Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.

译文

:最近,大量研究已将基于扩散张量成像(DTI)数据的牵引术作为一种工具来研究结构连接体。尽管DTI在提供独特的3D神经解剖学信息方面取得了巨大的成功,但它是一种间接的观察方法,其分辨率和准确性有限,其可靠性仍不清楚。因此,必须回答这个基本问题:DTI体层摄影术在构建大规模连接体中的可靠性如何?为了回答这个问题,我们以艾伦小鼠脑部连接图集(AMCA)发布的1772年小鼠大脑神经元追踪数据为基础,来评估DTI显像在推断白质纤维通路和区域间性能方面的表现。连接。通过与跟踪数据进行比较,已经首次在神经影像学领域中评估了全脑DTI描记法在构建大型连接体中的性能。我们的研究结果表明,只有优化的束线照相参数和适当的脑部碎裂方案规模,DTI才能产生相对可靠的纤维通路和大规模的连接组。同时,在优化的DTI影像学检查结果中也发现了相当多的错误,我们认为通过开发更好的DTI影像学检查方法可以潜在地减轻这种错误。在这种情况下,我们的框架可以用作可靠且定量的测试平台,以识别出物镜检查结果中的错误,这将有助于这种新颖的物镜检查算法的开发和最佳参数的选择。

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