Dataset Information

Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury.

ABSTRACT: Despite the association between brainstem lesions and coma, a mechanistic understanding of coma pathogenesis and recovery is lacking. We developed a coma model in the rat mimicking human brainstem coma, which allowed multimodal analysis of a brainstem tegmentum lesion's effects on behavior, cortical electrophysiology, and global brain functional connectivity. After coma induction, we observed a transient period (∼1h) of unresponsiveness accompanied by cortical burst-suppression. Comatose rats then gradually regained behavioral responsiveness concurrent with emergence of delta/theta-predominant cortical rhythms in primary somatosensory cortex. During the acute stage of coma recovery (∼1-8h), longitudinal resting-state functional MRI revealed an increase in functional connectivity between subcortical arousal nuclei in the thalamus, basal forebrain, and basal ganglia and cortical regions implicated in awareness. This rat coma model provides an experimental platform to systematically study network-based mechanisms of coma pathogenesis and recovery, as well as to test targeted therapies aimed at promoting recovery of consciousness after coma.

SUBMITTER: Pais-Roldan P

PROVIDER: S-EPMC6642798 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury.

Pais-Roldán Patricia P Edlow Brian L BL Jiang Yuanyuan Y Stelzer Johannes J Zou Ming M Yu Xin X

NeuroImage 20190129

Despite the association between brainstem lesions and coma, a mechanistic understanding of coma pathogenesis and recovery is lacking. We developed a coma model in the rat mimicking human brainstem coma, which allowed multimodal analysis of a brainstem tegmentum lesion's effects on behavior, cortical electrophysiology, and global brain functional connectivity. After coma induction, we observed a transient period (∼1h) of unresponsiveness accompanied by cortical burst-suppression. Comatose rats th ...[more]

PMID: 30708105

Similar Datasets

Project description:BackgroundThere is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma.MethodsThis proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both "functional data" from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and "structural data" (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants.ResultsThe final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94-0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes.ConclusionsA fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data. Clinical Trial Number http://ClinicalTrials.gov (No. NCT03482115).

Dataset Information

Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury.

Publications

Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets