Wednesday, August 6, 2014
DTI Analysis: Soup to Nuts Playlist
Instead of going through each DTI analysis step individually, I've collated everything into a Youtube playlist down below. Just remember that we are using data from the FSL practical course here, and also remember that suing somebody for giving out bad advice, although it is admittedly an easy way to become fantastically wealthy, won't necessarily make you happier.
In any case, just to briefly go over the rest of the steps: After correcting for magnetic field distortions and eddy currents, tensors are fitted using the dtifit command (or simply going through the FDT menu in the FSL interface). Once this has been done for each subject, a series of TBSS tools are used, each one prefixed by "tbss"; for example, tbss_1_preproc, tbss_2_reg, and so on. (You can find all of these in the $FSLDIR/bin directory, and if you have a good handle on Unix programming, you can inspect the code yourself.) After you have run all of those for your dataset, you set up the appropriate experimental design and contrasts, and use the randomise tool to perform statistics in your tractography mask.
Keep in mind that this is just beginner's material; and that if you were to compare your DTI competence to dating, if would be like you were still in that awkward teenager phase, unable to talk to anybody or make eye contact.
However, much of this material has already been covered in other blogs and online resources, provided by several other highly talented scientists and researchers, and - as much as I constantly fantasize about establishing a monopoly over neuroimaging information - there is no practical way to destroy them all.
Therefore, instead of posting redundant information, I highly recommend checking out an ongoing step-by-step series on TORTOISE by Peter Molfese, which you can compare to your results with FSL, and another blog dedicated to diffusion imaging, diffusion-imaging.com. The latter site covers virtually every piece of diffusion imaging software and pipeline out there, and is a good place to start.
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Hi Andy, I just discovered your blog and I"m loving it so far - very informative and written with humour :-)
ReplyDeleteForgive me if I'm asking a silly question (and posting in the wrong place), but is there a search function on this site to filter your posts by tags?
Hi Fiona,
DeleteHey, thanks! I'm glad that you like it and find it useful.
As for a search function, this is somewhat embarrassing, but I haven't had one since I began the blog. Now I find out that it's actually very easy to add, and so I've enabled a search box at the top right of the page. I've also added links to each individual label at the very bottom - there are a lot of labels, and I may cut them down to a more reasonable number, but for right now they give a pretty good overview on what is on the site.
Thanks again for the recommendation; I think it'll make the blog easier to navigate!
-Andy
That's brilliant, thanks so much.
ReplyDeleteI should tell you I'm in the midst of my master's thesis and discovering your blog and youtube videos has been a godsend!
Thanks again and enjoy your weekend.
Fiona
Glad to hear it! I hope it continues to be useful to you, and as always, let me know if there is anything you would like to know more about.
DeleteBest of luck,
-Andy
Hi Andrew,
ReplyDeleteThank you for the tutorial. I am new in DTI, so my question is very basic. I am trying to figure it out which are my input files. I do not have the nod if_AP file you enter from FSL dataset. I only have my DWI dataset. Do you know if this file can be extracted/constracted from the original dataset?
Thanks,
Pablo
Hi Pablo,
DeleteThe example files I used in the tutorial were from a sample dataset containing both volumes acquired in the anterior->posterior direction and the posterior->anterior direction; if you have any questions about whether this occurred, you can ask your scan technician.
However, if you only acquired volumes in one encoding direction, then your input will just be the DWI data that was output from the scanner. As far as I know, unless you know whether and which volumes were acquired in different encoding directions, you cannot extract these volumes from the original dataset.
Best,
-Andy
Thanks Andy.
ReplyDeleteSilly me I was assuming that nod_dif was a different type of file I didn't realised that this refer to the other direction that sample dataset was acquired. Mine was only A->P. So I am all set. Thanks for your blog it is really useful
Pablo
Hi andy,
ReplyDeleteyour blog is really helpfull!
I'm still looking for a way to look at the connectivity between 3 regions of interest... what's the best approach for this and did you already cover this somewhere on your blog? (which I haven't found yet)
thanks! This blog is really usefull!
cheers
Jago
Hey Jago,
DeleteAre you talking about looking at the connectivity between all three simultaneously? Is there a particular paper that you're thinking of as a template for that analysis? One related analysis I can think of might be a mediation analysis, where the correlation in activity between two regions is mediated by the activity in a third ROI.
-Andy
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ReplyDeleteYour blogs are so F-ing Funny!
ReplyDeleteYou always have me literally LOL every time.
While pushing out some really good information.
Thanks for making the painful experience of drudging through code a fun experience!
Hi Andy,
ReplyDeleteThanks for the amazing tutorials.
Just a quick question though - I would ideally want white matter masks to use as ROI for some analysis. But the "preview probability map" option is not available for JHU white matter atlas. Could you give me an idea on how to proceed with creating the masks?
Thanks a lot,
Surya