Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping.

Area of Science:

• Neuroimaging
• Systems Neuroscience
• Computational Neuroscience

Background:

• Brain connectivity analysis offers insights into functional architecture at the systems level.
• Deriving fine-scale functional structures from voxel-wise analyses remains a challenge.

Purpose of the Study:

• To introduce a novel method, filtered localized topo-connectivity mapping (filtered LTM), for identifying voxel-wise functional structures in the human brain.
• To validate filtered LTM using simulated and real 7T fMRI data.

Main Methods:

• Filtered localized topo-connectivity mapping (filtered LTM) utilizing singular-value-decomposition-informed filtering.
• Application to resting-state fMRI data from the Human Connectome Project.
• Validation with simulated data and comparison against anatomical and diffusion MRI data.

Main Results:

• Filtered LTM successfully identifies voxel-wise functional structures in the human brain.
• Functional structures identified by LTM generally align with anatomical boundaries but reveal subtle functional variations.
• LTM-derived functional parcels show significantly higher signal synchrony compared to geometric perturbations.

Conclusions:

• Filtered LTM is a powerful tool for investigating brain functional organization at the voxel scale using fMRI.
• The method enhances the characterization of brain networks by revealing functional details not apparent in anatomical data.
• Filtered LTM provides a novel approach to understanding the intricate functional architecture of the human brain.