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A computational approach towards the microscale mouse brain connectome from the mesoscale
Zhang, Tielin1; Zeng, Yi1,2,3; Xu, Bo1,2,3
AbstractThe wiring diagram of the mouse brain presents an indispensable foundation for the research on basic and applied neurobiology. It is also essential as a structural foundation for computational simulation of the brain. Different scales of the connectome give us different hints and clues to understand the functions of the nervous system and how they process information. However, compared to the macroscale and most recent mesoscale mouse brain connectome studies, there is no complete whole brain microscale connectome available because of the scalability and accuracy of automatic recognition techniques. Different scales of the connectivity data are comprehensive descriptions of the whole brain at different levels of details. Hence connectivity results from a neighborhood scale may help to predict each other. Here we report a computational approach to bring the mesoscale connectome a step forward towards the microscale from the perspective of neuron, synapse and network motifs distribution by the connectivity data at the mesoscale and some facts from the anatomical experiments at the microscale. These attempts make a step forward towards the efforts of microscale mouse brain connectome given the fact that the detailed microscale connectome results are still far to be produced due to the limitation of current nano-scale 3-D reconstruction techniques. The generated microscale mouse brain will play a key role on the understanding of the behavioral and cognitive processes of the mouse brain. In this paper, the conversion method which could get the approximate number of neurons and synapses in microscale is proposed and tested in sub-regions of Hippocampal Formation (HF), and is generalized to the whole brain. As a step forward towards understanding the microscale connectome, we propose a microscale motif prediction model to generate understanding on the microscale structure of different brain region from network motif perspective. Correlation analysis shows that the predicted motif distribution is very relevant to the real anatomical brain data at microscale.
KeywordMicroscale Mouse Brain Connectome Brain Connectivity Atlas Motif Distribution Synaptic Degree Distribution Analysis
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Funding OrganizationChinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z161100000216124)
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000402301000004
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Tielin,Zeng, Yi,Xu, Bo. A computational approach towards the microscale mouse brain connectome from the mesoscale[J]. JOURNAL OF INTEGRATIVE NEUROSCIENCE,2017,16(3):291-306.
APA Zhang, Tielin,Zeng, Yi,&Xu, Bo.(2017).A computational approach towards the microscale mouse brain connectome from the mesoscale.JOURNAL OF INTEGRATIVE NEUROSCIENCE,16(3),291-306.
MLA Zhang, Tielin,et al."A computational approach towards the microscale mouse brain connectome from the mesoscale".JOURNAL OF INTEGRATIVE NEUROSCIENCE 16.3(2017):291-306.
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