Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization

Mizuo Nagayama; Toshimitsu Aritake; Hideitsu Hino; Takeshi Kanda; Takehiro Miyazaki; Masashi Yanagisawa; Shotaro Akaho; Noboru Murata

doi:10.1007/978-3-030-30487-4_8

Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization

Mizuo Nagayama
, Toshimitsu Aritake
, Hideitsu Hino
, Takeshi Kanda
, Takehiro Miyazaki
, Masashi Yanagisawa
, Shotaro Akaho
, Noboru Murata

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Sleep is an essential process for the survival of animals. However, its phenomenon is poorly understood. To understand the phenomenon of sleep, the analysis should be made from the activities of a large number of cortical neurons. Calcium imaging is a recently developed technique that can record a large number of neurons simultaneously, however, it has a disadvantage of low time resolution. In this paper, we aim to discover phenomena which characterize sleep/wake states from calcium imaging data. We made an assumption that groups of neurons become active simultaneously and the neuronal activities of groups differ between sleep and wake states. We used non-negative matrix factorization (NMF) to identify those groups and their neuronal activities in time from calcium imaging data. NMF was used because neural activity can be expressed by the sum of individual neuronal activity and fluorescence intensity data are always positive values. We found that there are certain groups of neurons that behave differently between sleep and wake states.

Original language	English (US)
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2019
Subtitle of host publication	Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings
Editors	Igor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
Publisher	Springer Verlag
Pages	102-113
Number of pages	12
ISBN (Print)	9783030304867
DOIs	https://doi.org/10.1007/978-3-030-30487-4_8
State	Published - 2019
Externally published	Yes
Event	28th International Conference on Artificial Neural Networks: Workshop and Special Sessions, ICANN 2019 - Munich, Germany Duration: Sep 17 2019 → Sep 19 2019

Publication series

Name	Lecture Notes in Computer Science
Volume	11727 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Artificial Neural Networks: Workshop and Special Sessions, ICANN 2019
Country/Territory	Germany
City	Munich
Period	9/17/19 → 9/19/19

Keywords

Calcium imaging
Non-negative matrix factorization
Sleep state analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-30487-4_8

Cite this

Nagayama, M., Aritake, T., Hino, H., Kanda, T., Miyazaki, T., Yanagisawa, M., Akaho, S., & Murata, N. (2019). Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization. In I. V. Tetko, P. Karpov, F. Theis, & V. Kurková (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings (pp. 102-113). (Lecture Notes in Computer Science; Vol. 11727 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30487-4_8

Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization. / Nagayama, Mizuo; Aritake, Toshimitsu; Hino, Hideitsu et al.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. ed. / Igor V. Tetko; Pavel Karpov; Fabian Theis; Vera Kurková. Springer Verlag, 2019. p. 102-113 (Lecture Notes in Computer Science; Vol. 11727 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nagayama, M, Aritake, T, Hino, H, Kanda, T, Miyazaki, T, Yanagisawa, M, Akaho, S & Murata, N 2019, Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization. in IV Tetko, P Karpov, F Theis & V Kurková (eds), Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11727 LNCS, Springer Verlag, pp. 102-113, 28th International Conference on Artificial Neural Networks: Workshop and Special Sessions, ICANN 2019, Munich, Germany, 9/17/19. https://doi.org/10.1007/978-3-030-30487-4_8

Nagayama M, Aritake T, Hino H, Kanda T, Miyazaki T, Yanagisawa M et al. Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization. In Tetko IV, Karpov P, Theis F, Kurková V, editors, Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Springer Verlag. 2019. p. 102-113. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-30487-4_8

Nagayama, Mizuo ; Aritake, Toshimitsu ; Hino, Hideitsu et al. / Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. editor / Igor V. Tetko ; Pavel Karpov ; Fabian Theis ; Vera Kurková. Springer Verlag, 2019. pp. 102-113 (Lecture Notes in Computer Science).

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abstract = "Sleep is an essential process for the survival of animals. However, its phenomenon is poorly understood. To understand the phenomenon of sleep, the analysis should be made from the activities of a large number of cortical neurons. Calcium imaging is a recently developed technique that can record a large number of neurons simultaneously, however, it has a disadvantage of low time resolution. In this paper, we aim to discover phenomena which characterize sleep/wake states from calcium imaging data. We made an assumption that groups of neurons become active simultaneously and the neuronal activities of groups differ between sleep and wake states. We used non-negative matrix factorization (NMF) to identify those groups and their neuronal activities in time from calcium imaging data. NMF was used because neural activity can be expressed by the sum of individual neuronal activity and fluorescence intensity data are always positive values. We found that there are certain groups of neurons that behave differently between sleep and wake states.",

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author = "Mizuo Nagayama and Toshimitsu Aritake and Hideitsu Hino and Takeshi Kanda and Takehiro Miyazaki and Masashi Yanagisawa and Shotaro Akaho and Noboru Murata",

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AU - Miyazaki, Takehiro

AU - Yanagisawa, Masashi

AU - Akaho, Shotaro

AU - Murata, Noboru

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AB - Sleep is an essential process for the survival of animals. However, its phenomenon is poorly understood. To understand the phenomenon of sleep, the analysis should be made from the activities of a large number of cortical neurons. Calcium imaging is a recently developed technique that can record a large number of neurons simultaneously, however, it has a disadvantage of low time resolution. In this paper, we aim to discover phenomena which characterize sleep/wake states from calcium imaging data. We made an assumption that groups of neurons become active simultaneously and the neuronal activities of groups differ between sleep and wake states. We used non-negative matrix factorization (NMF) to identify those groups and their neuronal activities in time from calcium imaging data. NMF was used because neural activity can be expressed by the sum of individual neuronal activity and fluorescence intensity data are always positive values. We found that there are certain groups of neurons that behave differently between sleep and wake states.

KW - Calcium imaging

KW - Non-negative matrix factorization

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ER -

Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization

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