Phase synchronization in electroencephalographic recordings prognosticates outcome in paediatric coma
Vera Nenadovic1, Jose Luis Perez Velazquez2, James Saunders Hutchison3
1Division of Neurology Sick Kids, Toronto, Ontario, Canada; Brain and Mental Health, Toronto, Ontario, Canada.
Insights
Brain injury in children can be predicted by analyzing brain signal variability using electroencephalography (EEG). Lower EEG variability indicates a poorer patient outcome, aiding in early prognosis and intervention strategies.
Area of Science:
- Neuroscience
- Paediatric Critical Care
- Biomedical Engineering
Background:
- Brain injury is a leading cause of death and disability in children.
- Accurate outcome prediction is crucial for timely interventions but lacks a clinical model.
- Physiological signal variability, seen in heart rate, may apply to brain signals.
Purpose of the Study:
- To investigate the correlation between brain signal variability and patient outcomes after paediatric brain injury.
- To determine if electroencephalographic (EEG) phase synchrony variability can predict prognosis in children.
Main Methods:
- Retrospective analysis of scalp EEGs from children (1 month–17 years) in coma (GCS <8) post-brain injury (2000-2010).
- EEG phase synchrony evaluated using Hilbert transform; variability calculated.
- Patient outcome assessed via Paediatric Performance Category Score (PCPC) at discharge, dichotomized to good (1-3) or poor (4-6).
Main Results:
- Children with poor outcomes exhibited higher synchrony magnitude (R index) compared to those with good outcomes.
- Poor outcome group showed lower spatial complexity of synchrony patterns.
- Lower temporal variability of synchrony index values at 15 Hz was observed in children with poor outcomes.
Conclusions:
- EEG phase synchrony variability is a potential biomarker for predicting outcomes in paediatric brain injury.
- Reduced brain signal variability correlates with poorer prognosis after TBI, cardiac arrest, or stroke in children.
- This finding may inform the development of novel clinical models for early risk stratification.
Abstract:
Brain injury from trauma, cardiac arrest or stroke is the most important cause of death and acquired disability in the paediatric population. Due to the lifetime impact of brain injury, there is a need for methods to stratify patient risk and ultimately predict outcome. Early prognosis is fundamental to the implementation of interventions to improve recovery, but no clinical model as yet exists. Healthy physiology is associated with a relative high variability of physiologic signals in organ systems. This was first evaluated in heart rate variability research. Brain variability can be quantified through electroencephalographic (EEG) phase synchrony. We hypothesised that variability in brain signals from EEG recordings would correlate with patient outcome after brain injury. Lower variability in EEG phase synchronization, would be associated with poor patient prognosis. A retrospective study, spanning 10 years (2000-2010) analysed the scalp EEGs of children aged 1 month to 17 years in coma (Glasgow Coma Scale, GCS, <8) admitted to the paediatric critical care unit (PCCU) following brain injury from TBI, cardiac arrest or stroke. Phase synchrony of the EEGs was evaluated using the Hilbert transform and the variability of the phase synchrony calculated. Outcome was evaluated using the 6 point Paediatric Performance Category Score (PCPC) based on chart review at the time of hospital discharge. Outcome was dichotomized to good outcome (PCPC score 1 to 3) and poor outcome (PCPC score 4 to 6). Children who had a poor outcome following brain injury secondary to cardiac arrest, TBI or stroke, had a higher magnitude of synchrony (R index), a lower spatial complexity of the synchrony patterns and a lower temporal variability of the synchrony index values at 15 Hz when compared to those patients with a good outcome.
