Resting-state fMRI data seems incredibly popular. Moreover, the methods to analyze this type of data is quite numerous (Nilearn, CONN, XCD_P, …), and I am looking for guidance to select the appropriate tools for my own research.
Of course, the exact answer will depend on my research questions. But apart from that, I was wondering if there is some work (paper, review, or other) presenting the landscape of typical tools or software, including some comparison of the methods and functionalities, in particular regarding:
denoising (quite a huge topic by itself…)
connectivity measures (seed-based, seed-to-seed, voxel-to-voxel, parcel-to-parcel; and of course the measure itself - correlation, covariance, sparse covariance, graph theory etc) or dictionary learning and related tools,
statistical inference on connectivity measures (statistics and thresholding, multiple comparison correction at participant level - FDR, RFT, …)
group-level analyses (group-to-group or participant-to-group comparison, statistical inference, thresholding multiple comparison correction at group level - FDR, RFT, …).
So if you have your own preferences on all these (or related) things, I would guess it would be helpful to more than one if you could share this here
Thank a lot for your answer @Steven !
I am a bit surprised that there are not so much software out there that implement the most common steps in a somewhat automated fashion.
From what I could see, xcp_d is only for participant-level analysis.
CONN seems to be by (quite) far the most complete toolbox, in particular it has a lot of group-level analysis tools (including various methods to correct for multiple comparison, even on connectivity matrices in roi-to-roi analyses). To my knowledge (but correct me if I am wrong), this is the only software that does it. Probably nilearn with some scripting should also be useful here, but again, no (almost-) out-of-the-box" solution.
Also: I came across giga_connectome (bascially a BIDS wrapper for nilearn) but again, no group-level functionalities.
