A novel method for functional brain networks based on static cerebral blood flow

Area of Science:

• Neuroimaging
• Network Neuroscience
• Physiology

Background:

• Cerebral blood flow (CBF) is a key indicator of brain activity and functional network analysis.
• Current methods relying on temporal dynamics of CBF have limitations in signal-to-noise ratio and temporal resolution.

Purpose of the Study:

• To develop a novel method for constructing functional brain networks based on the shape similarity of regional static CBF probability distributions.
• To investigate the topological features, reliability, and neurobiological underpinnings of these novel CBF-based networks.

Main Methods:

• Utilized arterial spin labeling perfusion imaging to measure regional static CBF.
• Employed Jensen-Shannon divergence to quantify shape similarity in CBF probability distributions for network construction.
• Analyzed network topology, test-retest reliability, between-subject consistency, and spatial correlations with neurobiological markers.

Main Results:

• CBF networks demonstrated complex topological features, including small-world organization, modularity, and hub structures.
• Networks exhibited moderate test-retest reliability and high between-subject consistency.
• Interregional CBF similarity correlated with anatomical distance and differed between high- and low-order subnetworks.
• Network properties aligned with regional size, CBF levels, and neurobiological markers like dopamine transporter and gene expression.

Conclusions:

• The proposed method offers a novel, reliable, and neurobiologically meaningful approach to studying functional brain networks using static CBF.
• This method overcomes limitations of temporal dynamics-based approaches, providing new insights into brain organization.