biostudies-literatureTian XNIA NIH HHS7416https://www.ebi.ac.uk/biostudies/studies/S-EPMC9715556biostudies-literatureUnknown13(1)Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generaNature communicationsAn integrated resource for functional and structural connectivity of the marmoset brain.PMC9715556R24 AG073190Jiang HSilva ACLiang ZFeng FPerl YSMajka PTong CDeco GTian XRosa MGPChen YGlen DLiu CSzczupak DYen CCfalseAn integrated resource for functional and structural connectivity of the marmoset brain.Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation genera2022-01-01T00:00:00Z2022 Dec2025-04-19T03:32:15.317Z2025-04-07T13:41:54.882ZS-EPMC97155563645655810.1038/s41467-022-35197-2