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Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

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Related Experiment Video

Updated: Nov 29, 2025

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Published on: June 3, 2020

Patch-wise brain age longitudinal reliability.

Iman Beheshti¹, Olivier Potvin¹, Simon Duchesne^1,2

¹Centre de recherche CERVO, Québec, Canada.

Human Brain Mapping

|November 18, 2020

Summary

This summary is machine-generated.

This study validates a new patch-wise brain age estimation method using longitudinal MRI scans. The technique shows high accuracy and reliability for multi-site, long-term brain aging research.

Keywords:

anatomical MRI brain age estimation longitudinal study patch-wise grading reliability

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Area of Science:

Neuroimaging
Radiology
Biostatistics

Background:

Brain age estimation from anatomical T1-weighted magnetic resonance imaging (T1w MRI) is an emerging field.
Previous methods require validation for longitudinal and multi-site reliability.

Purpose of the Study:

To assess the longitudinal reliability of a recently developed patch-wise brain age estimation technique.
To evaluate the method's performance on a unique dataset of 99 longitudinal MRI scans from a single individual over 17 years.

Main Methods:

A patch-wise brain age estimation framework was developed using the Desikan-Killiany-Tourville atlas on 100 healthy individuals.
The framework was trained on the MindBoggle dataset and applied to a longitudinal dataset of 99 T1w MRI scans from one volunteer (aged 29-46 years).
The model's prediction accuracy and reliability across different manufacturers were evaluated.

Main Results:

The patch-wise technique demonstrated high prediction accuracy on an independent test set (R² = .94, Mean Absolute Error = 0.63 years).
No statistically significant differences in brain age estimation were found between different MRI manufacturers.
The method exhibited high reliability for longitudinal, multi-centric studies.

Conclusions:

The patch-wise brain age estimation technique is reliable for longitudinal studies.
This method is suitable for multi-centric research involving T1w MRI data.
The findings support the use of this technique for tracking brain aging over time and across different imaging sites.