Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data.

Autor: Shreya Saxena, Ian Kinsella, Simon Musall, Sharon H Kim, Jozsef Meszaros, David N Thibodeaux, Carla Kim, John Cunningham, Elizabeth M C Hillman, Anne Churchland, Liam Paninski
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: PLoS Computational Biology, Vol 16, Iss 4, p e1007791 (2020)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
88044459
DOI: 10.1371/journal.pcbi.1007791
Popis: 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.
Databáze: Directory of Open Access Journals
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