Hi,


Thank you very much for your fast response. I had thought that it would make sense to use the exact same pipeline, as I would be using in a longitudinal study in future. If I could just be a bit more specific about the details, as I have a publication under review at the moment and one of the reviewers isn't happy about this approach. I just wanted to check that we haven't misunderstood how the algorithm works if that is ok?


Reviewer's comment

“It is unclear to me what is the value of establishing test-retest reliability of applying FreeSurfer subfield segmentation to a within-subject template constructed by the Reuter algorithm. Once this template is constructed and mapped to the different time point images, test-retest measures become meaningless because the segmentations are not independent, but instead, highly dependent on each other.”


Our response

"our understanding is that the use of a within-subject’s template to initialise the segmentation will minimise potential bias or inaccuracy in segmentation in multi-session data. The segmentations are themselves run independently, but with the template being used to initialise (provide a starting point) for the nonlinear parameter fitting. We don’t believe that this would be expected to, and does not appear to, constrain the segmentation to the degree that it would produce “identical” values, as the reviewer suggested in a previous review. The alternative is to risk a biased segmentation which could potentially give a false impression of poor reliability and increased risk of false positive results. "


Any thoughts appreciated.


Thanks,


Amanda

From: Jovicich, Jorge <jorge.jovicich@unitn.it>
Sent: 20 September 2017 15:51:40
To: Worker, Amanda
Cc: Freesurfer support list
Subject: Re: [Freesurfer] Test-retest reliability in longitudinal data
 
Hi Amanda,


I would recommend you to quantify reliability using the same processing
pipeline you will use to test for longitudinal effects in your sample.
Relevant literature include those listed below. For reliability you
would want to evaluate subjects over a time period in which you do not
expect the main effects of your study (e.g., disease related or learning
related morphometric effects), but rather standard physiological and MRI
system variability. Ideally this group also has the same age/gender
representation of the group in which you later want to test for
longitudinal effects.


Cheers,

     Jorge


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On 20/09/2017 15:00, Worker, Amanda wrote:
>
> Hi all,
>
>
> I have a longitudinal dataset that I'd like to calculate test-retest
> reliability for. However, I am not sure whether to calculate this for
> the cross-sectionally processed data or longitudinal data? It would
> seem to make sense to use the cross-sectional data, as the time points
> are independent, but then it means that the test-retest results would
> not be applicable in a dataset processed longitudinally. On the other
> hand, calculating reliability metrics for data processed in the exact
> same way as will be used in further studies seems to make sense also,
> but would calculating test-retest on the longitudinally processed data
> bias the results, as the data points are not fully independent?
>
>
> Does anyone have any idea of the best way forward?
>
>
> Thanks,
>
>
> A
>
>
>
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