  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 methods utilize software toolkits to extract empirical
features from brain imaging to estimate effective connectivity. These methods
heavily rely on manual parameter settings and may result in large errors during
effective connectivity estimation. In this paper, a novel brain
imaging-to-graph generation (BIGG) framework is proposed to map functional
magnetic resonance imaging (fMRI) into effective connectivity for mild
cognitive impairment (MCI) analysis. To be specific, the proposed BIGG
framework is based on the diffusion denoising probabilistic models (DDPM),
where each denoising step is modeled as a generative adversarial network (GAN)
to progressively translate the noise and conditional fMRI to effective
connectivity. The hierarchical transformers in the generator are designed to
estimate the noise at multiple scales. Each scale concentrates on both spatial
and temporal information between brain regions, enabling good quality in noise
removal and better inference of causal relations. Meanwhile, the
transformer-based discriminator constrains the generator to further capture
global and local patterns for improving high-quality and diversity generation.
By introducing the diffusive factor, the denoising inference with a large
sampling step size is more efficient and can maintain high-quality results for
effective connectivity generation. Evaluations of the ADNI dataset 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 predicts MCI-related causal connections that are consistent with
clinical studies.
