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Popovych S, Macrina T, Kemnitz N, Castro M, Nehoran B, Jia Z, Bae JA, Mitchell E, Mu S, Trautman ET, Saalfeld S, Li K, Seung HS. Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat Commun 2024; 15:289. [PMID: 38177169 PMCID: PMC10767115 DOI: 10.1038/s41467-023-44354-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.
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Affiliation(s)
- Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Kai Li
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Computer Science Department, Princeton University, Princeton, NJ, USA.
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2
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Dorkenwald S, Li PH, Januszewski M, Berger DR, Maitin-Shepard J, Bodor AL, Collman F, Schneider-Mizell CM, da Costa NM, Lichtman JW, Jain V. Multi-layered maps of neuropil with segmentation-guided contrastive learning. Nat Methods 2023; 20:2011-2020. [PMID: 37985712 PMCID: PMC10703674 DOI: 10.1038/s41592-023-02059-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 10/02/2023] [Indexed: 11/22/2023]
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Affiliation(s)
- Sven Dorkenwald
- Google Research, Mountain View, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | | | | | | | | | | | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
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3
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Dorkenwald S, Schneider-Mizell CM, Brittain D, Halageri A, Jordan C, Kemnitz N, Castro MA, Silversmith W, Maitin-Shephard J, Troidl J, Pfister H, Gillet V, Xenes D, Bae JA, Bodor AL, Buchanan J, Bumbarger DJ, Elabbady L, Jia Z, Kapner D, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Reid RC, da Costa NM, Seung HS, Collman F. CAVE: Connectome Annotation Versioning Engine. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550598. [PMID: 37546753 PMCID: PMC10402030 DOI: 10.1101/2023.07.26.550598] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manual A. Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Jakob Troidl
- School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Valentin Gillet
- Lund University, Department of Biology, Lund Vision Group, Lund, Sweden
| | - Daniel Xenes
- Research & Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, United States
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | | | | | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - Kai Li
- Computer Science Department, Princeton University, Princeton, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Marc Takeno
- Allen Institute for Brain Science, Seattle, USA
| | | | - Nicholas L. Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, USA
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
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4
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | | | - Davi D. Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, USA
| | - Gregory S.X.E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
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5
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Wilson A, Babadi M. SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning. PATTERNS 2023; 4:100693. [PMID: 37123442 PMCID: PMC10140600 DOI: 10.1016/j.patter.2023.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/23/2022] [Accepted: 01/25/2023] [Indexed: 03/09/2023]
Abstract
3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.
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6
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Dorkenwald S, Turner NL, Macrina T, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Kemnitz N, Silversmith WM, Ih D, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Wong W, Castro M, Jordan CS, Wilson AM, Froudarakis E, Buchanan J, Takeno MM, Torres R, Mahalingam G, Collman F, Schneider-Mizell CM, Bumbarger DJ, Li Y, Becker L, Suckow S, Reimer J, Tolias AS, Macarico da Costa N, Reid RC, Seung HS. Binary and analog variation of synapses between cortical pyramidal neurons. eLife 2022; 11:e76120. [PMID: 36382887 PMCID: PMC9704804 DOI: 10.7554/elife.76120] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 11/15/2022] [Indexed: 11/17/2022] Open
Abstract
Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Sciences Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Agnes L Bodor
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - William Wong
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | | | - Marc M Takeno
- Allen Institute for Brain ScienceSeattleUnited States
| | - Russel Torres
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | | | - Yang Li
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lynne Becker
- Allen Institute for Brain ScienceSeattleUnited States
| | - Shelby Suckow
- Allen Institute for Brain ScienceSeattleUnited States
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
- Department of Electrical and Computer Engineering, Rice UniversityHoustonUnited States
| | | | - R Clay Reid
- Allen Institute for Brain ScienceSeattleUnited States
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
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7
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Hong B, Liu J, Zhai H, Liu J, Shen L, Chen X, Xie Q, Han H. Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes. BMC Bioinformatics 2022; 23:453. [PMID: 36316652 PMCID: PMC9623997 DOI: 10.1186/s12859-022-04991-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/17/2022] [Indexed: 11/14/2022] Open
Abstract
Background Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. Results In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. Conclusions We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis.
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Affiliation(s)
- Bei Hong
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Liu
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Zhai
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jiazheng Liu
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lijun Shen
- grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Chen
- grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qiwei Xie
- grid.28703.3e0000 0000 9040 3743Research Base of Beijing Modern Manufacturing Development, Beijing University of Technology, Beijing, China
| | - Hua Han
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China ,grid.507732.4CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
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8
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Kievits AJ, Lane R, Carroll EC, Hoogenboom JP. How innovations in methodology offer new prospects for volume electron microscopy. J Microsc 2022; 287:114-137. [PMID: 35810393 PMCID: PMC9546337 DOI: 10.1111/jmi.13134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/29/2022] [Accepted: 07/06/2022] [Indexed: 11/29/2022]
Abstract
Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.
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Affiliation(s)
- Arent J. Kievits
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
| | - Ryan Lane
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
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9
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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10
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Park C, Gim J, Lee S, Lee KJ, Kim JS. Automated Synapse Detection Method for Cerebellar Connectomics. Front Neuroanat 2022; 16:760279. [PMID: 35360651 PMCID: PMC8963724 DOI: 10.3389/fnana.2022.760279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.
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Affiliation(s)
- Changjoo Park
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Jawon Gim
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungjin Lee
- Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Kea Joo Lee
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
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11
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Wu J, Turner N, Bae JA, Vishwanathan A, Seung HS. RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations. Front Neuroinform 2022; 16:828169. [PMID: 35311003 PMCID: PMC8924549 DOI: 10.3389/fninf.2022.828169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 11/30/2022] Open
Abstract
Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets.
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Affiliation(s)
- Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- *Correspondence: Jingpeng Wu,
| | - Nicholas Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Department of Computer Science, Princeton University, Princeton, NJ, United States
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, United States
| | - Ashwin Vishwanathan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Department of Computer Science, Princeton University, Princeton, NJ, United States
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12
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Barsotti E, Correia A, Cardona A. Neural architectures in the light of comparative connectomics. Curr Opin Neurobiol 2021; 71:139-149. [PMID: 34837731 PMCID: PMC8694100 DOI: 10.1016/j.conb.2021.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/20/2021] [Accepted: 10/26/2021] [Indexed: 01/06/2023]
Abstract
Since the Cambrian, animals diversified from a few body forms or bauplans, into many extinct and all extant species. A characteristic neural architecture serves each bauplan. How the connectome of each animal differs from that of closely related species or whether it converged into an optimal architecture shared with more distant ones is unknown. Recent technological innovations in molecular biology, microscopy, digital data storage and processing, and computational neuroscience have lowered the barriers for whole-brain connectomics. Comparative connectomics of suitable, relatively small, representative species across the phylogenetic tree can infer the archetypal neural architecture of each bauplan and identify any circuits that possibly converged onto a shared and potentially optimal, structure.
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Affiliation(s)
- Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK.
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13
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Liu Y, Ji S. CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3507-3518. [PMID: 34129494 PMCID: PMC8674103 DOI: 10.1109/tmi.2021.3089547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine learning approaches have been employed to automatically predict synaptic clefts from EM images. In this work, we propose a novel and augmented deep learning model, known as CleftNet, for improving synaptic cleft detection from brain EM images. We first propose two novel network components, known as the feature augmentor and the label augmentor, for augmenting features and labels to improve cleft representations. The feature augmentor can fuse global information from inputs and learn common morphological patterns in clefts, leading to augmented cleft features. In addition, it can generate outputs with varying dimensions, making it flexible to be integrated in any deep network. The proposed label augmentor augments the label of each voxel from a value to a vector, which contains both the segmentation label and boundary label. This allows the network to learn important shape information and to produce more informative cleft representations. Based on the proposed feature augmentor and label augmentor, We build the CleftNet as a U-Net like network. The effectiveness of our methods is evaluated on both external and internal tasks. Our CleftNet currently ranks #1 on the external task of the CREMI open challenge. In addition, both quantitative and qualitative results in the internal tasks show that our method outperforms the baseline approaches significantly.
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14
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Lee K, Lu R, Luther K, Seung HS. Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3801-3811. [PMID: 34270419 PMCID: PMC8692755 DOI: 10.1109/tmi.2021.3097826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A "metric graph" on a set of edges between voxels is constructed from the dense voxel embeddings generated by a convolutional network. Partitioning the metric graph with long-range edges as repulsive constraints yields an initial segmentation with high precision, with substantial accuracy gain for very thin objects. The convolutional embedding net is reused without any modification to agglomerate the systematic splits caused by complex "self-contact" motifs. Our proposed method achieves state-of-the-art accuracy on the challenging problem of 3D neuron reconstruction from the brain images acquired by serial section electron microscopy. Our alternative, object-centered representation could be more generally useful for other computational tasks in automated neural circuit reconstruction.
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15
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An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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16
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Chunkflow: hybrid cloud processing of large 3D images by convolutional nets. Nat Methods 2021; 18:328-330. [PMID: 33750934 DOI: 10.1038/s41592-021-01088-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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17
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Parajuli LK, Koike M. Three-Dimensional Structure of Dendritic Spines Revealed by Volume Electron Microscopy Techniques. Front Neuroanat 2021; 15:627368. [PMID: 34135737 PMCID: PMC8200415 DOI: 10.3389/fnana.2021.627368] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/03/2021] [Indexed: 11/13/2022] Open
Abstract
Electron microscopy (EM)-based synaptology is a fundamental discipline for achieving a complex wiring diagram of the brain. A quantitative understanding of synaptic ultrastructure also serves as a basis to estimate the relative magnitude of synaptic transmission across individual circuits in the brain. Although conventional light microscopic techniques have substantially contributed to our ever-increasing understanding of the morphological characteristics of the putative synaptic junctions, EM is the gold standard for systematic visualization of the synaptic morphology. Furthermore, a complete three-dimensional reconstruction of an individual synaptic profile is required for the precise quantitation of different parameters that shape synaptic transmission. While volumetric imaging of synapses can be routinely obtained from the transmission EM (TEM) imaging of ultrathin sections, it requires an unimaginable amount of effort and time to reconstruct very long segments of dendrites and their spines from the serial section TEM images. The challenges of low throughput EM imaging have been addressed to an appreciable degree by the development of automated EM imaging tools that allow imaging and reconstruction of dendritic segments in a realistic time frame. Here, we review studies that have been instrumental in determining the three-dimensional ultrastructure of synapses. With a particular focus on dendritic spine synapses in the rodent brain, we discuss various key studies that have highlighted the structural diversity of spines, the principles of their organization in the dendrites, their presynaptic wiring patterns, and their activity-dependent structural remodeling.
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Affiliation(s)
- Laxmi Kumar Parajuli
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masato Koike
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Advanced Research Institute for Health Science, Juntendo University, Tokyo, Japan
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18
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Ofer N, Berger DR, Kasthuri N, Lichtman JW, Yuste R. Ultrastructural analysis of dendritic spine necks reveals a continuum of spine morphologies. Dev Neurobiol 2021; 81:746-757. [PMID: 33977655 DOI: 10.1002/dneu.22829] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 01/20/2023]
Abstract
Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in most mammalian neurons. Spines, with a bulbous head connected to the dendrite by a thin neck, have a variety of morphologies that likely impact their functional properties. Nevertheless, the question of whether spines belong to distinct morphological subtypes is still open. Addressing this quantitatively requires clear identification and measurements of spine necks. Recent advances in electron microscopy enable large-scale systematic reconstructions of spines with nanometer precision in 3D. Analyzing ultrastructural reconstructions from mouse neocortical neurons with computer vision algorithms, we demonstrate that the vast majority of spine structures can be rigorously separated into heads and necks, enabling morphological measurements of spine necks. We then used a database of spine morphological parameters to explore the potential existence of different spine classes. Without exception, our analysis revealed unimodal distributions of individual morphological parameters of spine heads and necks, without evidence for subtypes of spines. The postsynaptic density size was strongly correlated with the spine head volume. The spine neck diameter, but not the neck length, was also correlated with the head volume. Spines with larger head volumes often had a spine apparatus and pairs of spines in a post-synaptic cell contacted by the same axon had similar head volumes. Our data reveal a lack of morphological subtypes of spines and indicate that the spine neck length and head volume must be independently regulated. These results have repercussions for our understanding of the function of dendritic spines in neuronal circuits.
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Affiliation(s)
- Netanel Ofer
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Daniel R Berger
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, USA
| | | | - Jeff W Lichtman
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Rafael Yuste
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA.,Donostia International Physics Center, DIPC, San Sebastian, Spain
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19
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Pérez-Benito FJ, Signol F, Perez-Cortes JC, Fuster-Baggetto A, Pollan M, Pérez-Gómez B, Salas-Trejo D, Casals M, Martínez I, LLobet R. A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105668. [PMID: 32755754 DOI: 10.1016/j.cmpb.2020.105668] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. METHODS A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. RESULTS The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. CONCLUSIONS An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.
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Affiliation(s)
- Francisco Javier Pérez-Benito
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain.
| | - François Signol
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain.
| | - Juan-Carlos Perez-Cortes
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain.
| | - Alejandro Fuster-Baggetto
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain.
| | - Marina Pollan
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain.
| | - Beatriz Pérez-Gómez
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain.
| | - Dolores Salas-Trejo
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain.
| | - Maria Casals
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain.
| | - Inmaculada Martínez
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain.
| | - Rafael LLobet
- Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain.
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20
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Ishii S, Lee S, Urakubo H, Kume H, Kasai H. Generative and discriminative model-based approaches to microscopic image restoration and segmentation. Microscopy (Oxf) 2020; 69:79-91. [PMID: 32215571 PMCID: PMC7141893 DOI: 10.1093/jmicro/dfaa007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/02/2020] [Accepted: 02/17/2020] [Indexed: 11/14/2022] Open
Abstract
Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.
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Affiliation(s)
- Shin Ishii
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
| | - Sehyung Lee
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
| | - Hidetoshi Urakubo
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
| | - Hideaki Kume
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Haruo Kasai
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
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Kubota Y. Editorial: Electron-Microscopy-Based Tools for Imaging Cellular Circuits and Organisms. Front Neural Circuits 2019; 13:64. [PMID: 31680878 PMCID: PMC6797905 DOI: 10.3389/fncir.2019.00064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 09/24/2019] [Indexed: 11/30/2022] Open
Affiliation(s)
- Yoshiyuki Kubota
- Division of Cerebral Circuitry, National Institute for Physiological Sciences, Okazaki, Japan.,Department of Physiological Sciences, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Japan
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