deletions | additions       diff --git a/I_asked_colleagues_that_work__.tex b/I_asked_colleagues_that_work__.tex  index 3f3df95..c063ccd 100644  --- a/I_asked_colleagues_that_work__.tex  +++ b/I_asked_colleagues_that_work__.tex 
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I asked colleagues that work on ADNI data at UCSF, and they verified said  that the same healthy controls were not scanned at multiple sites, and so I wouldn't would not  be able to calculate a $CV_a$ from that data. However, its still really important to compare our results to other harmonization efforts, so I have added 2 tables that do this. In the first, our between site ICC results pre- and post- calibration are compared to the between site ICC results of \cite{cannon2014}. In \cite{cannon2014}, the sites were harmonized and an ADNI phantom was used to correct gradient distortions. The authors ran FSL's FIRST for subcortical segmentation, and a cortical pattern matching algorithm for gray/white segmentation. They then calculated the between-site ICC using variance components in the same way we did. The table below was added to the manuscript: \begin{table}   \begin{tabular}{ c c c c } 
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\label{comparetocannon}  \end{table}  The only ROI two ROIs  that does do  not compare to \cite{cannon2014} is the thalamus and the white matter volume (WMV). For the thalamus, it is possible that the FIRST algorithm is more reliable at segmenting this structure, while for white matter volume, Freesurfer is more reliable. I included another table by \cite{jovicich2013brain} where sites were not strictly harmonized, but different control subjects were scanned at each site, and the authors used the same freesurfer cross-sectional algorithm that we used. Instead of calculating between-site ICC's, they calculated the average within-site ICCs for each ROI. The following table (which is now included in the manuscript) compares our within-site ICC's pre- and post- calibraiton to \cite{jovicich2013brain} average within-site ICC values: \begin{table}   \begin{tabular}{ c c c c } 
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\caption{Comparing the within-site ICC before and after leave-one-out scaling factor calibration with the cross-sectional freesurfer results of \cite{jovicich2013brain}, where scanners used vendor-provided T1-weighted sequences, and the average within-site ICC is shown. The within-site ICCs of our study fall within the range of \cite{jovicich2013brain}, which shows the that sites in this study are as reliable as those in \cite{jovicich2013brain}.}   \end{table}  Here, we see that the within-site thalamus ICC values fall within the range of \cite{jovicich2013brain}, along with the other ROIs. Again, the calibration done here is to show that when the biases are applied to the data, the results are comparable to the harmonized results, and therefore the variability of biases ($CV_a$) could be trusted.