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Updated: May 1, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Age Prediction Using Resting-State Functional MRI.

Jose Ramon Chang¹, Zai-Fu Yao^2,3,4,5, Shulan Hsieh^6,7,8

¹Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.

Neuroinformatics

|February 11, 2024

Summary

This summary is machine-generated.

This study uses resting-state functional MRI to predict brain age, identifying Default Mode Network (DMN) changes in abnormal brain aging. This method offers a new way to screen for brain aging issues before cognitive decline occurs.

Keywords:

Abnormal brain aging Brain aging Default mode network Feature selection Least absolute shrinkage and selection operator Resting-state functional MRI

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Last Updated: May 1, 2026

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Published on: August 28, 2021

Area of Science:

Neuroscience
Radiology
Gerontology

Background:

Cognitive aging varies significantly, making brain health assessment crucial.
Brain age, a marker of neural health, can differ from chronological age and is linked to mortality and depression.
Functional brain imaging offers deeper insights into aging than structural methods alone.

Purpose of the Study:

To develop a predictive model for brain age using resting-state functional MRI (rsfMRI).
To identify neural network correlations associated with abnormal brain aging.
To establish a robust reference model for assessing brain health in adults.

Main Methods:

Utilized rsfMRI data from 176 healthy participants (aged 18-78).
Employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify 39 predictive rsfMRI correlations.
Developed a normal reference model by removing 68 outliers, achieving a low prediction error.

Main Results:

The developed model achieved a leave-one-out mean absolute error of 2.48 years.
Abnormal aging predictors were identified and linked to the Default Mode Network (DMN).
The model demonstrated superior accuracy compared to existing published models.

Conclusions:

The study provides an accurate model for predicting brain age and screening for abnormal aging.
The Default Mode Network (DMN) plays a significant role in brain aging processes.
This approach can identify brain aging issues before cognitive impairment is evident.