mfALFF, zfALFF, fALFF

Submitted by kkeuper on

YAN Chao-Gan

Fri, 05/15/2015 - 17:00

Permalink

Hi Kati,
1. fALFF is the raw fALFF score. mfALFF and zfALFF are the values after standardization (mean division or z-standardization). You can refer to Yan, C.G., Craddock, R.C., Zuo, X.N., Zang, Y.F., Milham, M.P., 2013. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80, 246-262.

2. For the mixed design, there is not an easy GUI yet. However, you can use specify regressors by using y_GroupAnalysis_Image. Or you can use SPM.

3. Yes, they are different multiple comparison correction methods. I use GRF more often.

Best,

Chao-Gan


On Fri, May 15, 2015 at 11:06 AM, The R-fMRI Network <rfmri.org@gmail.com> wrote:
[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

By Kati Keuper (kkeuper)

Dear all,
I am new to the area of resting state analyses. I preprocessed my resting state data using DPABI.

* Now I am wondering, which images to use best for further statistical analysis.

o Can you give some details on what mfALFF, zfALFF and fALFF images reflect?

* I would like to use a 2x2 factorial design with Session (pre vs. post) as a within group factor, and group (A vs B) as a between group factor.

o So far I have not found a possibility to specify equal vs. unequal variances or dependencies of my factors when using the "Statistical Analysis" Option in DPABI. Is there a way to specify this? If not, how do you deal with similar problems? Would you recommend to compute statistical analyses in SPM?

* A third question concerns the multiple-error correction in DPABI_VIEW. FDR, Alphasim, and GRF reveal quite different results in my data, with GRF being the most lenient correction. Has anybody faced similar problems? What could be the reason?
Thank you a lot for your advice.
Best,
Kati Keuper


Online version of this post: http://rfmri.org/content/mfalff-zfalff-falff


Many a little makes a mickle -- your kind contributions shall make our efforts not perish from the earth. Please help The R-fMRI Network at http://rfmri.org/#overlay=HelpUs
To manage subscriptions, please visit: http://rnet.co/mailman/listinfo/rfmri.org_rnet.co
Mail comment ID: http://rfmri.org/mailcomment/redirect/%3C31.2019.0.1431702374.d05023f42c51c7087ef563038015784c%40www.rfmri.org%3E

Dear DPABI team,

first, I would like to thank you for your work and effort – I enjoy working with DPABI. I found this entry in the forum and thought my question might fit in here.

After reading in this forum as well as the literature (I hope I did not miss important information), I am wondering if calculating a within- subject annual percentage signal change (from a ROI  between two timepoints) (fALFF, ReHo and DC using DPARSFA) is most suitable on raw maps compared to standardized maps (m or z) – after my knowledge the standardization will change the interpretation and standardization has its advantages, but at the same time the values can become very big after standardization and that might be a problem. Maybe you have an advice or further important points regarding relative signal change and used maps?

Many thanks in advance.

Kind regards

Marthe  

Dear all, I am new to the area of resting state analyses. I preprocessed my resting state data using DPABI. * Now I am wondering, which images to use best for further statistical analysis. o Can you give some details on what mfALFF, zfALFF and fALFF images reflect? * I would like to use a 2x2 factorial design with Session (pre vs. post) as a within group factor, and group (A vs B) as a between group factor. o So far I have not found a possibility to specify equal vs. unequal variances or dependencies of my factors when using the "Statistical Analysis" Option in DPABI. Is there a way to specify this? If not, how do you deal with similar problems? Would you recommend to compute statistical analyses in SPM? * A third question concerns the multiple-error correction in DPABI_VIEW. FDR, Alphasim, and GRF reveal quite different results in my data, with GRF being the most lenient correction. Has anybody faced similar problems? What could be the reason? Thank you a lot for your advice. Best, Kati Keuper