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Data-driven filtration and segmentation of mesoscale neural dynamics

View ORCID ProfileSydney C. Weiser, View ORCID ProfileBrian R. Mullen, View ORCID ProfileDesiderio Ascencio, View ORCID ProfileJames B. Ackman
doi: https://doi.org/10.1101/2020.12.30.424865
Sydney C. Weiser
1Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA
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  • For correspondence: jackman@ucsc.edu scweiser@ucsc.edu
Brian R. Mullen
1Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA
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Desiderio Ascencio
2Department of Psychology, University of California Santa Cruz, Santa Cruz, CA, USA
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James B. Ackman
1Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA
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  • For correspondence: jackman@ucsc.edu scweiser@ucsc.edu
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Abstract

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Minor adjustments to text and references; Addition of two supplemental videos.

  • https://github.com/ackmanlab/pySEAS

  • Abbreviations and Terms Defined

    ΔF/F (dFoF)
    change in fluorescence over mean fluorescence
    ICA
    Independent Component Analysis
    PCA
    Principal Component Analysis
    Domain Map
    maximum projection map of ICA components
    Domain
    A single contiguous unit from a domain map, represents an ICA component’s maximal region of influence
    Mosiac Movie
    a video representation of the time series extracted under each domain in the domain map
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    Posted February 24, 2021.
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    Data-driven filtration and segmentation of mesoscale neural dynamics
    Sydney C. Weiser, Brian R. Mullen, Desiderio Ascencio, James B. Ackman
    bioRxiv 2020.12.30.424865; doi: https://doi.org/10.1101/2020.12.30.424865
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    Data-driven filtration and segmentation of mesoscale neural dynamics
    Sydney C. Weiser, Brian R. Mullen, Desiderio Ascencio, James B. Ackman
    bioRxiv 2020.12.30.424865; doi: https://doi.org/10.1101/2020.12.30.424865

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