Short-to-Long Functional Connectivity Transfer via Structure-Aware Latent Diffusion

Resting-state functional connectivity (FC) is widely used tomodel brain organization and predict phenotypes, yet short fMRI acqui-sitions often yield unreliable estimates. We propose a Structure-AwareLatent Diffusion framework, SALD, to synthesize long-scan-like FC fromlimited observations. Our model conditions the generation process onshort-scan FC, structural connectivity (SC) as a lightweight anatomi-cal prior, and subject-level covariates. Crucially, in the Adolescent BrainCognitive Development Study (ABCD) cohort, our results suggest a fi-delity–utility tension in FC synthesis: likelihood-based diffusion trainingmay improve distributional fidelity while attenuating subtle inter-subjectvariation relevant to phenotype prediction. To address this, we introducea reward-guided Low-Rank Adaptation (LoRA) strategy that distills aguidance signal isolating the FC-congruent portion of phenotype vari-ance, steering generation toward preserving this signal while maintainingsample realism. Experiments on the ABCD cohort show that our methodbetter preserves phenotype-relevant signal in short-to-long FC synthesis,with the most pronounced improvements over short-scan baselines occur-ring in the low-scan-time regime across the evaluated phenotypes. Thesefindings suggest a promising direction for phenotype-aware short-to-longFC synthesis.