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.

该研究论文介绍了一种新的模型 MNGL（Multi-state Network Graphical Lasso），通过将 CGL（coherent graphical lasso）与 GMM（Gaussian Mixture Model）相结合，成功地建模了多状态脑网络，通过使用合成和真实的 ADHD 200 fMRI 数据集，证明 MNGL 比最新的最先进替代方案发现更多解释性和逼真的结果。