Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data. (November 2018)

• Record Type: Journal Article
• Authors: Zhang, Tuo; Kong, Jun; Jing, Ke; Chen, Hanbo; Jiang, Xi; Li, Longchuan; Guo, Lei; Lu, Jianfeng; Hu, Xiaoping; Liu, Tianming
• Is Part Of: Computerized medical imaging and graphics. Volume 69(2018)
• Page Start: 9
• Page End: 20
• Publication Date: 2018-11
• Subjects: Tract-tracing -- Myelin stain -- dMRI -- Multi-Scale and multi-modal fusion -- Connectome; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.06.001
• Languages: English
• ISSNs: 0895-6111

Abstract

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 localC 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 localC 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.