Data Preparation Pipeline/Code about pysustain

[noxtoby]

1. Extract biomarkers from your raw MRI, e.g. regional volumes of GM tissue.
2. Identify your control group => calculate mean of controls m and standard deviation of controls s for each biomarker
3. For each biomarker x, calculate z = (x - m)/s
4. Make sure that z increases with progression => swap sign if necessary

[lmz123321]

Hi, sorry to bother again. @noxtoby

I notice, in ADNI, the control group is defined as those CN subjects with CSF Abeta 1-42 > 192 pg/ml.

I do not understand why we need the restriction of CSF Abeta 1-42 > 192 pg/ml ?

Since we currently do not have the CSF data, can we just use all CN subjects as our control groups?

Thx.

[noxtoby]

Hope this doesn't sound too brutal, but you're asking about a study design choice, not pySuStaIn. This is not the place for that.

For the record, amyloid pathology is a hallmark of Alzheimer's disease and so CN individuals with low Abeta in CSF have high Abeta in the brain and therefore may develop Alzheimer's disease.

[lmz123321]

My apologies.

I know this is not an appropriate place to ask problems about the SuStaIn paper. I asked here only because I failed to receive any respondence from Dr. Young (both [at]ucl.ac.uk and [at]kcl.ac.uk) for about a weak. Any suggestions?

[noxtoby]

Again, sorry if this sounds harsh, but yours is a basic experimental design question. It's a conversation for your team.

We have provided a valuable tool for you to use. We are not collaborating with you on a research project.

We are busy.