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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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Neurophysiological correlates in Mild Cognitive Impairment detected using group Independent Component Analysis.

John F Ochoa, Mariana Ruiz, Diego Valle

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

|January 7, 2016

Summary

This study used electroencephalography (EEG) to identify biomarkers for Mild Cognitive Impairment (MCI). Findings suggest EEG analysis can reveal neurophysiological differences between MCI patients and healthy individuals.

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Area of Science:

Neuroscience
Biomarkers
Cognitive Impairment

Background:

Alzheimer's disease is the leading cause of dementia.
Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognition and dementia.
Electroencephalography (EEG) is explored for identifying dementia biomarkers.

Purpose of the Study:

To investigate neurophysiological differences in EEG signals between MCI subjects and healthy controls.
To apply advanced signal processing techniques to EEG data for biomarker discovery.
To explore the neurophysiological meaning of EEG signal generators.

Main Methods:

EEG recordings were obtained during an encoding task in MCI patients and healthy controls.
Group Independent Component Analysis (gICA) was used to decompose EEG signals.
Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC) were analyzed on neuronal components.

Main Results:

MCI subjects showed increased theta band PIC.
Healthy controls exhibited increased alpha band PsIC.
Correlations were found between PIC/PsIC and clinical scales.

Conclusions:

The proposed gICA-based methodology effectively extracts neurophysiologically meaningful information from EEG.
EEG analysis can differentiate between MCI and healthy cognitive states.
This approach holds potential for early detection and understanding of cognitive decline.