mlsanityData to 7 Oct 2026

Multi-scale brain networks

arXiv 1608.08828 ↗First posted 31 Aug 2016q-bio.NC physics.soc-phcited by 16

Not on the map. Why?

Abstract (arXiv)

The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales -- of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure -- irrespective of species, imaging modality, or spatial resolution.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 0; 2024: 3; 2025: 4; 2026: 2.

16 papers.

Sort:

  1. 2021Graph Neural Networks in Network Neurosciencecited by 91
  2. 2022EEG based Emotion Recognition: A Tutorial and Reviewcited by 42
  3. 2022Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivitycited by 8
  4. 2024Multiview Graph Learning with Consensus Graphcited by 3
  5. 2021Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Modelcited by 2
  6. 2024Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRIcited by 2
  7. 2025Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamicscited by 2
  8. 2025BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysiscited by 1
  9. 2026Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mappingcited by 1
  10. 2021Interpretable Network Representation Learning with Principal Component Analysiscited by 0
  11. 2022Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)cited by 0
  12. 2022Cognitive modelling with multilayer networks: Insights, advancements and future challengescited by 0
4 more
  1. 2024Identifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformercited by 0
  2. 2025Connecting the geometry and dynamics of many-body complex systems with message passing neural operatorscited by 0
  3. 2025Alzheimer's Disease Brain Network Miningcited by 0
  4. 2026Multiscale Community-Based Fingerprinting of Signed Functional Networkscited by 0