Neural circuit reconstruction

Neural circuit reconstruction is the reconstruction of the detailed circuitry of the nervous system (or a portion of the nervous system) of an animal. It is sometimes called EM reconstruction since the main method used is the electron microscope (EM).[1] This field is a close relative of reverse engineering of human-made devices, and is part of the field of connectomics, which in turn is a sub-field of neuroanatomy.

Model systems

Some of the model systems used for circuit reconstruction are the fruit fly,[1] the mouse,[2] and the nematode C. elegans.[3]

Sample preparation

The sample must be fixed, stained, and embedded in plastic.[4]

Imaging

The sample may be cut into thin slices with a microtome, then imaged using transmission electron microscopy. Alternatively, the sample may be imaged with a scanning electron microscope, then the surface abraded using a focused ion beam, or trimmed using an in-microscope microtome. Then the sample is re-imaged, and the process repeated until the desired volume is processed.[5]

Image processing

The first step is to align the individual images into a coherent three dimensional volume.

The volume is then annotated using one of two main methods. The first manually identifies the skeletons of each neurite.[6] The second techniques uses computer vision software to identify voxels belonging to the same neuron. The second technique uses Machine Learning software to identify voxels belonging to the same neuron. Popular approaches are U-Net architectures to predict voxel-wise affinities paired with a watershed segmentation[7] and flood-filling networks.[8] These approaches produce an over-segmentation which can be manually or automatically agglomerated to correctly represent a neuron. Even for automatically agglomerated segmentations, large manual proofreading efforts are employed for highest accuracy.[9]

Notable examples

  • The connectome of C. elegans was the seminal work in this field.[3] This circuit was obtained with great effort using manually cut sections and purely manual annotation on photographic film. For many years this was the only circuit reconstruction available.
  • The central brain of the fruit fly Drosophila Melanogaster was released in 2020.[10] This data release introduced the first on-line tools to query the connectome.
  • The Human Cortex H01, released in 2021, is a 1.4 petabyte volume of a small sample of human brain tissue imaged at nanoscale-resolution by serial section electron microscopy, reconstructed and annotated by automated computational techniques, and analyzed for preliminary insights into the structure of human cortex.[11]
  • In their 2022 study “Connectomic comparison of mouse and human cortex”, the researchers reconstructed 9 connectomes across species: Datasets of Mouse, Macaque and Human.[12]

Querying the connectome

Connectomes of higher organism's brains requires considerable data. For the fruit fly, for example, roughly 10 terabytes of image data are processed, by humans and computers, to generate several gigabyte of connectome data. Easy interaction with this data requires an interactive query interface, where researchers can look at the portion of data they are interested in without downloading the whole data set, and without specific training. A specific example of this technology is the NeuPrint interface to the connectomes generate at HHMI.[13] This mimics the infrastructure of genetics, where interactive query tools such as BLAST are normally used to look at genes of interest, which for most research comprise only a small portion of the genome.

Limitations and future work

Understanding the detailed operation of the reconstructed networks also requires knowledge of gap junctions (hard to see with existing techniques), the identity of neurotransmitters and the locations and identities of receptors. In addition, neuromodulators can diffuse across large distances and still strongly affect function.[14] Currently these features must be obtained through other techniques. Expansion microscopy may provide an alternative method.

References

  1. ^ a b Chklovskii, Dmitri B; Vitaladevuni, Shiv; Scheffer, Louis K (2010). "Semi-automated reconstruction of neural circuits using electron microscopy". Current Opinion in Neurobiology. 20 (5): 667–75. doi:10.1016/j.conb.2010.08.002. PMID 20833533. S2CID 206950616.
  2. ^ Bock, Davi D.; Lee, Wei-Chung Allen; Kerlin, Aaron M.; Andermann, Mark L.; Hood, Greg; Wetzel, Arthur W.; Yurgenson, Sergey; Soucy, Edward R.; et al. (2011). "Network anatomy and in vivo physiology of visual cortical neurons". Nature. 471 (7337): 177–82. Bibcode:2011Natur.471..177B. doi:10.1038/nature09802. PMC 3095821. PMID 21390124.
  3. ^ a b White, John G.; Southgate, Eileen; Nichol Thomson, J.; Brenner, Sydney (1986). "The structure of the nervous system of the nematode Caenorhabditis elegans". Philos Trans R Soc Lond B Biol Sci. 314 (1165): 1–340. Bibcode:1986RSPTB.314....1W. doi:10.1098/rstb.1986.0056. PMID 22462104.
  4. ^ Hayat, M. Arif (2000). Principles and techniques of scanning electron microscopy. Biological applications, fourth edition. Cambridge University Press. ISBN 978-0521632874.
  5. ^ Briggman, Kevin L.; Davi D. Bock (2012). "Volume electron microscopy for neuronal circuit reconstruction". Current Opinion in Neurobiology. 22 (1): 154–161. doi:10.1016/j.conb.2011.10.022. PMID 22119321. S2CID 22657332.
  6. ^ Saalfeld, Stephan, Albert Cardona, Volker Hartenstein, and Pavel Tomančák (2009). "CATMAID: collaborative annotation toolkit for massive amounts of image data". Bioinformatics. 25 (15): 1984–1986. doi:10.1093/bioinformatics/btp266. PMC 2712332. PMID 19376822.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ^ "Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction". scholar.google.com. Retrieved 2024-02-14.
  8. ^ Januszewski, Michał; Kornfeld, Jörgen; Li, Peter H.; Pope, Art; Blakely, Tim; Lindsey, Larry; Maitin-Shepard, Jeremy; Tyka, Mike; Denk, Winfried; Jain, Viren (August 2018). "High-precision automated reconstruction of neurons with flood-filling networks". Nature Methods. 15 (8): 605–610. doi:10.1038/s41592-018-0049-4. ISSN 1548-7105.
  9. ^ Chklovskii, Dmitri B., Shiv Vitaladevuni, and Louis K. Scheffer. (2010). "Semi-automated reconstruction of neural circuits using electron microscopy". Current Opinion in Neurobiology. 20 (5): 667–675. doi:10.1016/j.conb.2010.08.002. PMID 20833533. S2CID 206950616.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  10. ^ Jason Pipkin (Oct 8, 2020). "Connectomes: Mapping the mind of a fly". Elife Sciences.
  11. ^ Shapson-Coe, Alexander; Januszewski, Michał; Berger, Daniel R.; Pope, Art; Wu, Yuelong; Blakely, Tim; Schalek, Richard L.; Li, Peter H.; Wang, Shuohong (2021-11-25), A connectomic study of a petascale fragment of human cerebral cortex, doi:10.1101/2021.05.29.446289, retrieved 2024-02-14
  12. ^ Loomba, Sahil; Straehle, Jakob; Gangadharan, Vijayan; Heike, Natalie; Khalifa, Abdelrahman; Motta, Alessandro; Ju, Niansheng; Sievers, Meike; Gempt, Jens; Meyer, Hanno S.; Helmstaedter, Moritz (2022-07-08). "Connectomic comparison of mouse and human cortex". Science. 377 (6602). doi:10.1126/science.abo0924. ISSN 0036-8075.
  13. ^ "Analysis tools for connectomics". Howard Hughes Medical Institute.
  14. ^ Bargmann, Cornelia I. (2012). "Beyond the connectome: how neuromodulators shape neural circuits". BioEssays. 34 (6): 458–465. doi:10.1002/bies.201100185. PMID 22396302.