Done
- Build boilerplate ML pipeline for building predictive models (tested so far on behavioral features from HBN)
- Cross-referenced pipeline with pydra-ml to determine that similar steps are being implemented
- Summary analysis on clinical diagnosis and demographics from HBN to determine adaquete sample sizes for cerebellar fingerprinting paper
- First pass using UMAP unsupervised learning on child measures (cognitive testing, physical fitness features etc.)
- Research on methods, focusing mostly on these three papers:
To Do
- Try UMap in supervised mode to identify unique patterns in phenotypic data
- Questions to address:
- Within HBN assessment battery, which tests are most useful? (how does this vary across disorders?)
- can further subtyping of individuals be done based on patterns in behavioral/cognitive assessments? (especially for 200 people who were not diagnosed due to an incomplete evaluation)
- what is the reliability in the KSADS assessment across child and parent measures?
- Test python implementation of SUIT on subset (n=10) of hbn participants (T1w + T2)
- Perform VBM to measure local changes in structural abnormalities, following methods steps implemented in this paper - correcting for brain size etc.
Questions:
- Don’t yet have access to LORIS, will follow-up with Lindsay after labor day weekend
- CHOP Meeting - ask Satra to send email to folks there to set-up meeting sometime in Sep.?
- Remind Satra to provide info about other project that we forgot to discuss in our 09/01 meeting
- I’m happy to be included in other projects (meetings etc.) if any seem like a good fit for my interests/skills
- Set up weekly meetings w/Satra on Wednesdays
- I would like to recruit 1-2 undergrad students through UROP - can I hire for pay or academic credit alone?
- I requested edit access to sign up for a lab meeting slot: I’m thinking either end of this month or beginning of oct. I guess the neuroimaging subgroup would be most appropriate but perhaps also knowledge engineering as the meeting will be soliciting feedback about project ideas. Let me know what you think.