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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 studies mainly focus on using empirical functional timeseries to calculate effective connections, which may not comprehensivelycapture 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 mapfunctional magnetic resonance imaging (fMRI) into effective connectivity formild cognitive impairment (MCI) analysis. To be specific, the denoisingframework leverages a conditional diffusion process that progressivelytranslates the noise and conditioning fMRI to effective connectivity in anend-to-end manner. To ensure reverse diffusion quality and diversity, themulti-resolution enhanced transformer generator is designed to extract localand global spatiotemporal features. Furthermore, a multi-scale diffusivetransformer discriminator is devised to capture the temporal patterns atdifferent scales for generation stability. Evaluations of the ADNI datasetsdemonstrate the feasibility and efficacy of the proposed model. The proposedmodel not only achieves superior prediction performance compared with othercompeting methods but also identifies MCI-related causal connections that areconsistent with clinical studies.

Multi-resolution Spatiotemporal Enhanced Transformer Denoising with  Functional Diffusive GANs for Constructing Brain Effective Connectivity in  MCI analysis