Lab Philosophy

Lab motto: We do things the hard way!
Good science is slow, iterative, hard, but fun.
We do not take short cuts.
We design our experiements, collect our data, analyze our resutls with rigor and care.
We understand that productivity does not come without failure and dedication.
Happiest people do the best work.
We do whatever we can to stay committed, motivated, productive, and above all, be happy and stay happy.

Lab wiki

We have a lab wiki that lab members can access from here. In the wiki you will find information on:

  • Onboarding for new lab members.
  • Lab policy.
  • Lab meeting topics and guidelines.
  • List of suggested readings for new lab members.
  • Introduction to our computing resources.
  • Tutorials on various lab related topics.
  • Programing

    One must learn how to code to be an effective cognitive neuroscience researcher. Our lab uses Python and Bash scripting for data analyses and statistics. Below are some recommended resources to get started on these languages.

  • Learn Python The Hard Way is a great website for learning Python's syntax. Codecademy is also great for beginners.
  • Here is a Jupyter notebook with a great collection of scipy related links.
  • Check out the Data Science Handbook by Jake Vanerplas to learn Python packages for scientific computing (e.g., numpy, pandas, sci-kit learn).
  • Allmost all softwares developed for neuroimaging only run on Linux or MacOS, it is essential that you learn BASH command line for scripting. Also check out this more advanced tutorial.
  • Use Seaborn to make figures.
  • Learn Regular expression.
  • Atom is a nice, free text editor.
  • It is the lab's policy that all project code and scripts must be uploaded, tracked, and documented on our gitlab or github repository.
  • Statistics

    Some links on stats:

  • Mumford Brain Stats, for fMRI data analysis.
  • Read this great paper on how to do non-parametric statistics on neuroimaging data. Though written primarily for electrophysiology data, the principle is applicable to other types of neuroimaging data or related metrics. In my experience, non-parametric approach requires less asumption, thus more flexible. Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.
  • Here is a very accessible tutorial explaining the baisc logic behind bootstraping and resampling statistics.
  • fMRI, EEG/MEG

    Here is a list for students working with me interested in learning more about fMRI:

  • How to learn fMRI by Jonathan Peelle, I agree with him 100%.
  • Coursera course on fMRI, part 1
  • Coursera course on fMRI, part 2
  • AFNI bootcamp slides. I'm primarily a AFNI user.
  • A nice textbook: Huettel, Scott A., Allen W. Song, and Gregory McCarthy. Functional magnetic resonance imaging. Sunderland: Sinauer Associates.
  • EEG/MEG

  • We use MNE for EEG/MEG analysis.