Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Large-scale cortical dynamics arise from interactions between local circuits.
• Intrinsic properties of local circuits vary across cortical areas, potentially influencing network organization.
• Cortical hierarchy is a key organizational principle, but its role in shaping large-scale dynamics is not fully understood.

Purpose of the Study:

• To investigate how heterogeneity in local circuit properties, specifically along a hierarchical axis, shapes large-scale cortical dynamics.
• To develop and validate a large-scale dynamical circuit model of the human cortex incorporating hierarchical specialization.

Main Methods:

• Developed a large-scale dynamical circuit model of the human cortex.
• Incorporated heterogeneity of local synaptic strengths based on a hierarchical axis inferred from MRI T1w/T2w mapping.
• Fit the model using multimodal neuroimaging data, including functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG).

Main Results:

• Incorporating hierarchical heterogeneity significantly improved the model's fit to resting-state functional connectivity measured by fMRI.
• The model successfully captured the sensory-association organization observed in fMRI data.
• The model predicted hierarchically organized higher-frequency spectral power, which was supported by MEG data.

Conclusions:

• Local circuit properties and their hierarchical specialization are crucial for the large-scale organization of human cortical dynamics.
• This study provides circuit-level mechanisms linking different spatiotemporal levels of analysis in the brain.
• Findings highlight the importance of considering intrinsic local properties when studying brain network organization.