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New Lab Member Reading (Onboarding)
Software Carpentry Lessons covers the shell, R, python and git.
Additional resources from SciNet's Virtual Summer Training Program: (free for SciNet/Niagara users, usually runs from June-August - would highly recommend signing up for any courses that interest you!)
- Intro to supercomputing (+ Niagara)
- Advanced intro to the Linux command-line interface (NOTE: this video assumes some knowledge about the basic commands for working with files and folders; please refer to the Software Carpentry lesson on the Unix Shell for a comprehensive beginner-oriented tutorial)
The SciNet YouTube Channel has videos for the other courses of the Virtual Summer Training Program as well, most of which build on knowledge from the two above.
A text-based mystery to learn bash Command line mystery
Intro to data preprocessing in Python; log in to CIC computer and navigate to the following directory: /data/chamal/projects/nadia/intro_data_preprocessing_python
A common starting point for new T1w MRI projects is comparing cortical macrostructural measures (cortical thickness, surface area, etc.) between individuals or groups. If you have a raw dataset that you're not sure what to do with, below are links to our documentation for the typical workflow steps for transforming it into a useful format and obtaining the basic processed outputs we use for statistical analyses. QC = quality control
- Manual motion QC of raw T1w volumes
- Image preprocessing using minc-bpipe-library (outputs include things like brain masks and bias field-corrected brain volumes, which can improve the performance of any subsequent processing/analysis pipelines, like CIVET and MAGeT)
- Image processing for cortical surface extraction and analysis: running and QCing outputs of the CIVET pipeline
Most structural MRI tools used in the lab require that your images be in MINC format. See here for how to convert different file types to .mnc.
https://www.chiark.greenend.org.uk/~sgtatham/bugs.html
http://www.catb.org/~esr/faqs/smart-questions.html
https://en.wikipedia.org/wiki/XY_problem
https://stackoverflow.com/help/minimal-reproducible-example
Strunk and White, The Elements of Style
The Sense of Style: The Thinking Person�s Guide to Writing in the 21st Century, Steven Pinker
Style Exercises for Technical Writers
Getting Things Done, David Allen
Getting Things Done for Academics
Introduction to Statistical Learning
Elements of Statistical Learning
RPsychologist Guide to mixed-effect models
Covariance explained intuitively
Quantitative Methods for Linguistic Data (Good coverage of R modelling, including lmer)
Statistical Methods for Data Science - Elizabeth Purdom
3Blue1Brown's beautifully visualized Essence of Linear Algebra
Resources on browsing academic literature:
Seminal papers on CIVET
A good start would be our recent articles (Winterburn 2013, Chakravarty 2013, Pipitone 2014, Park 2014, Raznahan 2014).
Globally, I would say:
Collins and Pruessner NeuroImage 2010
This little ditty from Thompson and Toga: http://www.ncbi.nlm.nih.gov/pubmed/17115077
This little ditty from Paus, Keshavan, and Giedd http://www.ncbi.nlm.nih.gov/pubmed/?term=paus+keshavan+and+giedd
http://www.ncbi.nlm.nih.gov/pubmed/15148381
http://www.ncbi.nlm.nih.gov/pubmed/?term=how+does+your+cortex+grow
From an animal imaging stand point:
http://www.ncbi.nlm.nih.gov/pubmed/16084741
http://www.ncbi.nlm.nih.gov/pubmed/18502665
http://www.ncbi.nlm.nih.gov/pubmed/18387826
Alzheimer's disease:
http://www.ncbi.nlm.nih.gov/pubmed/23036450
http://www.ncbi.nlm.nih.gov/pubmed/24179747
CIVET:
http://www.ncbi.nlm.nih.gov/pubmed/15896981
http://www.ncbi.nlm.nih.gov/pubmed/16624590
http://www.ncbi.nlm.nih.gov/pubmed/15588607
http://www.ncbi.nlm.nih.gov/pubmed/19733347
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2678742/
Image Registration:
http://www.ncbi.nlm.nih.gov/pubmed/23756204
http://www.ncbi.nlm.nih.gov/pubmed/19195496
Other: