The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Existing deep learning solutions suffer from three major limitations. First, they cannot generalize to multi-trajectory prediction tasks, where each graph trajectory corresponds to a particular imaging modality or connectivity type (e.g., T1-w MRI). Second, existing models require extensive training datasets to achieve satisfactory performance which are often challenging to obtain. Third, they do not efficiently utilize incomplete time series data. To address these limitations, we introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets. As a result, we enhance the performance of each hospital's local generative model, while preserving data privacy. The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations. Our comprehensive experimental results showed the outperformance of FedGmTE-Net++ in brain multi-trajectory prediction from a single baseline graph in comparison with benchmark methods.

翻译：婴儿出生后第一年内脑网络复杂演化的理解对于识别早期脑连接性发展的动态至关重要。现有深度学习解决方案存在三大局限性：首先，它们无法泛化到多轨迹预测任务，其中每个图轨迹对应特定成像模态或连接类型（例如T1加权MRI）；其次，现有模型需要大量训练数据才能达到满意性能，而这类数据往往难以获取；第三，它们未能有效利用不完整的时间序列数据。为应对这些局限，我们提出FedGmTE-Net++——一种基于联邦学习的图多轨迹演化网络。通过联邦学习的优势，我们聚合了具有有限数据集的多个医院的局部学习成果，从而在保护数据隐私的同时提升每个医院局部生成模型的性能。FedGmTE-Net++的三项关键创新包括：(i) 提出首个专为数据稀缺环境下脑多轨迹演化预测设计的联邦学习框架，(ii) 在局部目标函数中引入辅助正则化项，以利用演化轨迹内所有纵向脑连接性并最大化数据利用率，(iii) 引入两阶段插补过程：先基于KNN进行初步预补全，再通过回归器改善相似度分数以细化插补结果。综合实验结果表明，FedGmTE-Net++在从单一基线图进行脑多轨迹预测方面优于基准方法。