Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data

Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has garnered significant attention in the fields of neuroinformatics and bioinformatics. However, existing usually require retraining model for each subject, which ignores knowledge shared across subjects. In this paper, we propose a novel framework estimating based on an amortization transformer, named AT-EC. detail, AT-EC first employs transformer dynamics fMRI time series infer different subjects, can train amortized that leverages Then, assisted mechanism is designed assist estimation network. Experimental results both simulated real-world demonstrate efficacy our method.