Coding, fMRI, Stats Help

Coding, fMRI, & Stats Help

Below are some resources that can be helpful for you if you are learning to code, or already know how but are looking to improve or learn new tricks.

Also included are resources for statistical analyses (behavior and fMRI), version control (git), learning about neural networks, and medial temporal lobe segmentation.

There are also a variety of summer schools you can apply to which will train you in various topics. Here is a list of a few that are offered and here is a github repository of a more comprehensive list.

Programming

General

Program better, for fun and profit

This website lists several different programming/analysis tutorials

Online Experiments

Running Experiments Online

Pavlovia – Run PsychoPy (Python) experiments online

Gorilla (for building online experiments)

Mechanical Turk Tutorial

Another Mechanical Turk Tutorial

Matlab Toolbox for communicating with MTurk

psiTurk (for use with Mechanical Turk; includes ready-made experiments shared by others)

Matlab

MathWorks Matlab tutorial

Matlab documentation

Matlab course (via coursera)

Overview of handy Matlab commands

Psychtoolbox tutorial

Psychtoolbox wiki

Princeton MVPA toolbox (Matlab-based)

Quest (Matlab-based toolbox for staircasing)

Sample Matlab Code

artmuse_nBack_sampleCode.m (Psychtoolbox code; from Aly & Turk-Browne, 2016, PNAS)

nBack stimuli (to use with above code)

artmuse_nBack_analysisCode.m (analysis code for n-back data)

addRandomPhase.m (make phase-scrambled images)

Unix

Unix Shell Tutorial from Foundations for Research Computing

An introduction to linear mixed-effects modeling in R

Power analysis in R

Intro to R – Manual

R for Data Science (free book)

Introduction to R (another free book)

Introductory R course

ANOVA in R (afex)

ggplot2

Elementary statistics with R

More advanced R tutorial

Introduction to mixed models in R

Aly-RCommands-Tutorial.R (Mariam’s excessive cheat sheet for R commands)

Python

Python on Codecademy

PsychoPy

A really useful PsychoPy tutorial

OpenSesame (experiment builder with a GUI and Python)

Python Data Science Handbook (free book)

Python tutorial from Foundations for Research Computing

scikit-learn (general-purpose machine learning in Python)

Nilearn (machine learning for neuroimaging in Python)

PyMVPA (MVPA in Python)

Stats and Analysis Help

General

An introduction to mixed-effects models for experimental psychology

Best practice guidance for linear mixed-effects models in psychological science

A (visual) introduction to mixed effects models

Andy Conway’s stats lectures

Effect Size Calculator

A primer for calculating effect sizes

Transforming stimulus size to visual angle, or vice versa

Robust correlation toolbox (for Matlab)

Two-way repeated-measures ANOVA (Matlab)

ROC toolbox (Matlab) [related paper]

ROC demo

Shiny app for analytic power analyses of two-way interactions

Shiny app for sample size justification

Simulating power for mixed-effects models

Simulating datasets for mixed-effects models (to pair with above reference on simulating power)

Power analysis in R

Conversions between partial eta squared and Cohen’s d

General fMRI

Andy’s Brain Book (fMRI tutorials)

fMRI Bootcamp (videos by Rebecca Saxe)

Neurostars (online community for fMRI help)

Neuropipe (pipeline for fMRI)

Simitar (Matlab-based searchlight analysis toolbox)

Neuroimaging Data Processing Wiki (intro to fMRI analyses)

BIDS (for organization & description of fMRI data)

mriqc (quality control)

fmriprep (preprocessing pipeline)

Mumford Brain Stats (fMRI help)

practiCal fMRI (fMRI education)

Neurodesign (toolbox to optimize fMRI expt design)

Power calculation guide for fMRI studies