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.
- https://gitlab.rc.uab.edu/mdefende/lladir
- 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.
- https://gitlab.rc.uab.edu/mdefende/image-to-surface
- 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:
- Methods for classifying eye movement phenotypes:
- https://github.com/Visscher-Lab/OculomotorStrategyToolkit
- The manuscript describing these methods is Maniglia, Visscher and Seitz, Journal of Vision, 2020, “A method to characterize compensatory oculomotor strategies following simulated central vision loss” available open access from https://jov.arvojournals.org/article.aspx?articleid=2770829
- For example, it identifies the precision of saccades to a preferred retinal locus, and the stability of fixation once the saccade has been made.
Neuroimaging tools
Much help with neuroimaging tools can be found with the CINL Brain Core
