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Connectome Literature Review (Japanese Abstract)

[!NOTE] For the latest implementation status, please refer to Functional Implementation Status (Remaining Functionality).

Purpose

  • We will summarize representative studies on the structural connectome of neurons and synapses, and summarize the objectives, methods, major discoveries, and limitations of each study in Japanese.

Representative studies (short summary)

  • White et al., 1986 — Full connectivity diagram of Caenorhabditis elegans
  • Overview: A classic study mapping all neuronal connections (synapses) in C. elegans. The wiring diagram for the entire individual is presented first.

  • Significance: Proof of concept for a complete connectome. Building the foundation for the analysis of the relationship between neural circuits and behavior.

  • Bock et al., 2011 — Mouse visual cortex: integrating function and EM reconstruction

  • Overview: Combining in vivo electrophysiology/calcium imaging with high-resolution reconstruction using EM to compare the function and structure of the same neuron group.

  • Significance: A pioneering example of linking functional data and structural connectome.

  • Kasthuri et al., 2015 — Reconstruction of neocortex region by saturated EM

  • Overview: We reconstructed a large-scale EM volume of a small neocortex in a saturated manner, showing the diversity of synaptic density and fine wiring.

  • Significance: Demonstration of high-density synapse environment and clarification of automation needs.

  • Januszewski et al., 2018 — Flood-Filling Networks (FFN)

  • Overview: High-precision automatic neuron reconstruction method (FFN) using neural networks. Significantly reduces manual proofreading.

  • Significance: Significant algorithmic improvements to advance automated processing of large-scale EM data.

  • Ronneberger et al., 2015 — U-Net

  • Overview: Encoder-decoder type CNN for medical images. Widely applied to bioimaging segmentation.

  • Significance: Frequently used as a basic technology for synapse detection/segmentation.

  • Wickersham et al., 2007 — Monosynaptic restricted lavatory tracing

  • Overview: A method for tracing monosynaptic connections using genetically modified Labis virus.

  • Significance: Effective for linking functions and wiring, such as identifying input sources for functional circuits.

  • Chung et al., 2013 (CLARITY), Chen et al., 2015 (Expansion Microscopy), etc.

  • Overview: A group of methods that enable multiple molecular labeling and optical imaging over large areas using tissue transparency and physical expansion.
  • Significance: Bridge between large-scale molecular imaging and structural analysis using optical methods.

Representative dataset projects

  • C. elegans connectome (all individuals)
  • Janelia FlyEM (hemibrain, etc.)
  • MICrONS (Mouse Visual Cortex: Function + EM)
  • Human Connectome Project (diffusion MRI-based large-scale human structural network)

Common workflow (outline)

  1. Sample preparation (fixation, staining, resin embedding or transparency)
  2. Imaging (EM or optical)
  3. Image preprocessing (correction/cropping)
  4. Segmentation (deep learning model examples: U-Net, FFN)
  5. Synapse detection and connection graph extraction
  6. Proofreading and adding metadata
  7. Network analysis/visualization

Main issues (summary)

  • Scale: Expansion to mouse whole brain/human whole brain has realistic cost and computational complexity.
  • Dissociation between structure and function: Static connectomes do not necessarily reflect short-term plasticity or state-dependent connections.
  • Limitations of automation: segmentation errors, false positives/false negatives in synapse identification.
  • Data management: PB grade storage, need for standardized metadata.
  1. When you specify a target of interest (model organism, brain region, scale), a detailed bibliography list and illustration candidates will be created.
  2. If necessary, include a short figure explanation (English source + DOI) for each paper.

References (starting point): - White et al., 1986. The structure of the nervous system of C. elegans. - Bock et al., 2011. Nature. - Kasthuri et al., 2015. Cell. - Januszewski et al., 2018. Nat Methods. - Ronneberger et al., 2015. MICCAI. - Wickersham et al., 2007. Neuron. - Chung et al., 2013. Nature (CLARITY). - Chen et al., 2015. Science (Expansion Microscopy).