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Predicting long-term prognosis in comatose patients through brain network analysis under name-evoked stimulation
Jiayu Ye1, Minwei Xu2, Jun Hu3
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Brain Research Bulletin
|February 27, 2026
Summary
Predicting recovery in comatose patients is difficult. Brain connectivity, measured using dynamic causal modeling (DCM) during an auditory task, accurately predicts long-term functional outcomes.
Area of Science:
- Neuroscience
- Neurocritical Care
- Computational Neuroscience
Background:
- Assessing prognosis for comatose patients is challenging.
- Brain connectivity is crucial for consciousness and recovery.
- Electroencephalography (EEG) offers a bedside tool for assessment.
Purpose of the Study:
- Investigate task-related dynamic causal modeling (DCM) connectivity in comatose patients.
- Determine if connectivity strengths correlate with functional recovery.
- Explore potential biomarkers for prognosis in neurocritical care.
Main Methods:
- Recorded bedside EEG from comatose patients during an auditory oddball name-calling task.
- Applied dynamic causal modeling (DCM) to analyze neural connectivity.
- Correlated DCM connectivity strengths with long-term functional outcomes (Glasgow Outcome Scale-Extended).
Main Results:
- A bidirectional model of connectivity among superior frontal gyri, superior parietal lobules, and auditory cortices was associated with neural processing.
- Connectivity strengths predicted long-term prognostic outcomes.
- Established DCM-derived biomarkers for evaluating functional prognosis.
Conclusions:
- Task-related DCM connectivity provides insights into neural processing in comatose patients.
- DCM connectivity strengths are potential predictors of functional recovery.
- DCM-derived biomarkers show promise for prognosis assessment in neurocritical care.
