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Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

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Published on: February 14, 2014

Age-level bias correction in brain age prediction.

Biao Zhang¹, Shuqin Zhang¹, Jianfeng Feng²

¹School of Mathematical Sciences, Fudan University, Shanghai 200433, China.

Neuroimage. Clinical

|January 12, 2023

Summary

Predicted age difference (PAD) is a common aging biomarker, but machine learning methods introduce bias. Even after correction, bias persists, questioning its reliability. We propose a new age-level bias correction method to improve accuracy.

Keywords:

Age prediction Bias correction Human brain MRI Machine learning

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Area of Science:

Neuroscience
Biomarkers
Machine Learning

Background:

Predicted age difference (PAD) is a key phenotype for aging and brain disease research.
Machine learning models for PAD often exhibit systematic bias.
Existing bias correction methods have proven insufficient.

Purpose of the Study:

To investigate persistent bias in PAD even after current correction methods.
To propose and validate a novel age-level bias correction approach for PAD.

Main Methods:

Analysis of PAD in individuals of the same chronological age.
Implementation of a novel age-level bias correction technique.
Numerical experiments to demonstrate method efficacy.

Main Results:

Systematic bias was found to persist in PAD even after applying existing correction methods.
The proposed age-level bias correction method demonstrated efficacy in numerical experiments.
Current PAD may not be a reliable phenotype without further refinement.

Conclusions:

Existing bias correction methods for PAD are insufficient.
A novel age-level bias correction method is proposed to address fundamental defects in PAD.
Further research is needed to establish PAD as a reliable phenotype.