Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Atypical Edema in Posterior Reversible Encephalopathy Syndrome: Clinical Associations and Outcome.

Journal of neuroimaging : official journal of the American Society of Neuroimaging·2025
Same author

Cerebrovascular morphology: Insights into normal variations, aging effects and disease implications.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2025
Same author

BRAIN-SIM: Leveraging Simulation for Neurocritical Care Education with an Innovative Multidisciplinary Approach.

Journal of intensive care medicine·2025
Same author

Continuous quantitative electroencephalography for early detection of acute low-pressure obstructive hydrocephalus in aneurysmal subarachnoid hemorrhage: illustrative case.

Journal of neurosurgery. Case lessons·2024
Same author

Update on Perioperative Delirium.

Clinics in geriatric medicine·2024
Same author

Update on Perioperative Delirium.

Anesthesiology clinics·2023

Related Experiment Video

Updated: Oct 16, 2025

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.3K

Hierarchical Cluster Analysis Identifies Distinct Physiological States After Acute Brain Injury.

Swarna Rajagopalan1, Wesley Baker2, Elizabeth Mahanna-Gabrielli3

  • 1Department of Neurology, Cooper Medical School of Rowan University, Camden, NJ, USA. Rajagopalan-swarna@cooperhealth.edu.

Neurocritical Care
|October 18, 2021
PubMed
Summary

Data-driven analysis of intracranial monitoring data identified distinct physiological states in acute brain injury patients. These states correlate with patient outcomes, offering potential for improved therapeutic strategies and outcome prediction.

Keywords:
Big dataBioinformaticsCluster analysisMultimodality neuromonitoringNeurocritical care

More Related Videos

Investigations on Alterations of Hippocampal Circuit Function Following Mild Traumatic Brain Injury
10:59

Investigations on Alterations of Hippocampal Circuit Function Following Mild Traumatic Brain Injury

Published on: November 19, 2012

15.4K
Stereotactic Atlas-Guided Laser Capture Microdissection of Brain Regions Affected by Traumatic Injury
09:29

Stereotactic Atlas-Guided Laser Capture Microdissection of Brain Regions Affected by Traumatic Injury

Published on: September 11, 2017

9.4K

Related Experiment Videos

Last Updated: Oct 16, 2025

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.3K
Investigations on Alterations of Hippocampal Circuit Function Following Mild Traumatic Brain Injury
10:59

Investigations on Alterations of Hippocampal Circuit Function Following Mild Traumatic Brain Injury

Published on: November 19, 2012

15.4K
Stereotactic Atlas-Guided Laser Capture Microdissection of Brain Regions Affected by Traumatic Injury
09:29

Stereotactic Atlas-Guided Laser Capture Microdissection of Brain Regions Affected by Traumatic Injury

Published on: September 11, 2017

9.4K

Area of Science:

  • Neuroscience
  • Critical Care Medicine
  • Data Science

Background:

  • Intracranial multimodality monitoring generates complex data.
  • Quantitative methods are needed to identify physiological signatures for therapeutic strategies and outcome prediction in acute brain injury.

Purpose of the Study:

  • To test if data-driven approaches can identify distinct physiological states from intracranial multimodality monitoring data.
  • To explore the relationship between these physiological states and patient outcomes.

Main Methods:

  • Retrospective observational study of patients with severe traumatic brain injury or subarachnoid hemorrhage.
  • Hierarchical cluster analysis of hourly physiological variables (heart rate, mean arterial pressure, intracranial pressure, brain tissue oxygen, cerebral microdialysis).
  • Comparison of physiological profiles and outcome distributions across identified clusters.

Main Results:

  • Four distinct physiological clusters were identified: cerebral ischemia, elevated intracranial pressure, normal physiological state, and cerebral hyperglycolysis.
  • Patients with favorable outcomes showed a higher proportion of normal physiological states (cluster 3).
  • Patients with unfavorable outcomes had a higher proportion of cerebral ischemia (cluster 1) and cerebral hyperglycolysis (cluster 4) events.

Conclusions:

  • A data-driven approach successfully identified distinct physiological groupings from neuromonitoring data.
  • These groupings have potential implications for therapeutic strategies and outcome prediction in acute brain injury.
  • Further validation in larger datasets is warranted for machine learning model development.