld ZD, Edlow BL. Default mode network dynamics in covert consciousness. Cortex. 2019; 119: 571– 574. 30791975 PMC6527357
  46. [50]Wu X, Gao J-H, Weng X, et al Intrinsic functional connectivity patterns predict consciousness level and recovery outcome in acquired brain injury. J Neurosci. 2015; 35: 12932– 12946. 26377477 PMC4571611
  47. [51]Di Perri C, Bahri MA, Amico E, et al Neural correlates of consciousness in patients who have emerged from a minimally conscious state: A cross-sectional multimodal imaging study. Lancet Neurol. 2016; 15: 830– 842. 27131917
  48. [52]Schiff ND. Cognitive motor dissociation following severe brain injuries. JAMA Neurol. 2015; 72: 1413– 1415. 26502348
  49. [53]Naccache L. Minimally conscious state or cortically mediated state? Brain. 2018; 141: 949– 960. 29206895 PMC5888986
  50. [54]Izzy S, Compton R, Carandang R, Hall W, Muehlschlegel S. Self-fulfilling prophecies through withdrawal of care: Do they exist in traumatic brain injury, too? Neurocrit Care. 2013; 19: 347– 363. 24132565
  51. [55]Span-Sluyter CAMFH, Lavrijsen JCM, Van Leeuwen E, Koopmans RTCM. Moral dilemmas and conflicts concerning patients in a vegetative state/unresponsive wakefulness syndrome: Shared or non-shared decision making? A qualitative study of the professional perspective in two moral case deliberations. BMC Med Ethics. 2018; 19: 10. 29471814 PMC5824545
  52. [56]Foster C. It is never lawful or ethical to withdraw life-sustaining treatment from patients with prolonged disorders of consciousness. J Med Ethics. 2019; 45: 265– 270. 30765416
  53. [57]Fins JJ, Bernat JL. Ethical, palliative, and policy considerations in disorders of consciousness. Arch Phys Med Rehabil. 2018; 99: 1927– 1931. 30098790
  54. [58]Pan J, Xie Q, Qin P, et al Prognosis for patients with cognitive motor dissociation identified by brain–computer interface. Brain. 2020; 143: 1177– 1189. 32101603 PMC7174053
  55. [59]Pistarini C, Maggioni G. Early rehabilitation of disorders of consciousness (DOC): Management, neuropsychological evaluation and treatment. Neuropsychol Rehabil. 2018; 28: 1319– 1330. 30033818
  56. [60]Kowalski RG, Hammond FM, Weintraub AH, et al Recovery of consciousness and functional outcome in moderate and severe traumatic brain injury. JAMA Neurol. 2021; 78: 548– 557. 33646273 PMC7922241
  57. [61]Giacino JT, Kalmar K, Whyte J. The JFK coma recovery scale-revised: Measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004; 85: 2020– 2029. 15605342
  58. [62]Kondziella D, Menon D, Helbok R, et al A precision medicine framework for classifying patients with disorders of consciousness: Advanced classification of consciousness endotypes (ACCESS). Neurocrit Care. 2021; 35(Suppl 1): 27– 36. 34236621
  59. [63]Sanz LRD, Aubinet C, Cassol H, et al SECONDs administration guidelines: A fast tool to assess consciousness in brain-injured patients. J Vis Exp. 2021; 2021: 1– 18. 33616111
  60. [64]Alvarez V, Rossetti AO. Clinical use of EEG in the ICU: Technical setting. J Clin Neurophysiol. 2015; 32: 481– 485. 26629758

Cited by

Sorted by citations

Authors
Amiri, M., Fisher, P. M., Raimondo, F., Sidaros, A., Cacic Hribljan, M., Othman, M. H., Zibrandtsen, I., Albrechtsen, S. S., Bergdal, O., Hansen, A. E., Hassager, C., Højgaard, J. L. S., Jakobsen, E. W., Jensen, H. R., Møller, J., Nersesjan, V., Nikolic, M., Olsen, M. H., Sigurdsson, S. T., Sitt, J. D., Sølling, C., Welling, K. L., Willumsen, L. M., Hauerberg, J., Larsen, V. A., Fabricius, M., Knudsen, G. M., Kjaergaard, J., Møller, K., & Kondziella, D.
Affiliations
  • Copenhagen University Hospital (DK)
  • Forschungszentrum Jülich (DE)
  • Department of Neurophysiology, Copenhagen University Hospital , Rigshospitalet, Copenhagen , Denmark
  • University of Copenhagen (DK)
  • Department of Neuroanaesthesiology, Copenhagen University Hospital , Rigshospitalet, Copenhagen , Denmark
  • Centre National de la Recherche Scientifique (FR)
  • Department of Clinical Medicine, University of Copenhagen , Copenhagen , Denmark
Corresponding author
Kondziella, Daniel
ORCID
Venue
Brain
Publisher
Oxford University Press (OUP)
Published
2022-09-13
Volume
146
Issue
1
Pages
50-64
ISSN
  • 0006-8950 (ISSN-L)
  • 1460-2156
DOI
10.1093/brain/awac335
PMC
PMC9825454
OpenAlex
W4295519935
Cited by
89
References
2
Citations by year
2026: 20 · 2025: 27 · 2024: 26 · 2023: 14 · 2022: 2
FWCI
12.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Brain (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
  • HAL (submittedVersion, cc-by-nc)
Funders
  • Offerfonden
  • Region Hovedstadens Forskningsfond
  • Lundbeck Foundation
  • Rigshospitalets Forskningspuljer
Topics
  • Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Neurology › Traumatic Brain Injury and Neurovascular Disturbances
Keywords
disorders of consciousness, residual consciousness, minimally conscious state, coma, functional MRI, resting-state EEG, multimodal prediction, support vector classifier, random forest, resting-state functional connectivity, brain injury, intensive care unit, neurocritical care, consciousness assessment, traumatic brain injury
MeSH terms
Adult, Aged, Brain Injuries, Consciousness, Consciousness Disorders, Female, Humans, Male, Middle Aged, Prospective Studies, Cohort Studies, Persistent Vegetative State

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review