An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.

ABSTRACT: Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural

SUBMITTER: Zhao M 

PROVIDER: S-EPMC9035078 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature