Week 1 Instructor Q&A
Channel: ABCD-ReproNim Course
Date: 2020-10-16

Week 1 Instructor Q&A Webinar
Friday October 16, 2020
Panelists: Drs. Angie Laird, David Kennedy, Satra Ghosh, JB Poline, Jessica Bartley, and ABCD-ReproNim Teaching Assistant Liza Levitis
Questions and answers asked during the Webinar can be accessed here: https://drive.google.com/file/d/1hmt_qxJOvbcTqLVIROetB6NLa9MdqHov/view?usp=sharing

Contents:
0:00 - Course Introduction

21:45 - Why do reproducible tools predominantly use Python? Are equivalents in R also being created?

24:56 - What are the downsides to using open-source analysis programs (if any)? Criticisms? Places for improvement?

26:40 - Do you think cognitive neuroscience should abstain from small-N studies because of their lack of power, or could they still be valuable (for exploration and meta-analyses) in the future when we fix the issues of transparency and publication bias?

28:45 - Is there perhaps a danger in focusing on reproducibility in a vacuum, i.e. without consideration of correctness. We could end up with reproducibly incorrect findings, what gets measured is what improves. Recent machine learning competitions have been based on Atlas’s that any competent anatomist quickly realizes are of horrid quality and it is well known that most  nonlinear warp algorithms do not align cortical gray matter with one exception (CVS), but anatomy is hard and most people hate it so they focus on the parts they like such as writing more computer programs.

32:15 - In open datasets different people can test many different hypotheses without correcting for the number of statistical tests performed by others before, do you think this could lead to an inflated type 1 error rate? If so, would this lead to some sort of dataset decay?

35:42 - The ReproIn gt BrainVerse gt NICEMAN gt NeuroBlast pipeline seems like it has potential to be a challenging data engineering problem if one is attempting to implement the approach in their lab. Is it best to try and establish this on our own as scientists or consult the imaging center we collect our data from? Thanks!

39:10 - What is the last day to gain access to the ABCD data set to participate in project week?

41:35 - Will there be opportunities for TA’s to give feedback on project topics for project week?

42:45 -  In the overview video and papers it states that protocols will evolve as the cohort matures. Are these changes in protocol comparable to previous? i.e. if a protocol changes as the age increases, is it comparable to previous years for longitudinal analysis? What exactly shifts as the age increases? Is there something we should keep an eye out for? And also in respect to changes in protocols as issues arise. Thanks!

46:15 - I am aware that there is a scanner difference between sites, but until I listened to Dr. Kennedy’s lecture, I didn’t know operating systems could affect brain volume calculations. Do we know what operating systems generated the brain volumes? Is it only one site or multiple? Thanks!

48:40 - In project proposals, what are concrete ways of indicating that you will focus on reproducible methods?

49:07 - What computational techniques will we learn to analyze our data? Will these be specific to longitudinal data?

50:35 - Will we also be learning how to analyze neuropsychological/demographic data as well? :)

51:15 - Will we be able to see the correct answers for the quizzes after they’re due?

51:35 - When is each quiz due?

52:25 - Is the genomic data available to us? If so, how do we access it?

53:30 - Do you have any suggestions for how observer students should download and analyze data if not on the Jupyter node?

55:15 - Were pain related measures collected in Year3? Would we have access to it by March 2021 or earlier?

55:50 - I have more questions about data decay and practical steps to tackle this, if not today, will this be part of what we do in this course or talk about more (e.g., will we be practicing using sequential comparision correction methods or another option) ?

57:05 - Does testing sensitivity to measures within ReproNim help with the generalizability concept and the ability to create a spectrum for a biomarker of interest to understand how a smaller subset could behave based on the generalized results established in a larger sample that is more representative of the heterogeneity of the real world?