Age-Related Differences in Neural Networks for Error Detection and Inhibitory Control: A LORETA-Based Comparative...

Area of Science:

• Neuroscience
• Cognitive Psychology
• Gerontology

Background:

• Inhibitory control and error detection are vital for identifying age-related cognitive decline.
• Understanding neural network changes in aging is crucial for early detection.

Purpose of the Study:

• Investigate neural network dynamics of inhibitory control and error detection in younger and older adults.
• Elucidate age-related alterations in cognitive control mechanisms.

Main Methods:

• Electroencephalograms (EEGs) recorded during an inhibitory control task in 17 older and 15 younger adults.
• Behavioral performance analyzed alongside directional functional connectivity using LORETA, iCoh, and Full Vector Field analysis.
• Analysis across theta, alpha, and beta frequency bands.

Main Results:

• Older adults exhibited lower accuracy and distinct beta-band connectivity patterns during incorrect responses (ACC to FPC).
• During correct responses, older adults showed alpha- and beta-band directionality (DLPFC to FPC).
• Younger adults displayed stronger intra-ACC connectivity and widespread network coherence during correct responses.

Conclusions:

• Efficient inhibitory control in older adults depends on higher-order error-monitoring networks.
• Altered neural dynamics indicate age-related decline in immediate cognitive control.
• EEGs offer a non-invasive biomarker for cognitive decline, targeting executive control for interventions.