Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data

Area of Science:

• Neuroimaging
• Brain Network Analysis
• Functional Connectivity

Background:

• Blood oxygenation level dependent (BOLD) resting-state functional MRI (rsfMRI) is commonly used for brain connectivity studies.
• The BOLD signal is an indirect measure of neuronal activity.
• Arterial spin labeling (ASL), specifically pulsed ASL (PASL), offers a more direct measure of blood flow changes related to neural activity.

Purpose of the Study:

• To investigate the feasibility of extracting major brain networks using PASL data.
• To compare brain networks derived from PASL with those from rsfMRI.
• To assess the utility of PASL for analyzing functional connectivity in existing datasets.

Main Methods:

• Retrospective analysis of a cohort dataset (21 mild traumatic brain injury patients, 29 healthy controls).
• Extraction of 10 major brain networks from both PASL and rsfMRI data.
• Contrastive analysis of similarities and differences between networks derived from the two modalities.

Main Results:

• PASL successfully extracted all 10 major brain networks.
• Eight out of 10 networks showed over 60% similarity between PASL and rsfMRI.
• Both modalities detected similar, though not identical, network changes in mild traumatic brain injury patients compared to controls.
• PASL-derived default mode network (DMN) included additional relevant regions compared to rsfMRI-derived DMN.

Conclusions:

• PASL data can be reliably analyzed to identify resting-state brain networks.
• PASL provides a viable alternative for functional connectivity analysis, especially for datasets where only ASL data is available.
• This technique enhances the utility of historical neuroimaging datasets.