TY - GEN
T1 - Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data
AU - Chen, Hanbo
AU - Zhao, Yu
AU - Zhang, Tuo
AU - Zhang, Hongmiao
AU - Kuang, Hui
AU - Li, Meng
AU - Tsien, Joe Z.
AU - Liu, Tianming
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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 s ame 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.
AB - 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 s ame 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.
KW - brain mapping
KW - DTI
KW - Multi-scale connectome
KW - neuron tracer
UR - http://www.scopus.com/inward/record.url?scp=84906969559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906969559&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10443-0_35
DO - 10.1007/978-3-319-10443-0_35
M3 - Conference contribution
C2 - 25320809
SN - 9783319104423
VL - 17
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 273
EP - 280
BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -