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Carbonero D, Noueihed J, Kramer MA, White JA. Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.11.561797. [PMID: 37905071 PMCID: PMC10614735 DOI: 10.1101/2023.10.11.561797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.
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Affiliation(s)
- Daniel Carbonero
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Jad Noueihed
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Mark A. Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
| | - John A. White
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
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Matera C, Bregestovski P. Light-Controlled Modulation and Analysis of Neuronal Functions. Int J Mol Sci 2022; 23:12921. [PMID: 36361710 PMCID: PMC9657357 DOI: 10.3390/ijms232112921] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 04/15/2024] Open
Abstract
Light is an extraordinary tool allowing us to read out and control neuronal functions thanks to its unique properties: it has a great degree of bioorthogonality and is minimally invasive; it can be precisely delivered with high spatial and temporal precision; and it can be used simultaneously or consequently at multiple wavelengths and locations [...].
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Affiliation(s)
- Carlo Matera
- Department of Pharmaceutical Sciences, University of Milan, 20133 Milan, Italy
| | - Piotr Bregestovski
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes, Aix-Marseille University, 13005 Marseille, France
- Institute of Neurosciences, Kazan State Medical University, 420111 Kazan, Russia
- Department of Normal Physiology, Kazan State Medical University, 420111 Kazan, Russia
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Low-Cost Platform for Multianimal Chronic Local Field Potential Video Monitoring with Graphical User Interface (GUI) for Seizure Detection and Behavioral Scoring. eNeuro 2022; 9:ENEURO.0283-22.2022. [PMID: 36192155 PMCID: PMC9581574 DOI: 10.1523/eneuro.0283-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/23/2022] [Accepted: 09/28/2022] [Indexed: 12/15/2022] Open
Abstract
Experiments employing chronic monitoring of neurophysiological signals and video are commonly used in studies of epilepsy to characterize behavioral correlates of seizures. Our objective was to design a low-cost platform that enables chronic monitoring of several animals simultaneously, synchronizes bilateral local field potential (LFP) and video streams in real time, and parses recorded data into manageable file sizes. We present a hardware solution leveraging Intan and Open Ephys acquisition systems and a software solution implemented in Bonsai. The platform was tested in 48-h continuous recordings simultaneously from multiple mice (male and female) with chronic epilepsy. To enable seizure detection and scoring, we developed a graphical user interface (GUI) that reads the data produced by our workflow and allows a user with no coding expertise to analyze events. Our Bonsai workflow was designed to maximize flexibility for a wide variety of experimental applications, and our use of the Open Ephys acquisition board would allow for scaling recordings up to 128 channels per animal.
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