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Effective connectivity can describe the causal patterns among brain regions.These patterns have the potential to reveal the pathological mechanism andpromote early diagnosis and effective drug development for cognitive disease.However, the current methods utilize software toolkits to extract empiricalfeatures from brain imaging to estimate effective connectivity. These methodsheavily rely on manual parameter settings and may result in large errors duringeffective connectivity estimation. In this paper, a novel brainimaging-to-graph generation (BIGG) framework is proposed to map functionalmagnetic resonance imaging (fMRI) into effective connectivity for mildcognitive impairment (MCI) analysis. To be specific, the proposed BIGGframework 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 effectiveconnectivity. The hierarchical transformers in the generator are designed toestimate the noise at multiple scales. Each scale concentrates on both spatialand temporal information between brain regions, enabling good quality in noiseremoval and better inference of causal relations. Meanwhile, thetransformer-based discriminator constrains the generator to further captureglobal and local patterns for improving high-quality and diversity generation.By introducing the diffusive factor, the denoising inference with a largesampling step size is more efficient and can maintain high-quality results foreffective connectivity generation. Evaluations of the ADNI dataset demonstratethe feasibility and efficacy of the proposed model. The proposed model not onlyachieves superior prediction performance compared with other competing methodsbut also predicts MCI-related causal connections that are consistent withclinical studies.

Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis