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Graphical abstract (a) Joint statistic matrix color-coded by TPs, FPs, FNs and TNs, which were obtained from the comparison between the connective matrices derived from tract-tracing database and dMRI data. The FVE brain map, the optimal brain map (out of the three brain maps in the present work) for global connectome, was used to construct connective matrices. The dMRI tractography connective matrix was constructed under the optimal tractography parameters (QA=0.2, Angle=70°) and binarized by the connective strength threshold corresponding to the maximal Youden index. Those parameters also apply to (b)–(i); (b) dMRI tractography fibers corresponding to the TP connections in (a). Fibers corresponding to the same connection have the same color; (c) The connective matrix color-coded by the average local C values on the dMRI fiber tracts corresponding to the TP connections. Pink block highlights those connecting brain sites in frontal lobe; (d) Top 1% connections in term of their large average local C values. Color blocks highlight some of these connections, and (e)-(i) show the corresponding dMRI fibers as well as the brain sites they connect. Dashed blocks indicate that those connections can find their symmetry counterparts in regions the solid blocks highlight. Interpretations of those blocks are found in the text. Highlights • Construction of a large-scale dMRI connectome was evaluated by gold-standard data: neuron tracing data and myelin stain data. • The presented framework is open for more data modalities and tractography models to be easily integrated in this framework. • With optimized tractography parameters and scales, we could substantially trust dMRI with more confidence. Abstract Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains. [ABSTRACT FROM AUTHOR]

For decades, seeking common, consistent and corresponding anatomical/functional regions across individual brains via cortical parcellation has been a longstanding challenging problem. In our opinion, two major barriers to solve this problem are determining meaningful cortical boundaries that segregate homogeneous regions and establishing correspondences among parcellated regions of multiple brains. To establish a corresponding system across subjects, we recently developed the Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system which possesses group-wise consistent white matter fiber connection patterns across individuals and thus provides a dense map of corresponding cortical landmarks. Despite this useful property, however, the DICCCOL landmarks are still far from covering the whole cerebral cortex and do not provide clear structural/functional cortical boundaries. To address the above limitation while leveraging the advantage of DICCCOL, in this paper, we present a novel approach for group-wise consistent parcellation of the cerebral cortex via a hierarchical scheme. In each hierarchical level, DICCCOLs are used as corresponding samples to automatically determine the cluster number so that other cortical surface vertices are iteratively classified into corresponding clusters across subjects within a group-wise classification framework . Experimental results showed that this approach can achieve consistent fine-granularity cortical parcellation with intrinsically-established structural correspondences across individual brains. Besides, comparisons with resting-state and task-based fMRI datasets demonstrated that the group-wise parcellation boundaries segregate functionally homogeneous areas. [ABSTRACT FROM AUTHOR]