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

• Published in: PLoS computational biology Vol. 16; no. 4; p. e1007791
• Main Authors: Saxena, Shreya, Kinsella, Ian, Musall, Simon, Kim, Sharon H, Meszaros, Jozsef, Thibodeaux, David N, Kim, Carla, Cunningham, John, Hillman, Elizabeth M C, Churchland, Anne, Paninski, Liam
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animals; Biology and Life Sciences; Biomedical engineering; Brain; Brain mapping; Brain Mapping - methods; Calcium; Calcium - chemistry; Calcium - metabolism; Calcium imaging; Cerebral cortex; Cortex (temporal); Datasets; Decomposition; Engineering; Factorization; Funding; Image Processing, Computer-Assisted - methods; Laboratories; Medicine and Health Sciences; Methods; Mice; Neural circuitry; Neuroimaging; Neurons; Neurosciences; Physical Sciences; Physiological aspects; Prefrontal Cortex - chemistry; Prefrontal Cortex - diagnostic imaging; Social Sciences; Software; Supervision; Video data; United States; New York; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007791

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.