Literature DB >> 28268521

Creation of a whole brain short association bundle atlas using a hybrid approach.

M Guevara, C Roman, J Houenou, D Duclap, C Poupon, J-F Mangin, P Guevara.   

Abstract

The Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we expanded a previously developed method for the automatic creation of a whole brain SWM bundle atlas. The method is based on a hybrid approach. First a cortical parcellation is used to extract fibers connecting two regions. Then an intra-and inter-subject hierarchical clustering are applied to find well-defined SWM bundles reproducible across subjects. In addition to the fronto-parietal and insula regions of the left hemisphere, the analysis was extended to the temporal and occipital lobes, including all their internal regions, for both hemispheres. Validation steps are performed in order to test the robustness of the method and the reproducibility of the obtained bundles. First the method was applied to two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Then, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres. The atlas can be used in clinical studies for segmentation of the SWM bundles in new subjects, and measure DW values or complement functional data.

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Year:  2016        PMID: 28268521     DOI: 10.1109/EMBC.2016.7590899

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder.

Authors:  Ye Wu; Fan Zhang; Nikos Makris; Yuping Ning; Isaiah Norton; Shenglin She; Hongjun Peng; Yogesh Rathi; Yuanjing Feng; Huawang Wu; Lauren J O'Donnell
Journal:  Neuroimage       Date:  2018-07-06       Impact factor: 6.556

2.  Automatic oculomotor nerve identification based on data-driven fiber clustering.

Authors:  Jiahao Huang; Mengjun Li; Qingrun Zeng; Lei Xie; Jianzhong He; Ge Chen; Jiantao Liang; Mingchu Li; Yuanjing Feng
Journal:  Hum Brain Mapp       Date:  2022-01-29       Impact factor: 5.038

  2 in total

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