Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

Hanbo Chen, Tao Liu, Yu Zhao, Tuo Zhang, Yujie Li, Meng Li, Hongmiao Zhang, Hui Kuang, Lei Guo, Joseph Zhuo Tsien, Tianming Liu

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)202-213
Number of pages12
StatePublished - 2015

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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