fsaverage subject for pycortex

<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, &amp; Sereno, 1999; Fischl, Dale &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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.<b>概述</b><br>本文件夹包含所有必要文件，用于基于FreeSurfer（Dale等，1999；Fischl等，1999；Fischl，2012）的fsaverage标准脑，在pycortex（Gao等，2015）中创建pycortex被试对象。这意味着，任何映射至MNI标准脑或fsaverage皮层表面顶点的数据，均可通过本数据集提供的皮层表面，使用pycortex进行可视化展示。<br><br>关于pycortex的使用方法，请参阅其GitHub页面（https://github.com/gallantlab/pycortex）、官方文档页面（https://gallantlab.github.io/）以及示例图库（https://gallantlab.github.io/auto_examples/index.html）。<br><br>完成pycortex安装后，您可通过在Python命令行执行以下代码，从本平台自动下载fsaverage所需文件：<br><br>import cortex<br>cortex.download_subject('fsaverage')<br><br>若您通过上述命令自动下载了本数据集，可通过在命令行执行以下代码定位文件：<br><br>import cortex<br>file_store = cortex.options.config.get('basic', 'filestore')<br>file_path = os.path.join(file_store, 'fsaverage', 'overlays.svg')<br>print(file_path)<br><br>该皮层表面标注了多个感兴趣区（ROIs，Regions of Interest），包括V1、V2、V3、V3A、V3B、V4、LO1、LO2、hMT、MST、VO1、VO2、IPS0、IPS1、IPS2、IPS3、IPS4、IPS5、SPL1、OFA、FFA及PPA，上述脑区的定义参考了多项研究，包括Wang等（2015）的概率图谱、人类连接组项目7T视网膜拓扑数据（Benson等，2018）以及FFA和PPA的跨被试概率图谱（Weiner等，2017、2018）。支撑这些感兴趣区定义的原始数据，可在本数据集附带的overlays.svg文件的图层中查看。<br><br><b>贡献说明</b><br>NB完成了从FreeSurfer中导入fsaverage被试的初始工作，并创建了适配多种图谱分辨率的转换方式；ML则完成了皮层的重新扁平化处理，筛选并将感兴趣区投射至皮层表面，并根据（）手动定义了感兴趣区与脑沟结构。<br><br><b>参考文献</b><br>Dale, A. M., Fischl, B., & Sereno, M. I. (1999). 基于皮层表面的分析Ⅰ：分割与表面重建。<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). 基于皮层表面的分析Ⅱ：膨胀、扁平化与基于表面的坐标系。<i>Neuroimage</i>, <i>9</i>(2), 195–207. https://doi.org/10.1006/nimg.1998.0396<br>Fischl, B. (2012). FreeSurfer. <i>NeuroImage</i>, 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：一款用于功能磁共振成像的交互式皮层可视化工具。<i>Frontiers in Neuroinformatics</i>, 9. https://doi.org/10.3389/fninf.2015.00023<br>Wang, L., Mruczek, R. E. B., Arcaro, M. J., & Kastner, S. (2015). 人类皮层视觉拓扑的概率图谱。<i>Cerebral Cortex</i>, 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). 人类高级视觉皮层功能特异性脑区的细胞构筑学研究。<i>Cerebral Cortex</i> <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). 基于皮层对齐与交叉验证定义海马旁回位置区的最可能位置。<i>Neuroimage</i> <i>170</i>, 373–384.