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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.0K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Identifying neurophenotypes of major depressive disorder through normative model of regional homogeneity.

Translational psychiatry·2026
Same author

White Matter Abnormalities in Bipolar II and Unipolar Depression - Evidence from Fixel-Based Analysis.

medRxiv : the preprint server for health sciences·2026
Same author

White matter free water and depressive symptoms in medication-free depressed adolescents: moderation by peripheral inflammation.

Translational psychiatry·2025
Same author

Focused low-intensity hippocampal transcranial ultrasound stimulation (TUS) for sleep disturbances in patients with chronic tinnitus: A study protocol for a pilot randomized controlled trial.

PloS one·2025
Same author

Pre-treatment subjective sleep quality as a predictive biomarker of tDCS effects in preclinical Alzheimer's disease patients: Secondary analysis of a randomised clinical trial.

PloS one·2025
Same author

Daily high-frequency transcranial random noise stimulation (hf-tRNS) for sleep disturbances and cognitive dysfunction in patients with mild vascular cognitive impairments: A study protocol for a pilot randomized controlled trial.

PloS one·2024

Related Experiment Video

Updated: Jun 15, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Decoding MRI-informed brain age using mutual information.

Jing Li1, Linda Chiu Wa Lam2, Hanna Lu3,4

  • 1Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China. ljing@link.cuhk.edu.hk.

Insights Into Imaging
|August 26, 2024
PubMed
Summary

Mutual information analysis reveals gray matter volume and cerebrospinal fluid volume are key to estimating brain age. These findings offer a benchmark for assessing regional contributions to brain age models.

Keywords:
Brain ageGray matter volumeMachine learningMutual informationStructural MRI

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

Related Experiment Videos

Last Updated: Jun 15, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Estimating brain age from neuroimaging data is crucial for understanding brain health and aging.
  • Existing methods lack standardized approaches to link brain age to specific regional brain features.
  • Developing intuitive and generalizable methods is essential for clinical and research applications.

Purpose of the Study:

  • To develop a standardized, generalizable, and interpretable method for investigating the relationship between estimated brain age and regional brain morphometric features.
  • To identify key regional morphometric features that contribute significantly to estimated brain age.
  • To establish a benchmark for assessing regional contributions to brain age using mutual information.

Main Methods:

  • Utilized T1-weighted MRI data from the Cambridge Centre for Ageing and Neuroscience (N=609) and the brain development project (N=547).
  • Trained a brain age model using a support vector regression method.
  • Applied Kraskov (KSG) estimator to compute mutual information (MI) between estimated brain age and regional morphometric features (GMV, WMV, CSF, CT).

Main Results:

  • Gray matter volume (GMV) exhibited the highest MI value (8.71), with the pre-central gyrus showing the peak (0.69).
  • Cerebrospinal fluid (CSF) volume was second (7.76), with the cingulate gyrus having the highest MI (0.87).
  • White matter volume (WMV) had the lowest MI (4.59), while cortical thickness (CT) ranked third (6.22).

Conclusions:

  • Mutual information (MI) analysis provides a benchmark for assessing regional contributions to estimated brain age.
  • GMV and CSF volume are identified as pivotal features in determining estimated brain age.
  • Findings enhance computational models of brain age by highlighting key regional contributions and brain regions.