{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Run-level analyses\n", "In this notebook, we will explain how to aggregate data from different fMRI runs using \"run-level analyses\". We will use both simulated and real data to explain this concept and show how to implement this in Python as well as FSL.\n", "\n", "If you haven't done the other two notebooks (`linux_and_the_CMD.ipynb` and `first_level_analyses.ipynb` yet), please go through these first.\n", "\n", "**What you'll learn**: after this lab, you'll be able to ...\n", "\n", "* set up a run-level model in FSL FEAT\n", "\n", "**Estimated time needed to complete**: 1-2 hours" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run-level analyses\n", "More often than not, researchers split their experiment across different fMRI *runs* (a period of continuous fMRI acquisition). These runs may be grouped within a particular *session* (a set of MRI scans within a particular period that the participant is in the scanner) or split across different sessions (e.g., run 1 and 2 are done on day 1, and run 3 and 4 are done on day 2)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "