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--- biac:analysis:resting_pipeline [2013/09/16 18:26] – admin
+++ biac:analysis:resting_pipeline [2013/09/17 17:20] – [Python/FSL Resting State Pipeline] admin
@@ Line 17: / Line 17: @@
 - normalize data
 - regress out WM/CSF
-- lowpass filter
+- bandpass filter
 - do parcellation and produce correlation matrix from label file
       * or split it up:
@@ Line 68: / Line 68: @@
 .5
   --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
                         correlation search. default is the 116 region AAL
@@ Line 216: / Line 216: @@
 ==== Step 6 ====
   * 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 **
 ==== Step 7 ====
@@ Line 264: / Line 265: @@
 </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 =====
   * this was designed to be modular, so that you only need to run the steps you need