Estimating Task-Evoked Neurovascular Coupling Using Mutual Information Between BOLD and Perfusion-Weighted fMRI...

Area of Science:

• Neuroimaging
• Neuroscience
• Physiology

Background:

• Neurovascular coupling (NVC) is crucial for interpreting functional MRI (fMRI) data, especially during task-based studies.
• Previous research indicates widespread BOLD-ASL coupling during resting states, but its behavior during tasks and the role of nonlinear dependencies remain less understood.

Purpose of the Study:

• To investigate BOLD-ASL coupling during visual and motor tasks.
• To assess the contribution of nonlinear dependencies to NVC using mutual information (MI) compared to traditional correlation (corr).

Main Methods:

• Simultaneous BOLD and ASL fMRI data were collected during visual checkerboard and finger-tapping tasks.
• Coupling was assessed using Pearson correlation and mutual information at zero and maximum lag.
• Spatial overlap with task activation was quantified using Dice coefficients within Yeo 17-network regions.

Main Results:

• Strong spatial correspondence was observed between task activation and BOLD-ASL coupling in relevant sensory and motor regions for both tasks.
• Mutual information demonstrated greater overlap with GLM-based task activation models than Pearson correlation.
• This suggests MI captures more complex NVC aspects than linear methods.

Conclusions:

• Multimodal fMRI approaches are vital for a comprehensive characterization of NVC.
• Mutual information provides valuable insights into nonlinear NVC, enhancing our understanding beyond traditional linear analyses.
• Findings highlight the importance of considering nonlinear dynamics in neurovascular coupling during task-based fMRI studies.