A transformer model for learning spatiotemporal contextual representation in fMRI data.

ABSTRACT: Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account. This approach takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features that can in turn be used in various downstream tasks such as classification, feature extraction, and statistical analysis. The proposed spatiot

SUBMITTER: Asadi N 

PROVIDER: S-EPMC10270708 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature