Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts

基于连接组的空间统计方法能够对不同队列的人类连接组进行大规模群体分析

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Abstract

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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