INTRODUCTION

The study of brain differences across Eastern and Western populations using neuroimaging provides important insights for understanding potential cultural and genetic influences on the process of cognition and mental health^[1–3]. Brain MRI atlases serve as essential tools for standardizing anatomical references in the assessment of brain structure and function across different populations^[4–6], and have been used to measure variations in brain morphology, cortical thickness, and functional connectivity between Eastern and Western people^{[1–3,7–11]}. Yet, there has been no comprehensive investigation into the comparison of white matter (WM) tracts between Eastern and Western populations.

This study presents a diffusion MRI (dMRI) tractography atlas that enables concurrent mapping of brain WM connections across Eastern and Western people. The atlas is created concurrently using the high-quality dMRI data provided in the Human Connectome Project (HCP)^[12] and the Chinese Human Connectome Project (CHCP)^[13], where each parcel includes streamlines from both populations. We assess population-wise differences between these two cohorts, leveraging the HCP and CHCP components within the curated atlas.

METHODS

Dataset: The HCP dataset includes dMRI data acquired from healthy adults living in the USA, and the CHCP dataset includes data from healthy adults living in China. We selected 153 subjects from each dataset with matched age and sex distributions (HCP: 24.2±1.4 years, 72 females and 81 males; CHCP: 23.9±2.4 years, 68 females and 85 males) to create the atlas.

Atlas creation: Figure-1 gives the method overview. First, dMRI Harmonization is performed to remove the inter-site variability resulting from scanner differences while preserving inter-subject variability. We use our novel machine learning algorithm^[14] to reconcile the raw dMRI signals across the two datasets. Second, whole-brain tractography is performed to reconstruct WM fibers from the entire brain. We use our advanced two-tensor Unscented Kalman filter (UKF) algorithm^[15,16], which accounts for crossing fibers and offers sensitive and reliable fiber tracking within a wide range of populations^[17–19]. Third, fiber clustering is performed to subdivide the tractography into fine-scale parcels. We use our well-established spectral clustering pipeline for simultaneous tractography streamline clustering across all HCP and CHCP subjects^[20,21]. This generates a fiber clustering atlas including 800 fiber clusters, with a total of 2.4 million streamlines. Last, anatomical atlas curation is performed to annotate each fiber cluster with an anatomical label. We use our ORG atlas built only using the HCP data as a reference^[18,22]. We calculate the distances between the clusters in the new atlas and those in the ORG atlas and then assign each new atlas cluster with the label of the closest ORG cluster. In total, our curated atlas contains an anatomical tract parcellation including 53 major anatomical WM tracts (Table-1).

Comparison between HCP and CHCP: Our atlas is created using the HCP and CHCP datasets concurrently so that each cluster includes streamlines from both populations. This enables an unbiased comparison of the corresponding WM structures between the populations. We separate each cluster and each tract in the atlas into two components according to the origin of the streamlines (HCP or CHCP). For each cluster and tract, we measure the number of streamlines and perform t-tests (with an FDR correction) to assess between-population differences.

RESULTS

Figure-2 gives a visualization of example anatomical tracts in the proposed atlas, as well as those belonging to the HCP and CHCP components, with a comparison to the ORG atlas. Overall, the shape of these anatomical tracts across the new atlas, the ORG atlas, the HCP component, and the CHP component are highly visually similar. In addition, we quantify the existence of streamlines belonging to a certain population and find that 100% of fiber clusters comprised streamlines from both populations. These results show a generally comparable WM parcellation between the HCP and CHCP populations.

For the cluster-level and tract-level comparison results, we find a large number of clusters and tracts with significant differences between the two populations. Across the 800 fiber clusters, 454 clusters (56.75%) have a significantly different number of streamlines, where 201 clusters in HCP have more streamlines and 253 clusters in CHCP have more streamlines. Among the 53 fiber tracts, 30 tracts (56.60%) have a significantly different number of streamlines, where 19 tracts in HCP have more streamlines and 11 tracts in CHCP have more streamlines.

DISCUSSION & CONCLUSION

This study presents a novel cross-population WM atlas for concurrent mapping of brain connections between Eastern and Western people. We perform a comparative assessment of the WM connections and identify widespread differences between the two populations in terms of streamline counts.

Acknowledgements

This work is in part supported by the National Natural Science Foundation of China (No. 62371107) and the National Institutes of Health (R01MH125860, R01MH119222, R01MH132610, R01NS125781).

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