Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

International evidence-based recommendations for point-of-care lung ultrasound : 2025 focused update of the 2012 recommendations.

Intensive care medicine·2026

Same author

Thalamic nuclei insights into Alzheimer's disease.

bioRxiv : the preprint server for biology·2026

Same author

Core Blood-Brain Barrier Disruption in Patients With Large Vessel Occlusion.

Stroke·2026

Same author

Stepwise clinical and diagnostic strategy for coma of unknown origin.

Intensive care medicine·2026

Same author

Memory and Resting-State Connectivity in Acute Transient Global Amnesia: A Case-Control fMRI Study.

Annals of clinical and translational neurology·2026

Same author

The exposome of brain aging across 34 countries.

Nature medicine·2026

Same journal

Modeling the Cost-Effectiveness of Incorporating Extracorporeal Cardiopulmonary Resuscitation Strategy in Out-of-Hospital Cardiac Arrest Patients.

Critical care medicine·2026

Same journal

Ethical Implications of the Slow Code: A Systematic Review of Ethics of Slow Codes in U.S. Hospitals.

Critical care medicine·2026

Same journal

Association Between Emergency Department-to-ICU Transfer Time and Hospital Mortality Across ICU Admission Diagnoses: A Post Hoc Subgroup Analysis.

Critical care medicine·2026

Same journal

Large Language Models Provide Accurate but Potentially Unsafe Answers to Multimodal Critical Care Medicine Board Review Questions.

Critical care medicine·2026

Same journal

Concordance Between Bedside and Electronic Detection of Intracranial Pressure Crises: Insights From the Brain Tissue Oxygen Monitoring and Management in Severe Traumatic Brain Injury II Trial.

Critical care medicine·2026

Same journal

Impact of Molecular Adsorbent Recirculating System Therapy on Piperacillin Concentration in Critically Ill Patients: An Observational Cohort Study.

Critical care medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Explainable Machine Learning for Coma Outcome Prediction Based on Structural and Functional Brain MRI.

Benjamine Sarton^1,2, Giulia Maria Mattia², Eve Cervoni^1,2

¹Critical Care Unit, University Teaching Hospital of Purpan, Toulouse, France.

Critical Care Medicine

|February 27, 2026

Summary

This summary is machine-generated.

Machine learning models analyzing advanced MRI data can accurately predict coma diagnosis, brain injury type, and patient recovery. These models identify key brain network metrics for assessing coma and predicting outcomes.

Keywords:

coma explainable artificial intelligence machine learning mesocircuit multimodal magnetic resonance imaging prognosis

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Feb 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Neuroimaging
Machine Learning
Neurology

Background:

Advanced MRI is recommended for coma evaluation, but identifying relevant metrics is challenging.
Existing methods struggle to extract meaningful information from complex MRI data for coma patients.

Purpose of the Study:

To develop and validate an explainable machine learning (ML) pipeline for analyzing advanced MRI data in coma patients.
To identify specific MRI-derived metrics indicative of coma state, etiology, and neurological recovery potential.

Main Methods:

A prospective cross-sectional study involving 64 coma patients (traumatic or anoxic) and 55 controls.
Advanced structural MRI and resting-state functional connectivity analysis were performed.
An ensemble of explainable ML methods was applied and cross-validated for analysis.

Main Results:

ML models demonstrated high accuracy in coma diagnosis (93.4%), injury discrimination (76.2%), and outcome prediction (82.4%).
50% of coma patients experienced an unfavorable neurological outcome at 3 months.
The models showed strong generalization capabilities across different tasks.

Conclusions:

A novel set of brain MRI-derived metrics effectively characterizes coma, its cause, and recovery potential.
The structural and functional integrity of mesocircuit and frontoparietal networks are crucial indicators.
This ML approach offers a promising tool for clinical coma assessment and prognosis.