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Related Experiment Video

Updated: Oct 31, 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

Pitfalls in brain age analyses.

Ellyn R Butler¹, Andrew Chen^2,3, Rabie Ramadan⁴

¹Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Human Brain Mapping

|June 30, 2021

Summary

This summary is machine-generated.

The brain age gap, a measure of brain aging, is influenced by chronological age. Adjusting for age may artificially inflate model accuracy, necessitating new methods to quantify brain deviations.

Keywords:

age brain development deviation prediction residual

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Area of Science:

Neuroimaging
Biomarkers
Aging Research

Background:

The brain age gap, comparing predicted brain age to chronological age, is a widely studied metric.
Previous research indicates the brain age gap is dependent on chronological age.
Group differences in the brain age gap may reflect age disparities rather than true biological differences.

Purpose of the Study:

To critically evaluate the methodology of brain age gap analyses.
To address the limitations of current approaches in quantifying deviations from normal brain aging.
To propose theoretical advancements for accurately measuring brain age discrepancies.

Main Methods:

Analysis of the brain age gap as a linear transformation of residuals.
Examination of the impact of regressing chronological age out of the brain age gap.
Theoretical assessment of model accuracy statistics (e.g., R-squared) following age correction.

Main Results:

The brain age gap is inherently dependent on chronological age.
Regressing age out of the brain age gap artificially inflates model accuracy metrics (R-squared).
Achieving R-squared values below 0.85 after age correction is highly improbable, irrespective of true model accuracy.

Conclusions:

Current methods for analyzing the brain age gap are limited by their dependence on chronological age.
Age correction inflates statistical measures, potentially misrepresenting the true accuracy of brain age prediction models.
Further theoretical development is required to establish robust methods for quantifying deviation from normal brain aging.