Cultural differences and biological diversity play important roles in shaping human brain structure and function. To date, most large-scale multimodal neuroimaging datasets have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions. The Chinese Human Connectome Project (CHCP) aims to address these resource and knowledge gaps by acquiring imaging, genetic and behavioral data from a large sample of participants living in an Eastern culture. The CHCP collected multimodal neuroimaging data from healthy Chinese adults using a protocol comparable to that of the Human Connectome Project. Comparisons between the CHCP and Human Connectome Project revealed both commonalities and distinctions in brain structure, function and connectivity. The corresponding large-scale brain parcellations were highly reproducible across the two datasets, with the language processing task showing the largest differences. The CHCP dataset is publicly available in an effort to facilitate transcultural and cross-ethnic brain–mind studies.
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All CHCP data (including all brain imaging modalities: T1W/T2W structure images, DWI, resting-state fMRI and task-evoked fMRI; essential behavioral and demographic information data) are available on the Science Data Bank website at https://doi.org/10.11922/sciencedb.01374 and the CHCP website at https://www.Chinese-HCP.cn. Data sharing plan for nonimaging data (including physiological data, tfMRI onset/offset and duration, full behavioral data and genetic data) is to make them available as soon as feasible via quarterly release on the Science Data Bank and CHCP websites, after careful data processing, quality control and privacy protection. HCP data were provided by the Human Connectome Project (https://humanconnectome.org/), WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH institutes and centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
All the codes used in this study are available at the GitHub repository (https://github.com/ChineseHCP/CHCP).
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This work was supported by the National Natural Scientific Foundation of China (81790650, 81790651, 81727808, 81627901 to J.H.G. and 31771253 to J.G.); the National Science and Technology Innovation 2030 Program (2021ZD0200500 to J.H.G.); the Beijing Municipal Science and Technology Commission (Z171100000117012 and Z181100001518003 to J.H.G.); the Collaborative Research Fund of the Chinese Institute for Brain Research (2020-NKX-PT-02 to J.H.G.) and Changping Laboratory. J.H.G. was supported by Shenzhen Science and Technology Research Funding Program (JCYJ20200109144801736). X.N.Z. receives support from the start-up funds for Leading Talents at Beijing Normal University and the National Basic Science Data Center, Chinese Data-sharing Warehouse for In-vivo Imaging Brain (NBSDC-DB-15). We thank the National Center for Protein Sciences at Peking University for assistance with data acquisition. We thank the Science Data Bank for facilitation on data sharing.
The authors declare no competing interests.
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Extended Data Fig. 1 Reliability in tfMRI activation maps measured by ICC for both the CHCP and HCP data.
The image-wise intraclass correlation coefficient (ICC) values in seven tasks were calculated across the two different phase-encoding runs for both the CHCP (left column) and HCP (right column) data. Together with the tfMRI activation maps shown in Fig. 4a, these results suggested that the brain areas which were significantly activated in tfMRI tend to show higher ICC values than brain areas which were not activated.
Extended Data Fig. 2 Maps of two-sample t-test and effect size on tfMRI activation for CHCP vs. HCP.
Sample sizes were 140 in both the CHCP and HCP groups. a, Group-level difference of the CHCP vs. HCP in the brain activations for the seven tasks. The white contours represent the area with a threshold (z ≤ −5.13 and z ≥ + 5.13, P < 0.05 vertex-level, two-sided, Bonferroni corrected) across all 91,282 grayordinates. b, The between-group effect size of the CHCP vs. HCP in the seven tasks. The effect size was measured by the index of Cohen’s d, and the white contours represent the areas with Cohen’s d values larger than 0.5 or smaller than −0.5.
Extended Data Fig. 3 Distribution of the activation intensity in tasks from the CHCP and HCP data.
Sample sizes were 140 in both the CHCP and HCP groups. a, Gambling task. No significant differences were found in distributions between the CHCP and HCP datasets with a threshold of two-sided P < 0.001 FDR corrected. b, Relational task. No significant differences were found. c, Social task. The difference in the distributions of the ventral attention network was marginally significant between groups (P = 0.003, two-sided, FDR corrected). d, Emotion task. The distributions of the visual and dorsal attention networks were significantly different between the CHCP and HCP (P < 0.001, two-sided, FDR corrected). e, Working memory task. The visual, dorsal attention, frontoparietal and somatomotor networks were significantly different between groups (P < 0.001, two-sided, FDR corrected). The results for the motor and language tasks are shown in Fig. 4b. The density refers to the probability of the subject number under a certain contrast value, and all of the statistics shown in this figure were based on the K-S test with FDR correction.
Extended Data Fig. 4 Volume-based activation maps in seven tasks from the CHCP and HCP data.
For volume-based group-level analyses, to estimate the average effects across runs for each subject, FEAT was first used, and then, FMRIB’s local analysis of mixed effects (FLAME) tools were employed to estimate the average effects of interest for each group. The group maps are displayed with a threshold of z =± 5 (P < 10−5 voxel-level, two-sided, uncorrected). The left and right columns show the results of the activation maps for the seven tasks from the CHCP and HCP datasets, respectively.
Extended Data Fig. 5 Topographic difference on the resting-state 7-network atlases between CHCP and HCP groups.
Sample sizes were 140 in both the CHCP and HCP groups. The topographic difference on 7-network atlases across CHCP and HCP were shown with parcel size correction (left) and without parcel size correction (right) in the left and right hemispheres (two-sided Pperm < 0.001 for each value of 1-Dice coefficient, permutation test between CHCP and HCP, FDR corrected).
Extended Data Fig. 6 Topographic difference on the dMRI brainnetome atlas across the CHCP and HCP data.
Sample sizes were 140 in both the CHCP and HCP groups. The distribution of variability (1-Dice coefficient) across brain regions between the CHCP and HCP brainnetome atlases were shown with parcel size correction (left) and without parcel size correction (right).
Extended Data Fig. 7 Test for reliability of the brainnetome atlas for the CHCP and HCP data.
The brainnetome atlases are highly consistent across the “Parcellation” sample (n = 110) and “Validation” sample (n = 110) for both groups. A total of 89.1% and 88.9% of the vertices were assigned to the same regions across the “Parcellation” and “Replication” brainnetome atlases for the CHCP and HCP subsets, respectively.
Extended Data Fig. 8 Test for reliability of the 7-network atlas for the CHCP and HCP data.
The 7-network estimates are highly consistent across the “Parcellation” sample (n = 110) and “Validation” sample (n = 110) for both groups. A total of 95.2% and 95.0% of the vertices were assigned to the same networks across the “Parcellation” and “Replication” 7-network atlases for the CHCP and HCP subsets, respectively.
Extended Data Fig. 9 Distributions of the accuracy measures from behavioral data during the task fMRI for the CHCP and HCP subsets.
The distributions of accuracy are shown in histograms. The mean response accuracy values were calculated for each subset (relational: CHCP: 77.63%, HCP: 77.49%; social: CHCP: 80.09%, HCP: 90.49%; emotion: CHCP: 95.82%, HCP: 97.52%; working memory: CHCP: 90.47%, HCP: 88.25%; language: CHCP: 90.31%, HCP: 89.04%).
Supplementary Methods, Tables 1–7 and References 1–12.
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Ge, J., Yang, G., Han, M. et al. Increasing diversity in connectomics with the Chinese Human Connectome Project. Nat Neurosci 26, 163–172 (2023). https://doi.org/10.1038/s41593-022-01215-1