Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling

Data-sharing in neuroimaging research alleviates the cost and time constraints of collecting large sample sizes at a single location, aiding the development of foundational models with deep learning. Yet, challenges to data sharing, such as data privacy, ownership, and regulatory compliance, exist. Federated learning enables collaborative training across sites while addressing many of these concerns. Connectomes are a promising data type for data sharing and creating foundational models. Yet, the field lacks a single, standardized atlas for constructing connectomes. Connectomes are incomparable between these atlases, limiting the utility of connectomes in federated learning. Further, fully reprocessing raw data in a single pipeline is not a solution when sample sizes range in the 10–100’s of thousands. Dedicated frameworks are needed to efficiently harmonize previously processed connectomes from various atlases for federated learning. We present Federate Learning for Existing Connectomes from Heterogeneous Atlases (FLECHA) to addresses these challenges. FLECHA learns a mapping between atlas spaces on an independent dataset, enabling the transformation of connectomes to a common target space before federated learning. We assess FLECHA using functional and structural connectomes processed with five atlases from the Human Connectome Project. Our results show improved prediction performance for FLECHA. They also demonstrate the potential of FLECHA to generalize connectome-based models across diverse silos, potentially enhancing the application of deep learning in neuroimaging.