Combining ROIs

Once you've used a tool like fslmaths, 3dcalc, or Marsbar to create a single ROI, you can combine several of these ROIs using the same tools. This might be useful, for example, when creating a larger-scale masks encompassing several different areas.

In each case, combining ROIs is simply a matter of creating new images using a calculator-like tool; think of your TI-83 from the good old days, minus those frustrating yet addictive games such as FallDown. (Personal record: 1083.) With fslmaths, use the -add flag to concatenate several different ROIs together, e.g.:

fslmaths roi1 -add roi2 -add roi3 outputfile

With AFNI:

3dcalc -a roi1 -b roi2 -c roi3 -expr '(a+b+c)' -prefix outputfile

With Marsbar is a bit more involved, but also easier since you can do it from the GUI, as shown in the following video.




Many thanks to alert reader Anonymous, who is both too cool to register a username and once scored a 1362 on FallDown. Now all you gotta do is lay back and wait for the babe stampede!

Creating Spherical ROIs in AFNI Using 3dUndump

Regions of interest; everybody wants them, but nobody knows how to get them. However, as Megatron once said, power flows to the one who knows how; desire alone is not enough.

Aware of this, I have created a script which will disenthrall you from the pit of ignorance and give you the power to create ROIs just about anywhere you please. The script uses AFNI's 3dUndump, which creates a spherical ROI of a given radius from which parameter values can be extracted using a tool like 3dmaskdump. The rationale is similar to creating ROIs using fslmaths or SPM's marsbar; and if you understand those, using 3dUndump is essentially the same thing.

The only caveat is that you must know the orientation of your dataset before using 3dUndump. AFNI defaults to RAI orientation, in which numbers increase from right to left, anterior to posterior, and inferior to superior; in other words, coordinates to the right of the origin will be negative (since numbers decrease going from left to right), and coordinates anterior to the origin will be negative (since numbers again decrease going from posterior to anterior). Always make sure to check the orientation using a command like 3dinfo -orient before creating your ROI, or open up your anatomical dataset in the AFNI viewer and navigate to the location that you want (e.g., right nucleus accumbens) and then write down the coordinates displayed in the upper left corner of the viewer. You can also use the option -orient LPI, if you're using coordinates from a paper.

This Python script that will let you input the coordinates, and then output a dataset ROI that can be overlaid on your anatomical image. The script can be found here.


Tutorial on 3dUndump:

Tutorial on MakeSpheres.py

Unbiased FMRI Analysis: Leave One Subject Out

Neuroimaging researchers are incessantly bedeviled by the problem of biased region of interest (ROI) analysis. One is constantly lured by the siren song of significant results and large effect sizes radiating from the stygian depths of a non-independent ROI; and while one can at times point toward their use of independent ROIs from other studies, there is always the lurking suspicion that the researcher already knew where the activation was before the ROI was chosen. I have witnessed men, otherwise Samsons in the field and Solomons in counsel, who have had their heads shorn by the harlot of biased analysis.

The most straightforward and appropriate way to do this, of course, is with a region defined on a priori assumptions about where your quarry might lie, based on theory or based on the results of other studies. This ensures that any results extracted from that region are uninfluenced by the model used to generate the statistical maps, therefore circumventing the issue of "double-dipping", or circular analyses (see Kriegeskorte et al, 2009). Another method is to use anatomical regions based on atlases, which again should be motivated by theory.

However, there is yet another option that I was unaware of until recently: Leaving one subject out (LOSO). According to this procedure, non-independence can be mitigated by constructing a general linear model (GLM) with every subject in the study except for one; statistics such as beta weights, time courses, etc., can then be extracted from the resulting parametric map for the subject that was left out, as this subject is no longer contributing to the signal observed in the given region. This process is then repeated and the appropriate parameter extracted for each subject. It is unlikely that there will be perfect overlap between all of the subjects included in each LOSO analysis, but if the effect is real and robust, then it should survive each of these non-overlapping regions.

One consideration with this procedure is what threshold to use for each LOSO analysis. One approach is to hold the p-value constant, in which case a higher t-threshold is used for each analysis due to a reduction in the degrees of freedom. The other approach is to hold the t-value constant, leading to a slightly increased p-value. Both approaches are defensible, although if there are wide variations in the ROI results with each approach, one may want to reconsider the reliability of their finding.

More details can be found in the paper by Esterman et al (2010); I hope this provides the necessary edification and enlightenment for those benighted souls wading about in the filth of their own squalor.

Let's Talk about Masks (Live Video)

I've been experimenting more with Camtasia, and I've uploaded a new video showing how masks are drawn on an actual human, rubber brain, which involves the use of R studio, Excel, and colored pens. My hope is that this makes the learning experience more interactive; in addition, you get to see what my mug looks like.