PET PIPELINE AUTOMATION AND STRUCTURAL MRI EXPLORATION

PhD project: cholinergic PET imaging of people with REM sleep behaviour disorder (prodromal stage of Parkinson's / Lewy body dementia). Taking place at the Montreal Neurological Institute. Part of the ongoing Montreal RBD cohort study that tracks disease progression across time.

My lab's particular focus is a new PET radiotracer (FEOBV) that shows great promise in quantifying brain cholinergic systems.

I want to attempt to see what can be done to garner more attention to this radiotracer.

At a birds-eye-view, I have two intentions:

1. Grasp the current pipeline and replicate it on existing data.
2. Learn about software and statistical techniques that I can implement in the lab and bring to the longitudinal study.

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PROJECT 1: LEARN CURRENT PREPROCESSING PIPELINE

This part I'm less excited about. Why?

• I'm at BrainHack because I like analyzing data, less so preprocessing it. (But, I need to get over this hump.)
• The instructions I've been passed down are a little rough.

Goals

1. Understand my lab's preprocessing pipeline.
2. Successfully replicate it.
   • Become comfortable working with minctools, CBrain, CIVET
3. If time allows, run data through ANTS, dartel

Deliverable 1

Write a script that automates the lab's pipeline steps.

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PROJECT 2: STRUCTURAL MRI ANALYSIS - ML CLASSIFIER.

I plan to then switch gears to machine learning on structural MRI data of individuals with Alzheimer's disease (AD). I would like to create a classifier to determine whether scan comes from an individual with AD or a healthy control.

Goals

• Download a subset of the PREVENT-AD or OASIS datasets
• Feature extraction
  • Basic morphology: cortical thickness, brain volume, etc.
• Put these features into workable matrices using numpy and pandas
• Dimensionality reduction via PCA
• Enter remaining features into model
  • Model type: SVM, random forest?
• Learn about cross-validation techniques thanks to break-out session
• Nilearn to implement the above
• Matplotlib plots along the way to visualize correlation matrices, model error, ROC curves, etc.

If extra time allows, delve into:

• Longitudinal data
• Model to predict onset of disease conversion
  • Naive bayes classifiers?
  • Survival analysis?
  • Cox hazards functions?

Deliverable 2

A Jupyter notebook walking through each of the above steps, with plots saved inline.

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Project medium

I will present these jupyter notebooks in a lab meeting and provide all documents on Github.

During my PhD, I hope to apply these ML techniques to my PET imaging, and to get involved in the cohort study.

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COMMENTS?

FEEDBACK?

THANKS !

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