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

Prosopagnosia01:24

Prosopagnosia

153
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
153

You might also read

Related Articles

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

Sort by
Same author

A Review of <i>Canarii Fructus</i> (<i>Canarium album</i>) Polyphenols: From Efficient Extraction to Mechanistic Understanding and Functional Food Development.

Foods (Basel, Switzerland)·2026
Same author

METTL3/YTHDC1 axis-mediated m<sup>6</sup>A modification of Foxo1 mRNA promote endothelial autophagic apoptosis in diabetic atherosclerosis.

Molecular medicine (Cambridge, Mass.)·2026
Same author

Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study.

BMC medical informatics and decision making·2026
Same author

Engineering Asymmetric Cu<sup>0</sup>/Cu<sup>+</sup> Interfaces for Record-Efficiency Ammonia Electrosynthesis From Dilute Nitrate in Neutral Media.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Potentiated remediation of imazethapyr-contaminated soil by phosphate-doped biochar immobilized with Bacillus cereus MZ-1.

Bioresource technology·2026
Same author

Zoology, traditional uses, processing technology, chemical compositions and pharmacological activities of Hirudo: A reviews.

Journal of ethnopharmacology·2026

Related Experiment Video

Updated: Jun 19, 2025

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
10:33

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

8.5K

Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain

Xiang Zhang1, Yizhen Pan1, Tingting Wu1

  • 1The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.

Neuroimage
|July 24, 2024
PubMed
Summary

This study developed an interpretable 3D CNN model to accurately predict brain age using MRI scans. The model identified key brain regions involved in aging and revealed that mild traumatic brain injury (mTBI) accelerates brain aging, correlating with cognitive decline.

Keywords:
Atlas-based occlusion analysisBrain ageConvolutional neural networkMild traumatic brain injury

More Related Videos

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury
09:49

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

Published on: January 20, 2023

3.2K
A Mouse Model of Single and Repetitive Mild Traumatic Brain Injury
04:19

A Mouse Model of Single and Repetitive Mild Traumatic Brain Injury

Published on: June 20, 2017

11.1K

Related Experiment Videos

Last Updated: Jun 19, 2025

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
10:33

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

8.5K
Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury
09:49

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

Published on: January 20, 2023

3.2K
A Mouse Model of Single and Repetitive Mild Traumatic Brain Injury
04:19

A Mouse Model of Single and Repetitive Mild Traumatic Brain Injury

Published on: June 20, 2017

11.1K

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Neurology

Background:

  • Convolutional neural networks (CNNs) accurately predict brain age in healthy individuals using MRI structural features.
  • Previous studies often relied on single features, neglecting multimodal information.
  • Brain aging patterns post-mild traumatic brain injury (mTBI) remain unclear.

Purpose of the Study:

  • To develop an interpretable 3D combined CNN model for accurate brain-age prediction.
  • To identify age-stratified brain regions contributing to age prediction in healthy controls (HCs) and mTBI patients.
  • To investigate the correlation between brain predicted age gap (brain-PAG) in mTBI and cognitive impairment/neurodegeneration markers.

Main Methods:

  • Utilized a large, heterogeneous dataset (N=1464) with structural MRI data.
  • Implemented an interpretable 3D combined CNN model incorporating multiple structural features.
  • Employed an atlas-based occlusion analysis with the Brainnetome Atlas for region identification.

Main Results:

  • Achieved high accuracy in brain-age prediction (MAE: 3.08 years, Pearson's r: 0.97) on HCs, with strong cross-center generalizability.
  • Identified the caudate and thalamus as key contributors to brain age prediction in both HCs and mTBI patients.
  • Demonstrated significantly higher brain-PAG in mTBI patients, correlating with cognitive impairment and plasma neurofilament light levels, indicating persistent effects.

Conclusions:

  • An interpretable deep learning framework accurately predicts brain age in HCs and mTBI patients.
  • The caudate and thalamus are critical for age prediction across the lifespan in both groups.
  • Accelerated brain aging in mTBI is linked to cognitive deficits and neurodegeneration, highlighting potential for future therapeutic assessments.