Network analysis identifies consensus physiological measures of neurovascular coupling in humans

Area of Science:

• Neuroscience
• Physiology
• Medical Imaging

Background:

• Neurovascular coupling links brain activity with blood flow, crucial for neuronal metabolism.
• Research in human neurovascular coupling is growing due to clinical relevance and advanced in vivo models.
• Current methods for characterizing neurovascular responses lack standardization and optimal metrics.

Purpose of the Study:

• To identify the most powerful and consistent metrics for quantifying human neurovascular coupling.
• To address the knowledge gap in characterizing neurovascular responses in humans.
• To develop novel, sensitive metrics for assessing neurovascular coupling in health and disease.

Main Methods:

• Amassed a large human neurovascular coupling dataset (>300 trials).
• Employed a network-based approach including dimensionality reduction and community-based clustering.
• Utilized traditional (e.g., peak response) and non-traditional (e.g., time windows, pulsatility) metrics.

Main Results:

• Identified key metrics that predominantly characterize neurovascular coupling, showing stability within and across participants.
• Determined metrics that explain the majority of variance in the neurovascular coupling dataset.
• Developed novel metrics: iAmplitude, iRate, and iPulsatility, enhancing sensitivity for population differences.

Conclusions:

• Established a robust methodology for selecting optimal neurovascular coupling metrics.
• Introduced novel metrics (iAmplitude, iRate, iPulsatility) that improve the sensitivity of neurovascular coupling assessment.
• These findings facilitate a better understanding of neurovascular coupling in both healthy and diseased states.