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Updated: Jul 8, 2025

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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Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task.

Prerna Singh, Ayush Tripathi, Lalan Kumar

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

This summary is machine-generated.

This study explores brain connectivity in middle-aged adults using electroencephalography (EEG) during multisensory integration tasks. Findings show distinct patterns that can accurately predict age groups, offering insights into cognitive changes.

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Infant Auditory Processing and Event-related Brain Oscillations

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Published on: July 1, 2015

Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Multisensory integration, the brain's ability to combine sensory inputs, declines with age.
Research on age-related cognitive decline primarily focuses on older adults, with limited data on middle-aged groups.
Understanding cognitive changes in middle age is crucial for early detection and intervention.

Purpose of the Study:

To investigate electroencephalography (EEG)-based functional connectivity during audiovisual temporal asynchrony integration in middle-aged individuals.
To analyze changes in functional connectivity across different age groups.
To develop a classification method for distinguishing between age groups based on connectivity features.

Main Methods:

EEG data acquisition during unimodal auditory, unimodal visual, and audiovisual stimulus tasks.
Correlation-based functional connectivity analysis.
Feature extraction from connectivity graphs for machine learning classification.
Random Forest classifier utilized for age group prediction.

Main Results:

Significant differences in functional connectivity were observed among young (18-25), early middle-aged (25-33), and middle-aged (33-41) groups.
High classification accuracies of 89.4% (Audio) and 88.4% (Audio-50-Visual) were achieved.
The proposed method effectively differentiates between age groups based on EEG connectivity patterns.

Conclusions:

EEG-based functional connectivity analysis is a viable method for studying age-related cognitive changes in middle age.
The findings highlight the potential of using brain connectivity patterns for non-invasive age-group classification.
This research provides a foundation for further investigation into cognitive health during the middle-adult lifespan.