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--- biac:analysis:physiological [2011/11/03 18:40]
petty [Physiological Correction Methods]
+++ biac:analysis:physiological [2023/02/23 18:43]
@@ Line 1: / Line 1: @@
-====== Physiological Correction Methods ======
-Currently this assumes that you have recorded your physiological responses through Cigal.  Also, these types of corrections should be done **before** any type of slice-time correction or pre-processing.
-There's a python program, than can take your run's bxh header along with your cigal pdigm file to create various types of corrected text files to be used for physio corrections in a number of different packages.
-This tool uses various fields from the bxh and pdigm file to output "corrected" data.  "Corrected" here means accounting for potential time-locking issues, different TR in the cigal recording verses the actual data acquisition, ddqs, etc.
-The python tool will call cigal functions directly to do the corrections, then output new text data in the format requested.
-<code>
-physio_create.py --help
-</code>
-<code>
-physio_create.py --bxh /path/to/run4.bxh --pdigm /path/to/pdigm5_12345_4_2 -f fsl --outpath /here/
-Program to produce formatted physiological data:
-    Data will be corrected and recreated from the pdigm file based on information from run data
-    BXH and PDIGM required
-    OUTPATH defaults to PWD
-    HZ sampling rate
-    FORMAT: defaults to fsl
-        - fsl = cardio,resp,TR pulse ( 3 column file )
-        - npm = time,cardio,resp ( 3 column file )
-        - afni = cardiac.txt, respiration.txt
-Options:
-  -h, --help            show this help message and exit
-  -b FILE, --bxh=FILE   bxh file for run
-  -p FILE, --pdigm=FILE
-                        pdigm file for run
-  -f string, --format=string
-                        output format ( fsl,npm,afni )
-  --hz                  sampling rate is hz ( 100 )
-  -o PATH, --outpath=PATH
-                        location to store output files
-</code>
-===== AFNI =====
-Create the AFNI formated physiological text data.
-<code bash>physio_create.py -b Data/Func/20111025_12345/run004.bxh -p Data/Behav/12345/pdigm5_12345_4_2 -f afni</code>
-This would create my "cardiac.txt" and "respiration.txt".
-Now set-up build the regressors that will be used in afni with their matlab tool.
-<code matlab>
-addpath /usr/local/packages/MATLAB/afni/
-Opt.Respfile = '/path/to/respiration.txt'
-Opt.Cardfile = '/path/to/cardiac.txt'
-Opt.VolTR = 1.5
-Opt.Nslices = 34
-Opt.PhysFS = 100
-RetroTS(Opt)
-</code>
-The output will be **"oba.slibase.1D"**, which is a text file containing regressors to remove from your data on a slice-by-slice basis.
-Convert your data to 4D nifti if you haven't already done so:
-<code bash>bxh2analyze --niigz -s input.bxh run004</code>
-Create the afni script which you will use to actually run the 3dretroicor functionality:
-<code bash>afni_proc.py -subj_id 12345 -dsets run004.nii.gz -blocks despike -do_block ricor -tcat_remove_first_trs 0 -ricor_regs *.slibase.1D -ricor_regs_nfirst 0 -ricor_regre
-ss_method 'per-run'</code>
-The result of the above command would be a tcsh script, which would run the despiking and 3dretroicor correction only.  Run it:
-<code bash>tcsh -xef proc.12345 |& tee output.proc.12345</code>
-There will be a resulting folder with all the afni data inside.  You can convert the afni BRIK to a nifti file, then wrap it with a BXH, then proceed to whatever you're doing afterwards:
-<code bash>
-#convert afni to nii, create bxh
-dAFNItoNIFTI 12345.results/pb02.12345.r01.ricor+orig.BRIK
-fslwrapbxh pb02.12345.r01.ricor.BRIK
-</code>
-Run the resting state pipeline:
-<code bash>resting_pipeline.py -f pb02.12345.r01.ricor.bxh -s 1,2,3,4,5,6,7 -p func --sliceorder odd</code>
-===== FSL Feat =====
-We've gotten the FSL beta functions for physiological correction, which we'll test.
-They are based on the same algorithms used in afni with retroicor.  Once things are functioning, they'll be installed on the cluster.
-More information can be seen here:
-[[http://www.fmrib.ox.ac.uk/Members/jon/physiological-noise-correction]]
-The beta options will allow you to plug in the resulting regressors directly into Feat.  After pre-processing you can use the **res4d.nii.gz** files for further analysis ( filtered_func_data would not have the physio regressor removed ).

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