Large-scale recording of neuronal activity in freely-moving mice at cellular resolution

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作者
Aniruddha Das
Sarah Holden
Julie Borovicka
Jacob Icardi
Abigail O’Niel
Ariel Chaklai
Davina Patel
Rushik Patel
Stefanie Kaech Petrie
Jacob Raber
Hod Dana
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[1] Cleveland Clinic Foundation,Department of Neurosciences, Lerner Research Institute
[2] Oregon Health and Science University,Department of Behavioral Neuroscience
[3] Oregon Health and Science University,Jungers Center
[4] Oregon Health and Science University,Departments of Neurology and Radiation Medicine, Division of Neuroscience, ONPRC
[5] Case Western Reserve University,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, School of Medicine
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Current methods for recording large-scale neuronal activity from behaving mice at single-cell resolution require either fixing the mouse head under a microscope or attachment of a recording device to the animal’s skull. Both of these options significantly affect the animal behavior and hence also the recorded brain activity patterns. Here, we introduce a different method to acquire snapshots of single-cell cortical activity maps from freely-moving mice using a calcium sensor called CaMPARI. CaMPARI has a unique property of irreversibly changing its color from green to red inside active neurons when illuminated with 400 nm light. We capitalize on this property to demonstrate cortex-wide activity recording without any head fixation, tethering, or attachment of a miniaturized device to the mouse’s head. Multiple cortical regions were recorded while the mouse was performing a battery of behavioral and cognitive tests. We identified task-dependent activity patterns across motor and somatosensory cortices, with significant differences across sub-regions of the motor cortex and correlations across several activity patterns and task parameters. This CaMPARI-based recording method expands the capabilities of recording neuronal activity from freely-moving and behaving mice under minimally-restrictive experimental conditions and provides large-scale volumetric data that are currently not accessible otherwise.
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