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). 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 nematode C. elegans, fruit fly,{{cite journal |last1=Chklovskii |first1=Dmitri B |last2=Vitaladevuni |first2=Shiv |last3=Scheffer |first3=Louis K |title=Semi-automated reconstruction of neural circuits using electron microscopy |journal=Current Opinion in Neurobiology |volume=20 |issue=5 |pages=667–75 |year=2010 |pmid=20833533 |doi=10.1016/j.conb.2010.08.002|s2cid=206950616 }} the mouse,{{cite journal |last1=Bock |first1=Davi D. |last2=Lee |first2=Wei-Chung Allen |last3=Kerlin |first3=Aaron M. |last4=Andermann |first4=Mark L. |last5=Hood |first5=Greg |last6=Wetzel |first6=Arthur W. |last7=Yurgenson |first7=Sergey |last8=Soucy |first8=Edward R. |last9=Kim |first9=Hyon Suk |last10=Reid |first10=R. Clay |title=Network anatomy and in vivo physiology of visual cortical neurons |journal=Nature |volume=471 |issue=7337 |pages=177–82 |year=2011 |pmid=21390124 |doi=10.1038/nature09802|display-authors=8 |pmc=3095821 |bibcode=2011Natur.471..177B }} and the human.

{{cite journal

|title=A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution}

|last31= Lichtman |first31= Jeff W.

|journal=Science

|volume=384

|issue=6696

|pages=eadk4858

|year=2024

|publisher=American Association for the Advancement of Science}}

Sample preparation

The sample must be fixed, stained, and embedded in plastic.{{cite book |author=Hayat, M. Arif |title=Principles and techniques of scanning electron microscopy. Biological applications, fourth edition. |publisher=Cambridge University Press |year=2000 |isbn=978-0521632874}}

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.{{cite journal |author1=Briggman, Kevin L. |author2=Davi D. Bock |title=Volume electron microscopy for neuronal circuit reconstruction |journal=Current Opinion in Neurobiology |volume=22 |issue=1 |year=2012 |pages=154–161 |doi=10.1016/j.conb.2011.10.022|pmid=22119321 |s2cid=22657332 |url=https://zenodo.org/record/1258851 }}

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.{{cite journal |author=Saalfeld, Stephan, Albert Cardona, Volker Hartenstein, and Pavel Tomančák |title=CATMAID: collaborative annotation toolkit for massive amounts of image data |journal=Bioinformatics |volume=25 |issue=15 |year=2009 |pages=1984–1986 |doi=10.1093/bioinformatics/btp266|pmid=19376822 |pmc=2712332 }} 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{{Cite web |title=Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction |url=https://scholar.google.com/citations?view_op=view_citation&hl=de&user=7rqAapgAAAAJ&citation_for_view=7rqAapgAAAAJ:Se3iqnhoufwC |access-date=2024-02-14 |website=scholar.google.com}} and flood-filling networks.{{Cite journal |last=Januszewski |first=Michał |last2=Kornfeld |first2=Jörgen |last3=Li |first3=Peter H. |last4=Pope |first4=Art |last5=Blakely |first5=Tim |last6=Lindsey |first6=Larry |last7=Maitin-Shepard |first7=Jeremy |last8=Tyka |first8=Mike |last9=Denk |first9=Winfried |last10=Jain |first10=Viren |date=August 2018 |title=High-precision automated reconstruction of neurons with flood-filling networks |url=https://www.nature.com/articles/s41592-018-0049-4 |journal=Nature Methods |language=en |volume=15 |issue=8 |pages=605–610 |doi=10.1038/s41592-018-0049-4 |issn=1548-7105}} 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.{{cite journal |author=Chklovskii, Dmitri B., Shiv Vitaladevuni, and Louis K. Scheffer. |title=Semi-automated reconstruction of neural circuits using electron microscopy |journal=Current Opinion in Neurobiology |volume=20 |issue=5 |year=2010 |pages=667–675 |url=https://www.researchgate.net/publication/46220031 |doi=10.1016/j.conb.2010.08.002 |pmid=20833533|s2cid=206950616 }}

Notable examples

The connectome of C. elegans was the seminal work in this field.{{cite journal |author1=White, John G.|author2= Southgate, Eileen|author2-link= Eileen Southgate|author3= Nichol Thomson, J. |author4=Brenner, Sydney |title=The structure of the nervous system of the nematode Caenorhabditis elegans |journal=Philos Trans R Soc Lond B Biol Sci |volume=314 |issue=1165 |year=1986 |pages=1–340 |doi=10.1098/rstb.1986.0056 |pmid=22462104 |bibcode= 1986RSPTB.314....1W|doi-access=free }} 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.{{cite web |url=https://elifesciences.org/articles/62451 |title=Connectomes: Mapping the mind of a fly |publisher=Elife Sciences |author=Jason Pipkin |date=Oct 8, 2020}} This data release introduced the first on-line tools to query the connectome.
The Human Cortex [https://h01-release.storage.googleapis.com/landing.html 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.{{Citation |last=Shapson-Coe |first=Alexander |title=A connectomic study of a petascale fragment of human cerebral cortex |date=2021-11-25 |url=https://www.biorxiv.org/content/10.1101/2021.05.29.446289v4 |access-date=2024-02-14 |language=en |doi=10.1101/2021.05.29.446289 |last2=Januszewski |first2=Michał |last3=Berger |first3=Daniel R. |last4=Pope |first4=Art |last5=Wu |first5=Yuelong |last6=Blakely |first6=Tim |last7=Schalek |first7=Richard L. |last8=Li |first8=Peter H. |last9=Wang |first9=Shuohong}}
In their 2022 study “Connectomic comparison of mouse and human cortex”, the researchers reconstructed 9 connectomes across species: Datasets of [https://wklink.org/9045 Mouse], [https://wklink.org/1186 Macaque] and [https://wklink.org/7861 Human].{{Cite journal |last=Loomba |first=Sahil |last2=Straehle |first2=Jakob |last3=Gangadharan |first3=Vijayan |last4=Heike |first4=Natalie |last5=Khalifa |first5=Abdelrahman |last6=Motta |first6=Alessandro |last7=Ju |first7=Niansheng |last8=Sievers |first8=Meike |last9=Gempt |first9=Jens |last10=Meyer |first10=Hanno S. |last11=Helmstaedter |first11=Moritz |date=2022-07-08 |title=Connectomic comparison of mouse and human cortex |url=https://www.science.org/doi/10.1126/science.abo0924 |journal=Science |language=en |volume=377 |issue=6602 |doi=10.1126/science.abo0924 |issn=0036-8075}}

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.{{cite web |url=https://neuprint.janelia.org/ |title=Analysis tools for connectomics |publisher=Howard Hughes Medical Institute}} 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.{{cite journal |author=Bargmann, Cornelia I. |title=Beyond the connectome: how neuromodulators shape neural circuits |journal=BioEssays |volume=34 |issue=6 |year=2012 |pages=458–465 |doi=10.1002/bies.201100185|pmid=22396302 |doi-access=free }} Currently these features must be obtained through other techniques.

Expansion microscopy may provide an alternative to EM for circuit reconstruction. In 2024, a new technique called LICONN combined hydrogel expansion with light microscopy (as opposed to electron microscopy) to generate neuron level connectomes.{{cite bioRxiv

|last13= Danzl |first13= Johann

|date=1 Mar 2024 |title=Light-microscopy based dense connectomic reconstruction of mammalian brain tissue

|biorxiv=10.1101/2024.03.01.582884}} Potential advantages include cheaper equipment, faster data acquisition, and multi-color labelling. The equipment is cheaper as a confocal microscope is less costly than an electron microscope. The data acquisition is faster since only a change of focus, not physical sectioning, is required. Multi-color labelling is helpful as some features such as gap junctions are hard to spot in electron microscopy, but are easily labelled by antibodies in optical images. Additional colors, likely feasible, can aid in neurotransmitter identification and other tasks that are difficult in the monochrome EM images.

References

Category:Brain

Category:Neural coding

Category:Neuroimaging

Category:Neuroinformatics