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retinotopic mapping tools

Project description

# retinotopic_mapping package

by Jun Zhuang © 2016 Allen Institute email: junz<AT>alleninstitute<DOT>org

For a more thorough introduction and explanation of the module please see our [documentation](http://retinotopic-mapping.readthedocs.io/en/latest/).

The retinotopic mapping package is a self-contained module for performing automated segmentation of the mouse visual cortex. The experimental setup and analysis routine was modified from Garrett et al. 2014 (1), and closely follows the protocols and procedures documented in Juavinett et al. 2016 (2).

The code base contains several stimulus routines which are highly customizable and designed to give the user significant flexibility and control in creative experimental design. There are two distinct but connected aspects to the package:

1. an online experimental component comprised of the MonitorSetup, StimulusRoutines, and DisplayStimulus modules

2. an offline automated analysis component provided by the RetinotopicMapping module

The analysis takes visual altitude and azimuth maps of mouse cortex as inputs, calculates the visual sign of each pixel and auto-segments the cortical surface into primary visual cortex and multiple higher visual cortices. Ideally, the visual altitude and azimuth maps can be generated by fourier analysis of population cortical responses to periodic sweeping checker board visual stimuli (3, 4).

The package also provides some useful plotting functions to visualize the results.

Please check the jupyter notebook in the ‘examples’ folder for a documented that takes an experimental data set generated from the StimulusRoutine.py module and then performs an automated visual segmentation of the mouse cortex using the Retinotopic.py module

https://github.com/zhuangjun1981/retinotopic_mapping/blob/master/retinotopic_mapping/examples/retinotopic_mapping_example.ipynb

### Contributors: * Jun Zhuang @zhuang1981 * John Yearseley @yearsj * Derric Williams @derricw

#### Language:

  1. python 2.7

#### Install: ` cd <package_path> python setup.py install `

#### Dependencies:

  1. numpy, version 1.10.4 or later
  2. scipy, version 0.17.0 or later
  3. OpenCV-Python, version 2.4.8 or later
  4. scikit-image, version 0.12.3 or later
  5. matplotlib, version 1.5.1 or later
  6. tifffile, version 0.7.0 or later
  7. PsychoPy, version 1.7 or later
  8. PyDAQmx, version 1.2 or later * requires National Instruments DAQmx driver, version 15.0 or later

#### References:

  1. Garrett ME, Nauhaus I, Marshel JH, Callaway EM (2014) Topography and areal organization of mouse visual cortex. J Neurosci 34:12587-12600.
  2. Juavinett AL, Nauhaus I, Garrett ME, Zhuang J, Callaway EM (2017). Automated identification of mouse visual areas with intrinsic signal imaging. Nature Protocols. 12: 32-43.
  3. Kalatsky VA, Stryker MP (2003) New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron 38:529-545.
  4. Marshel JH, Kaye AP, Nauhaus I, Callaway EM (2012) Anterior-posterior direction opponency in the superficial mouse lateral geniculate nucleus. Neuron 76:713-720.
  5. Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW, Brady TJ, Rosen BR, Tootell RB (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268:889-893.
  6. Sereno MI, McDonald CT, Allman JM (1994) Analysis of retinotopic maps in extrastriate cortex. Cereb Cortex 4:601-620.

#### Issues:

  1. Most image analysis parameters are defined as number of pixels, not microns.
  2. Works in windows, but not fully tested on Mac and Linux.

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