Metric structural human connectomes: localization and multifractality of eigenmodes
Anna Bobyleva, Alexander Gorsky, Sergei Nechaev, Olga Valba, Nikita, Pospelov

TL;DR
This paper investigates the spectral properties and eigenmode localization of human brain connectomes, revealing multifractality, localized modes, and critical behavior, and proposes a generative model capturing these features.
Contribution
It introduces a simple generative model combining nonlinear preferential attachment with spatial penalties to replicate connectome spectral features and analyzes multifractality and localization of eigenmodes.
Findings
Eigenmodes exhibit multifractality across the spectrum.
Localized scar-like eigenmodes are identified with high IPR.
Spectral statistics suggest critical behavior and multifractal phases.
Abstract
In this study, we explore the fundamental principles behind the architecture of the human brain's structural connectome, from the perspective of spectral analysis of Laplacian and adjacency matrices. Building on the idea that the brain strikes a balance between efficient information processing and minimizing wiring costs, we aim to understand the impact of the metric properties of the connectome and how they relate to the existence of an inherent scale. We demonstrate that a simple generative model, combining nonlinear preferential attachment with an exponential penalty for spatial distance between nodes, can effectively reproduce several key characteristics of the human connectome, including spectral density, edge length distribution, eigenmode localization and local clustering properties. We also delve into the finer spectral properties of the human structural connectomes by…
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