Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

ABSTRACT: Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-tempora

SUBMITTER: Zheng W 

PROVIDER: S-EPMC11428273 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature