A mutual information approach to automate identification of neuronal clusters in Drosophila brain images
Mapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derive...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-06-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2012.00021/full |
| _version_ | 1818515107952459776 |
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| author | Nicolas Yvan Masse Nicolas Yvan Masse Sebastian eCachero Aaron eOstrovsky Gregory S X E Jefferis |
| author_facet | Nicolas Yvan Masse Nicolas Yvan Masse Sebastian eCachero Aaron eOstrovsky Gregory S X E Jefferis |
| author_sort | Nicolas Yvan Masse |
| collection | DOAJ |
| description | Mapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derived from the same neural stem cell (neuroblast clones) are functionally related and morphologically highly stereotyped across animals. However identifying these neuroblast clones (approximately 180 per central brain hemisphere) manually remains challenging and time consuming. Here, we take advantage of the stereotyped nature of neural circuits in Drosophila to automatically identify clones, requiring manual annotation of only an initial, smaller set of images. Our procedure starts by filtering the images to accentuate neural projections and cell bodies, and then skeletonises the projections with a dimension reduction algorithm. Images are then registered onto a common template brain, allowing us to determine which projections and cell bodies are shared across different brains. We then determine whether the presence of a cell body or projection is associated with the presence of a clone, allowing us identify the neural structures that can reliably indicate whether a brain contains a specific clone. This enables us to detect the presence of clones in novel images by mapping their cell bodies and projections and matching them against these informative neural structures. The approach is not limited to a specific labeling strategy and can be used to identify partial (e.g. individual neurons) as well as complete matches. Furthermore this approach could be generalised to studies of neural circuits in other organisms. |
| first_indexed | 2024-12-11T00:24:40Z |
| format | Article |
| id | doaj.art-fa3699ab8aa74c7fa9da1ce5125d96d0 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-11T00:24:40Z |
| publishDate | 2012-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-fa3699ab8aa74c7fa9da1ce5125d96d02022-12-22T01:27:37ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962012-06-01610.3389/fninf.2012.0002118153A mutual information approach to automate identification of neuronal clusters in Drosophila brain imagesNicolas Yvan Masse0Nicolas Yvan Masse1Sebastian eCachero2Aaron eOstrovsky3Gregory S X E Jefferis4MRC Laboratory of Molecular BiologyUniversity of ChicagoMRC Laboratory of Molecular BiologyMRC Laboratory of Molecular BiologyMRC Laboratory of Molecular BiologyMapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derived from the same neural stem cell (neuroblast clones) are functionally related and morphologically highly stereotyped across animals. However identifying these neuroblast clones (approximately 180 per central brain hemisphere) manually remains challenging and time consuming. Here, we take advantage of the stereotyped nature of neural circuits in Drosophila to automatically identify clones, requiring manual annotation of only an initial, smaller set of images. Our procedure starts by filtering the images to accentuate neural projections and cell bodies, and then skeletonises the projections with a dimension reduction algorithm. Images are then registered onto a common template brain, allowing us to determine which projections and cell bodies are shared across different brains. We then determine whether the presence of a cell body or projection is associated with the presence of a clone, allowing us identify the neural structures that can reliably indicate whether a brain contains a specific clone. This enables us to detect the presence of clones in novel images by mapping their cell bodies and projections and matching them against these informative neural structures. The approach is not limited to a specific labeling strategy and can be used to identify partial (e.g. individual neurons) as well as complete matches. Furthermore this approach could be generalised to studies of neural circuits in other organisms.http://journal.frontiersin.org/Journal/10.3389/fninf.2012.00021/fullDrosophilaconfocal microscopyNeuronimage registrationimage classificationmutual information |
| spellingShingle | Nicolas Yvan Masse Nicolas Yvan Masse Sebastian eCachero Aaron eOstrovsky Gregory S X E Jefferis A mutual information approach to automate identification of neuronal clusters in Drosophila brain images Frontiers in Neuroinformatics Drosophila confocal microscopy Neuron image registration image classification mutual information |
| title | A mutual information approach to automate identification of neuronal clusters in Drosophila brain images |
| title_full | A mutual information approach to automate identification of neuronal clusters in Drosophila brain images |
| title_fullStr | A mutual information approach to automate identification of neuronal clusters in Drosophila brain images |
| title_full_unstemmed | A mutual information approach to automate identification of neuronal clusters in Drosophila brain images |
| title_short | A mutual information approach to automate identification of neuronal clusters in Drosophila brain images |
| title_sort | mutual information approach to automate identification of neuronal clusters in drosophila brain images |
| topic | Drosophila confocal microscopy Neuron image registration image classification mutual information |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2012.00021/full |
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