RMINC is a library for the R statistical environment designed to read and write MINC volumes as well as perform optimized statistical routines at every voxel of a set of files.



The main development home for RMINC can be found here: https://launchpad.net/rminc - that page includes access to the source code, downloads, bug reporting, and facilities for asking questions (and getting them answered, of course).


The main RMINC documentation comes in the form of a mini-book; find the most recent version here: https://launchpad.net/rminc/+download, or a direct link to the pdf here: VBMstats.pdf (Note: this document was last updated on April 16, 2008)

Analyzing volume differences in neuroanatomy with a single set of labels

  1. When you are analyzing structures, make sure that the label file you use accurately segments out your final non-linear model, otherwise your analysis will be meaningless
  2. Use the Jacobian determinants that capture the absolute differences (in our case the *log-determinant-scaled* )
  3. Use little blurring, for instance for files with a 56 micron resolution use 100 micron blur (in our case the *log-determinant-scaled-fwhm0.1* )


To analyze individual structures in the brain, you will need two things: a set of registered images and an atlas that segments the final average of your registration pipeline into To analyze individual structures in the brain, an atlas was used to segment the brain into different structures/regions. (Information about how you can align a segmented set of labels to your data set can be found here: Atlas to Atlas Segmentation) The main idea is the following. Through the registration pipeline we have information about the change in volume in the voxels of each of the individual mouse brain. This is captured in the Jacobian determinant files. Using the label file, we can integrate the information captured in the Jacobian determinants to find out the volume of the structures for each of the individual input files.

The Jacobian determinant fields are fairly noisy if you use them unprocessed. For this reason we blur them before we do analysis on them. The amount of blurring you want to use depends on what kind of changes you want to capture. When looking at neuroanatomy it is important to use only a small amount of blurring, because you want to avoid smoothing out information around borders of structures. The mouse brains we generally look at have a resolution of 56 micron, and we do structural analysis on Jacobian determinants that have been blurred using a 100 micron kernel.

A second thing to keep in mind is that the registration pipeline produces two types of Jacobian determinants. One that reflects relative changes (overall scaling or brain size differences have been taking out) and absolute changes which still contain the brain size differences. When analyzing neuroanatomical structures you want to use the determinants that reflect the absolute changes. Currently the naming convention for those files is as follows:


Here is an example of how to do the analysis in R

> library(RMINC)
# There is a variable that was loaded into R containing the genotype and the location of the Jacobian determinants called filenames, it contains the following:
> filenames
 scaled_jacobians                              genotype
1 01_wt-log-determinant-scaled-fwhm0.1.mnc           wt
2 02_wt-log-determinant-scaled-fwhm0.1.mnc           wt
3 03_wt-log-determinant-scaled-fwhm0.1.mnc           wt
4 04_wt-log-determinant-scaled-fwhm0.1.mnc           wt
5 05_mut-log-determinant-scaled-fwhm0.1.mnc         mut
6 06_mut-log-determinant-scaled-fwhm0.1.mnc         mut
7 07_mut-log-determinant-scaled-fwhm0.1.mnc         mut
8 08_mut-log-determinant-scaled-fwhm0.1.mnc         mut

# the first thing to do now is to get the volume information of all the structures 




Installing the RMINC library the easy way:

If you want to make it nice and complicated: