  Brain network analysis has emerged as pivotal method for gaining a deeper
understanding of brain functions and disease mechanisms. Despite the existence
of various network construction approaches, shortcomings persist in the
learning of correlations between structural and functional brain imaging data.
In light of this, we introduce a novel method called BrainNetDiff, which
combines a multi-head Transformer encoder to extract relevant features from
fMRI time series and integrates a conditional latent diffusion model for brain
network generation. Leveraging a conditional prompt and a fusion attention
mechanism, this method significantly improves the accuracy and stability of
brain network generation. To the best of our knowledge, this represents the
first framework that employs diffusion for the fusion of the multimodal brain
imaging and brain network generation from images to graphs. We validate
applicability of this framework in the construction of brain network across
healthy and neurologically impaired cohorts using the authentic dataset.
Experimental results vividly demonstrate the significant effectiveness of the
proposed method across the downstream disease classification tasks. These
findings convincingly emphasize the prospective value in the field of brain
network research, particularly its key significance in neuroimaging analysis
and disease diagnosis. This research provides a valuable reference for the
processing of multimodal brain imaging data and introduces a novel, efficient
solution to the field of neuroimaging.
