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2014 | OriginalPaper | Chapter

Construct and Assess Multimodal Mouse Brain Connectomes via Joint Modeling of Multi-scale DTI and Neuron Tracer Data

Authors : Hanbo Chen, Yu Zhao, Tuo Zhang, Hongmiao Zhang, Hui Kuang, Meng Li, Joe Z. Tsien, Tianming Liu

Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

Publisher: Springer International Publishing

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Mapping the neuronal wiring diagrams in the brain at multiple spatial scales has been one of the major brain mapping objectives. Macro-scale medical imaging modalities such as diffusion tensor imaging (DTI) and meso-scale biological imaging such as serial two-photon tomography have emerged as the prominent tools to reveal structural connectivity patterns at multiple scales. However, a significant gap that whether/how DTI data and microscopic data are correlated with each other for the same species of mammalian brains, e.g., mouse brains, has been rarely explored. To bridge this knowledge gap, this work aims to construct multi-modal mouse brain connectomes via joint modeling of macro-scale DTI data and meso-scale neuronal tracing data. Specifically, the high-resolution DTI data and its streamline tractography result are mapped to the Allen Mouse Brain Atlas, in which the high-density axonal projections were already mapped by microscopic serial two-photon tomography. Then, multi-modal connectomes were constructed and the multi-view spectral clustering method is employed to assess consistent and discrepant connectivity patterns across the multi-scale multi-modal connectomes. Experimental results demonstrated the importance of fusing multimodal, multi-scale imaging modalities for structural connectivity and connectome mapping.

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Metadata
Title
Construct and Assess Multimodal Mouse Brain Connectomes via Joint Modeling of Multi-scale DTI and Neuron Tracer Data
Authors
Hanbo Chen
Yu Zhao
Tuo Zhang
Hongmiao Zhang
Hui Kuang
Meng Li
Joe Z. Tsien
Tianming Liu
Copyright Year
2014
Publisher
Springer International Publishing
DOI
https://doi.org/10.1007/978-3-319-10443-0_35

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