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Author  : Zhian N. Kamvar <zkamvar@gmail.com>
Time    : 2022-11-15 00:42:01 +0000
Message : [custom] fix lesson contents

01-intro-and-preprocessing.md

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+---
+title: Course Overview and Introduction
+teaching: 25
+exercises: 0
+---
+
+This document is based on the slides here [Google Slides](https://docs.google.com/presentation/d/1er6dQcERL-Yeb5-7A29tJnmqgHNaLpTLXM3e-SmpjDg/edit#slide=id.g484812a0c7_6_1)
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- What steps do I need to take before beginning to work with fMRI data?
+- Learn about fMRIPrep derivatives
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Functional Neuroimaging in Python
+
+Welcome to the **Functional Neuroimaging Analysis in Python** workshop! In this workshpo we'll get you up to speed with the current tools and techniques used in the analysis of functional MRI (fMRI) data. The primary goals of this workshop are:
+
+1. Understand how neuroimaging data is stored, and how it helps us perform analysis
+2. Gain familiarity with the issues surrounding the analysis of fMRI data, and how we can combat it in pre-processing and analysis
+3. Learn how to analyze neuroimaging data, working from beginning to end
+4. Get comfortable with Python as a tool for analysis and visualization of data
+
+## The Central Objective
+
+This workshop is designed to teach you the basics and work up to performing an **intra-network functional connectivity analysis** of the **Default Mode Network** in individuals with Schizophrenia and compare them to a Control population.
+
+All of this sounds fancy, but we'll explain in depth what this looks like in practice as the course goes along.
+
+## A breakdown of material
+
+The material covered will be:
+
+1. Preprocessing fMRI data
+2. Exploring fMRIPrep preprocessing pipeline outputs using pyBIDS
+3. Introduction to Nilearn and Image manipulation
+4. Integrating functional time-series data
+5. Parcellating your data
+6. Confound cleaning fMRI time-series signals
+7. Functional Connectivity Analysis
+
+## Pre-processing
+
+You're a researcher who's collected some nice MR images, and put in some work organizing your data into a BIDS dataset. Now you're rarin' to go and want to play with some data and get some science done. However, *fMRI data is messy*, there are a ton of issues that you need to overcome before you can even begin to analyze your data, **this is called pre-processing**. Here are some of the issues:
+
+1. We have whole head images, *we just want the brain*
+2. Your fMRI image and T1 (anatomical) image are not aligned with each other
+3. Your fMRI image is distorted due to changing magnetic fields in some areas of the brain
+4. People move, the fMRI image is misaligned *through time*
+5. Movement influences the fMRI signal itself! We want *brain signals not motion signals*
+6. All subject images aren't aligned with each other, and furthermore have different brain shapes and sizes. How can we perform a group analysis (i.e averaging etc..) if all our samples are different from one another? We need to *normalize our data*
+
+This seems like a lot of problems to deal with... A pictoral guide of what dealing with these problems looks like follows below:
+
+### Visual Guide to Pre-processing T1 images
+
+First we'll want to deal with our structural data; this is called the **T1 image**. Preprocessing the T1 image consists of the following steps:
+
+1. Brain extraction - we want to analyze brains, not skulls
+2. Normalization - since brains are different across people, we need a method to make them look more alike so we can perform group analysis. This is achieved using a non-linear warp which *squishes and pulls* the brain to look like a *template image*
+
+![](fig/animated_t1_mni.gif){alt='T1 Normalization' class="img-responsive"}
+
+### Visual Guide to Pre-processing fMRI data
+
+With fMRI data things are a bit more complicated since you have to deal with:
+
+1. Motion across time
+2. Distortion artifacts due to magnetic field inhomogeneities
+
+The following steps are required (at minimum!):
+
+1. Brain extraction - again we're only interested in the brain
+2. Motion correction - we need to align the fMRI data *across time*
+3. Susceptibility Distortion Correction (SDC) - we need to correct for magnetic field inhomogeneities
+4. Alignment to the T1 image - aligning to the T1 image allows us to perform the \*squishy/pully" normalization to make everyone's brain more alike
+5. Confound regression - not only does motion *misalign brains* it also corrupts the signal with *motion signal artifacts*, this also needs to be cleaned!
+
+![](fig/animated_fmri_preproc.gif){alt='fMRI Preprocessing Steps' class="img-responsive"}
+
+So **how does one begin to even accomplish this**? Traditionally, neuroimagers used a plethora of tools like, but not limited to: **FSL**, **AFNI**, **FREESURFER**, **ANTS**, **SPM**. Each with their own quirks and file format requirements.
+
+Unfortunately this is difficult to navigate, and each tool develops new techniques to better peform each of these pre-processing steps. Luckily, if your data is in a **BIDS Format**, there exists a tool, [**fMRIPrep**](https://fmriprep.org), which does this all for you while using the best methods across *most of these tools*!. An image below from their website depicts the processing steps they use:
+
+![](https://github.com/oesteban/fmriprep/raw/38a63e9504ab67812b63813c5fe9af882109408e/docs/_static/fmriprep-workflow-all.png){alt="fMRIPrep's Workflow" class="img-responsive"}
+
+Ultimately, fMRIPrep is an end-to-end pipeline - meaning that you feed it your raw organized data and it'll produce a bunch of outputs that you can use for analysis! What follows below are explanations of what those outputs are:
+
+#### fMRIPrep anatomical outputs
+
+![](fig/fmriprep_anat_out.png){alt='fMRIPrep Anatomical Outputs' class="img-static"}
+
+##### Native Space
+
+1. **sub-xxxxx\_T1w\_brainmask.nii** - a binary mask which can be used to pull out just the brain
+2. **sub-xxxxx\_T1w\_preproc.nii** - the fully cleaned T1 image which *has not been normalized*
+
+##### MNI (Normalized) Space
+
+1. **sub-xxxxx\_T1w\_space-MNI152NLin2009cAsym\_brainmask.nii.gz** - also a brain mask, but warped to fit a template brain (the template is MNI152NLin2009cAsym)
+2. **sub-xxxxx\_T1w\_space-MNI152NLin2009cAsym\_class-(CSF/GM/WM)\_probtissue.nii.gz** - probability values for each of the tissue types. We won't get into too much detail with this one
+3. **sub-xxxxx\_T1w\_space-MNI152NLin2009cAsym\_preproc..nii** - the cleaned up T1 image that has been squished and warped into the MNI152NLin2009cAsym template space
+
+#### fMRIPrep functional outputs
+
+![](fig/fmriprep_func_out.png){alt='fMRIPrep Functional Outputs' class="img-static"}
+
+As above we have both **Native** and **Normalized** versions of the fMRI brain, we have a mask of each one as well as the preprocessed fMRI brain.
+
+**Note**: These have *not* been cleaned of motion artifacts. They have only been *aligned*, *distortion corrected*, and *skull-stripped*.
+
+fMRIPrep also outputs a **sub-xxxxx\_task-...\_confounds.tsv** tab-delimited spreadsheet which contains a set of **nuisance regressors** that you can use to clean the *signal itself of motion artifacts*. We'll explore this in a later section.
+
+## End
+
+In the next section we'll start exploring the outputs generated by fMRIPrep to get a better handle of how to use them to manipulate images, clean motion signals, and perform analysis!
+
+
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- fMRI data is dirty and needs to be cleaned, aligned, and transformed before being able to use
+- There are standards in place which will allow you to effortlessly pull the data that you need for analysis
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+