Common deactivation patterns during working memory and visual attention tasks: an intra-subject fMRI study at 4 Tesla

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Functional Neuroimaging

Background:

• Functional magnetic resonance imaging (fMRI) studies brain activity by measuring blood oxygenation level-dependent (BOLD) signals.
• Understanding the interplay between brain activation and deactivation is crucial for comprehending cognitive processes.
• Previous research has explored task-specific brain networks, but the dynamic balance between positive and negative BOLD signals requires further investigation.

Purpose of the Study:

• To investigate the balance between positive and negative fMRI signals during visual attention (VA) and working memory (WM) tasks.
• To examine how task difficulty modulates brain activation and deactivation in specific brain networks.
• To explore the relationship between task-dependent BOLD responses and global cerebral blood flow variations.

Main Methods:

• Employed parametric functional magnetic resonance imaging (fMRI) with visual attention (VA) and working memory (WM) tasks.
• Utilized graded levels of task difficulty to systematically alter cognitive load.
• Analyzed brain activation (signal change, volume) and deactivation patterns across frontal, temporal, occipital, and limbic regions.

Main Results:

• Brain activation was greater for VA tasks compared to WM tasks.
• Task deactivation was more pronounced during WM tasks.
• Load-related BOLD responses showed strong cross-correlation in the deactivated network during VA tasks, but less so during WM tasks.

Conclusions:

• The deactivated network's variability across cognitive tasks supports the hypothesis of task-dependent global cerebral blood flow.
• Global cerebral blood flow varies across different tasks, but not necessarily between different difficulty levels of the same task.
• The observed task-dependent balance of activation and deactivation may serve to maximize neural resources for the active cognitive network.