Graph Alignment Exploiting the Spatial Organisation Improves the Similarity of Brain Networks

Anna Calissano (1, 2) , Théodore Papadopoulo (1, 3) , Xavier Pennec (1, 2) , Samuel Deslauriers-Gauthier (1, 3)

1 UniCA - Université Côte d'Azur
2 EPIONE - E-Patient : Images, données & mOdèles pour la médeciNe numériquE
3 ATHENA - Computational Imaging of the Central Nervous System

Every brain is unique, having its structural and functional organisation 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 a common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. This paper questions such assumption: can the structural and functional 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. We perform graph matching on a set of control patients and on parcellations of different granularity to understand which is the connectivity misalignment between regions. The graph matching is unsupervised and reveals interesting insight on local misalignment of brain regions across subjects.

Domaines: Statistiques [math.ST]; Topologie géométrique [math.GT]; Neurosciences [q-bio.NC]

Anna Calissano, Théodore Papadopoulo, Xavier Pennec, Samuel Deslauriers-Gauthier. Graph Alignment Exploiting the Spatial Organisation Improves the Similarity of Brain Networks. 2022. ⟨hal-03910761v1⟩