Friday, November 11, 2016

Updated Functional Connectivity Tutorial using AFNI

As part of a new course in neuroimaging methods at Haskins Laboratories, I've begun updating videos on topics such as functional connectivity, context-dependent correlations, and how to accept bribes as a reviewer. The first topic we've covered is resting-state functional connectivity, a sophisticated-sounding name designed to make the subject think he is doing something of immense scientific importance by lying still and doing nothing, when in reality it's to distract him while we find out how to hell to hook up the experimental laptop.

Aside from its usefulness as a stall tactic, resting-state connectivity can also reveal resting-state networks, or correlations between the signal of distant regions of the brain. This provides clues to how structural connectivity - i.e., white matter connections - interact with the BOLD signal, as well as whether differences in resting-state connectivity is a marker for mental disorders such as Alzheimer's or schizophrenia.

The following video takes you step-by-step through functional connectivity analysis, using an online dataset from openfmri.org. One major change from my previous tutorials is condensing all the information into one long video, and providing time markers for each segment in the "Show More" box. This way the viewer can jump around to the information that they need, without having to keep track of several different videos detailing different steps. I hope it's an improvement, and I would like to get feedback.


I've also posted the lecture on resting-state analysis given at Haskins Laboratories on November 3rd. You won't learn much new here that isn't in the video above, but it does have more information. For most of the lecture you can only see the top of my head bobbing around, but that's OK. Eyes on the slides, not the hair.


Exercises:


  1. Set the errts dataset as the underlay, and select "Graph". From the "Opt" menu, select "Write Center." Rename the output 1D file, and use 1dplot to see the timecourse. This can be used as a seed for another connectivity analysis.
  2. Other resting state networks include the somatosensory network, the visual network, and the language network. Research one of these networks, determine where the hubs are, and run a resting state analysis on a seed placed in that hub.
  3. Run correlations for a group of subjects, convert to z-scores, and do a second-level t-test using uber_ttest.py.
  4. Modify the afni_proc.py script to apply 3dRSFC to your data (see Example 10b in afni_proc.py -help)


6 comments:

  1. Hello Andrew, Can you please upload a video on how to install fink or homebrew to analyze resting data?

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    Replies
    1. Hi Sage, try this link regarding installing AFNI on El Capitan: http://andysbrainblog.blogspot.com/2016/01/afni-install-on-osx-el-capitan.html. It has a video on there about how to do all the steps, and one of them is installing homebrew.

      Best,

      -Andy

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  2. Hi Andrew,

    Is there any link providing the scripts (including PPI slides and data) that you mention in your video?

    As I can see, you do provide a link in the video that it does not exist anymore.

    Best,

    Kostas

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    Replies
    1. Unfortunately, those were deleted during a website upgrade at IU; I'll have to create them again. I've been meaning to update those scripts anyway, so this might be a good incentive to do so.

      -Andy

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  3. This comment has been removed by the author.

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  4. Hi Andy,

    Thanks for your very helpful blogs.
    I had a very specific question related to gPPI (perhaps this isn't the best post to ask on but wasn't sure where I should!). I want to investigate patterns of functional connectivity between two different networks (let's say a social network A and a non-social network B). I can functionally localise these two networks using localiser tasks. Now I want to find whether regions in network A are functionally correlated with regions in network B. The way I've been doing this till now is to identify seed regions (let's say I take regions from network A as seed regions) and then see if they are functionally correlated with any other region in the brain, and then use a SVC in SPM to see if these regions are part of network B. However, are you aware of any analysis/toolbox that can allow me to use the functionally localised regions within network B as my search space instead of using the whole brain or SVC. Or more specifically can I use a seed to voxel based connectivity analysis where my voxels are only those which are functionally localised part of network B?

    I hope this ramble is more than a ramble and makes some sense! Any help would be very much appreciated!

    Thanks so much!
    Isra

    ReplyDelete