# Graph analysis of functional brain networks: practical issues in translational neuroscience

arXiv: https://arxiv.org/abs/1406.7391 · HTML: https://mlsanity.com/paper/1406.7391/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2014.

## Built on by (most cited first, 10 of 10)

- [MentorGNN: Deriving Curriculum for Pre-Training GNNs](https://mlsanity.com/paper/2208.09905/) (2022)
- [Curriculum Graph Machine Learning: A Survey](https://mlsanity.com/paper/2302.02926/) (2023)
- [EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs](https://mlsanity.com/paper/2305.00664/) (2023)
- [Functional connectivity ensemble method to enhance BCI performance (FUCONE)](https://mlsanity.com/paper/2111.03122/) (2021)
- [HappyFeat -- An interactive and efficient BCI framework for clinical applications](https://mlsanity.com/paper/2310.02948/) (2023)
- [RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020](https://mlsanity.com/paper/2102.06015/) (2021)
- [Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning](https://mlsanity.com/paper/2206.13465/) (2022)
- [Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics](https://mlsanity.com/paper/2312.00811/) (2023)
- [Multiscale Graph Construction Using Non-local Cluster Features](https://mlsanity.com/paper/2411.08371/) (2024)
- [Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding](https://mlsanity.com/paper/2603.11965/) (2026)

## Abstract (arXiv)

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.