DOI: 10.6084/m9.figshare.9916166.v1
title: fsaverage subject for pycortex

description: <b>Summary</b><br>This is folder containing all the files necessary to create a pycortex subject for the fsaverage brain from freesurfer (Dale, Fischl, & Sereno, 1999; Fischl, Dale & Sereno, 1999; Fischl, 2012) in pycortex (Gao et al, 2015). This means that any data mapped to the MNI brain or to the vertices of the fsaverage surface can be displayed with pycortex on the surface provided in this dataset. For usage of pycortex, see the pycortex git page (https://github.com/gallantlab/pycortex), the pycortex documentation page (https://gallantlab.github.io/), and the pycortex gallery (https://gallantlab.github.io/auto_examples/index.html).Once you have installed pycortex, the files for fsaverage can be automatically downloaded from this site by calling the following at a python command prompt: <br><br>import cortex<br>cortex.download_subject('fsaverage')<br><br>If you automatically downloaded this dataset using the command above, you can find the files by calling the following at the command prompt:<br>import cortexfile_store = cortex.options.config.get('basic', 'filestore')file_path = os.path.join(file_store, 'fsaverage', 'overlays.svg')<br>print(file_path)<br><br>The surface has labeled regions of interest (ROIs) for V1, V2, V3, V3A, V3B, V4, LO1, LO2, hMT, MST, VO1, VO2, IPS0, IPS1, IPS2, IPS3, IPS4, IPS5, SPL1, OFA, FFA, and PPA, defined according to multiple sources, including the Wang et al (2015) probabilistic atlas, Human Connectome Project 7T retinotopy data (Benson et al 2018), and cross-subject probabilistic maps of FFA and PPA (from Weiner et al 2017, 2018). The data that provides the basis for these ROIs can be viewed in the layers of the overlays.svg file included in this dataset. <br><br><b>Contributions</b>NB performed the initial import of the fsaverge subject from freesurfer and created and transforms to various atlas resolutionsML re-flattened the brain, curated and projected the regions of interest onto the brain, and manually defined the regions of interest and sulci according to (). <br><br><b>References</b>Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. <i>Neuroimage</i>, <i>9</i>(2), 179–194. https://doi.org/10.1006/nimg.1998.0395<br>Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. <i>Neuroimage</i>, <i>9</i>(2), 195–207. https://doi.org/10.1006/nimg.1998.0396<br>Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. https://doi.org/10.1016/J.NEUROIMAGE.2012.01.021<br><br>Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015). Pycortex: an interactive surface visualizer for fMRI. Frontiers in Neuroinformatics, 9. https://doi.org/10.3389/fninf.2015.00023<br>Wang, L., Mruczek, R. E. B., Arcaro, M. J., & Kastner, S. (2015). Probabilistic Maps of Visual Topography in Human Cortex. Cerebral Cortex, 25(10), 3911–3931. https://doi.org/10.1093/cercor/bhu277<br>Weiner, K.S., Barnett, M.A., Lorenz, S., Caspers, J., Stigliani, A., Amunts, K., Zilles, K., Fischl, B., and Grill-Spector, K. (2017). The Cytoarchitecture of Domain-specific Regions in Human High-level Visual Cortex. Cereb. Cortex <i>27</i>, 146–161.<br>Weiner, K.S., Barnett, M.A., Witthoft, N., Golarai, G., Stigliani, A., Kay, K.N., Gomez, J., Natu, V.S., Amunts, K., Zilles, K., et al. (2018). Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation. Neuroimage <i>170</i>, 373–384.