Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages
- University of British Columbia, Canada
- University of Britsih Columbia, Canada
Abstract
We report improved automated open-source methodology for head-fixed mesoscale cortical imaging and/or behavioral training of home cage mice using Raspberry Pi-based hardware. Staged partial and probabilistic restraint allows mice to adjust to self-initiated headfixation over 3 weeks' time with ~50% participation rate. We support a cue-based behavioral licking task monitored by a capacitive touch-sensor water spout. While automatically head-fixed, we acquire spontaneous, movement-triggered, or licking task-evoked GCaMP6 cortical signals. An analysis pipeline marked both behavioral events, as well as analyzed brain fluorescence signals as they relate to spontaneous and/or task-evoked behavioral activity. Mice were trained to suppress licking and wait for cues that marked the delivery of water. Correct rewarded go-trials were associated with widespread activation of midline and lateral barrel cortex areas following a vibration cue and delayed frontal and lateral motor cortex activation. Cortical GCaMP signals predicted trial success and correlated strongly with trial-outcome dependent body movements.
Data availability
The name of each brain imaging file which contains both the mouse ID and the time stamp can be found in the SQL database (RFIDtag_xxxx_timestamp.raw; see Methods for URL and hosted as a full text file archive on Zenodo (https://doi.org/10.5281/zenodo.3268838) for the 5 cages of male mice that compose figures 1-7 and cage 6 female mice https://doi.org/10.5683/SP2/9RFXRP.All text file behavioral data is included online as well as image data for figures 8 and supplemental figure 2 are found on https://doi.org/10.5281/zenodo.3243572, all data files and code for figures 9 and 10 are found in https://doi.org/10.5683/SP2/ZTOPUM and female mouse behavioral data https://doi.org/10.5683/SP2/9RFXRP. All Python data acquisition code can be found on https://github.com/jamieboyd/AutoHeadFix/ and https://github.com/ubcbraincircuits/AutoHeadFix.
-
Code and .mat files for automated homecage paper (Figs 9 and 10)https://doi.org/10.5683/SP2/ZTOPUM.
-
Home cage data female mice cage 6 all text file and database informationhttps://doi.org/10.5683/SP2/9RFXRP.
Article and author information
Author details
Funding
Canadian Institutes of Health Research (FDN-143209)
- Timothy H Murphy
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- David Kleinfeld, University of California, San Diego, United States
Ethics
Animal experimentation: All procedures were conducted with approval from the University of British Columbia Animal Care Committee and in accordance with guidelines set forth by the Canadian Council for Animal Care.
Version history
- Received: February 12, 2020
- Accepted: May 7, 2020
- Accepted Manuscript published: May 15, 2020 (version 1)
- Version of Record published: July 2, 2020 (version 2)
Copyright
© 2020, Murphy et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 4,661
- views
-
- 488
- downloads
-
- 23
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages
eLife 9:e55964.
https://doi.org/10.7554/eLife.55964
Further reading
-
- Neuroscience
Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.
-
- Neuroscience
The first neuronal wiring diagram of an insect nerve cord, which includes biological information on cell type and organisation, enables further investigation into premotor circuit function.
