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BOLD Frequency Power Indexes Working Memory Performance.

Joshua Henk Balsters¹, Ian H Robertson, Vince D Calhoun

¹Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin Dublin, Ireland ; Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich Zurich, Switzerland.

Frontiers in Human Neuroscience

|May 31, 2013

Summary

This summary is machine-generated.

This study reveals that BOLD spectral power in specific brain networks correlates with working memory accuracy. This novel approach using independent component analysis (ICA) offers a more sensitive measure of cognitive performance than traditional methods.

Keywords:

BOLD oscillations ICA aging delayed match-to-sample fMRI

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Area of Science:

Neuroscience
Cognitive Neuroscience
Functional Neuroimaging

Background:

Electrophysiology studies neural oscillations and task performance.
The Blood-Oxygen-Level-Dependent (BOLD) response's sluggishness limits spectral analysis.
Few studies explore BOLD signal spectral properties in relation to cognition.

Purpose of the Study:

To investigate the relationship between working memory performance and BOLD spectral power within functional brain networks.
To apply group independent component analysis (ICA) to functional magnetic resonance imaging (fMRI) data.
To explore novel indices of cognitive performance.

Main Methods:

Group ICA applied to fMRI data during a delayed match-to-sample working memory task.
Multivariate analysis to assess correlations between BOLD spectral power and working memory performance (accuracy, reaction time).
Comparison with traditional General Linear Model (GLM) analysis.

Main Results:

BOLD spectral power in visual, temporal-parietal, default-mode, salience, and basal ganglia networks correlated with task accuracy.
The relationship between task accuracy and BOLD spectral power was stronger than with age, gender, head movement, or neuropsychological measures.
Traditional GLM analysis failed to detect significant group differences or regions covarying with task accuracy.

Conclusions:

BOLD spectral power, analyzed with ICA, provides unique information about cognitive performance.
This method is potentially more sensitive to brain-behavior relationships than standard GLM approaches.
The combination of ICA and BOLD spectral power is a promising novel index for assessing cognitive function.