Attention module improves both performance and interpretability of four-dimensional functional magnetic resonance imaging decoding neural network.

ABSTRACT: Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in-depth interpretation of DNN channels. A four-dimensional (4D) convolution operation was also included to extract temporo-spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the seven different task benchmarks from the Human Connectome Project (HCP) datase

SUBMITTER: Jiang Z 

PROVIDER: S-EPMC9057093 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature