eliminated caveat on tsv files based on recent discussions, added explanations to correlation matrices

Author: Eric Feczko

docs/derivatives.md

@@ -253,8 +253,4 @@ There is a minor bug in the executive summary visual report in this round of pro
 
 ### Executive Summary labeled as fMRI `scan_type` in records
 
-For all NDA records within this first release the `scan_type` variable was set to fMRI for the executive summary visual reports.
-
-### motion.tsv files are no longer supported
-
-The file desc-includingFD_motion.tsv was produced post-processing as an interface for loading frame-by-frame motion numbers (e.g. X,Y,Z, roll, pitch, and yaw) as tab separated values. It is no longer going to be supported since this is not part of the standard processing stream. For future reference, it is found in the "func" derivatives folder, as described above.
+For all NDA records within this first release the `scan_type` variable was set to fMRI for the executive summary visual reports.

docs/pipeline.md

@@ -99,4 +99,4 @@ The ExecutiveSummary stage produces an HTML visual quality control page that dis
 [File Mapper](https://github.com/DCAN-Labs/file-mapper) is responsible for mapping the HCP pipeline outputs into valid BIDS derivatives.
 
 ### Stage 10: Connectivity Matrix Generation
-Parcellated connectivity matrices (pconns) are much smaller, and can be further explored to estimate within-study results reproducibility. Using the different sets of parcellated timeseries, we calculated the lag-zero pearson’s correlation coefficient between every pair of parcellated regions of interest (ROIs). Per subject and parcellation scheme, this results in an ROI x ROI correlation matrix. The provided connectivity derivatives were extracted with an FD threshold of 0.2 mm for [5 minutes and 10 minutes of data](https://collection3165.readthedocs.io/en/stable/derivatives/#4-functional).
+Parcellated connectivity matrices (pconns) are much smaller, and can be further explored to estimate within-study results reproducibility. Using the different sets of parcellated timeseries, we calculated the lag-zero pearson’s correlation coefficient between every pair of parcellated regions of interest (ROIs). Per subject and parcellation scheme, this results in an ROI x ROI correlation matrix. The provided connectivity derivatives were extracted with an FD threshold of 0.2 mm for [5 minutes and 10 minutes of data](https://collection3165.readthedocs.io/en/stable/derivatives/#4-functional). A variance stabilization procedure was applied to the correlations prior to uploading. Specifically, the inverse hyperbolic tangent was applied to the correlations: z = arctanh(r). The maximum value displayed is 7.254329. Applying the hyperbolic tangent will recover the pearson's correlation: r = tanh(z).