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Dear AFNI users-
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
https://discuss.afni.nimh.nih.gov
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely,
AFNI HQ
History of AFNI updates
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Hello AFNI team, and 3dvolreg experts in particular,
SMS has increased both the spatial and temporal resolution of fMRI data to the point where motion correction can use quite a bit of memory. From my naive understanding, 3dvolreg reads the entire input dataset into memory, performs motion correction on the entire dataset (resulting in an output dataset) and then writes the output to disk. In
by
cameron
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AFNI Message Board
Glad to hear! I will pull in the changes and let you know how it works.
Thanks!
Cameron
by
cameron
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AFNI Message Board
Here is everything that you requested. I do not see slow performance with the data from the bootcamp. You can download the data I am using from here: https://utexas.box.com/s/7penj3t5k5w3p4ah6ipfxxlfbdbp9lgx (it is from FCP-INDI/CoRR)
afni_system_check.py -check_all
-------------------------------- general ---------------------------------
architecture: 64bit ELF
system:
by
cameron
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AFNI Message Board
Hello Daniel,
The input data is on the local file system, which uses to PCIe 4.0 M.2 SSDs in a RAID 0 configuration with ext4. I also suspected the file system at first so reran from a ramdisk and saw the exact same performance.
by
cameron
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AFNI Message Board
Here yo go ...
++ Compile date = Feb 25 2021 {AFNI_21.0.12:linux_ubuntu_16_64}
afni -no_detach -quiet -VAFNI_COMPRESSOR=
<BLANK>
afni -no_detach -quiet -VAFNI_AUTOGZIP=
YES
afni -no_detach -quiet -VAFNI_NOMMAP=
<BLANK>
time 3dROIstats -mask mask2.nii.gz func.nii.gz
File Sub-brick Mean_1
func.nii.gz 0[?] 9957.563410
func.nii.gz 1[?] 9947.920665
func.nii.gz 2[?
by
cameron
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AFNI Message Board
I am really sorry that it has taken almost a year and a half for me to respond to this and for the poor documentation of 3dECM.
If you are using the full power method, described in Lohman et al. 2010, then the first subbrik corresponds to ECM calculated on a binarized graph and the second corresponds to ECM calculated on a weighted graph. The binarized graph is created by setting the weight o
by
cameron
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AFNI Message Board
Hello Colleagues,
I recently installed AFNI_21.0.12 on Ubuntu 20.04 (built Feb 23 or 24, 2021) and noticed that 3dROIstats was very slow. Taking 2.5 minutes for 3.5x3.5x4 data with 300 TRs. I creating a whole brain mean time course using a whole-brain mask (e.g. 1 inside the brain 0 outside). I verified this same behavior on Ubuntu 16.04. I get the same slow performance with and without PIGZ e
by
cameron
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AFNI Message Board