Neural coding of autonomic functions in different states of consciousness

Detecting signs of residual neural activity in patients with altered states of consciousness is a crucial issue for the customization of neurorehabilitation treatments and clinical decision-making. With this large observational prospective study, we propose an innovative approach to detect residual...

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Bibliographic Details
Published in:Journal of neuroengineering and rehabilitation 2023-07, Vol.20 (1), p.96-96, Article 96
Main Authors: Liuzzi, Piergiuseppe, Hakiki, Bahia, Scarpino, Maenia, Burali, Rachele, Maiorelli, Antonio, Draghi, Francesca, Romoli, Anna Maria, Grippo, Antonello, Cecchi, Francesca, Mannini, Andrea
Format: Article
Language:English
Subjects:
Adult
Analysis
Autonomic nervous system
Behavior
Brain
Brain damage
Brain–heart interaction
Coding
Coma
Consciousness
Decision making
Disorders of consciousness
EEG
EEG-ECG mutual information
EKG
Electrocardiogram
Electrocardiography
Electroencephalography
Female
Health aspects
Heart rate
Hemorrhage
Humans
Injuries
Libraries
Medical imaging
Metabolism
Minimally Consciousness State
Neural coding
Neuroimaging
Neurology
Persistent Vegetative State - diagnosis
Prospective Studies
Quantitative Correlates of Consciousness
Rehabilitation
Sleep
Sleep and wakefulness
Subgroups
Unconsciousness
Wakefulness
Young Adult
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Summary:Detecting signs of residual neural activity in patients with altered states of consciousness is a crucial issue for the customization of neurorehabilitation treatments and clinical decision-making. With this large observational prospective study, we propose an innovative approach to detect residual signs of consciousness via the assessment of the amount of autonomic information coded within the brain. The latter was estimated by computing the mutual information (MI) between preprocessed EEG and ECG signals, to be then compared across consciousness groups, together with the absolute power and an international qualitative labeling. One-hundred seventy-four patients (73 females, 42%) were included in the study (median age of 65 years [IQR = 20], MCS +: 29, MCS -: 23, UWS: 29). Electroencephalography (EEG) information content was found to be mostly related to the coding of electrocardiography (ECG) activity, i.e., with higher MI (p 
ISSN:1743-0003
1743-0003
DOI:10.1186/s12984-023-01216-6