Neuroimaging Analysis Tools created by the lab:

  • Leveraging Large, Available Datasets to Improve Reliability
    • This approach uses machine learning algorithms to create a model of cortical thickness as a function of various anatomic and retinotopic properties in cortex. The goal is to reduce variance due to these factors in order to better identify differences between participant groups.
  • Image to Surface Pipeline
    • The image-to-surface pipeline represents a simple way to take images from the retina and map them to their predicted locations on cortex.  This is especially useful for identifying anticipated projections of retinal lesions.
  • Tools for segmenting V1V1 labels: fsaverage segmented V1 label file, for segmenting V1 for eccentricity-specific analyses
    • V1 analysis code: Code to distribute V1 labels from fsaverage brain to individual participants
    • If you use these tools, please cite Burge, W. K., Griffis, J. C., Nenert, R., Elkhetali, A., DeCarlo, D. K., Ver Hoef, L. W., & … Visscher, K. M. (2016). Cortical thickness in human V1 associated with central vision loss. Scientific Reports, 623268. doi:10.1038/srep23268 pdf

Eye movement measurement tools created by the lab:

Neuroimaging tools
Much help with neuroimaging tools can be found with the CINL Brain Core