Publication

Optimizing intact skull intrinsic signal imaging for subsequent targeted electrophysiology across mouse visual cortex

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Last modified
  • 05/20/2025
Type of Material
Authors
    Armel Nsiangani, Georgia Institute of TechnologyJospeh Del Rosario, Georgia Institute of TechnologyAlan C Yeh, Georgia Institute of TechnologyDonghoon Shin, Georgia Institute of TechnologyShea Wells, Georgia Institute of TechnologyTidhar Lev-Ari, Georgia Institute of TechnologyBrice Williams, Georgia Institute of TechnologyBilal Haider, Emory University
Language
  • English
Date
  • 2022-02-08
Publisher
  • NATURE PORTFOLIO
Publication Version
Copyright Statement
  • © The Author(s) 2022
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Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 12
Issue
  • 1
Start Page
  • 2063
End Page
  • 2063
Supplemental Material (URL)
Abstract
  • Understanding brain function requires repeatable measurements of neural activity across multiple scales and multiple brain areas. In mice, large scale cortical neural activity evokes hemodynamic changes readily observable with intrinsic signal imaging (ISI). Pairing ISI with visual stimulation allows identification of primary visual cortex (V1) and higher visual areas (HVAs), typically through cranial windows that thin or remove the skull. These procedures can diminish long-term mechanical and physiological stability required for delicate electrophysiological measurements made weeks to months after imaging (e.g., in subjects undergoing behavioral training). Here, we optimized and directly validated an intact skull ISI system in mice. We first assessed how imaging quality and duration affect reliability of retinotopic maps in V1 and HVAs. We then verified ISI map retinotopy in V1 and HVAs with targeted, multi-site electrophysiology several weeks after imaging. Reliable ISI maps of V1 and multiple HVAs emerged with ~ 60 trials of imaging (65 ± 6 min), and these showed strong correlation to local field potential (LFP) retinotopy in superficial cortical layers (r2 = 0.74–0.82). This system is thus well-suited for targeted, multi-area electrophysiology weeks to months after imaging. We provide detailed instructions and code for other researchers to implement this system.
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  • Computer Science

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