Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus.

Using functional connectivity (FC) or effective connectivity (EC) alone cannot effectively delineate brain networks based on functional magnetic resonance imaging (fMRI) data, limiting the understanding of the mechanism of tinnitus and its treatment. Investigating brain FC is a foundational step in exploring EC. This study proposed a functionally guided EC (FGEC) method based on reinforcement learning (FGECRL) to enhance the precision of identifying EC between distinct brain regions.

MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation.

Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has gradually become one of the hot subjects in the fields of neuroscience. In particular, the encoder-decoder based methods can effectively extract the connections in fMRI time series, which have achieved promising performance. However, these methods generally use Granger causality model, which may identify false directions due to the non-stationary characteristic of fMRI data.

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 methods usually require retraining the model for each subject, which ignores the knowledge shared across subjects. In this paper, we propose a novel framework for estimating effective connectivity based on an amortization transformer, named AT-EC.

Brain effective connectivity analysis facilitates the treatment outcome expectation of sound therapy in patients with tinnitus.

Tinnitus is associated with abnormal functional connectivity of multiple regions of the brain. However, previous analytic methods have disregarded information on the direction of functional connectivity, leading to only a moderate efficacy of pretreatment planning. We hypothesized that the pattern of directional functional connectivity can provide key information on treatment outcomes.