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Naik A, Kenyon R, Taheri A, BergerWolf T, Ibrahim B, Shinagawa Y, Llano D. V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data. J Neurosci Res 2023; 101:217-231. [PMID: 36309817 PMCID: PMC9742979 DOI: 10.1002/jnr.25139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/07/2022] [Accepted: 10/12/2022] [Indexed: 12/14/2022]
Abstract
Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.
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Affiliation(s)
- A.G. Naik
- Department of Computer Science, University of Illinois at Chicago, USA
| | - R.V. Kenyon
- Department of Computer Science, University of Illinois at Chicago, USA
| | - A. Taheri
- Department of Computer Science, University of Illinois at Chicago, USA
| | - T. BergerWolf
- Department of Computer Science Engineering, Ohio State University, USA
| | - B. Ibrahim
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
| | - Y. Shinagawa
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
| | - D.A. Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
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2
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de Kraker L, Seignette K, Thamizharasu P, van den Boom BJ, Ferreira Pica I, Willuhn I, Levelt CN, Togt CVD. SpecSeg is a versatile toolbox that segments neurons and neurites in chronic calcium imaging datasets based on low-frequency cross-spectral power. CELL REPORTS METHODS 2022; 2:100299. [PMID: 36313805 PMCID: PMC9606108 DOI: 10.1016/j.crmeth.2022.100299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 02/11/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022]
Abstract
Imaging calcium signals in neurons of animals using single- or multi-photon microscopy facilitates the study of coding in large neural populations. Such experiments produce massive datasets requiring powerful methods to extract responses from hundreds of neurons. We present SpecSeg, an open-source toolbox for (1) segmentation of regions of interest (ROIs) representing neuronal structures, (2) inspection and manual editing of ROIs, (3) neuropil correction and signal extraction, and (4) matching of ROIs in sequential recordings. ROI segmentation in SpecSeg is based on temporal cross-correlations of low-frequency components derived by Fourier analysis of each pixel with its neighbors. The approach is user-friendly, intuitive, and insightful and enables ROI detection around neurons or neurites. It works for single- (miniscope) and multi-photon microscopy data, eliminating the need for separate toolboxes. SpecSeg thus provides an efficient and versatile approach for analyzing calcium responses in neuronal structures imaged over prolonged periods of time.
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Affiliation(s)
- Leander de Kraker
- Netherlands Institute for Neuroscience, Molecular Visual Plasticity Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Koen Seignette
- Netherlands Institute for Neuroscience, Molecular Visual Plasticity Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Premnath Thamizharasu
- Netherlands Institute for Neuroscience, Molecular Visual Plasticity Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Bastijn J.G. van den Boom
- Netherlands Institute for Neuroscience, Neuromodulation and Behavior Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Ildefonso Ferreira Pica
- Netherlands Institute for Neuroscience, Molecular Visual Plasticity Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Ingo Willuhn
- Netherlands Institute for Neuroscience, Neuromodulation and Behavior Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, the Netherlands
| | - Christiaan N. Levelt
- Netherlands Institute for Neuroscience, Molecular Visual Plasticity Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, the Netherlands
| | - Chris van der Togt
- Netherlands Institute for Neuroscience, Molecular Visual Plasticity Group, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
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Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Calcium imaging has rapidly become a methodology of choice for real-time in vivo neuron analysis. Its application to large sets of data requires automated tools to annotate and segment cells, allowing scalable image segmentation under reproducible criteria. In this paper, we review and summarize the most recent methods for computational segmentation of calcium imaging. The contributions of the paper are three-fold: we provide an overview of the main algorithms taxonomized in three categories (signal processing, matrix factorization and machine learning-based approaches), we highlight the main advantages and disadvantages of each category and we provide a summary of the performance of the methods that have been tested on public benchmarks (with links to the public code when available).
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Li M, Liu C, Cui X, Jung H, You H, Feng L, Zhang S. An Open-Source Real-Time Motion Correction Plug-In for Single-Photon Calcium Imaging of Head-Mounted Microscopy. Front Neural Circuits 2022; 16:891825. [PMID: 35814484 PMCID: PMC9265215 DOI: 10.3389/fncir.2022.891825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022] Open
Abstract
Single-photon-based head-mounted microscopy is widely used to record the brain activities of freely-moving animals. However, during data acquisition, the free movement of animals will cause shaking in the field of view, which deteriorates subsequent neural signal analyses. Existing motion correction methods applied to calcium imaging data either focus on offline analyses or lack sufficient accuracy in real-time processing for single-photon data. In this study, we proposed an open-source real-time motion correction (RTMC) plug-in for single-photon calcium imaging data acquisition. The RTMC plug-in is a real-time subpixel registration algorithm that can run GPUs in UCLA Miniscope data acquisition software. When used with the UCLA Miniscope, the RTMC algorithm satisfies real-time processing requirements in terms of speed, memory, and accuracy. We tested the RTMC algorithm by extending a manual neuron labeling function to extract calcium signals in a real experimental setting. The results demonstrated that the neural calcium dynamics and calcium events can be restored with high accuracy from the calcium data that were collected by the UCLA Miniscope system embedded with our RTMC plug-in. Our method could become an essential component in brain science research, where real-time brain activity is needed for closed-loop experiments.
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Affiliation(s)
- Mingkang Li
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Changhao Liu
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Xin Cui
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Hayoung Jung
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Heecheon You
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Linqing Feng
- Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China
- *Correspondence: Linqing Feng
| | - Shaomin Zhang
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Shaomin Zhang
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Mahadevan AS, Long BL, Hu CW, Ryan DT, Grandel NE, Britton GL, Bustos M, Gonzalez Porras MA, Stojkova K, Ligeralde A, Son H, Shannonhouse J, Robinson JT, Warmflash A, Brey EM, Kim YS, Qutub AA. cytoNet: Spatiotemporal network analysis of cell communities. PLoS Comput Biol 2022; 18:e1009846. [PMID: 35696439 PMCID: PMC9191702 DOI: 10.1371/journal.pcbi.1009846] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 01/18/2022] [Indexed: 11/18/2022] Open
Abstract
We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet’s capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.
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Affiliation(s)
- Arun S. Mahadevan
- Department of Bioengineering, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - Byron L. Long
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
- Department of Computer Science, University of Texas at San Antonio, San Antonio, Texas, United States of America
| | - Chenyue W. Hu
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - David T. Ryan
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - Nicolas E. Grandel
- Systems, Synthetic and Physical Biology Program, Rice University, Houston, Texas, United States of America
| | - George L. Britton
- Systems, Synthetic and Physical Biology Program, Rice University, Houston, Texas, United States of America
| | - Marisol Bustos
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
| | - Maria A. Gonzalez Porras
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
| | - Katerina Stojkova
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
| | - Andrew Ligeralde
- Biophysics Graduate Program, University of California, Berkeley, California, United States of America
| | - Hyeonwi Son
- Department of Oral & Maxillofacial Surgery, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - John Shannonhouse
- Department of Oral & Maxillofacial Surgery, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Jacob T. Robinson
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, United States of America
| | - Aryeh Warmflash
- Systems, Synthetic and Physical Biology Program, Rice University, Houston, Texas, United States of America
- Department of Biosciences, Rice University, Houston, Texas, United States of America
| | - Eric M. Brey
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
- UTSA–UT Health Joint Graduate Group in Biomedical Engineering, San Antonio, Texas, United States of America
| | - Yu Shin Kim
- Department of Oral & Maxillofacial Surgery, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
- UTSA–UT Health Joint Graduate Group in Biomedical Engineering, San Antonio, Texas, United States of America
- Programs in Integrated Biomedical Sciences, Translational Sciences, Radiological Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Amina A. Qutub
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
- UTSA–UT Health Joint Graduate Group in Biomedical Engineering, San Antonio, Texas, United States of America
- UTSA AI MATRIX Consortium, San Antonio, Texas, United States of America
- * E-mail:
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Tjahjono N, Jin Y, Hsu A, Roukes M, Tian L. Letting the little light of mind shine: Advances and future directions in neurochemical detection. Neurosci Res 2022; 179:65-78. [PMID: 34861294 PMCID: PMC9508992 DOI: 10.1016/j.neures.2021.11.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022]
Abstract
Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.
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Affiliation(s)
- Nikki Tjahjono
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA, 95616, USA
| | - Yihan Jin
- Neuroscience Graduate Group, University of California, Davis, Davis, CA, 95618, USA
| | - Alice Hsu
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Michael Roukes
- Department of Physics, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, 95616, USA.
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Etter G, Manseau F, Williams S. A Probabilistic Framework for Decoding Behavior From in vivo Calcium Imaging Data. Front Neural Circuits 2020; 14:19. [PMID: 32499681 PMCID: PMC7243991 DOI: 10.3389/fncir.2020.00019] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 04/06/2020] [Indexed: 11/13/2022] Open
Abstract
Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.
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Affiliation(s)
- Guillaume Etter
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | | | - Sylvain Williams
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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Oh J, Lee C, Kaang BK. Imaging and analysis of genetically encoded calcium indicators linking neural circuits and behaviors. THE KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY : OFFICIAL JOURNAL OF THE KOREAN PHYSIOLOGICAL SOCIETY AND THE KOREAN SOCIETY OF PHARMACOLOGY 2019; 23:237-249. [PMID: 31297008 PMCID: PMC6609268 DOI: 10.4196/kjpp.2019.23.4.237] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/29/2019] [Accepted: 05/29/2019] [Indexed: 12/21/2022]
Abstract
Confirming the direct link between neural circuit activity and animal behavior has been a principal aim of neuroscience. The genetically encoded calcium indicator (GECI), which binds to calcium ions and emits fluorescence visualizing intracellular calcium concentration, enables detection of in vivo neuronal firing activity. Various GECIs have been developed and can be chosen for diverse purposes. These GECI-based signals can be acquired by several tools including two-photon microscopy and microendoscopy for precise or wide imaging at cellular to synaptic levels. In addition, the images from GECI signals can be analyzed with open source codes including constrained non-negative matrix factorization for endoscopy data (CNMF_E) and miniscope 1-photon-based calcium imaging signal extraction pipeline (MIN1PIPE), and considering parameters of the imaged brain regions (e.g., diameter or shape of soma or the resolution of recorded images), the real-time activity of each cell can be acquired and linked with animal behaviors. As a result, GECI signal analysis can be a powerful tool for revealing the functions of neuronal circuits related to specific behaviors.
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Affiliation(s)
- Jihae Oh
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Chiwoo Lee
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Bong-Kiun Kaang
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
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