# Analyzing complex functional brain networks: fusing statistics and network science to understand the brain

arXiv: https://arxiv.org/abs/1302.5721 · HTML: https://mlsanity.com/paper/1302.5721/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2013.

## Built on by (most cited first, 8 of 8)

- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2205.12465/) (2022)
- [BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations](https://mlsanity.com/paper/2405.00077/) (2024)
- [R-Mixup: Riemannian Mixup for Biological Networks](https://mlsanity.com/paper/2306.02532/) (2023)
- [Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help](https://mlsanity.com/paper/2501.17207/) (2025)
- [Effective and Interpretable fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2107.11247/) (2021)
- [BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification](https://mlsanity.com/paper/2505.11538/) (2025)
- [MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder](https://mlsanity.com/paper/2409.06163/) (2024)

## Abstract (arXiv)

Complex functional brain network analyses have exploded over the last eight years, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.