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Updated: Jun 19, 2026

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

Predicting executive functioning from brain networks: modality specificity and age effects.

Marisa K Heckner^1,2, Edna C Cieslik^1,2, Lya K Paas Oliveros^1,2

¹Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany.

Cerebral Cortex (New York, N.Y. : 1991)

|October 2, 2023

Summary

This summary is machine-generated.

Brain structure and function changes with age, impacting executive functioning (EF). This study found limited success in predicting individual EF from brain metrics like gray matter volume and functional connectivity, suggesting new approaches are needed.

Keywords:

cognitive aging mental abilities multilevel resting-state fMRI predictive modeling structural MRI

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Published on: February 14, 2014

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Area of Science:

Neuroscience
Cognitive Psychology
Aging Research

Background:

Healthy aging involves brain network alterations linked to executive functioning (EF) decline.
Neural underpinnings of individual EF differences remain unclear.
Biomarker potential of resting-state functional connectivity (RSFC) for EF is debated.

Purpose of the Study:

To predict individual executive functioning (EF) abilities from brain metrics in young and old adults.
To investigate modality-specific prediction accuracies across different brain networks.
To determine age- and task-dependency of brain-behavior relationships for EF.

Main Methods:

Examined gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC.
Utilized uni- and multivariate analysis frameworks for out-of-sample prediction.
Compared prediction accuracies between young and old adults across different brain networks.

Main Results:

Overall low prediction accuracies (R2 < 0.07) and weak brain-behavior associations (r < 0.28) were found.
Regional GMV was most informative for older adults' EF, while fALFF was more predictive for younger adults.
Prediction accuracy was largely modality-specific and varied with age.

Conclusions:

Current brain metrics (GMV, fALFF, RSFC) have limited utility as individual EF biomarkers.
Future research should explore global brain properties and task-based functional connectivity.
Adaptive behavioral testing may yield more sensitive predictors for EF in different age groups.