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biac:analysis:resting_pipeline

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biac:analysis:resting_pipeline [2013/09/16 18:26]
admin 		biac:analysis:resting_pipeline [2014/02/10 20:23]
cmp12 [Download Source]
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     4 - normalize data     4 - normalize data
     5 - regress out WM/CSF     5 - regress out WM/CSF
-    6 - lowpass filter+    6 - bandpass filter
     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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                         0.5                         0.5
   --lpfreq=0.08         frequency cutoff for lowpass filtering in HZ.  default   --lpfreq=0.08         frequency cutoff for lowpass filtering in HZ.  default
-                        is .08hz+                        is .08hz.  highpass is fixed at .001hz.
   --corrlabel=FILE      pointer to 3D label containing ROIs for the   --corrlabel=FILE      pointer to 3D label containing ROIs for the
                         correlation search. default is the 116 region AAL                         correlation search. default is the 116 region AAL
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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 lowpass is 0.08 HZ 
 +  * 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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 </code> </code>
  
 +
 +==== Step 8 ==== 
 +  * Functional connectivity density mapping
 +  * Takes functional data from last step and calculates how connected they are to the voxels around them
 +  * uses ( --fcdmthresh and --refgm ) as the pearson r-value and gray matter mask
 +  * if defaults are used, then a dilated gray matter mask is used from FAST segmentation of MNI brain and a pearson r value of 0.6
 +  * Iteratively goes to all neighboring voxels and counts the number that have correlated signal until they are under the r threshold
 +  * adapted from Dardo Tomasi, PNAS(2010), vol. 107, no. 21. 9885–9890
 +  * resulting file with be "fcdm.nii.gz"; higher voxel values indicate more correlation from neighbors
 +
 +{{:biac:analysis:fcdm.png?direct&400|}}
 ===== Things to consider ===== ===== Things to consider =====
   * this was designed to be modular, so that you only need to run the steps you need   * this was designed to be modular, so that you only need to run the steps you need
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-Still under-development 
 3D VTK: 3D VTK:
  
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 +
 +
 +----
 +
 +===== Download Source =====
 +{{:biac:analysis:rsfmri_python.tgz|}}
 +  - source files assume you have a working install of FSL and all imported python modules
 +  - need a working install of the [[http://www.nitrc.org/projects/bxh_xcede_tools/|BIRN BXH/Xcede tools]]
 +  - 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 )
 +  - **Chen et al. holder**
 +  - fcdm algorithm adapted from **Dardo Tomasi, PNAS(2010), vol. 107, no. 21. 9885–9890**
  
  
  
  
biac/analysis/resting_pipeline.txt · Last modified: 2023/02/23 18:43 (external edit)

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