Detecting cell assemblies by NMF-based clustering from calcium imaging data. (May 2022)

• Record Type: Journal Article
• Authors: Nagayama, Mizuo; Aritake, Toshimitsu; Hino, Hideitsu; Kanda, Takeshi; Miyazaki, Takehiro; Yanagisawa, Masashi; Akaho, Shotaro; Murata, Noboru
• Is Part Of: Neural networks. Volume 149(2022)
• Page Start: 29
• Page End: 39
• Publication Date: 2022-05
• Subjects: Calcium imaging -- Clustering -- NMF; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.01.023
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: A large number of neurons form cell assemblies that process information in the brain. Recent developments in measurement technology, one of which is calcium imaging, have made it possible to study cell assemblies. In this study, we aim to extract cell assemblies from calcium imaging data. We propose a clustering approach based on non-negative matrix factorization (NMF). The proposed approach first obtains a similarity matrix between neurons by NMF and then performs spectral clustering on it. The application of NMF entails the problem of model selection. The number of bases in NMF affects the result considerably, and a suitable selection method is yet to be established. We attempt to resolve this problem by model averaging with a newly defined estimator based on NMF. Experiments on simulated data suggest that the proposed approach is superior to conventional correlation-based clustering methods over a wide range of sampling rates. We also analyzed calcium imaging data of sleeping/waking mice and the results suggest that the size of the cell assembly depends on the degree and spatial extent of slow wave generation in the cerebral cortex. Highlights: A method of estimating an adjacency matrix of cells based on NMF is proposed. Model averaging enables us to avoid choosing the number of components in NMF. Cell assemblies are extracted robustly with spectral clustering. Our method finds that the larger slow wave state, the larger cell assembly.