AFNI Class Notes 12/15/17:
Imposing blur
3dBlurToFWHM iteratively blur inside a mask to global FWHM
3dBlurInMask will blur a dataset inside a mask without measuring FWHM
3dmerge -1blur_fwhm will blur data by blur function
Calculating blur
3dFWHMx calculates Gaussian and autocorrelation function
3dLocalstat -stat FWHM
3dLocalACF -stat ACF
3dFWHM totally superseded by 3dFWHMx
Using blur for calculations:
AlphaSim mostly superseded by 3dClustSim
cdf calculate p to t/F levels
Where to cluster? A paper by Tor Wager's Group
Why do we care about FWHM v. ACF? A paper by Knutsson (pdf)
As Imposing blur was discussed in afni_proc.py we will start the discussion with calculating blur.
While 3dLocalstat provides a voxel based calculation of blur it uses only Gaussian based on 3dFWHM which is outdated. Thus 3dFWHMx is strongly suggested.
3dFWHMx This function provides important information regarding spatial smoothness of data in preparation for cluster based thresholding of data.
-input Dataset to calculate blur. You can use either:
Raw data going into 3dDeconvolve-use detrend in this caseError data coming out of 3dDeconvolve-don't use detrend in this case
-detrend [q] Suggested: removes polynomial trends [Default = off; default q=length/30]
Definitely use for time series data such as data prior to deconvolution
-demed Same as detrend 0 [Default = off]
-unif Makes time series variance uniform [Default = off; used in detrend]
-automask Mask data based on 3dAutomask [Default = use all voxels]
-mask mmm Provide planned mask
-detprefix d Save the detrended file for datachecking
-geom/-arith Provide mean per subbrik }= If the input dataset has more than one sub-brick,
-combine x y z into one value
-out ttt Write output to file 'ttt' [3 columns of numbers; ttt= - sends to stdout]
-ACF [anam] Computes autocorrelation function for data as well as Gaussian
ACF(r) = a * exp(-r*r/(2*b*b)) + (1-a)*exp(-r/c)
-acf [anam] same as ACF but comments "#" are removed
Example:
3dFWHMx -acf acf.test -detrend -automask pb05.CAJe_110.r01.scale+tlrc.
Output (stdout):
4.62998 4.51046 4.29648 4.47683
0.86879 2.49856 17.8253 6.1694
this gives FWHM in first row (x/y/z/global) and acf in second row (a/b/c/approx fwhm).
Output (png): acf.text.png
3dClustSim Estimates the probability of false positive (noise-only) clusters.
-mask mset Provide explicit mask for size, shape, dimensions
-nxyz n1 n2 n3 Size of 3D grid to use for simulation [Default values = 64 64 32]
-dxyz d1 d2 d3 Voxel dimensions [Default values = 3.5 3.5 3.5]
-BALL Treat grid as describing a sphere not a box
-inset iset [iset ...] Use to do multiple ROIs
-acf a b c 3 autocorrelation values
-fwhm s single Gaussian filter width
-fwhmxyz sx sy sz 3 dimensional Gaussian
-pthr p1 .. pn voxel thresholds
[default = 0.05 0.02 0.01 0.005 0.002 0.001 0.0005 0.0002 0.0001]
-athr a1 .. an cluster thresholds [default = 0.10 0.05 0.02 0.01]
-LOTS same as using '-pthr LOTS -athr LOTS'
-MEGA more than LOTS -iter n number of Monte Carlo simulations [default = 10000]
-seed S random number seed [default seed = 123456789; use 0 for quasi-randomized]
-prefix ppp Write output for NN method #k to file 'ppp.NNk_Xsided.1D',
3dClustSim does 3 different types of thresholding: as X in file name
1-sided as above
2-sided where positive and negative values are clustered together
bi-sided where positive values and negative are clustered SEPARATELY
3dClustSim does 3 different NN methods are: as k in file name
1 Use first-nearest neighbor clustering= faces touch
2 Use second-nearest neighbor clustering= faces OR edges touch
3 Use third-nearest neighbor clustering= faces OR edges OR corners touc
-cmd ccc Write command in file (ccc)
-quiet
Example:
3dClustSim -acf 0.86879 2.49856 17.8253 -iter 10000 -pthr .05 .01 .005 .001 -athr .05 .01 .005 .001 -mask mask_epi_extents+tlrc. -prefix ClustOutput
Output: ClustOutput.NN1_bisided.1D
-quiet
Example:
3dClustSim -acf 0.86879 2.49856 17.8253 -iter 10000 -pthr .05 .01 .005 .001 -athr .05 .01 .005 .001 -mask mask_epi_extents+tlrc. -prefix ClustOutput
Output: ClustOutput.NN1_bisided.1D
pthr\athr |
0.05 |
0.01 |
0.005 |
0.001 |
0.05 |
50.8 |
69.7 |
79.8 |
112.0 |
0.01 |
13.0 |
17.1 |
19.0 |
24.6 |
0.005 |
8.9 |
11.5 |
12.6 |
16.0 |
0.001 |
4.7 |
6.0 |
6.7 |
8.1 |