Mach, MathieuAmico, EnricoLiégeois, RaphaëlPreti, Maria GiuliaGriffa, AlessandraVan De Ville, DimitriPedersen, Mangor2026-05-202026-05-202024-06-04Network Neuroscience, ISSN: 2472-1751 (Print); 2472-1751 (Online), The MIT Press, 8(4), 1-51. doi: 10.1162/netn_a_003932472-17512472-1751http://hdl.handle.net/10292/21142Connectomes’ 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.© 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.https://creativecommons.org/licenses/by/4.0/ConnectomeDistanceGlobal brainGraph spaceNetwork analysisSingle brain region5202 Biological Psychology32 Biomedical and Clinical Sciences3209 Neurosciences52 PsychologyNeurosciencesNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceNeurological3209 Neurosciences5202 Biological psychologyJournal ArticleOpenAccess10.1162/netn_a_00393