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@ARTICLE{Amiri:916035,
      author       = {Amiri, Moshgan and Fisher, Patrick M and Raimondo, Federico
                      and Sidaros, Annette and Hribljan, Melita Cacic and Othman,
                      Marwan H and Zibrandtsen, Ivan and Albrechtsen, Simon S and
                      Bergdal, Ove and Hansen, Adam Espe and Hassager, Christian
                      and Højgaard, Joan Lilja S and Jakobsen, Elisabeth Waldemar
                      and Jensen, Helene Ravnholt and Møller, Jacob and
                      Nersesjan, Vardan and Nikolic, Miki and Olsen, Markus Harboe
                      and Sigurdsson, Sigurdur Thor and Sitt, Jacobo D and
                      Sølling, Christine and Welling, Karen Lise and Willumsen,
                      Lisette M and Hauerberg, John and Larsen, Vibeke Andrée and
                      Fabricius, Martin and Knudsen, Gitte Moos and Kjaergaard,
                      Jesper and Møller, Kirsten and Kondziella, Daniel},
      title        = {{M}ultimodal prediction of residual consciousness in the
                      intensive care unit: the {CONNECT}-{ME} study},
      journal      = {Brain},
      volume       = {146},
      number       = {1},
      issn         = {0006-8950},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2022-05881},
      pages        = {50-64},
      year         = {2023},
      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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36097353},
      UT           = {WOS:000892013000001},
      doi          = {10.1093/brain/awac335},
      url          = {https://juser.fz-juelich.de/record/916035},
}