Multi-resolution Spatiotemporal Enhanced Transformer Denoising with
  Functional Diffusive GANs for Constructing Brain Effective Connectivity in
  MCI analysis
Qiankun Zuo Chi-Man Pun Yudong Zhang Hongfei Wang Jin Hong
Effective connectivity can describe the causal patterns among brain regions.
These patterns have the potential to reveal the pathological mechanism and
promote early diagnosis and effective drug development for cognitive disease.
However, the current studies mainly focus on using empirical functional time
series to calculate effective connections, which may not comprehensively
capture the complex causal relationships between brain regions. In this paper,
a novel Multi-resolution Spatiotemporal Enhanced Transformer Denoising (MSETD)
network with an adversarially functional diffusion model is proposed to map
functional magnetic resonance imaging (fMRI) into effective connectivity for
mild cognitive impairment (MCI) analysis. To be specific, the denoising
framework leverages a conditional diffusion process that progressively
translates the noise and conditioning fMRI to effective connectivity in an
end-to-end manner. To ensure reverse diffusion quality and diversity, the
multi-resolution enhanced transformer generator is designed to extract local
and global spatiotemporal features. Furthermore, a multi-scale diffusive
transformer discriminator is devised to capture the temporal patterns at
different scales for generation stability. Evaluations of the ADNI datasets
demonstrate the feasibility and efficacy of the proposed model. The proposed
model not only achieves superior prediction performance compared with other
competing methods but also identifies MCI-related causal connections that are
consistent with clinical studies.