autoRM: an Automatic Retinotopic Mapping Tool for Mice

version 1.0

release date 2021/4/19

written by Ruix. Li

autoRM provides a fully automatic tool for mice retinotopic mapping. It can help you to locate the primary and several higher-order visual cortex of mice with calcium or optical imaging (figure from ref [1]).

What you need to use autoRM

Psychopy3 (>=2020.1.3) to present visual stimulus
NI-DAQ USB-6501 digital I/O Device and NI-DAQmx driver for synchronization
MATLAB (>=2019a) with image processing toolbox

The content of autoRM

RetinotopicMappingv5.py is a Python script to present visual stimulus for retinotopic mapping, a typical experiment last about 30 minutes.
RMDegMap.m is a MATLAB function to calculate visual degree, naming azimuth and elevation.
RMSetParam.m is a MATLAB app helps you to determine parameters used in RMAreaMAp.m.
RMAreaMap.m is a MATLAB function to identify visual areas, see ref [2] for details.
autoRM.m is the MATLAB function you use, it calls RMDegMap RMSetParam and RMAreaMap, usually you don't need to use other functions.

How to use

MATLAB image processing toolbox is required.
Run RetinotopicMappingv5.py with psychopy3 before start recording. Recommend using a NI-DAQ digital I/O device to synchronize your camera with the visual stimulus.
The Python program will return the frame number required for recording. Set the frame number in your camera control interface.

After stimulus finish, RetinotopicMappingv5.py will save a txt log file and a json configuration file. The json file is required in the following steps.
Convert your recording data into .mat data.
Add RMDegMap.m, RMSetParam.m and RMAreaMap.m to your MATLAB path, then call autoRM in MATLAB command window and follow the instructions to select recording data .mat file and .json configration files.
A GUI will pop-up for you to adjust parameters.

autoRM will create a figure for you at the path of .json config file. This figure can be used as retinotopic mapping reference for your subsequential experiments.

Inputs and outputs

autoRM need the following input to work

XYT image data: the image data for analysis
configuration json file: the json file generated by RetinotopicMappingv5.py, which contains experiment configurations for data process.

autoRM save the following data

degMap mat file contains the following data

FOV: a image of field of view
dataL2R, dataR2L: trial average data of left to right and right to left
dataD2U, dataU2D: trial average data of down to up and up to down
phaseMaps: phase maps of trial average data obtained from FFT.
degMaps: degree maps connect visual areas with visual field.
degMapAzi: azimuth map. Here assume the azimuth at front of mouse is 0. Azimuth usually ranges from 0 to 120 degree.
degMapElv: elevation map. Here assume the elevation at front of mouse is 0. Elevation ususally ranges from -40 to 40 degree.

areaMap mat file contains the following data

rSignMap: raw sign map calculated from degMapAzi and degMapElv
signMap: sign map after image process to reduce noise and artifacts.
areaMap: a bit map indicate visal areas.
signFOV: align sign map to FOV.
hltFOV: high light visual areas in FOV.

autoRM save 2 figures

Endnote

References:

[1] Marshel, James H., et al. "Functional specialization of seven mouse visual cortical areas." Neuron 72.6 (2011): 1040-1054.

[2] Juavinett, Ashley L., et al. "Automated identification of mouse visual areas with intrinsic signal imaging." Nature protocols 12.1 (2017): 32.

[3] Zhuang, Jun, et al. "An extended retinotopic map of mouse cortex." Elife 6 (2017): e18372.

RuixLi / autoRM