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

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Aging

Aging

Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Protein and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

The Effect of Aging on Tissues

The Effect of Aging on Tissues

Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

You might also read

Related Articles

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

Sort by

Same author

Aging differentially affects memory vividness and remembered salience for emotionally negative images.

Psychology and aging·2026

Same author

AI-assisted teams outperform AI-led teams but not human-only teams in assessing research reproducibility in quantitative social science.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026

Same author

Effects of Initial Experiences on Risky Choice.

Quarterly journal of experimental psychology (2006)·2026

Same author

Bias in Structural MRI Correlates of Delay Discounting due to Head Motion.

Human brain mapping·2026

Same author

Recovery sleep after total sleep deprivation preserves neutral and enhances emotional declarative memory.

Sleep advances : a journal of the Sleep Research Society·2026

Search research articles

Related Experiment Video

Updated: Feb 15, 2026

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

Predicting age from cortical structure across the lifespan.

Christopher R Madan^1,2, Elizabeth A Kensinger²

¹School of Psychology, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.

The European Journal of Neuroscience

|January 24, 2018

Summary

This summary is machine-generated.

Brain structure changes systematically with age, allowing for reliable age prediction. Researchers used machine learning on MRI data to predict age from cortical morphology with a 6-7 year error.

Keywords:

aging brain morphology cortical complexity fractal dimensionality gyrification structural MRI

More Related Videos

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Published on: September 29, 2011

Measurement of Lifespan in Drosophila melanogaster

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Related Experiment Videos

Last Updated: Feb 15, 2026

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Published on: September 29, 2011

Measurement of Lifespan in Drosophila melanogaster

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Area of Science:

Neuroimaging
Neuroscience
Radiology

Background:

Individual brain structures vary, but population-level changes in brain morphology with age show consistency.
Previous studies indicate systematic age-related differences in brain structure.

Purpose of the Study:

To assess the accuracy of predicting an individual's age using cortical morphology metrics.
To compare the effectiveness of different structural measures (thickness, gyrification, fractal dimensionality) for age prediction.
To investigate the impact of parcellation approaches on age prediction accuracy.

Main Methods:

Utilized T1-weighted MRI volumes from 1056 healthy adults (aged 18-97) for training a machine learning model.
Calculated cortical structural measures (thickness, gyrification, fractal dimensionality) across seven parcellation strategies.
Applied a machine learning framework incorporating nonlinear age-related changes.

Main Results:

Age prediction achieved a median error of 6-7 years in two independent test datasets.
Combining cortical thickness and fractal dimensionality yielded the most accurate age predictions.
The study demonstrates systematic age-related patterns in brain structure.

Conclusions:

Cortical morphology metrics are sufficiently systematic for reliable age prediction.
Machine learning models can accurately estimate chronological age from brain structure.
Brain imaging provides a valuable tool for understanding age-related neurological changes.