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

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

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

2026
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Overview
In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a local area are ordered or disordered. We demonstrate how spatial entropy can be quantified for each voxel in the brain according to the network community affiliations of surrounding voxels. This process results in interpretable maps of brain network entropy. We show that local entropy decreases in predictable brain regions during working memory and music‐listening tasks. We suggest that these regional entropy reductions reflect self‐organization of neural processes in support of functionally localized cognitive tasks. In summary, we propose a method that allows group‐level comparison of the brain network community structure identified in individuals. Analyses in this work provide a framework for future analyses of spatial entropy in complex networks that can be mapped to Euclidean space—both within the brain and in other contexts. Key Points We introduce an approach for quantifying the spatial entropy of functional brain network community structure. We demonstrate the biological relevance of the measure in three independent datasets; two of these compare working memory task scans to resting‐state scans while the third compares music‐listening to resting‐state. Our proposed approach is data‐driven, easy to implement, and highly interpretable. It also allows investigators to visualize complex data by mapping values into the brain rather than storing values in extremely high‐dimensional and data structures. We introduce a method for mapping the spatial entropy (or disorder) of functional brain network community structure images. We show that entropy decreased in the central executive network during working memory engagement in two independent samples. We also show that entropy decreased in the auditory cortex during music‐listening.