Open AccessNeural oscillations track recovery of consciousness in acute traumatic brain injury patients
Author(s) -
Frohlich Joel,
Crone Julia S.,
Johnson Micah A.,
Lutkenhoff Evan S.,
Spivak Norman M.,
Dell'Italia John,
Hipp Joerg F.,
Shrestha Vikesh,
Ruiz Tejeda Jesús E.,
Real Courtney,
Vespa Paul M.,
Monti Martin M.
Publication year - 2022
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25725
Subject(s) - electroencephalography , consciousness disorders , traumatic brain injury , persistent vegetative state , coma (optics) , psychology , minimally conscious state , quantitative electroencephalography , arousal , magnetic resonance imaging , level of consciousness , functional magnetic resonance imaging , medicine , neuroscience , consciousness , anesthesia , psychiatry , radiology , physics , optics
Electroencephalography (EEG), easily deployed at the bedside, is an attractive modality for deriving quantitative biomarkers of prognosis and differential diagnosis in severe brain injury and disorders of consciousness (DOC). Prior work by Schiff has identified four dynamic regimes of progressive recovery of consciousness defined by the presence or absence of thalamically‐driven EEG oscillations. These four predefined categories (ABCD model) relate, on a theoretical level, to thalamocortical integrity and, on an empirical level, to behavioral outcome in patients with cardiac arrest coma etiologies. However, whether this theory‐based stratification of patients might be useful as a diagnostic biomarker in DOC and measurably linked to thalamocortical dysfunction remains unknown. In this work, we relate the reemergence of thalamically‐driven EEG oscillations to behavioral recovery from traumatic brain injury (TBI) in a cohort of N = 38 acute patients with moderate‐to‐severe TBI and an average of 1 week of EEG recorded per patient. We analyzed an average of 3.4 hr of EEG per patient, sampled to coincide with 30‐min periods of maximal behavioral arousal. Our work tests and supports the ABCD model, showing that it outperforms a data‐driven clustering approach and may perform equally well compared to a more parsimonious categorization. Additionally, in a subset of patients ( N = 11), we correlated EEG findings with functional magnetic resonance imaging (fMRI) connectivity between nodes in the mesocircuit—which has been theoretically implicated by Schiff in DOC—and report a trend‐level relationship that warrants further investigation in larger studies.