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Brain Gray Matter MRI Morphometry for Neuroprognostication After Cardiac Arrest
Stein Silva1, Patrice Peran, Lionel Kerhuel
11Department of Anaesthesiology and Critical Care, Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France.2Critical Care and Anaesthesiology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France.3Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France.4Department of Anaesthesiology and Critical Care, Critical Care Unit, Hopital Dieu Hospital, Narbonne, France.5Department of Anaesthesiology and Critical Care, School of medicine and Surgery, University Milano Bicocca and Hospital San Gerardo, Monza, Italy.6Department of Neuroradiology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.7Department of Anaesthesiology and Critical Care, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.8Laboratoire d'Imagerie Biomédicale (UMR S 1146/UMR 7371), Université Pierre-et-Marie-Curie-Paris 06, Paris, France.9Critical Care and Anaesthesiology Department, Groupe Hospitalier Pitié-Salpétrière, APHP, Paris, France.10Department of Neuroradiology, Groupe Hospitalier Pitié-Salpétrière, APHP, Paris, France.11Cyclotron Research Center and Department of Neurology, University Hospital and University of Liège, Liège, Belgium.12Algology and Palliative Care Department, University Hospital and University of Liège, Liège, Belgium.
Brain imaging using MRI can predict long-term outcomes after cardiac arrest. Measuring cortical thickness and subcortical gray matter volume accurately assesses brain damage, aiding neuroprognostication.
Area of Science:
- Neurology
- Radiology
- Intensive Care Medicine
Background:
- Cardiac arrest (CA) can lead to anoxic brain injury.
- Accurate neuroprognostication is crucial for patient management after CA.
- Current methods for assessing brain damage may have limitations.
Purpose of the Study:
- To investigate the utility of combined MRI cortical thickness and subcortical gray matter volumetry for early neuroprognostication after CA.
- To develop and validate a predictive model for long-term outcomes using brain morphometric data.
Main Methods:
- Prospective cohort study involving 126 anoxic coma patients and 70 controls.
- High-resolution T1-weighted MRI scans were acquired to measure cortical thickness and subcortical gray matter volumes.
- Machine learning techniques were used to build and test predictive models.
Main Results:
- Patients showed significant cortical and subcortical brain volume atrophy compared to controls.
- The combined morphometric model achieved high accuracy in predicting outcomes (AUC = 0.87 in learning sample, 0.96 in test sample).
- Atrophy in specific regions including the frontal cortex, posterior cingulate cortex, thalamus, and basal ganglia correlated with patient outcomes.
Conclusions:
- Combined MRI-based quantitative morphometry provides an accurate in vivo assessment of structural brain damage post-cardiac arrest.
- These findings support the hypothesis of striatopallidal-thalamo-cortical mesocircuit disruption.
- This approach holds promise for routine use in long-term neuroprognostication after cardiac arrest.
