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Dimensionality reduction of calcium-imaged neuronal population activity

  • Tze Hui Koh
  • , William E. Bishop
  • , Takashi Kawashima
  • , Brian B. Jeon
  • , Ranjani Srinivasan
  • , Yu Mu
  • , Ziqiang Wei
  • , Sandra J. Kuhlman
  • , Misha B. Ahrens
  • , Steven M. Chase
  • , Byron M. Yu
  • Carnegie Mellon University
  • Center for the Neural Basis of Cognition
  • Howard Hughes Medical Institute
  • Weizmann Institute of Science
  • Johns Hopkins University
  • Center for Excellence in Brain Science and Intelligence Technology

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality-reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear whether the dimensionality-reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality-reduction methods. We have also developed a method to perform deconvolution and dimensionality reduction simultaneously (calcium imaging linear dynamical system, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium-imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

Original languageEnglish
Pages (from-to)71-85
Number of pages15
JournalNature Computational Science
Volume3
Issue number1
DOIs
StatePublished - Jan 2023

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