# Multi-scale brain networks

arXiv: https://arxiv.org/abs/1608.08828 · HTML: https://mlsanity.com/paper/1608.08828/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 16 of 16)

- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity](https://mlsanity.com/paper/2209.11232/) (2022)
- [Multiview Graph Learning with Consensus Graph](https://mlsanity.com/paper/2401.13769/) (2024)
- [Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model](https://mlsanity.com/paper/2102.13135/) (2021)
- [Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI](https://mlsanity.com/paper/2410.00410/) (2024)
- [Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics](https://mlsanity.com/paper/2501.06762/) (2025)
- [BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis](https://mlsanity.com/paper/2511.17604/) (2025)
- [Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mapping](https://mlsanity.com/paper/2602.01482/) (2026)
- [Interpretable Network Representation Learning with Principal Component Analysis](https://mlsanity.com/paper/2106.14238/) (2021)
- [Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)](https://mlsanity.com/paper/2206.10569/) (2022)
- [Cognitive modelling with multilayer networks: Insights, advancements and future challenges](https://mlsanity.com/paper/2210.00500/) (2022)
- [Identifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformer](https://mlsanity.com/paper/2409.11174/) (2024)
- [Connecting the geometry and dynamics of many-body complex systems with message passing neural operators](https://mlsanity.com/paper/2502.15913/) (2025)
- [Alzheimer's Disease Brain Network Mining](https://mlsanity.com/paper/2512.17276/) (2025)
- [Multiscale Community-Based Fingerprinting of Signed Functional Networks](https://mlsanity.com/paper/2608.27483/) (2026)

## 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.