BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model

TL;DR

BrainNetDiff introduces a novel multimodal diffusion model combining Transformer encoders and latent diffusion for accurate brain network generation from multimodal imaging data, enhancing neuroimaging analysis and disease diagnosis.

Contribution

This work is the first to employ diffusion models for fusing multimodal brain imaging data and generating brain networks from images to graphs.

Findings

Improved accuracy and stability in brain network generation.

Effective in classifying healthy and impaired cohorts.

Demonstrates potential in neuroimaging and disease diagnosis.

Abstract

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.