Finally, I’ve made it to the last day of bootcamp. Got an AFNI pen to prove that I was here, and pictures of the workshop should be available shortly, since I’m assuming everyone must be curious to know what it looked like (I would be).
Gang opened with a presentation on the AFNI tools available for the primary types of connectivity: functional connectivity and effective connectivity. Functional connectivity is a slightly misleading term in my opinion, since you are simply looking at correlations between regions based on a seed-voxel timeseries. The correlation we claim to be looking at is merely a goodness of fit of the timeseries of one voxel with another, and from there we state whether these regions are somehow talking to each other or not, although that is a nebulous term. Much safer would be to go with a term like timeseries correlation analysis, since that is more descriptive of what is actually going on. As for effective connectivity and other types of intra and inter-voxel correlations such as structural equation modeling, I have not had as much experience in those areas, and will not touch on them right now. When I get to those sometime down the line, I will discuss those in more detail, and whether and when they appear to be useful.
The remained of the day was a SUMA demo from Ziad, showcasing how easy it is to visualize surface activity using their surface mapping program. SUMA is, in my experience, much faster and easier to manipulate than FreeSurfer, and, notwithstanding a few technical hurdles, is simple to use when interfacing with volumetric data. Also demonstrated was AFNI’s InstaCorr tool, which allows for instantaneous visualization of functional connectivity throughout the entire brain. One simply sets a voxel as a seed region, and can see how it correlates with every other voxel in the brain. The most interesting (and fun) feature of this tool is the ability to simply hold down the control and shift keys, and then drag the mouse cursor around to see the functional connectivity maps update in less time than it takes to refresh the monitor. This can be done on the surface maps as well. Although I still have the same reservations about resting state data as mentioned previously, this appears to be an excellent method for visualizing these experiments.
Beyond that, I took the opportunity to get in some additional face time with each of the AFNI members, and had a conversation with Daniel about how to examine registration between anatomical and epi datasets. By adding the –AddEdge option to the alignment part of the script (such as align_epi_anat.py), an additional folder named “AddEdge” is created with the anatomical and EPI datasets both before and after registration. Contours of the gyri and sulci are also shown, as well as any overlap between the two after registration. Apparently, the functional data I showed him wasn’t particularly defined (although we were acquiring at 3.5x3.5x3.75), the registration was still OK. One method for improving it may be to use scans acquired pre-steady-state, since those have been spatial contrast than the scans that are acquired during the experiment.
Before I left, I asked Bob about using grey matter masks for smoothing. The rationale for smoothing within a grey matter mask is to avoid smoothing in air and other stuff that we don’t care about (e.g., CSF, ventricles, bone), and as a result improve SNR relative to traditional smoothing methods that take place over the entire brain. However, Bob brought up the point that smoothing within GM on an individual subject basis can introduce biases into the group analysis, since not every subject experiences the same smoothing in the same voxel location. When we smooth, for example, after normalizing to a standardized space, all of the brains fit within the magic Talairach box, and so everything within the bounding box receives the same smoothing kernel. However, since each subject’s grey matter boundaries are stereotyped, we may be smoothing in different areas for each subject; in fact, it is guaranteed to happen. To alleviate this, one could either create a group grey matter mask and use that for smoothing, or take both the white and grey matter segmentation maps from FreeSurfer and, combining them, smooth across a whole brain mask that leaves out non-brain related areas, such as ventricles. I will have to think more about this and try a couple of approaches before deciding on what is feasible, and whether it makes that big of a difference or not.
That’s about it from the bootcamp. It has been an intense four days, but I have enjoyed it immensely, and I plan to continue using AFNI in the future, at least for double-checking the work that goes on in my lab. I’ll be experimenting more in the near future and posting figures of my results, as well as screencasts, when I find the time to pick those up again. For now, it’s onto CNS at Chicago.