Project idea: "Knowledge" transfer from "Phantom data analyses"- add scanning "properties" to the model

[yarikoptic]

Continuing on our "quick" https://f1000research.com/articles/9-1131 publication/finding. I think we should take some published study (or better multiple) with data available, and ideally with a reproducible analysis workflow available and see if addition of such variables as "SAR", "ImageOrientationPatient" which either were extracted/placed into sidecar .json files (or original DICOMs still available and could be extracted) into the model significantly effect the higher-level analyses results of the study.

Those properties are present in DICOMs and often extracted (e.g. if using heudiconv with dcm2niix) and placed into BIDS side car files

a list of openneuro datasets having SAR and/or ImageOrientationPatient

(git)smaug:/mnt/btrfs/datasets/datalad/crawl/openneuro[master]git
$> for ds in ds*; do git -C $ds grep -e '"ImageOrientationPatient' -e '"SAR":' -q && echo "- https://openneuro.org/datasets/$ds" ; done

• https://openneuro.org/datasets/ds000031
• https://openneuro.org/datasets/ds000113
• https://openneuro.org/datasets/ds000117
• https://openneuro.org/datasets/ds000228
• https://openneuro.org/datasets/ds000249
• https://openneuro.org/datasets/ds000256
• https://openneuro.org/datasets/ds001107
• https://openneuro.org/datasets/ds001241
• https://openneuro.org/datasets/ds001293
• https://openneuro.org/datasets/ds001353
• https://openneuro.org/datasets/ds001399
• https://openneuro.org/datasets/ds001415
• https://openneuro.org/datasets/ds001430
• https://openneuro.org/datasets/ds001439
• https://openneuro.org/datasets/ds001450
• https://openneuro.org/datasets/ds001454
• https://openneuro.org/datasets/ds001461
• https://openneuro.org/datasets/ds001517
• https://openneuro.org/datasets/ds001531
• https://openneuro.org/datasets/ds001545
• https://openneuro.org/datasets/ds001553
• https://openneuro.org/datasets/ds001554
• https://openneuro.org/datasets/ds001555
• https://openneuro.org/datasets/ds001563
• https://openneuro.org/datasets/ds001583
• https://openneuro.org/datasets/ds001600
• https://openneuro.org/datasets/ds001608
• https://openneuro.org/datasets/ds001621
• https://openneuro.org/datasets/ds001645
• https://openneuro.org/datasets/ds001652
• https://openneuro.org/datasets/ds001715
• https://openneuro.org/datasets/ds001722
• https://openneuro.org/datasets/ds001728
• https://openneuro.org/datasets/ds001740
• https://openneuro.org/datasets/ds001747
• https://openneuro.org/datasets/ds001748
• https://openneuro.org/datasets/ds001761
• https://openneuro.org/datasets/ds001762
• https://openneuro.org/datasets/ds001775
• https://openneuro.org/datasets/ds001838
• https://openneuro.org/datasets/ds001840
• https://openneuro.org/datasets/ds001847
• https://openneuro.org/datasets/ds001875
• https://openneuro.org/datasets/ds001919
• https://openneuro.org/datasets/ds001921
• https://openneuro.org/datasets/ds001923
• https://openneuro.org/datasets/ds001942
• https://openneuro.org/datasets/ds001946
• https://openneuro.org/datasets/ds001981
• https://openneuro.org/datasets/ds002011
• https://openneuro.org/datasets/ds002014
• https://openneuro.org/datasets/ds002033
• https://openneuro.org/datasets/ds002040
• https://openneuro.org/datasets/ds002087
• https://openneuro.org/datasets/ds002105
• https://openneuro.org/datasets/ds002116
• https://openneuro.org/datasets/ds002147
• https://openneuro.org/datasets/ds002153
• https://openneuro.org/datasets/ds002155
• https://openneuro.org/datasets/ds002156
• https://openneuro.org/datasets/ds002158
• https://openneuro.org/datasets/ds002168
• https://openneuro.org/datasets/ds002185
• https://openneuro.org/datasets/ds002236
• https://openneuro.org/datasets/ds002241
• https://openneuro.org/datasets/ds002242
• https://openneuro.org/datasets/ds002250
• https://openneuro.org/datasets/ds002274
• https://openneuro.org/datasets/ds002278
• https://openneuro.org/datasets/ds002293
• https://openneuro.org/datasets/ds002294
• https://openneuro.org/datasets/ds002306
• https://openneuro.org/datasets/ds002320
• https://openneuro.org/datasets/ds002328
• https://openneuro.org/datasets/ds002336
• https://openneuro.org/datasets/ds002338
• https://openneuro.org/datasets/ds002345
• https://openneuro.org/datasets/ds002358
• https://openneuro.org/datasets/ds002363
• https://openneuro.org/datasets/ds002367
• https://openneuro.org/datasets/ds002372
• https://openneuro.org/datasets/ds002374
• https://openneuro.org/datasets/ds002393
• https://openneuro.org/datasets/ds002411
• https://openneuro.org/datasets/ds002547
• https://openneuro.org/datasets/ds002574
• https://openneuro.org/datasets/ds002614
• https://openneuro.org/datasets/ds002634
• https://openneuro.org/datasets/ds002655
• https://openneuro.org/datasets/ds002672
• https://openneuro.org/datasets/ds002674
• https://openneuro.org/datasets/ds002683
• https://openneuro.org/datasets/ds002702
• https://openneuro.org/datasets/ds002715
• https://openneuro.org/datasets/ds002717
• https://openneuro.org/datasets/ds002727
• https://openneuro.org/datasets/ds002766
• https://openneuro.org/datasets/ds002773
• https://openneuro.org/datasets/ds002776
• https://openneuro.org/datasets/ds002785
• https://openneuro.org/datasets/ds002790
• https://openneuro.org/datasets/ds002793
• https://openneuro.org/datasets/ds002799
• https://openneuro.org/datasets/ds002809
• https://openneuro.org/datasets/ds002814
• https://openneuro.org/datasets/ds002837
• https://openneuro.org/datasets/ds002842
• https://openneuro.org/datasets/ds002862
• https://openneuro.org/datasets/ds002872
• https://openneuro.org/datasets/ds002873
• https://openneuro.org/datasets/ds002878
• https://openneuro.org/datasets/ds002898
• https://openneuro.org/datasets/ds002900
• https://openneuro.org/datasets/ds002902
• https://openneuro.org/datasets/ds002912
• https://openneuro.org/datasets/ds002938
• https://openneuro.org/datasets/ds002939
• https://openneuro.org/datasets/ds002940
• https://openneuro.org/datasets/ds002982
• https://openneuro.org/datasets/ds002990
• https://openneuro.org/datasets/ds002994
• https://openneuro.org/datasets/ds002995
• https://openneuro.org/datasets/ds003005
• https://openneuro.org/datasets/ds003008
• https://openneuro.org/datasets/ds003011
• https://openneuro.org/datasets/ds003017
• https://openneuro.org/datasets/ds003037
• https://openneuro.org/datasets/ds003098
• https://openneuro.org/datasets/ds003103
• https://openneuro.org/datasets/ds003114
• https://openneuro.org/datasets/ds003126
• https://openneuro.org/datasets/ds003145
• https://openneuro.org/datasets/ds003151
• https://openneuro.org/datasets/ds003171
• https://openneuro.org/datasets/ds003192
• https://openneuro.org/datasets/ds003216
• https://openneuro.org/datasets/ds003242
• https://openneuro.org/datasets/ds003338
• https://openneuro.org/datasets/ds003342
• https://openneuro.org/datasets/ds003346
• https://openneuro.org/datasets/ds003382
• https://openneuro.org/datasets/ds003419
• https://openneuro.org/datasets/ds003433
• https://openneuro.org/datasets/ds003434
• https://openneuro.org/datasets/ds003436
• https://openneuro.org/datasets/ds003438
• https://openneuro.org/datasets/ds003439
• https://openneuro.org/datasets/ds003440
• https://openneuro.org/datasets/ds003441
• https://openneuro.org/datasets/ds003442
• https://openneuro.org/datasets/ds003443
• https://openneuro.org/datasets/ds003444
• https://openneuro.org/datasets/ds003445
• https://openneuro.org/datasets/ds003446
• https://openneuro.org/datasets/ds003452
• https://openneuro.org/datasets/ds003465
• https://openneuro.org/datasets/ds003466
• https://openneuro.org/datasets/ds003470
• https://openneuro.org/datasets/ds003477
• https://openneuro.org/datasets/ds003479
• https://openneuro.org/datasets/ds003481
• https://openneuro.org/datasets/ds003487
• https://openneuro.org/datasets/ds003495
• https://openneuro.org/datasets/ds003511
• https://openneuro.org/datasets/ds003521
• https://openneuro.org/datasets/ds003540
• https://openneuro.org/datasets/ds003558
• https://openneuro.org/datasets/ds003563
• https://openneuro.org/datasets/ds003568
• https://openneuro.org/datasets/ds003574
• https://openneuro.org/datasets/ds003604
• https://openneuro.org/datasets/ds003606
• https://openneuro.org/datasets/ds003612
• https://openneuro.org/datasets/ds003633
• https://openneuro.org/datasets/ds003639
• https://openneuro.org/datasets/ds003642
• https://openneuro.org/datasets/ds003643
• https://openneuro.org/datasets/ds003653
• https://openneuro.org/datasets/ds003658
• https://openneuro.org/datasets/ds003701
• https://openneuro.org/datasets/ds003707
• https://openneuro.org/datasets/ds003714

The simplest kind would be some analysis on anatomical or DWI per subject, so there is just a single input data file at higher level analysis. If it is an fMRI study with multiple runs, those might be incorporated into lower level model only if there is a large number of runs per subject (unlikely), but as for an initial approximation, we could take average in values between all runs for those and introduce them into higher level analysis. After all I expect SAR be comparable, and ImageOrientationPatient nearly the same across runs (if subjects were not taken out from the MRI between runs, and/or FOV was transferred across sequences).

Note: ImageOrientationPatient is likely a correlated with participant demographics (age, weight and gender). If those were not modeled (unlikely) in the study, those might need to be added into the model first. That all also depends on the particulars of the study and its claims. SAR itself might be a correlate of demographics (weight in particular), didn't check...

Based on our results in aforementioned paper, my hypotheses would be

• those additional "properties of the scanning session" would provide stat significant explanation of variance in the results
• by providing better explanatory power, we should see a better detection (more supra-thresholded results) in case of "spatial" results and normalized effect sizes overall

Additional possible observations/points to keep in mind

• data preprocessing and analysis pipelines differ in their ability to account for "noise" and homogeneity in data. So it might well be that my hypotheses from above would not find support in some studies using more "robust" pipelines than the others. Not sure if we would be able to explore the "spectrum" of pipelines here, it would all depend on sample studies we decide to tackle.
• this idea might not be novel at all and some studies might have already done something along the lines. Would be nice to discover/identify them

[parteekcoder]

@yarikoptic hi could please assign me this issue , i would like be part of your GSOC projects this year

[yarikoptic]

I don't think this project will be a part of this GSOC.

[parteekcoder]

Is incf participating in GSoC this year

[raamana]

Hi Parteek, like I said in another issue, please reach out to and confirm with INCF directly. If they are, they would likely refer you to participating mentors/projects, who are often great IMHO. We have no knowledge of their plans.