Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data Article Swipe
Shreya Saxena
,
Ian Kinsella
,
Simon Musall
,
Sharon Kim
,
József Mészáros
,
David N. Thibodeaux
,
Carla Y. Kim
,
John Cunningham
,
Elizabeth M. C. Hillman
,
Anne K. Churchland
,
Liam Paninski
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/650093
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/650093
Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.
Related Topics
Concepts
Non-negative matrix factorization
Calcium imaging
Matrix decomposition
Subspace topology
Computer science
Pattern recognition (psychology)
Neuroscience
Neural activity
Artificial neural network
Representation (politics)
Artificial intelligence
Physics
Calcium
Chemistry
Biology
Organic chemistry
Political science
Eigenvalues and eigenvectors
Quantum mechanics
Politics
Law
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/650093
- https://www.biorxiv.org/content/biorxiv/early/2019/11/06/650093.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2944978764
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2944978764Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/650093Digital Object Identifier
- Title
-
Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-05-25Full publication date if available
- Authors
-
Shreya Saxena, Ian Kinsella, Simon Musall, Sharon Kim, József Mészáros, David N. Thibodeaux, Carla Y. Kim, John Cunningham, Elizabeth M. C. Hillman, Anne K. Churchland, Liam PaninskiList of authors in order
- Landing page
-
https://doi.org/10.1101/650093Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2019/11/06/650093.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2019/11/06/650093.full.pdfDirect OA link when available
- Concepts
-
Non-negative matrix factorization, Calcium imaging, Matrix decomposition, Subspace topology, Computer science, Pattern recognition (psychology), Neuroscience, Neural activity, Artificial neural network, Representation (politics), Artificial intelligence, Physics, Calcium, Chemistry, Biology, Organic chemistry, Political science, Eigenvalues and eigenvectors, Quantum mechanics, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 5, 2021: 3, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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