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Sanchez M, Moore D, Johnson EC, Wester B, Lichtman JW, Gray-Roncal W. Connectomics Annotation Metadata Standardization for Increased Accessibility and Queryability. Front Neuroinform 2022; 16:828458. [PMID: 35651719 PMCID: PMC9150677 DOI: 10.3389/fninf.2022.828458] [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: 12/03/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroscientists can leverage technological advances to image neural tissue across a range of different scales, potentially forming the basis for the next generation of brain atlases and circuit reconstructions at submicron resolution, using Electron Microscopy and X-ray Microtomography modalities. However, there is variability in data collection, annotation, and storage approaches, which limits effective comparative and secondary analysis. There has been great progress in standardizing interfaces for large-scale spatial image data, but more work is needed to standardize annotations, especially metadata associated with neuroanatomical entities. Standardization will enable validation, sharing, and replication, greatly amplifying investment throughout the connectomics community. We share key design considerations and a usecase developed for metadata for a recent large-scale dataset.
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Affiliation(s)
- Morgan Sanchez
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Dymon Moore
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Erik C. Johnson
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, United States
| | - William Gray-Roncal
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
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Timonidis N, Bakker R, Tiesinga P. Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics 2020; 18:611-626. [PMID: 32448958 PMCID: PMC7498447 DOI: 10.1007/s12021-020-09471-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.
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Affiliation(s)
- Nestor Timonidis
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands.
| | - Rembrandt Bakker
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Wilhelm-Johnen-Strasse, 52425, Jülich, Germany
| | - Paul Tiesinga
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands
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Johnson EC, Wilt M, Rodriguez LM, Norman-Tenazas R, Rivera C, Drenkow N, Kleissas D, LaGrow TJ, Cowley HP, Downs J, K. Matelsky J, J. Hughes M, P. Reilly E, A. Wester B, L. Dyer E, P. Kording K, R. Gray-Roncal W. Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets. Gigascience 2020; 9:giaa147. [PMID: 33347572 PMCID: PMC7751400 DOI: 10.1093/gigascience/giaa147] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 08/19/2020] [Accepted: 12/18/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. RESULTS We developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. CONCLUSIONS Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains.
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Affiliation(s)
- Erik C Johnson
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Miller Wilt
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Luis M Rodriguez
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Raphael Norman-Tenazas
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Corban Rivera
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Nathan Drenkow
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Dean Kleissas
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Theodore J LaGrow
- School of Electrical & Computer Engineering, Georgia Institute of Technology, 777 Atlantic Dr. NW, Atlanta, GA, 30332 USA
| | - Hannah P Cowley
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Joseph Downs
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Jordan K. Matelsky
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Marisa J. Hughes
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Elizabeth P. Reilly
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Brock A. Wester
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
| | - Eva L. Dyer
- School of Electrical & Computer Engineering, Georgia Institute of Technology, 777 Atlantic Dr. NW, Atlanta, GA, 30332 USA
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, 313 Ferst Dr., Atlanta, GA, 30332 USA
| | - Konrad P. Kording
- Department of Biomedical Engineering, University of Pennsylvania, 210 South 33rd St., Philadelphia, PA, 19104 USA
| | - William R. Gray-Roncal
- Research And Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd., Laurel, MD, 20723 USA
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