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

Assessment of body composition in breast cancer patients: concordance between transverse computed tomography analysis at the fourth thoracic and third lumbar vertebrae.

Frontiers in nutrition·2024
Same author

Occupational models from 42 million unstructured job postings.

Patterns (New York, N.Y.)·2023
Same author

A data science approach to 138 years of congressional speeches.

Heliyon·2020
Same author

The First 48 Consecutive Patients with 3-Year Symptom Score Follow-Up Post-Prostate Artery Embolization (PAE) at a Single-Centre University Hospital.

Cardiovascular and interventional radiology·2019
Same author

Composite Aging Markers Can Be Used for Quantitative Profiling of Aging.

Gerontology·2015
Same author

Comprehensive Analysis of Large Sets of Age-Related Physiological Indicators Reveals Rapid Aging around the Age of 55 Years.

Gerontology·2015

Related Experiment Video

Updated: Mar 22, 2026

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

12.7K

Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging.

Lior Shamir1, Joe Long

  • 121000 W Ten Mile Rd., Department of Math and Computer Science, Lawrence Technological University, Southfield, Michigan 48075, United States.

Current Aging Science
|April 14, 2016
PubMed
Summary
This summary is machine-generated.

Brain aging is not linear, with rapid changes occurring around ages 55 and 65. This suggests biological pathways, not just environmental damage, drive brain aging processes.

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

41.0K

Related Experiment Videos

Last Updated: Mar 22, 2026

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

12.7K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

41.0K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Gerontology

Background:

  • Cognitive function declines with age.
  • The drivers of brain aging—environmental damage versus biological pathways—remain unclear.

Purpose of the Study:

  • To quantitatively profile age-related alterations in brain tissues using MRI.
  • To investigate the non-linear nature of brain aging.

Main Methods:

  • Quantitative image analysis of 463 brain MRI scans from 416 subjects.
  • Computation of image content descriptors correlated with chronological age.
  • Machine learning profiling of age-related brain tissue alterations.

Main Results:

  • Global image content descriptors highly correlated with chronological age (~0.9822).
  • Brain aging exhibits non-linear patterns with periods of rapid change around ages 55 and 65.
  • Identified distinct periods of accelerated brain aging.

Conclusions:

  • Brain aging is a dynamic, non-linear process.
  • Findings support the role of biological pathways in brain aging, beyond environmental damage.
  • Publicly available code and data facilitate further research.