Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm

Area of Science:

• Neuroscience
• Computational Neuroscience
• Brain Imaging

Background:

• Imaging complex neural processes in the brain is challenging.
• Existing methods lack the resolution and efficiency for macroscopic brain-wide analysis.
• Understanding macroscopic neural dynamics is crucial for brain function research.

Purpose of the Study:

• To introduce a novel framework, Neurophysiological Process Imaging (NPI), for space-time resolved brain imaging.
• To enable efficient and accurate inference and imaging of neural processes across the cerebral cortex.
• To demonstrate NPI's utility in analyzing resting-state brain activity.

Main Methods:

• Developed a novel nonlinear inference method to fit uncoupled neural mass models to electromagnetic source time-series.
• Applied the framework to resting-state magnetoencephalographic (MEG) source estimates.
• Utilized high-performance computing for efficient processing and overnight results.

Main Results:

• Successfully inferred and imaged population-averaged membrane potentials and synaptic connection strengths across the entire cerebral cortex.
• Identified endogenous inputs to cingulate, occipital, and inferior frontal cortex as key modulators of resting-state alpha power.
• Revealed varied roles of endogenous input and neural populations in mediating alpha power across different resting-state subnetworks.

Conclusions:

• Neurophysiological Process Imaging (NPI) is a practical and novel imaging tool for macroscopic brain analysis.
• The framework can be applied to arbitrary neural mass models, offering broad applicability.
• NPI facilitates a deeper understanding of neural processes in various brain states, including resting-state dynamics.