Connectome Embedding in Multidimensional Graph Spaces
Date
Authors
Mach, Mathieu
Amico, Enrico
Liégeois, Raphaël
Preti, Maria Giulia
Griffa, Alessandra
Van De Ville, Dimitri
Pedersen, Mangor
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
The MIT Press
Abstract
Connectomes’ topological organization can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to 10 graph theoretic nodal properties. These properties assign a score to nodes, reflecting their meaning in the network. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated various connectomes (structural/functional, binary/weighted). We observed that nodal properties are correlated (i.e., they carry similar information) at whole-brain and subnetwork level. We conducted an exploratory machine learning analysis to test whether high-dimensional network information differs between sensory and association areas. Brain regions of sensory and association networks were classified with an 80–86% accuracy in a 10-dimensional (10D) space. We observed the largest gain in machine learning accuracy going from a 2D to 3D space, with a plateauing accuracy toward 10D space, and nonlinear Gaussian kernels outperformed linear kernels. Finally, we quantified the Euclidean distance between nodes in a 10D graph space. The multidimensional Euclidean distance was highest across subjects in the default mode network (in structural networks) and frontoparietal and temporal lobe areas (in functional networks). To conclude, we propose a new framework for quantifying network features in high-dimensional spaces that may reveal new network properties of the brain.Description
Keywords
Connectome, Distance, Global brain, Graph space, Network analysis, Single brain region, 5202 Biological Psychology, 32 Biomedical and Clinical Sciences, 3209 Neurosciences, 52 Psychology, Neurosciences, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Neurological, 3209 Neurosciences, 5202 Biological psychology
Source
Network Neuroscience, ISSN: 2472-1751 (Print); 2472-1751 (Online), The MIT Press, 8(4), 1-51. doi: 10.1162/netn_a_00393
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© 2024 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.