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.

通过引入一种名为 BrainNetDiff 的新方法，结合多头变换器编码器从 fMRI 时间序列中提取相关特征，并结合条件潜在扩散模型进行大脑网络生成，从图像到图形的融合，显著提高了大脑网络生成的准确性和稳定性，验证了该框架在构建健康和神经学受损队列中的大脑网络方面的适用性，实验证明了该方法在下游疾病分类任务中的显著效果，突出了大脑网络研究的前景价值，特别是在神经影像学分析和疾病诊断中的关键意义，为多模态大脑影像数据处理提供了有价值的参考，并为神经影像领域引入了一种新的高效解决方案。