deletions | additions       diff --git a/This_important_point_was_also__.tex b/This_important_point_was_also__.tex  index f02a665..32f02dc 100644  --- a/This_important_point_was_also__.tex  +++ b/This_important_point_was_also__.tex 
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This important point was also made by reviewer 2, and we were definitely not clear about have clarified  the overall goal of this project! I project. We  will restate my response, which our response here. The overall goal of this project  wasthat we are  not to claiming claim  that the method of scanning 12 phantom subjects travel is in any way was  cost effective! The effective. Rather, the  goal was to measure MRI-related biases when systems are not standardized, and then see how we one  can overcome these biases with proper sample sizes, rather than our a  costly calibration method, method  or ADNI-like harmonization. If sites don't need to harmonize, they can include retrospective data in harmonization (for  the analysis (which is cost-effective). It case of retrospective data). This  also allows sites the freedom to upgrade hardware/software or even change sequences during a study, which study. This  might be an incentive for sites to contribute data even if they are given little financial incentive. We focused too much on the support. The  phantom calibration aspect when actually we should have emphasized has been minimized and  our statistical model that accounts for MRI-related biases, the biases has been emphasized. The  measurements of that bias (which were estimatedand validated  via calibration), calibration) are an important part of this study because they validate the scaling assumption of the statistical model  and provide researchers values to plug into  the idea that this is power equation. Our framework provides  an alternative method to ADNI harmonization, rather than a strict improvement. The human  phantom calibration is still important to show showed  thatwhen you apply our assumption of scaled bias to our measurements,  the overall absolute agreement between sites improves to the same level of ADNI ADNI-type  harmonization. I followed up on this in my response Our results are compared  to other harmonization efforts in  the second reviewer manuscript  and to in  the following major concern, which compares our results to other harmonization efforts. response.  It is also true that some of our Our  sites have used sequences that are similar to  the vendor provided-T1 sequence, sequences,  and \cite{jovicich2013brain} found that high multicenter reliability can be achieved using these standard vendor sequences with very few calibration efforts. However, many of the sites in our consortium are in the middle of longitudinal studies within their sites, and are hesitant to make even very small protocol changes, despite the result from \cite{jovicich2013brain}, which was for the longitudinal processing stream. Since some multicenter studies rely only on Our statistical model was for  a cross-sectional stream, we think it is still important to evaluate this in terms design, and the evaluation  of between-site variability, scaling bias,  even between standard vendor sequences, is important  to optimize sample sizes. sizes for the cross-sectional case.