# Multi-State Brain Network Discovery

arXiv: https://arxiv.org/abs/2311.02466 · HTML: https://mlsanity.com/paper/2311.02466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks](https://mlsanity.com/paper/2101.05348/) (2021)
- [ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration](https://mlsanity.com/paper/2212.03277/) (2022)
- [One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data](https://mlsanity.com/paper/2307.15198/) (2023)
- [Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation](https://mlsanity.com/paper/1301.2194/) (2013)
- [ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data](https://mlsanity.com/paper/2212.03306/) (2022)

## Abstract (arXiv)

Brain network discovery aims to find nodes and edges from the spatio-temporal signals obtained by neuroimaging data, such as fMRI scans of human brains. Existing methods tend to derive representative or average brain networks, assuming observed signals are generated by only a single brain activity state. However, the human brain usually involves multiple activity states, which jointly determine the brain activities. The brain regions and their connectivity usually exhibit intricate patterns that are difficult to capture with only a single-state network. Recent studies find that brain parcellation and connectivity change according to the brain activity state. We refer to such brain networks as multi-state, and this mixture can help us understand human behavior. Thus, compared to a single-state network, a multi-state network can prevent us from losing crucial information of cognitive brain network. To achieve this, we propose a new model called MNGL (Multi-state Network Graphical Lasso), which successfully models multi-state brain networks by combining CGL (coherent graphical lasso) with GMM (Gaussian Mixture Model). Using both synthetic and real world ADHD 200 fMRI datasets, we demonstrate that MNGL outperforms recent state-of-the-art alternatives by discovering more explanatory and realistic results.