Bootstrap FAIRlyBig

- a trivially reproducible HTC workflow

Dr. Felix Hoffstaedter

Cognitive and Affective Biopsychology Group

Institute for Neuroscience and Medicine, INM-7 Research Centre Jülich

Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany

Brainage prediction in Parkinson's Disease

Distributed Data Management

• clone Data from repository (built-in backup)
   
• Look at data structure without content present
   
• Automatic download of single files or all data
   
• Multiple representations i.e. folders & names of same data
   
• Easy collaboration on data via common platforms (i.e OSF)

• git init dataset
   
• git clone URL        #code repo
   
• git add file/s *              #text
  git commit -m "info"
   
• git remote add URL
   
• git push / fetch / pull
   
• git branch / switch branch name
   
• git submodule add URL

• datalad create dataset
   
• datalad clone URL  #code & DATA
   
• datalad save -m "info" \
  file/s *                        #ANYTHING
   
• datalad siblings add URL
   
• datalad push / update (--merge)
  - - - - - - - - - - - - - - - - - -
• datalad get / drop file/s * #content

>>> datalad run -m "info" command <<<

DATA PROCESSING

Reproducible DATA PROCESSING

 Code

- archived -

- archived -

DATA PROCESSING

 Code

DATA PROCESSING

Reproducible DATA PROCESSING

datalad run 

wrapper for trivially reproducible processing

Bootstrap FAIRlyBig

- a trivially reproducible HTC workflow

Distributed data management

Transparent data processing

1. Datalad        - distributed data management tool

2. Singularity  - software hosting environment

3. CON/duct    - lightweigt compute resource monitoring

4. HTCondor & SLURM    - job scheduling systems

Bootstrap FAIRlyBig

- a trivially reproducible HTC workflow

Reproducible DATA PROCESSING

Execute FAIRly Big Workflow

Bootstrap FAIRlyBig

- a trivially reproducible HTC workflow

https://cerebra.fz-juelich.de/f.hoffstaedter/bootstrap_MRIQC

Brain Age prediction: Why & How

Brain Age prediction in Parkinson's Disease

• Biggest & most reliable effect on brain structure measured with MRI

Brain Ageing

Brainage prediction

Brainage prediction in Parkinson's Disease

• Disease Progression modeled using LTJMM  (latent time joint mixed-effects model)
  Input:   UPDRS, PIGH, MoCA & SCOPA
• Parkinson's Disease subtyping via VaDER (variational deep embedding with recurrence)
  Input:   LTJMM outcome trajectories

• Segmentation with CAT12.8.1 for VBM
• GLM difference: PD subtypes & Controls

• No significant difference between PD subtypes

• Brain age workflow selection on PPMI HC
  using 3 best models from Moore et al. 2022 + 2 bias corrections

• 4mm Resolution LASSO regression model trained on PPMI HC + Beheshti Bias correction

• increased Brainaging is associated with increased cognitive, not motor decline

•  each year increase in baseline BAG was associated with:
  • 2% increased hazard for cognitive decline
  • 4% increased hazard for mild cognitive impairment

Scale: 1) normal cognition, 2) subjective cogn. impairment,
3) mild cognitive impairment, and 4) dementia

• Power & sample size estimation for a clinical train assuming an
  effect of 30% on the progression of cognitive decline over 2 years