Journal
BIOMEDICINES
Volume 10, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/biomedicines10081897
Keywords
disorders of consciousness; traumatic brain injury; electroencephalography; brain plasticity and connectivity; post-anoxic coma; severe acquired brain injury; acquired brain damage; linear discriminant analyses; brain functional impairment; neurocognitive disorders
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Funding
- IRCCS Istituto delle Scienze Neurologiche di Bologna
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The study found that functional connectivity and dominant frequency based on EEG can accurately predict clinical outcomes in patients with disorders of consciousness. This has significant implications for making appropriate rehabilitative decisions.
Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients' etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.
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