Graph alignment exploiting the spatial organization improves the similarity of brain networks

• Published in: Human brain mapping Vol. 45; no. 1; pp. e26554 - n/a
• Main Authors: Calissano, Anna, Papadopoulo, Theodore, Pennec, Xavier, Deslauriers‐Gauthier, Samuel
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2024; Wiley
• Subjects: Algorithms; Alignment; Brain; Brain - diagnostic imaging; Center of gravity; Cerebral Cortex; Connectome - methods; Environmental factors; Functional morphology; Geometric Topology; Graph matching; graph space; Graph theory; Graphs; group‐wise parcellation; healthy subjects; Humans; Life Sciences; Magnetic Resonance Imaging - methods; Mathematics; Misalignment; network alignment; Neural networks; Neuroimaging; Neurons and Cognition; Permutations; Similarity; Spatial distribution; Statistics; structural parcellation; Structure-function relationships; unlabeled graphs; graph space; group-wise parcellation; healthy subjects; graph matching; structural parcellation; network alignment; unlabeled graphs
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26554

Every brain is unique, having its structural and functional organization shaped by both genetic and environmental factors over the course of its development. Brain image studies tend to produce results by averaging across a group of subjects, under the common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. We investigate this assumption: can the structural properties of a specific region of an atlas be assumed to be the same across subjects? This question is addressed by looking at the network representation of the brain, with nodes corresponding to brain regions and edges to their structural relationships. Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects, considering parcellations of different granularity, to understand the connectivity misalignment between regions. First, we compare the obtained permutations with four different algorithm initializations: Spatial Adjacency, Identity, Barycenter, and Random. Our results suggest that applying an alignment strategy improves the similarity across subjects when the number of parcels is above 100 and when using Spatial Adjacency and Identity initialization (the most plausible priors). Second, we characterize the obtained permutations, revealing that the majority of permutations happens between neighbors parcels. Lastly, we study the spatial distribution of the permutations. By visualizing the results on the cortex, we observe no clear spatial patterns on the permutations and all the regions across the context are mostly permuted with first and second order neighbors. Can the structural properties of a specific region of an atlas be assumed to be the same across subjects? Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects and with parcellations of different granularity to understand which is the connectivity misalignment between regions.