{ "info": { "author": "Jun Zhuang @ Allen Institute for Brain Science", "author_email": "junz@alleninstitute.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python" ], "description": "# retinotopic_mapping package \n\nby Jun Zhuang \n© 2016 Allen Institute \nemail: junz<AT>alleninstitute<DOT>org \n\nFor a more thorough introduction and explanation of the module please \nsee our [documentation](http://retinotopic-mapping.readthedocs.io/en/latest/).\n\nThe retinotopic mapping package is a self-contained module\nfor performing automated segmentation of the mouse\nvisual cortex. The experimental setup and analysis routine was\nmodified from Garrett et al. 2014 (1), and closely follows\nthe protocols and procedures documented in Juavinett et al. 2016\n(2).\n\nThe code base contains several stimulus routines which are\nhighly customizable and designed to give the user significant\nflexibility and control in creative experimental design. There\nare two distinct but connected aspects to the package:\n\n1. an online experimental component comprised of the\n`MonitorSetup`, `StimulusRoutines`, and\n`DisplayStimulus` modules\n\n2. an offline automated analysis component provided\nby the `RetinotopicMapping` module\n\nThe analysis takes visual altitude and azimuth maps of mouse cortex as inputs, calculates the visual \nsign of each pixel and auto-segments the cortical surface into primary visual cortex and multiple higher\nvisual cortices. Ideally, the visual altitude and azimuth maps can be generated by fourier analysis of\npopulation cortical responses to periodic sweeping checker board visual stimuli (3, 4). \n\nThe package also provides some useful plotting functions to visualize the results.\n\nPlease check the jupyter notebook in the '\\examples' folder for a documented that takes an experimental\ndata set generated from the `StimulusRoutine.py` module and then performs an automated visual segmentation\nof the mouse cortex using the `Retinotopic.py` module\n\nhttps://github.com/zhuangjun1981/retinotopic_mapping/blob/master/retinotopic_mapping/examples/retinotopic_mapping_example.ipynb\n\n### Contributors:\n* Jun Zhuang @zhuang1981\n* John Yearseley @yearsj\n* Derric Williams @derricw\n\n#### Language:\n\n1. python 2.7\n\n\n#### Install:\n```\ncd \npython setup.py install\n```\n\n\n#### Dependencies:\n\n1. numpy, version 1.10.4 or later\n2. scipy, version 0.17.0 or later\n3. OpenCV-Python, version 2.4.8 or later\n4. scikit-image, version 0.12.3 or later\n5. matplotlib, version 1.5.1 or later\n6. tifffile, version 0.7.0 or later\n7. PsychoPy, version 1.7 or later\n8. PyDAQmx, version 1.2 or later \n * requires National Instruments DAQmx driver, version 15.0 or later\n\n#### References:\n\n1. Garrett ME, Nauhaus I, Marshel JH, Callaway EM (2014) Topography and areal organization of mouse visual cortex. J Neurosci 34:12587-12600.\n\n2. 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.\n\n3. Kalatsky VA, Stryker MP (2003) New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron 38:529-545.\n\n4. Marshel JH, Kaye AP, Nauhaus I, Callaway EM (2012) Anterior-posterior direction opponency in the superficial mouse lateral geniculate nucleus. Neuron 76:713-720.\n\n5. 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.\n\n6. Sereno MI, McDonald CT, Allman JM (1994) Analysis of retinotopic maps in extrastriate cortex. Cereb Cortex 4:601-620.\n\n\n#### Issues:\n\n1. Most image analysis parameters are defined as number of pixels, not microns.\n2. 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