BrainCSD: a hierarchical consistency-driven MoE framework for connectome synthesis and multitask brain trait prediction

Functional and structural connectomes (FC/SC) are informative biomarkers for brain disorders, yet their clinical use remains limited by acquisition cost, lengthy connectome construction pipelines, and missing modalities in real-world neuroimaging studies. To address these challenges, we introduce BrainCSD (Brain Connectivity Synthesis and Decode), a hierarchical Mixture-of-Experts (MoE) framework for connectome-oriented representation learning and missing-modality connectome synthesis. BrainCSD does not require precomputed FC/SC matrices as direct inputs, but learns neuroanatomically constrained representations from fMRI or dMRI inputs with template- and atlas-based anatomical correspondence.

Its architecture incorporates three consistency constraints: ROI-level alignment, encoding-level consistency, and network-level refinement. We evaluate BrainCSD across 7248 participants, 22 imaging sites, 9 datasets, 5 neurological and neurodevelopmental disorders, and 15 downstream tasks, including connectome synthesis, disease classification, brain-age prediction, and MMSE estimation. BrainCSD-generated connectomes provide useful complementary information for downstream diagnostic and trait prediction tasks.

Additional balanced metrics, graph-topological analyses, and subgroup evaluations further support the potential utility of BrainCSD for connectome-based analysis. Nevertheless, the current results are based primarily on within-dataset validation, and larger prospective external validation, uncertainty calibration, and clinical expert assessment are required before any clinical use. This work was supported by the National Natural Science Foundation of China (Grants 62306089, 32361143787, and 82102032), the Key Project of Basic Research of Shenzhen (Grant JCYJ20200109113603854), the Shenzhen Science and Technology Program (Grants ZDSYS20230626091203008), and the Guangxi Natural Science Foundation (Grant 2023GXNS-FBA026073).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, China Xiongri Shen, Jiaqi Wang, Yi Zhong, Leilei Zhao & Liling Li School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, China The Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China Department of Automation, Tsinghua University, Beijing, China Center for Biomedical Information Technology, The Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China School of Biomedical Engineering, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China Center for Language, Intelligence and Machines, Shenzhen Loop Area Institute, Shenzhen, China The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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