Learning Resources

Scientific Writing:

• Good data need good writing

• The science of scientific writing (MUST READ)

• A few books:

  • Style: The Basics of Clarity and Grace

  • The Sense of Style

Math and statistics: 

• Statistical Thinking for the 21st Century by Russ Poldrack

• Linear Algebra Youtube video playlist by 3Blue1Brown

• Calculus by Gilbert Strang

• Numpy exercises using linear algebra (other Numpy functions included)

• Linear model with Python cheat sheet

Coding:

• A great course on computing to learn fMRI

• Python Data Science Handbook

• Python for everybody

• Python 3 for Scientists

• Software Carpentry for Python

• Scipy lecture notes

• Basics of translation from Matlab to Python (cheatsheet)

Neuroscience:

• BrainFacts book by SfN

• Coursera: Medical Neuroscience

Neuroimaging:

• Data Analysis Lecturelets by Mike X Cohen (for EEG primarily)

• Coursera Course on Principles of fMRI (1)

• Coursera Course: Fundamental Neuroscience for Neuroimaging

• Information on MRI data header/organization NIFTI-1 Header (although NIFT-2 for volume space is more typical, the data dimension storage described here is ~true)

• Information on MRI CIFTI-2 format (composed of surface vertices and 3D/4D subcortical volume data

Statistics:

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. 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 interested in learning more about fMRI:

• How to learn fMRI by Jonathan Peelle,

• Coursera course on fMRI, part 1

• Coursera course on fMRI, part 2

• AFNI bootcamp slides.

• A nice textbook: Huettel, Scott A., Allen W. Song, and Gregory McCarthy. Functional magnetic resonance imaging. Sunderland: Sinauer Associates.

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. 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 basIc logic behind bootstraping and resampling statistics.