Wide-Field Calcium Imaging of Neuronal Network Dynamics In Vivo

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Abstract

A central tenet of neuroscience is that sensory, motor, and cognitive behaviors are generated by the communications and interactions among neurons, distributed within and across anatomically and functionally distinct brain regions. Therefore, to decipher how the brain plans, learns, and executes behaviors requires characterizing neuronal activity at multiple spatial and temporal scales. This includes simultaneously recording neuronal dynamics at the mesoscale level to understand the interactions among brain regions during different behavioral and brain states. Wide-field Ca2+ imaging, which uses single photon excitation and improved genetically encoded Ca2+ indicators, allows for simultaneous recordings of large brain areas and is proving to be a powerful tool to study neuronal activity at the mesoscopic scale in behaving animals. This review details the techniques used for wide-field Ca2+ imaging and the various approaches employed for the analyses of the rich neuronal-behavioral data sets obtained. Also discussed is how wide-field Ca2+ imaging is providing novel insights into both normal and altered neural processing in disease. Finally, we examine the limitations of the approach and new developments in wide-field Ca2+ imaging that are bringing new capabilities to this important technique for investigating large-scale neuronal dynamics.

Original languageEnglish (US)
Article number1601
JournalBiology
Volume11
Issue number11
DOIs
StatePublished - Nov 2022

Bibliographical note

Funding Information:
This work was supported in part by National Institutes of Health grants, R01 NS111028 to S.B.K. and T.J.E., P30 DA048742 to S.B.K. and T.J.E., and K99 NS121274 to M.L.S., as well as by an Academic Investment Research Program grant from the University of Minnesota Medical School to T.J.E.

Publisher Copyright:
© 2022 by the authors.

Keywords

  • calcium imaging
  • functional connectivity
  • mesoscopic imaging
  • neuronal dynamics
  • spatial independent component analysis

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