Low computational-cost cell detection method for calcium imaging data

Ito, Takehiro; Ota, Ken‐ichiro; Ueno, Kanako; Oisi, Yasuhiro; Matsubara, Chie; Kobayashi, Kenta; Ohkura, Masamichi; Nakai, Junichi; Murayama, Miyuki; Aonishi, Toru

doi:10.1016/j.neures.2022.02.008

Cited by 6 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the technology that records cells becomes more advanced and eventually increases the number of cells that can be recorded at a time, more efficient image analysis methods are required. 59,60 3 Biological Insights Obtained From Multiphoton Imaging of Hippocampal Circuits…”

Section: Key Innovations and Experimental And Analytical Practice In ...mentioning

confidence: 99%

Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions

Mizuta,

Sato

2024

Neurophoton.

View full text Add to dashboard Cite

The function of the hippocampus in behavior and cognition has long been studied primarily through electrophysiological recordings from freely moving rodents. However, the application of optical recording methods, particularly multiphoton fluorescence microscopy, in the last decade or two has dramatically advanced our understanding of hippocampal function. This article provides a comprehensive overview of techniques and biological findings obtained from multiphoton imaging of hippocampal neural circuits.

show abstract

Section: Key Innovations and Experimental And Analytical Practice In ...mentioning

confidence: 99%

Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions

Mizuta,

Sato

2024

Neurophoton.

View full text Add to dashboard Cite

show abstract

“…All the obtained images were first subjected to motion correction using NoRMCorre [78]. We then used a low-computational-cost cell-detection (LCCD) algorithm [79] to identify the ROIs corresponding to single neurons. The fluorescence time course F ROI (t) of each ROI was calculated by averaging all 15 pixels within the ROI, and the fluorescence signal of the cell body was estimated as F (t) = F ROI − r × F neuropil (t), where r = 0.7.…”

Section: Imaging Data Analysismentioning

confidence: 99%

Single-cell resolution functional networks during sleep are segregated into spatially intermixed modules

Kiyooka,

Oomoto,

Kitazono

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The neural mechanisms responsible for the reduction of consciousness during sleep remain elusive. Previous studies investigating macro/mesoscale neural data have suggested that functional networks are segregated into spatially localized modules, and that these modules are more segregated during sleep than during wakefulness. However, large-scale single-cell resolution functional networks remain largely unexplored. Here, we simultaneously recorded the activities of up to 10,000 cortical neurons from multiple brain regions in mice during wakefulness and sleep using a fast, single-cell resolution, and wide-field-of-view two-photon calcium imaging technique. We examined how networks were integrated or segregated between brain states in terms of modularity and spatial distribution in the cortex. We found that modularity during non-rapid eye movement sleep was higher than that during wakefulness, indicating a more segregated network. However, these modules were not spatially localized but rather intermixed across regions in both states. Our results provide novel insights into differences in the cellular-scale organization of functional networks during altered states of consciousness.

show abstract