Select then fusion: An effective multi-atlas brain network analysis method with sparse and uncertain mechanism

Multi-atlas brain networks offer a more comprehensive and intricate understanding than a single atlas in identifying brain disorders. Traditional multi-atlas analysis methods depend on some simple fusion methods (i.e., addition and concatenation) but do not consider the information redundancy caused by increased brain regions and uncertain information between multiple atlases. To address this, we propose an effective multi-atlas brain network analysis method with a sparse and uncertain mechanism, called Sparse and Uncertain Fusion Neural Network (SUFNN). We first construct a multi-atlas brain network based on functional magnetic resonance imaging (fMRI) using different atlases. Then, an attention-enhanced module is used to learn the features of each atlas. These features are fed into the multi-atlas brain regions selection module, which can select disease-related brain regions based on their importance scores. Subsequently, the model employs the selected features for downstream processing. Finally, we employ an uncertain fusion module that determines the uncertainty of each atlas and performs an uncertain fusion strategy to get the results at the evidence level. Experimental results on the SRPBS dataset demonstrate that our SUFNN outperforms several state-of-the-art methods in identifying brain disorders.