Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings
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 sta...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78448-6 |
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| author | Daniel Carbonero Jad Noueihed Mark A. Kramer John A. White |
| author_facet | Daniel Carbonero Jad Noueihed Mark A. Kramer John A. White |
| author_sort | Daniel Carbonero |
| collection | DOAJ |
| description | 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. Nonnegative 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. |
| format | Article |
| id | doaj-art-7e48120f9ab8476ba2f4fb8abc17ccaa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7e48120f9ab8476ba2f4fb8abc17ccaa2024-11-17T12:28:54ZengNature PortfolioScientific Reports2045-23222024-11-0114111710.1038/s41598-024-78448-6Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordingsDaniel Carbonero0Jad Noueihed1Mark A. Kramer2John A. White3Department of Biomedical Engineering, Boston UniversityDepartment of Biomedical Engineering, Boston UniversityDepartment of Mathematics and Statistics, Boston UniversityDepartment of Biomedical Engineering, Boston UniversityAbstract 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. Nonnegative 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.https://doi.org/10.1038/s41598-024-78448-6Dimensionality reductionNonnegative matrix factorizationCalcium imagingNeuronal network dynamicsNeuronal network analysis |
| spellingShingle | Daniel Carbonero Jad Noueihed Mark A. Kramer John A. White Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings Scientific Reports Dimensionality reduction Nonnegative matrix factorization Calcium imaging Neuronal network dynamics Neuronal network analysis |
| title | Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings |
| title_full | Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings |
| title_fullStr | Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings |
| title_full_unstemmed | Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings |
| title_short | Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings |
| title_sort | nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings |
| topic | Dimensionality reduction Nonnegative matrix factorization Calcium imaging Neuronal network dynamics Neuronal network analysis |
| url | https://doi.org/10.1038/s41598-024-78448-6 |
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