Spatial Entropy of Brain Network Landscapes: A Novel Method to Assess Spatial Disorder in Brain Networks.

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Functional brain networks exhibit complex community structures.
• Understanding the spatial organization and dynamics of these networks is crucial for cognitive neuroscience.
• Quantifying local order and disorder within brain networks remains a challenge.

Purpose of the Study:

• To introduce a novel method for mapping spatial entropy of functional brain network community structure.
• To demonstrate the quantification of spatial entropy at the voxel level within brain space.
• To provide interpretable maps of brain network entropy for group-level comparisons.

Main Methods:

• Developed a method to quantify spatial entropy based on the community affiliations of neighboring voxels.
• Applied the method to functional brain network data.
• Analyzed entropy changes during working memory and music-listening tasks.

Main Results:

• Generated interpretable maps of brain network entropy.
• Observed significant decreases in local spatial entropy in predictable brain regions during cognitive tasks.
• Demonstrated that the method allows for group-level comparison of brain network community structure.

Conclusions:

• The proposed method enables the mapping and quantification of spatial entropy in brain networks.
• Regional entropy reductions during cognitive tasks suggest neural self-organization.
• This framework supports future analyses of spatial entropy in complex networks within and beyond the brain.