Camille Chatelle1,2, Eric S Rosenthal3,4,5, Yelena G Bodien3,6, Camille A Spencer-Salmon3, Joseph T Giacino6, Brian L Edlow3,7. 1. GIGA Consciousness, Coma Science Group, University of Liège, Avenue de l'Hôpital, 11, 4000, Liège, Belgium. camillechatelle@gmail.com. 2. Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA. camillechatelle@gmail.com. 3. Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA. 4. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 5. Clinical Data Animation Center, Massachusetts General Hospital, Boston, MA, USA. 6. Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA. 7. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Abstract
BACKGROUND/ OBJECTIVE: Behavioral examinations may fail to detect language function in patients with severe traumatic brain injury (TBI) due to confounds such as having an endotracheal tube. We investigated whether resting and stimulus-evoked electroencephalography (EEG) methods detect the presence of language function in patients with severe TBI. METHODS: Four EEG measures were assessed: (1) resting background (applying Forgacs' criteria), (2) reactivity to speech, (3) background and reactivity (applying Synek's criteria); and (4) an automated support vector machine (classifier for speech versus rest). Cohen's kappa measured agreement between the four EEG measures and evidence of language function on a behavioral coma recovery scale-revised (CRS-R) and composite (CRS-R or functional MRI) reference standard. Sensitivity and specificity of each EEG measure were calculated against the reference standards. RESULTS: We enrolled 17 adult patients with severe TBI (mean ± SD age 27.0 ± 7.0 years; median [range] 11.5 [2-1173] days post-injury) and 16 healthy subjects (age 28.5 ± 7.8 years). The classifier, followed by Forgacs' criteria for resting background, demonstrated the highest agreement with the behavioral reference standard. Only Synek's criteria for background and reactivity showed significant agreement with the composite reference standard. The classifier and resting background showed balanced sensitivity and specificity for behavioral (sensitivity = 84.6% and 80.8%; specificity = 57.1% for both) and composite reference standards (sensitivity = 79.3% and 75.9%, specificity = 50% for both). CONCLUSIONS: Methods applying an automated classifier, resting background, or resting background with reactivity may identify severe TBI patients with preserved language function. Automated classifier methods may enable unbiased and efficient assessment of larger populations or serial timepoints, while qualitative visual methods may be practical in community settings.
BACKGROUND/ OBJECTIVE: Behavioral examinations may fail to detect language function in patients with severe traumatic brain injury (TBI) due to confounds such as having an endotracheal tube. We investigated whether resting and stimulus-evoked electroencephalography (EEG) methods detect the presence of language function in patients with severe TBI. METHODS: Four EEG measures were assessed: (1) resting background (applying Forgacs' criteria), (2) reactivity to speech, (3) background and reactivity (applying Synek's criteria); and (4) an automated support vector machine (classifier for speech versus rest). Cohen's kappa measured agreement between the four EEG measures and evidence of language function on a behavioral coma recovery scale-revised (CRS-R) and composite (CRS-R or functional MRI) reference standard. Sensitivity and specificity of each EEG measure were calculated against the reference standards. RESULTS: We enrolled 17 adult patients with severe TBI (mean ± SD age 27.0 ± 7.0 years; median [range] 11.5 [2-1173] days post-injury) and 16 healthy subjects (age 28.5 ± 7.8 years). The classifier, followed by Forgacs' criteria for resting background, demonstrated the highest agreement with the behavioral reference standard. Only Synek's criteria for background and reactivity showed significant agreement with the composite reference standard. The classifier and resting background showed balanced sensitivity and specificity for behavioral (sensitivity = 84.6% and 80.8%; specificity = 57.1% for both) and composite reference standards (sensitivity = 79.3% and 75.9%, specificity = 50% for both). CONCLUSIONS: Methods applying an automated classifier, resting background, or resting background with reactivity may identify severe TBIpatients with preserved language function. Automated classifier methods may enable unbiased and efficient assessment of larger populations or serial timepoints, while qualitative visual methods may be practical in community settings.
Authors: Leandro R D Sanz; Aurore Thibaut; Brian L Edlow; Steven Laureys; Olivia Gosseries Journal: Curr Opin Neurol Date: 2021-08-01 Impact factor: 6.283
Authors: Jan Claassen; Yama Akbari; Sheila Alexander; Mary Kay Bader; Kathleen Bell; Thomas P Bleck; Melanie Boly; Jeremy Brown; Sherry H-Y Chou; Michael N Diringer; Brian L Edlow; Brandon Foreman; Joseph T Giacino; Olivia Gosseries; Theresa Green; David M Greer; Daniel F Hanley; Jed A Hartings; Raimund Helbok; J Claude Hemphill; H E Hinson; Karen Hirsch; Theresa Human; Michael L James; Nerissa Ko; Daniel Kondziella; Sarah Livesay; Lori K Madden; Shraddha Mainali; Stephan A Mayer; Victoria McCredie; Molly M McNett; Geert Meyfroidt; Martin M Monti; Susanne Muehlschlegel; Santosh Murthy; Paul Nyquist; DaiWai M Olson; J Javier Provencio; Eric Rosenthal; Gisele Sampaio Silva; Simone Sarasso; Nicholas D Schiff; Tarek Sharshar; Lori Shutter; Robert D Stevens; Paul Vespa; Walter Videtta; Amy Wagner; Wendy Ziai; John Whyte; Elizabeth Zink; Jose I Suarez Journal: Neurocrit Care Date: 2021-07-08 Impact factor: 3.210