HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Brain regions exhibit specialized functions and communicate via the white matter network.
• Passive diffusion models explain significant functional connectivity, posing a challenge to isolating active brain processes.
• Distinguishing active neural signaling from passive signal spread is crucial for understanding brain function.

Purpose of the Study:

• To develop a method for isolating active brain signals from passive diffusion in functional MRI (fMRI).
• To quantify the active component of brain signals by deconvolving passive spread effects.
• To explore the impact of the isolated active signal on brain network organization and task-related activity.

Main Methods:

• Utilized a higher-order network diffusion (HONeD) model to spatially deconvolve passive signal spread.
• Calculated an estimate for the active signal (HONeD-innovation or HONeD-in) in closed-form.
• Applied the method to functional MRI data from 770 Human Connectome Project subjects.

Main Results:

• The HONeD-innovation signal sparsifies functional connectivity while preserving network integrity.
• This active signal remodels resting-state networks (RSNs), altering their hierarchical organization.
• Deblurred task-activation maps, suggesting improved spatial resolution of brain activity.
• Revealed a circular mixing of unimodal and multimodal RSNs, challenging existing hierarchical models.

Conclusions:

• HONeD deconvolution offers a novel and generalizable approach to analyzing both resting-state and task-based fMRI data.
• The HONeD-innovation signal provides a clearer view of active neural processes distinct from passive signal propagation.
• This method enhances the study of brain network dynamics and functional organization.