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Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data.
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.
Affiliation
  • Saxena S; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
  • Kinsella I; Department of Statistics, Columbia University, New York, New York, United States of America.
  • Musall S; Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
  • Kim SH; Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America.
  • Meszaros J; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
  • Thibodeaux DN; Department of Statistics, Columbia University, New York, New York, United States of America.
  • Kim C; Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
  • Cunningham J; Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.
  • Hillman EMC; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
  • Churchland A; Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America.
  • Paninski L; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
PLoS Comput Biol ; 16(4): e1007791, 2020 04.
Article in En | MEDLINE | ID: mdl-32282806
ABSTRACT
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Brain Mapping / Calcium / Prefrontal Cortex Type of study: Prognostic_studies Limits: Animals Language: En Journal: PLoS Comput Biol Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Brain Mapping / Calcium / Prefrontal Cortex Type of study: Prognostic_studies Limits: Animals Language: En Journal: PLoS Comput Biol Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: Estados Unidos