Klein, A., Ghosh, S. S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., ... & Keshavan, A. (2017). Mindboggling morphometry of human brains. PLoS computational biology, 13(2), e1005350.
Brain images have been used a biomarkers for mental illness, but there are still not a lot of reliable biomarkers out there.
A significant impediment to understanding mental health is variation in human brain anatomy etc. The normal variation must first be established to determine what is out of range. To know normal variation, correspondence between brains must first be established, but that is very difficult. registrations methods are highly variable etc.
instead neuroanatomists prefer to use high level features, such as distinctive cortical folding patterns etc. To compare these features across individuals we need to quantify them. Current methods for quantification can be via greyscale values in a volume, deformation based morphometry, voxel based morphometry, directly measure shape, volume, surface area, cortical thickness.
More subtle shape measures may provide more sensitive and specific biomarkers and combining shape measures in a multivariate analysis could improve results.
Design and Implementation
Mindboggle takes in preprocessed T1 data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. It can be run command line, in python, and via docker.
I will be using this blog space as a repository of notes on various articles and lectures related to my research. Click on the categories below to organize the posts by topic.