Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts
Li, T.; Wang, X.; Cole, M.; Sun, Z.; Jiang, Z.; Qian, X.; Gao, S.; Luo, T.; Descoteaux, M.; Stein, J. L.; Wang, X.; Nichols, T. E.; Zhang, H.; Zhang, Z.; Zhu, H.
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Large-scale population analyses of structural connectome organization remain challenging because of cross-subject alignment, pathway interpretability and computational burden. No widely adopted standard exists for systematic evaluation across processing methods. We developed connectome-based spatial statistics (CBSS), a scalable framework for anatomically aligned and functionally informed quantification of white-matter microstructure that yields atlas-defined pathways organized into 13 functional networks. Using data from 56,510 UK Biobank participants together with five independent lifespan cohorts, we evaluated the streamline-, voxel- and network-level measures in the aspects of reliability, heritability, structure-function coupling, cognitive and behavioral prediction, brain aging patterns and lifespan trajectories across cohorts. The systematic evaluation workflow compares population-level white-matter representations across methods, spatial scales, tasks and datasets. The results support CBSS as a common connectome reference for large-scale, cross-cohort diffusion MRI studies.
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