4.8 Article

Connectomics-Based Analysis of Information Flow in the Drosophila Brain

Journal

CURRENT BIOLOGY
Volume 25, Issue 10, Pages 1249-1258

Publisher

CELL PRESS
DOI: 10.1016/j.cub.2015.03.021

Keywords

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Funding

  1. National Science Council in Taiwan [NSC-100-2112-M-029-001-MY3, NSC-101-2311-B-007-008-MY3]
  2. Ministry of Science and Technology in Taiwan [MOST-103-2633-B-007-001, MOST-103-2221-E-492-023]
  3. NSF [CIF-BCSP-1212778]
  4. Mathers Foundation
  5. Ministry of Science and Technology
  6. Ministry of Education in Taiwan
  7. Direct For Computer & Info Scie & Enginr
  8. Division of Computing and Communication Foundations [1212778] Funding Source: National Science Foundation

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Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra-and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain.

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