biac:analysis:resting_pipeline
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biac:analysis:resting_pipeline [2013/09/16 18:34] – [Step 7] admin | biac:analysis:resting_pipeline [2024/06/21 15:44] (current) – external edit 127.0.0.1 | ||
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4 - normalize data | 4 - normalize data | ||
5 - regress out WM/CSF | 5 - regress out WM/CSF | ||
- | 6 - lowpass | + | 6 - bandpass |
7 - do parcellation and produce correlation matrix from label file | 7 - do parcellation and produce correlation matrix from label file | ||
* or split it up: | * or split it up: | ||
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(1, | (1, | ||
even=interleaved (2, | even=interleaved (2, | ||
- | this from input image, if available. | + | this from input BXH, if available. |
--tr=MSEC | --tr=MSEC | ||
--ref=FILE | --ref=FILE | ||
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0.5 | 0.5 | ||
--lpfreq=0.08 | --lpfreq=0.08 | ||
- | is .08hz | + | is .08hz. highpass is fixed at .001hz. |
--corrlabel=FILE | --corrlabel=FILE | ||
correlation search. default is the 116 region AAL | correlation search. default is the 116 region AAL | ||
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* run fsl's slice time correction ( slicetimer ) | * run fsl's slice time correction ( slicetimer ) | ||
* if starting with a BXH header, you'll likely have the sliceorder field which will be used to create a custom sliceorder file to be used by fsl | * if starting with a BXH header, you'll likely have the sliceorder field which will be used to create a custom sliceorder file to be used by fsl | ||
+ | * the default is to run bxh_slicetiming to extract a timing file from the BXH header | ||
* if this isn't present, then you'll need to define **--sliceorder** so that we can generate the file for you | * if this isn't present, then you'll need to define **--sliceorder** so that we can generate the file for you | ||
* " | * " | ||
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* " | * " | ||
* " | * " | ||
+ | * this gets much more complicated with multi-band data, so using the default extraction is recommended | ||
==== Step 2 ==== | ==== Step 2 ==== | ||
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==== Step 6 ==== | ==== Step 6 ==== | ||
* this step will band-pass filter data to remove high-frequency noise using custom python code | * this step will band-pass filter data to remove high-frequency noise using custom python code | ||
- | * the default is 0.08 HZ | + | * the default |
+ | * highpass is fixed at .001 HZ | ||
* if you'd like to chose a different frequency, please use ** --lpfreq ** | * if you'd like to chose a different frequency, please use ** --lpfreq ** | ||
==== Step 7 ==== | ==== Step 7 ==== | ||
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- | Still under-development | ||
3D VTK: | 3D VTK: | ||
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+ | |||
+ | |||
+ | ---- | ||
+ | |||
+ | ===== Download Source ===== | ||
+ | {{: | ||
+ | - source files assume you have a working install of FSL and all imported python modules | ||
+ | - need a working install of the [[http:// | ||
+ | - will need to edit any paths that may be different at your install location ( FSL FAST segmentations of the MNI brain and base sets of ROIs ) | ||
+ | - **Chou et al. AJNAR(2012), | ||
+ | - fcdm algorithm adapted from **Dardo Tomasi, PNAS(2010), vol. 107, no. 21. 9885–9890** | ||
biac/analysis/resting_pipeline.txt · Last modified: 2024/06/21 15:44 by 127.0.0.1