A quantitative model for human neurovascular coupling with translated mechanisms from animals

Area of Science:

• Neuroscience
• Computational Biology
• Physiology

Background:

• Neurovascular coupling (NVC) links neural activity to blood flow.
• Understanding NVC is vital for interpreting functional brain imaging data.
• Existing models lack integration across diverse experimental data types.

Purpose of the Study:

• To develop a unified quantitative mathematical model of NVC.
• To integrate diverse experimental and species-specific NVC data.
• To preserve and translate mechanistic insights across datasets.

Main Methods:

• Developed a comprehensive mathematical model of NVC.
• Integrated data from optogenetics, microscopy, LFP, BOLD, and hemoglobin measurements.
• Model trained and validated on data from rodents, primates, and humans.

Main Results:

• Identified specific neuronal cell types (NO-interneurons, pyramidal neurons, NPY-interneurons) driving distinct phases of vascular response.
• Quantified hemoglobin dynamics and the interplay between local field potential (LFP) and blood oxygenation level-dependent (BOLD) signals.
• Model successfully predicted independent validation data, demonstrating its predictive power.

Conclusions:

• The unified model provides a mechanistic understanding of NVC across different stimuli and species.
• It enables integrative analysis of complex human brain imaging data.
• This approach enhances the interpretation of functional imaging by linking neural activity to hemodynamic responses.