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Related Experiment Video

Updated: Jul 26, 2025

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

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

Published on: May 7, 2014

Classification and prediction of cognitive performance differences in older age based on brain network patterns using

Camilla Krämer^1,2, Johanna Stumme^1,2, Lucas da Costa Campos^1,2

¹Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.

Network Neuroscience (Cambridge, Mass.)

|June 20, 2023

Summary

Resting-state functional connectivity (RSFC) parameters show limited ability to predict cognitive performance in healthy aging adults. Machine learning models struggled to classify or predict cognitive differences using these brain network measures.

Keywords:

Aging Cognition Graph-theoretical analyses Machine learning Resting-state functional connectivity

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

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

Published on: February 14, 2014

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Last Updated: Jul 26, 2025

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

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

Published on: May 7, 2014

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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

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

Published on: February 14, 2014

Area of Science:

Neuroscience
Cognitive Science
Machine Learning

Background:

Cognitive aging varies significantly among healthy older adults, potentially due to differences in brain network functional architecture.
Resting-state functional connectivity (RSFC) parameters are established markers of brain architecture and aid in diagnosing neurodegenerative diseases.

Purpose of the Study:

To investigate the utility of RSFC parameters in classifying and predicting cognitive performance variations in the normally aging brain using machine learning (ML).
To evaluate the classifiability and predictability of global and domain-specific cognitive performance from nodal and network-level RSFC measures.

Main Methods:

Utilized data from healthy older adults (55-85 years) from the 1000BRAINS study.
Employed machine learning (ML) to analyze nodal and network-level RSFC strength measures.
Systematically evaluated ML performance across various analytical choices using a robust cross-validation scheme.

Main Results:

Classification accuracy for global and domain-specific cognition did not surpass 60%.
Prediction performance was low, with high mean absolute errors (MAE ≥ 0.75) and minimal explained variance (R² ≤ 0.07).
These outcomes were consistent across different cognitive targets, feature sets, and pipeline configurations.

Conclusions:

Functional network parameters have limited potential as sole biomarkers for cognitive aging.
Predicting cognitive function from functional network patterns in healthy aging is challenging.